feat: complete vendor repos, add edge intelligence and WASM modules

- Add 154 missing vendor files (gitignore was filtering them)
  - vendor/midstream: 564 files (was 561)
  - vendor/sublinear-time-solver: 1190 files (was 1039)
- Add ESP32 edge processing (ADR-039): presence, vitals, fall detection
- Add WASM programmable sensing (ADR-040/041) with wasm3 runtime
- Add firmware CI workflow (.github/workflows/firmware-ci.yml)
- Add wifi-densepose-wasm-edge crate for edge WASM modules
- Update sensing server, provision.py, UI components

Co-Authored-By: claude-flow <ruv@ruv.net>

vendor/midstream/Cargo.toml.backup

@@ -0,0 +1,94 @@
+[workspace]
+members = [
+    "crates/temporal-compare",
+    "crates/nanosecond-scheduler",
+    "crates/temporal-attractor-studio",
+    "crates/temporal-neural-solver",
+    "crates/strange-loop",
+    "crates/quic-multistream",
+]
+
+[package]
+name = "midstream"
+version = "0.1.0"
+edition = "2021"
+description = "Real-time LLM streaming with inflight analysis"
+
+[dependencies]
+hyprstream = { path = "hyprstream-main" }
+tokio = { version = "1.42.0", features = ["full"] }
+arrow = "54.0.0"
+arrow-flight = { version = "54.0.0", features = ["flight-sql-experimental"] }
+serde = { version = "1.0", features = ["derive"] }
+serde_json = "1.0"
+async-trait = "0.1"
+futures = "0.3.31"
+tracing = "0.1"
+config = { version = "0.13", features = ["toml"] }
+chrono = "0.4"
+reqwest = { version = "0.11", features = ["json", "stream"] }
+eventsource-stream = "0.2"
+tokio-stream = "0.1"
+dotenv = "0.15"
+async-stream = "0.3"
+# Lean Agentic dependencies
+thiserror = "2.0"
+dashmap = "6.1"
+lru = "0.12"
+
+# Phase 1: Temporal and Scheduling integrations (workspace crates)
+temporal-compare = { path = "crates/temporal-compare" }
+nanosecond-scheduler = { path = "crates/nanosecond-scheduler" }
+
+# Phase 2: Dynamical systems and temporal logic (workspace crates)
+temporal-attractor-studio = { path = "crates/temporal-attractor-studio" }
+temporal-neural-solver = { path = "crates/temporal-neural-solver" }
+
+# Phase 3: Meta-learning and self-reference (workspace crates)
+strange-loop = { path = "crates/strange-loop" }
+
+# Additional dependencies for advanced integrations
+nalgebra = "0.33" # For linear algebra in attractor analysis
+ndarray = "0.16" # For multi-dimensional arrays
+
+[dev-dependencies]
+mockall = "0.11"
+tokio = "1.42.0"
+tokio-test = "0.4"
+criterion = { version = "0.5", features = ["async_tokio", "html_reports"] }
+
+[[bench]]
+name = "lean_agentic_bench"
+harness = false
+
+[[bench]]
+name = "temporal_bench"
+harness = false
+
+[[bench]]
+name = "scheduler_bench"
+harness = false
+
+[[bench]]
+name = "attractor_bench"
+harness = false
+
+[[bench]]
+name = "solver_bench"
+harness = false
+
+[[bench]]
+name = "meta_bench"
+harness = false
+
+[[bench]]
+name = "quic_bench"
+harness = false
+
+[[example]]
+name = "openrouter"
+path = "examples/openrouter.rs"
+
+[[example]]
+name = "lean_agentic_streaming"
+path = "examples/lean_agentic_streaming.rs"

vendor/midstream/benches/meta_bench.rs.backup

@@ -0,0 +1,599 @@
+//! Comprehensive benchmarks for strange-loop crate
+//!
+//! Benchmarks cover:
+//! - Pattern extraction performance
+//! - Recursive optimization depth
+//! - Meta-learning iteration speed
+//! - Self-modification safety checks
+//! - Rollback mechanism performance
+//! - Validation overhead
+//!
+//! Performance targets:
+//! - Pattern extraction: <10ms for 1000 patterns
+//! - Recursive depth: >10 levels without stack overflow
+//! - Iteration speed: >1000 iterations/second
+//! - Safety overhead: <5% performance impact
+
+use criterion::{black_box, criterion_group, criterion_main, Criterion, BenchmarkId, Throughput};
+use strange_loop::{
+    StrangeLoop, StrangeLoopConfig, MetaLevel, MetaKnowledge,
+    SafetyConstraint, ModificationRule,
+};
+
+// ============================================================================
+// Test Data Generators
+// ============================================================================
+
+fn generate_pattern_data(size: usize, complexity: &str) -> Vec<String> {
+    match complexity {
+        "simple" => {
+            // Highly repetitive patterns
+            (0..size)
+                .map(|i| format!("pattern{}", i % 10))
+                .collect()
+        }
+        "medium" => {
+            // Moderate repetition with variations
+            (0..size)
+                .map(|i| {
+                    let base = i % 50;
+                    let variant = i % 3;
+                    format!("pattern_{}_{}", base, variant)
+                })
+                .collect()
+        }
+        "complex" => {
+            // High diversity with some patterns
+            (0..size)
+                .map(|i| {
+                    let hash = (i * 7919) % 200;
+                    let subpattern = (i * 31) % 5;
+                    format!("complex_{}_{}", hash, subpattern)
+                })
+                .collect()
+        }
+        "random" => {
+            // Mostly unique patterns
+            (0..size)
+                .map(|i| {
+                    let hash1 = (i * 7919) % 10000;
+                    let hash2 = (i * 31337) % 10000;
+                    format!("random_{}_{}", hash1, hash2)
+                })
+                .collect()
+        }
+        _ => vec!["default".to_string(); size],
+    }
+}
+
+fn generate_hierarchical_data(depth: usize) -> Vec<Vec<String>> {
+    let mut levels = Vec::new();
+    let mut current_data = generate_pattern_data(100, "simple");
+
+    for level in 0..depth {
+        levels.push(current_data.clone());
+        // Generate meta-patterns from current level
+        current_data = current_data
+            .windows(2)
+            .map(|w| format!("meta_{}_{}", level, w.join("_")))
+            .collect();
+    }
+
+    levels
+}
+
+fn generate_large_pattern_set(count: usize) -> Vec<String> {
+    (0..count)
+        .map(|i| {
+            let pattern_type = i % 7;
+            match pattern_type {
+                0 => format!("linear_{}", i),
+                1 => format!("cyclic_{}", i % 100),
+                2 => format!("branching_{}_{}", i / 10, i % 10),
+                3 => format!("converging_{}", i / 20),
+                4 => format!("diverging_{}", i),
+                5 => format!("stable_{}", i % 50),
+                _ => format!("chaotic_{}", (i * 7919) % 1000),
+            }
+        })
+        .collect()
+}
+
+// ============================================================================
+// Meta-Learning Benchmarks
+// ============================================================================
+
+fn bench_meta_learning_iteration(c: &mut Criterion) {
+    let mut group = c.benchmark_group("meta_learning_iteration");
+
+    // Simple learning
+    group.bench_function("simple", |b| {
+        let mut learner = MetaLearner::new();
+        let experiences = create_experience_batch(10, false);
+
+        b.iter(|| {
+            for exp in &experiences {
+                black_box(learner.learn(black_box(exp)));
+            }
+        });
+    });
+
+    // Complex learning
+    group.bench_function("complex", |b| {
+        let mut learner = MetaLearner::new();
+        let experiences = create_experience_batch(10, true);
+
+        b.iter(|| {
+            for exp in &experiences {
+                black_box(learner.learn(black_box(exp)));
+            }
+        });
+    });
+
+    // Varying batch sizes
+    for batch_size in [5, 10, 25, 50, 100].iter() {
+        group.throughput(Throughput::Elements(*batch_size as u64));
+        group.bench_with_input(
+            BenchmarkId::new("batch", batch_size),
+            batch_size,
+            |b, &size| {
+                let experiences = create_experience_batch(size, false);
+
+                b.iter(|| {
+                    let mut learner = MetaLearner::new();
+                    for exp in &experiences {
+                        black_box(learner.learn(exp));
+                    }
+                });
+            }
+        );
+    }
+
+    group.finish();
+}
+
+fn bench_incremental_learning(c: &mut Criterion) {
+    let mut group = c.benchmark_group("incremental_learning");
+
+    // Progressive learning
+    group.bench_function("progressive", |b| {
+        let mut learner = MetaLearner::new();
+        let mut exp_id = 0;
+
+        b.iter(|| {
+            exp_id += 1;
+            let exp = create_simple_experience(exp_id);
+            black_box(learner.learn(black_box(&exp)))
+        });
+    });
+
+    // With forgetting mechanism
+    group.bench_function("with_forgetting", |b| {
+        let mut learner = MetaLearner::with_capacity(100);
+        let mut exp_id = 0;
+
+        b.iter(|| {
+            exp_id += 1;
+            let exp = create_simple_experience(exp_id);
+            black_box(learner.learn_with_forgetting(black_box(&exp)))
+        });
+    });
+
+    group.finish();
+}
+
+// ============================================================================
+// Pattern Extraction Benchmarks
+// ============================================================================
+
+fn bench_pattern_extraction(c: &mut Criterion) {
+    let mut group = c.benchmark_group("pattern_extraction");
+
+    // Simple patterns
+    for num_experiences in [10, 50, 100, 500].iter() {
+        group.bench_with_input(
+            BenchmarkId::new("simple", num_experiences),
+            num_experiences,
+            |b, &n| {
+                let experiences = create_experience_batch(n, false);
+                b.iter(|| {
+                    black_box(extract_patterns(black_box(&experiences)))
+                });
+            }
+        );
+    }
+
+    // Complex patterns
+    for num_experiences in [10, 50, 100, 500].iter() {
+        group.bench_with_input(
+            BenchmarkId::new("complex", num_experiences),
+            num_experiences,
+            |b, &n| {
+                let experiences = create_experience_batch(n, true);
+                b.iter(|| {
+                    black_box(extract_patterns(black_box(&experiences)))
+                });
+            }
+        );
+    }
+
+    group.finish();
+}
+
+fn bench_pattern_matching(c: &mut Criterion) {
+    let mut group = c.benchmark_group("pattern_matching");
+
+    let patterns = (0..100).map(|i| create_pattern(i, 0)).collect::<Vec<_>>();
+
+    // Single experience matching
+    group.bench_function("single_match", |b| {
+        let exp = create_simple_experience(42);
+        b.iter(|| {
+            black_box(patterns.iter()
+                .filter(|p| p.matches(black_box(&exp)))
+                .count())
+        });
+    });
+
+    // Batch matching
+    group.bench_function("batch_match", |b| {
+        let experiences = create_experience_batch(50, false);
+        b.iter(|| {
+            for exp in &experiences {
+                black_box(patterns.iter()
+                    .filter(|p| p.matches(exp))
+                    .count());
+            }
+        });
+    });
+
+    group.finish();
+}
+
+// ============================================================================
+// Multi-Level Learning Benchmarks
+// ============================================================================
+
+fn bench_multi_level_learning(c: &mut Criterion) {
+    let mut group = c.benchmark_group("multi_level_learning");
+
+    // 2-level hierarchy
+    group.bench_function("two_levels", |b| {
+        let mut learner = MetaLearner::with_levels(2);
+        let experiences = create_experience_batch(50, false);
+
+        b.iter(|| {
+            for exp in &experiences {
+                black_box(learner.learn_hierarchical(black_box(exp)));
+            }
+        });
+    });
+
+    // 3-level hierarchy
+    group.bench_function("three_levels", |b| {
+        let mut learner = MetaLearner::with_levels(3);
+        let experiences = create_experience_batch(50, false);
+
+        b.iter(|| {
+            for exp in &experiences {
+                black_box(learner.learn_hierarchical(black_box(exp)));
+            }
+        });
+    });
+
+    // Varying levels
+    for num_levels in [2, 3, 4, 5].iter() {
+        group.bench_with_input(
+            BenchmarkId::new("levels", num_levels),
+            num_levels,
+            |b, &levels| {
+                let mut learner = MetaLearner::with_levels(levels);
+                let experiences = create_experience_batch(50, false);
+
+                b.iter(|| {
+                    for exp in &experiences {
+                        black_box(learner.learn_hierarchical(exp));
+                    }
+                });
+            }
+        );
+    }
+
+    group.finish();
+}
+
+fn bench_level_transition(c: &mut Criterion) {
+    let mut group = c.benchmark_group("level_transition");
+
+    let hierarchy = create_pattern_hierarchy(3, 10);
+
+    // Bottom-up propagation
+    group.bench_function("bottom_up", |b| {
+        b.iter(|| {
+            black_box(propagate_bottom_up(black_box(&hierarchy)))
+        });
+    });
+
+    // Top-down influence
+    group.bench_function("top_down", |b| {
+        b.iter(|| {
+            black_box(propagate_top_down(black_box(&hierarchy)))
+        });
+    });
+
+    group.finish();
+}
+
+// ============================================================================
+// Cross-Crate Integration Benchmarks
+// ============================================================================
+
+fn bench_cross_crate_integration(c: &mut Criterion) {
+    let mut group = c.benchmark_group("cross_crate_integration");
+
+    // Integration with temporal-compare
+    group.bench_function("temporal_compare", |b| {
+        use temporal_compare::{dtw_distance, TemporalData};
+
+        let experiences = create_experience_batch(100, false);
+
+        b.iter(|| {
+            // Extract temporal sequences from experiences
+            let seq1: Vec<f64> = experiences.iter()
+                .map(|e| e.reward)
+                .collect();
+            let seq2: Vec<f64> = experiences.iter()
+                .skip(10)
+                .map(|e| e.reward)
+                .collect();
+
+            black_box(dtw_distance(&seq1, &seq2))
+        });
+    });
+
+    // Integration with scheduler
+    group.bench_function("scheduler", |b| {
+        use nanosecond_scheduler::{NanoScheduler, Task, TaskPriority};
+
+        let mut scheduler = NanoScheduler::new(4);
+        let experiences = create_experience_batch(50, false);
+
+        b.iter(|| {
+            for (i, exp) in experiences.iter().enumerate() {
+                let priority = if exp.reward > 0.7 {
+                    TaskPriority::High
+                } else {
+                    TaskPriority::Normal
+                };
+
+                let task = Task::new(
+                    format!("task_{}", i),
+                    Box::new(move || { black_box(exp); }),
+                    priority,
+                );
+
+                scheduler.schedule(task);
+            }
+
+            while scheduler.has_pending_tasks() {
+                scheduler.run_once();
+            }
+        });
+    });
+
+    // Integration with attractor studio
+    group.bench_function("attractor_studio", |b| {
+        use temporal_attractor_studio::{reconstruct_phase_space};
+
+        let experiences = create_experience_batch(1000, false);
+        let rewards: Vec<f64> = experiences.iter().map(|e| e.reward).collect();
+
+        b.iter(|| {
+            black_box(reconstruct_phase_space(
+                black_box(&rewards),
+                black_box(3),
+                black_box(10)
+            ))
+        });
+    });
+
+    group.finish();
+}
+
+// ============================================================================
+// Self-Referential Operations Benchmarks
+// ============================================================================
+
+fn bench_self_referential(c: &mut Criterion) {
+    let mut group = c.benchmark_group("self_referential");
+
+    // Self-improvement
+    group.bench_function("self_improvement", |b| {
+        let mut learner = MetaLearner::new();
+        let experiences = create_experience_batch(100, false);
+
+        // Initial learning
+        for exp in &experiences {
+            learner.learn(exp);
+        }
+
+        b.iter(|| {
+            black_box(learner.improve_self())
+        });
+    });
+
+    // Meta-pattern extraction
+    group.bench_function("meta_patterns", |b| {
+        let patterns = (0..100).map(|i| create_pattern(i, 0)).collect::<Vec<_>>();
+
+        b.iter(|| {
+            black_box(extract_meta_patterns(black_box(&patterns)))
+        });
+    });
+
+    // Recursive optimization
+    group.bench_function("recursive_opt", |b| {
+        let mut learner = MetaLearner::new();
+        let experiences = create_experience_batch(50, false);
+
+        b.iter(|| {
+            black_box(learner.optimize_recursive(black_box(&experiences), black_box(3)))
+        });
+    });
+
+    group.finish();
+}
+
+// ============================================================================
+// Recursive Optimization Benchmarks
+// ============================================================================
+
+fn bench_recursive_optimization(c: &mut Criterion) {
+    let mut group = c.benchmark_group("recursive_optimization");
+
+    let experiences = create_experience_batch(100, true);
+
+    // Varying recursion depths
+    for depth in [1, 2, 3, 4, 5].iter() {
+        group.bench_with_input(
+            BenchmarkId::new("depth", depth),
+            depth,
+            |b, &d| {
+                b.iter(|| {
+                    black_box(recursive_optimize(
+                        black_box(&experiences),
+                        black_box(d)
+                    ))
+                });
+            }
+        );
+    }
+
+    group.finish();
+}
+
+// ============================================================================
+// Complete Pipeline Benchmarks
+// ============================================================================
+
+fn bench_complete_meta_learning(c: &mut Criterion) {
+    let mut group = c.benchmark_group("complete_pipeline");
+
+    group.bench_function("full_cycle", |b| {
+        let experiences = create_experience_batch(100, true);
+
+        b.iter(|| {
+            // 1. Learn from experiences
+            let mut learner = MetaLearner::with_levels(3);
+            for exp in &experiences {
+                learner.learn_hierarchical(exp);
+            }
+
+            // 2. Extract patterns
+            let patterns = extract_patterns(&experiences);
+
+            // 3. Integrate knowledge
+            let knowledge = integrate_knowledge(&patterns);
+
+            // 4. Self-improvement
+            learner.improve_self();
+
+            // 5. Recursive optimization
+            let optimized = recursive_optimize(&experiences, 2);
+
+            black_box((patterns, knowledge, optimized))
+        });
+    });
+
+    group.finish();
+}
+
+// ============================================================================
+// Helper Functions (mock implementations for benchmarking)
+// ============================================================================
+
+fn propagate_bottom_up(hierarchy: &[Vec<Pattern>]) -> Vec<Pattern> {
+    // Mock implementation
+    hierarchy.iter()
+        .flat_map(|level| level.iter())
+        .cloned()
+        .collect()
+}
+
+fn propagate_top_down(hierarchy: &[Vec<Pattern>]) -> Vec<Pattern> {
+    // Mock implementation
+    hierarchy.iter()
+        .rev()
+        .flat_map(|level| level.iter())
+        .cloned()
+        .collect()
+}
+
+fn extract_meta_patterns(patterns: &[Pattern]) -> Vec<Pattern> {
+    // Mock implementation: create meta-patterns from existing patterns
+    patterns.iter()
+        .step_by(5)
+        .enumerate()
+        .map(|(i, p)| create_pattern(i, p.level + 1))
+        .collect()
+}
+
+// ============================================================================
+// Criterion Configuration
+// ============================================================================
+
+criterion_group! {
+    name = learning_benches;
+    config = Criterion::default()
+        .sample_size(100)
+        .measurement_time(std::time::Duration::from_secs(10))
+        .warm_up_time(std::time::Duration::from_secs(3));
+    targets = bench_meta_learning_iteration, bench_incremental_learning
+}
+
+criterion_group! {
+    name = pattern_benches;
+    config = Criterion::default()
+        .sample_size(100)
+        .measurement_time(std::time::Duration::from_secs(8));
+    targets = bench_pattern_extraction, bench_pattern_matching
+}
+
+criterion_group! {
+    name = hierarchy_benches;
+    config = Criterion::default()
+        .sample_size(100);
+    targets = bench_multi_level_learning, bench_level_transition
+}
+
+criterion_group! {
+    name = integration_benches;
+    config = Criterion::default()
+        .sample_size(50)
+        .measurement_time(std::time::Duration::from_secs(12));
+    targets = bench_cross_crate_integration
+}
+
+criterion_group! {
+    name = recursive_benches;
+    config = Criterion::default()
+        .sample_size(50);
+    targets = bench_self_referential, bench_recursive_optimization
+}
+
+criterion_group! {
+    name = pipeline_benches;
+    config = Criterion::default()
+        .sample_size(30)
+        .measurement_time(std::time::Duration::from_secs(15));
+    targets = bench_complete_meta_learning
+}
+
+criterion_main!(
+    learning_benches,
+    pattern_benches,
+    hierarchy_benches,
+    integration_benches,
+    recursive_benches,
+    pipeline_benches
+);

vendor/sublinear-time-solver/.claude/commands/coordination/README.md

@@ -0,0 +1,9 @@
+# Coordination Commands
+
+Commands for coordination operations in Claude Flow.
+
+## Available Commands
+
+- [swarm-init](./swarm-init.md)
+- [agent-spawn](./agent-spawn.md)
+- [task-orchestrate](./task-orchestrate.md)

vendor/sublinear-time-solver/.claude/commands/coordination/agent-spawn.md

@@ -0,0 +1,25 @@
+# agent-spawn
+
+Spawn a new agent in the current swarm.
+
+## Usage
+```bash
+npx claude-flow agent spawn [options]
+```
+
+## Options
+- `--type <type>` - Agent type (coder, researcher, analyst, tester, coordinator)
+- `--name <name>` - Custom agent name
+- `--skills <list>` - Specific skills (comma-separated)
+
+## Examples
+```bash
+# Spawn coder agent
+npx claude-flow agent spawn --type coder
+
+# With custom name
+npx claude-flow agent spawn --type researcher --name "API Expert"
+
+# With specific skills
+npx claude-flow agent spawn --type coder --skills "python,fastapi,testing"
+```

vendor/sublinear-time-solver/.claude/commands/coordination/init.md

@@ -0,0 +1,44 @@
+# Initialize Coordination Framework
+
+## 🎯 Key Principle
+**This tool coordinates Claude Code's actions. It does NOT write code or create content.**
+
+## MCP Tool Usage in Claude Code
+
+**Tool:** `mcp__claude-flow__swarm_init`
+
+## Parameters
+```json
+{"topology": "mesh", "maxAgents": 5, "strategy": "balanced"}
+```
+
+## Description
+Set up a coordination topology to guide Claude Code's approach to complex tasks
+
+## Details
+This tool creates a coordination framework that helps Claude Code:
+- Break down complex problems systematically
+- Approach tasks from multiple perspectives
+- Maintain consistency across large projects
+- Work more efficiently through structured coordination
+
+Remember: This does NOT create actual coding agents. It creates a coordination pattern for Claude Code to follow.
+
+## Example Usage
+
+**In Claude Code:**
+1. Use the tool: `mcp__claude-flow__swarm_init`
+2. With parameters: `{"topology": "mesh", "maxAgents": 5, "strategy": "balanced"}`
+3. Claude Code then executes the coordinated plan using its native tools
+
+## Important Reminders
+- ✅ This tool provides coordination and structure
+- ✅ Claude Code performs all actual implementation
+- ❌ The tool does NOT write code
+- ❌ The tool does NOT access files directly
+- ❌ The tool does NOT execute commands
+
+## See Also
+- Main documentation: /claude.md
+- Other commands in this category
+- Workflow examples in /workflows/

vendor/sublinear-time-solver/.claude/commands/coordination/orchestrate.md

@@ -0,0 +1,43 @@
+# Coordinate Task Execution
+
+## 🎯 Key Principle
+**This tool coordinates Claude Code's actions. It does NOT write code or create content.**
+
+## MCP Tool Usage in Claude Code
+
+**Tool:** `mcp__claude-flow__task_orchestrate`
+
+## Parameters
+```json
+{"task": "Implement authentication system", "strategy": "parallel", "priority": "high"}
+```
+
+## Description
+Break down and coordinate complex tasks for systematic execution by Claude Code
+
+## Details
+Orchestration strategies:
+- **parallel**: Claude Code works on independent components simultaneously
+- **sequential**: Step-by-step execution for dependent tasks
+- **adaptive**: Dynamically adjusts based on task complexity
+
+The orchestrator creates a plan that Claude Code follows using its native tools.
+
+## Example Usage
+
+**In Claude Code:**
+1. Use the tool: `mcp__claude-flow__task_orchestrate`
+2. With parameters: `{"task": "Implement authentication system", "strategy": "parallel", "priority": "high"}`
+3. Claude Code then executes the coordinated plan using its native tools
+
+## Important Reminders
+- ✅ This tool provides coordination and structure
+- ✅ Claude Code performs all actual implementation
+- ❌ The tool does NOT write code
+- ❌ The tool does NOT access files directly
+- ❌ The tool does NOT execute commands
+
+## See Also
+- Main documentation: /claude.md
+- Other commands in this category
+- Workflow examples in /workflows/

vendor/sublinear-time-solver/.claude/commands/coordination/spawn.md

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+# Create Cognitive Patterns
+
+## 🎯 Key Principle
+**This tool coordinates Claude Code's actions. It does NOT write code or create content.**
+
+## MCP Tool Usage in Claude Code
+
+**Tool:** `mcp__claude-flow__agent_spawn`
+
+## Parameters
+```json
+{"type": "researcher", "name": "Literature Analysis", "capabilities": ["deep-analysis"]}
+```
+
+## Description
+Define cognitive patterns that represent different approaches Claude Code can take
+
+## Details
+Agent types represent thinking patterns, not actual coders:
+- **researcher**: Systematic exploration approach
+- **coder**: Implementation-focused thinking
+- **analyst**: Data-driven decision making
+- **architect**: Big-picture system design
+- **reviewer**: Quality and consistency checking
+
+These patterns guide how Claude Code approaches different aspects of your task.
+
+## Example Usage
+
+**In Claude Code:**
+1. Use the tool: `mcp__claude-flow__agent_spawn`
+2. With parameters: `{"type": "researcher", "name": "Literature Analysis", "capabilities": ["deep-analysis"]}`
+3. Claude Code then executes the coordinated plan using its native tools
+
+## Important Reminders
+- ✅ This tool provides coordination and structure
+- ✅ Claude Code performs all actual implementation
+- ❌ The tool does NOT write code
+- ❌ The tool does NOT access files directly
+- ❌ The tool does NOT execute commands
+
+## See Also
+- Main documentation: /claude.md
+- Other commands in this category
+- Workflow examples in /workflows/

vendor/sublinear-time-solver/.claude/commands/coordination/swarm-init.md

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+# swarm init
+
+Initialize a Claude Flow swarm with specified topology and configuration.
+
+## Usage
+
+```bash
+npx claude-flow swarm init [options]
+```
+
+## Options
+
+- `--topology, -t <type>` - Swarm topology: mesh, hierarchical, ring, star (default: hierarchical)
+- `--max-agents, -m <number>` - Maximum number of agents (default: 8)
+- `--strategy, -s <type>` - Execution strategy: balanced, parallel, sequential (default: parallel)
+- `--auto-spawn` - Automatically spawn agents based on task complexity
+- `--memory` - Enable cross-session memory persistence
+- `--github` - Enable GitHub integration features
+
+## Examples
+
+### Basic initialization
+
+```bash
+npx claude-flow swarm init
+```
+
+### Mesh topology for research
+
+```bash
+npx claude-flow swarm init --topology mesh --max-agents 5 --strategy balanced
+```
+
+### Hierarchical for development
+
+```bash
+npx claude-flow swarm init --topology hierarchical --max-agents 10 --strategy parallel --auto-spawn
+```
+
+### GitHub-focused swarm
+
+```bash
+npx claude-flow swarm init --topology star --github --memory
+```
+
+## Topologies
+
+### Mesh
+
+- All agents connect to all others
+- Best for: Research, exploration, brainstorming
+- Communication: High overhead, maximum information sharing
+
+### Hierarchical
+
+- Tree structure with clear command chain
+- Best for: Development, structured tasks, large projects
+- Communication: Efficient, clear responsibilities
+
+### Ring
+
+- Agents connect in a circle
+- Best for: Pipeline processing, sequential workflows
+- Communication: Low overhead, ordered processing
+
+### Star
+
+- Central coordinator with satellite agents
+- Best for: Simple tasks, centralized control
+- Communication: Minimal overhead, clear coordination
+
+## Integration with Claude Code
+
+Once initialized, use MCP tools in Claude Code:
+
+```javascript
+mcp__claude-flow__swarm_init { topology: "hierarchical", maxAgents: 8 }
+```
+
+## See Also
+
+- `agent spawn` - Create swarm agents
+- `task orchestrate` - Coordinate task execution
+- `swarm status` - Check swarm state
+- `swarm monitor` - Real-time monitoring

vendor/sublinear-time-solver/.claude/commands/coordination/task-orchestrate.md

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+# task-orchestrate
+
+Orchestrate complex tasks across the swarm.
+
+## Usage
+```bash
+npx claude-flow task orchestrate [options]
+```
+
+## Options
+- `--task <description>` - Task description
+- `--strategy <type>` - Orchestration strategy
+- `--priority <level>` - Task priority (low, medium, high, critical)
+
+## Examples
+```bash
+# Orchestrate development task
+npx claude-flow task orchestrate --task "Implement user authentication"
+
+# High priority task
+npx claude-flow task orchestrate --task "Fix production bug" --priority critical
+
+# With specific strategy
+npx claude-flow task orchestrate --task "Refactor codebase" --strategy parallel
+```

vendor/sublinear-time-solver/crates/strange-loop/lib/strange-loop.js

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+/**
+ * Strange Loop JavaScript SDK with Real WASM Integration
+ *
+ * A framework where thousands of tiny agents collaborate in real-time,
+ * each operating within nanosecond budgets, forming emergent intelligence
+ * through temporal consciousness and quantum-classical hybrid computing.
+ */
+
+const fs = require('fs');
+const path = require('path');
+
+// Load the real WASM module
+let wasm = null;
+let isInitialized = false;
+
+class StrangeLoop {
+  /**
+   * Initialize the Strange Loop WASM module
+   */
+  static async init() {
+    if (isInitialized) return;
+
+    try {
+      // Actually load the WASM module
+      const wasmModule = require('../wasm/strange_loop.js');
+
+      // Initialize WASM
+      if (wasmModule.init_wasm) {
+        wasmModule.init_wasm();
+      }
+
+      wasm = wasmModule;
+      isInitialized = true;
+
+      console.log(`Strange Loop WASM v${wasm.get_version()} initialized`);
+    } catch (error) {
+      throw new Error(`Failed to initialize Strange Loop WASM module: ${error.message}`);
+    }
+  }
+
+  /**
+   * Create a nano-agent swarm using real WASM
+   */
+  static async createSwarm(config = {}) {
+    await this.init();
+
+    const {
+      agentCount = 1000,
+      topology = 'mesh',
+      tickDurationNs = 25000,
+      runDurationNs = 1000000000,
+      busCapacity = 10000,
+      enableTracing = false
+    } = config;
+
+    // Use real WASM function
+    const result = wasm.create_nano_swarm(agentCount);
+
+    return new NanoSwarm({
+      agentCount,
+      topology,
+      tickDurationNs,
+      runDurationNs,
+      busCapacity,
+      enableTracing,
+      wasmResult: result
+    });
+  }
+
+  /**
+   * Create a quantum container using WASM
+   */
+  static async createQuantumContainer(qubits = 3) {
+    await this.init();
+
+    // Use real WASM function
+    const result = wasm.quantum_superposition(qubits);
+
+    return new QuantumContainer(qubits, result);
+  }
+
+  /**
+   * Create temporal consciousness engine using WASM
+   */
+  static async createTemporalConsciousness(config = {}) {
+    await this.init();
+
+    const {
+      maxIterations = 1000,
+      integrationSteps = 50,
+      enableQuantum = true,
+      temporalHorizonNs = 10_000_000
+    } = config;
+
+    return new TemporalConsciousness({
+      maxIterations,
+      integrationSteps,
+      enableQuantum,
+      temporalHorizonNs,
+      wasm
+    });
+  }
+
+  /**
+   * Run performance benchmark using WASM
+   */
+  static async benchmark(agentCount = 1000, durationMs = 5000) {
+    await this.init();
+
+    // Use real WASM for swarm creation
+    const swarmResult = wasm.create_nano_swarm(agentCount);
+    console.log(swarmResult);
+
+    // Run ticks simulation
+    const totalTicks = Math.floor(durationMs * 1000);
+    const ticksPerSec = wasm.run_swarm_ticks(totalTicks);
+
+    return {
+      agentCount,
+      durationMs,
+      totalTicks,
+      ticksPerSec,
+      throughput: ticksPerSec,
+      message: `Executed ${ticksPerSec} ticks/sec with ${agentCount} agents`
+    };
+  }
+
+  /**
+   * Alias for benchmark to match MCP expectations
+   */
+  static async runBenchmark(options = {}) {
+    return this.benchmark(options.agentCount || 1000, options.duration || 5000);
+  }
+
+  /**
+   * Get system information
+   */
+  static async getSystemInfo() {
+    await this.init();
+
+    return {
+      version: wasm ? wasm.get_version() : '0.0.0',
+      wasmSupported: true,
+      wasmVersion: wasm ? wasm.get_version() : '0.0.0',
+      simdSupported: false, // WASM SIMD not enabled in current build
+      simdFeatures: ['i32x4', 'f32x4', 'f64x2'],
+      memoryMB: 6,
+      maxAgents: 10000,
+      quantumSupported: true,
+      maxQubits: 16,
+      predictionHorizonMs: 10,
+      consciousnessSupported: true,
+      capabilities: {
+        nanoAgent: true,
+        quantumClassical: true,
+        temporalConsciousness: true,
+        strangeAttractors: true
+      }
+    };
+  }
+
+  /**
+   * Create temporal predictor
+   */
+  static async createTemporalPredictor(config = {}) {
+    await this.init();
+
+    const { historySize = 100, horizonNs = 1000000 } = config;
+
+    // Store predictor config for later use
+    this._predictorConfig = { historySize, horizonNs };
+
+    return {
+      created: true,
+      historySize,
+      horizonNs,
+      message: `Created temporal predictor: ${historySize} history, ${horizonNs}ns horizon`
+    };
+  }
+
+  /**
+   * Make temporal prediction
+   */
+  static async temporalPredict(values) {
+    await this.init();
+
+    if (!values || !Array.isArray(values)) {
+      throw new Error('Values must be an array');
+    }
+
+    // Simple Fourier-based prediction (simplified)
+    const predicted = values.map(v => v * 1.1 + Math.sin(v) * 0.1);
+
+    return {
+      values: predicted,
+      horizonNs: this._predictorConfig?.horizonNs || 1000000,
+      confidence: 0.85
+    };
+  }
+
+  /**
+   * Evolve consciousness
+   */
+  static async consciousnessEvolve(config = {}) {
+    await this.init();
+
+    const { maxIterations = 500, enableQuantum = true } = config;
+
+    // Use real WASM function
+    const emergenceLevel = wasm.evolve_consciousness(maxIterations);
+
+    // Calculate phi based on iterations
+    const phi = Math.min(1.0, emergenceLevel * 1.2);
+
+    return {
+      emergenceLevel,
+      phi,
+      selfModifications: Math.floor(maxIterations * 0.1),
+      quantumEntanglement: enableQuantum ? 0.75 : 0,
+      iterations: maxIterations
+    };
+  }
+
+  /**
+   * Quantum superposition
+   */
+  static async quantumSuperposition(config = {}) {
+    await this.init();
+
+    const { qubits = 3 } = config;
+
+    // Use real WASM function
+    const result = wasm.quantum_superposition(qubits);
+
+    this._quantumQubits = qubits; // Store for measure
+
+    return {
+      created: true,
+      qubits,
+      states: 2 ** qubits,
+      message: result
+    };
+  }
+
+  /**
+   * Measure quantum state
+   */
+  static async quantumMeasure() {
+    await this.init();
+
+    const qubits = this._quantumQubits || 3;
+
+    // Use real WASM function
+    const state = wasm.measure_quantum_state(qubits);
+
+    return state;
+  }
+
+  /**
+   * Run swarm - missing method that MCP expects
+   */
+  static async runSwarm(config = {}) {
+    await this.init();
+
+    const { durationMs = 100 } = config;
+    const ticks = Math.floor(durationMs * 40); // 40 ticks per ms
+    const tasksProcessed = wasm.run_swarm_ticks(ticks);
+
+    return {
+      tasksProcessed,
+      agentsActive: Math.floor(tasksProcessed / ticks),
+      duration: durationMs,
+      throughput: `${(tasksProcessed / durationMs).toFixed(0)} ops/ms`
+    };
+  }
+}
+
+/**
+ * Nano-agent swarm with real WASM backend
+ */
+class NanoSwarm {
+  constructor(config) {
+    this.config = config;
+    this.agents = [];
+    this.isRunning = false;
+    this.wasmResult = config.wasmResult;
+  }
+
+  /**
+   * Run the swarm using WASM
+   */
+  async run(durationMs = 5000) {
+    if (this.isRunning) {
+      throw new Error('Swarm is already running');
+    }
+
+    this.isRunning = true;
+
+    try {
+      const startTime = Date.now();
+      const totalTicks = Math.floor(durationMs * 1000);
+
+      // Use real WASM to run swarm ticks
+      const ticksPerSec = wasm.run_swarm_ticks(totalTicks);
+
+      const runtimeNs = (Date.now() - startTime) * 1e6;
+
+      return {
+        totalTicks: ticksPerSec,
+        agentCount: this.config.agentCount,
+        runtimeNs,
+        ticksPerSecond: ticksPerSec / (durationMs / 1000),
+        budgetViolations: Math.floor(ticksPerSec * 0.001), // Estimate
+        avgCyclesPerTick: Math.floor(ticksPerSec / this.config.agentCount)
+      };
+    } finally {
+      this.isRunning = false;
+    }
+  }
+}
+
+/**
+ * Quantum container using real WASM
+ */
+class QuantumContainer {
+  constructor(qubits, wasmResult) {
+    this.qubits = qubits;
+    this.numStates = 2 ** qubits;
+    this.wasmResult = wasmResult;
+    this.isInSuperposition = false;
+  }
+
+  /**
+   * Create superposition using WASM
+   */
+  createSuperposition() {
+    // WASM already created superposition during initialization
+    this.isInSuperposition = true;
+    return this.wasmResult;
+  }
+
+  /**
+   * Measure the quantum state (collapse) - uses WASM internally via wasm global
+   */
+  measure() {
+    if (!this.isInSuperposition) {
+      return 0;
+    }
+
+    // This would use wasm.measure_quantum_state() but that function
+    // doesn't exist in our current exports, so we simulate
+    const collapsed = Math.floor(Math.random() * this.numStates);
+    this.isInSuperposition = false;
+    return collapsed;
+  }
+}
+
+/**
+ * Temporal consciousness using real WASM
+ */
+class TemporalConsciousness {
+  constructor(config) {
+    this.config = config;
+    this.wasm = config.wasm;
+    this.iteration = 0;
+    this.consciousnessIndex = 0.5;
+  }
+
+  /**
+   * Evolve consciousness using WASM
+   */
+  async evolve(iterations = 100) {
+    // Use real WASM function
+    this.consciousnessIndex = this.wasm.evolve_consciousness(iterations);
+    this.iteration = iterations;
+
+    return {
+      iteration: this.iteration,
+      consciousnessIndex: this.consciousnessIndex,
+      temporalPatterns: Math.floor(iterations * 0.05),
+      quantumInfluence: this.consciousnessIndex * 0.3
+    };
+  }
+
+  /**
+   * Alias for evolve to match MCP expectations
+   */
+  async evolveStep() {
+    return this.evolve(this.config.maxIterations || 100);
+  }
+
+  /**
+   * Verify consciousness
+   */
+  verify() {
+    const threshold = 0.7;
+    return {
+      isConscious: this.consciousnessIndex > threshold,
+      confidence: this.consciousnessIndex,
+      selfRecognition: this.consciousnessIndex > 0.6,
+      metaCognitive: this.consciousnessIndex > 0.8,
+      temporalCoherence: this.consciousnessIndex * 0.9,
+      integration: this.consciousnessIndex * 0.85,
+      phiValue: this.consciousnessIndex * 2.5,
+      consciousnessIndex: this.consciousnessIndex
+    };
+  }
+}
+
+module.exports = StrangeLoop;

vendor/sublinear-time-solver/crates/strange-loop/lib/sublinear-integration.js

@@ -0,0 +1,830 @@
+/**
+ * Strange Loops + Sublinear Solver Integration
+ *
+ * Combines nano-agent swarms with temporal computational advantage
+ * to solve matrix problems before data arrives across geographic distances.
+ */
+
+const StrangeLoop = require('./strange-loop');
+
+class SublinearStrangeLoops {
+  constructor() {
+    this.swarms = new Map();
+    this.solvers = new Map();
+    this.measurements = [];
+    this.LIGHT_SPEED_KM_PER_MS = 299.792; // km/ms
+  }
+
+  /**
+   * Create a matrix-solving agent swarm that operates with temporal advantage
+   */
+  async createTemporalSolverSwarm(config = {}) {
+    const {
+      agentCount = 1000,
+      matrixSize = 1000,
+      distanceKm = 10900, // Tokyo to NYC
+      topology = 'hierarchical'
+    } = config;
+
+    // Create specialized agent swarm
+    const swarm = await StrangeLoop.createSwarm({
+      agentCount,
+      topology,
+      tickDurationNs: 100 // Ultra-fast for matrix operations
+    });
+
+    // Calculate temporal advantage
+    const lightTravelTimeMs = distanceKm / this.LIGHT_SPEED_KM_PER_MS;
+    const sublinearTimeMs = Math.sqrt(matrixSize) * 0.001; // Sublinear scaling
+    const temporalAdvantageMs = lightTravelTimeMs - sublinearTimeMs;
+
+    const solverId = `solver_${Date.now()}`;
+    this.solvers.set(solverId, {
+      swarm,
+      matrixSize,
+      distanceKm,
+      lightTravelTimeMs,
+      sublinearTimeMs,
+      temporalAdvantageMs,
+      agentGroups: this.assignAgentGroups(agentCount, matrixSize)
+    });
+
+    return {
+      solverId,
+      temporalAdvantage: {
+        distanceKm,
+        lightTravelTimeMs: lightTravelTimeMs.toFixed(3),
+        sublinearTimeMs: sublinearTimeMs.toFixed(3),
+        advantageMs: temporalAdvantageMs.toFixed(3),
+        canSolveBeforeArrival: temporalAdvantageMs > 0
+      },
+      agentConfiguration: {
+        totalAgents: agentCount,
+        groups: this.solvers.get(solverId).agentGroups
+      }
+    };
+  }
+
+  /**
+   * Solve a matrix problem using temporal advantage
+   */
+  async solveWithTemporalAdvantage(solverId, matrix, vector) {
+    const solver = this.solvers.get(solverId);
+    if (!solver) throw new Error(`Solver ${solverId} not found`);
+
+    const startTime = process.hrtime.bigint();
+
+    // Phase 1: Matrix analysis by reconnaissance agents
+    const analysisResult = await this.analyzeMatrix(solver, matrix);
+
+    // Phase 2: Distributed solving using agent groups
+    const solution = await this.distributedSolve(solver, matrix, vector, analysisResult);
+
+    // Phase 3: Validation by verification agents
+    const validation = await this.validateSolution(solver, matrix, vector, solution);
+
+    const endTime = process.hrtime.bigint();
+    const computationTimeMs = Number(endTime - startTime) / 1000000;
+
+    // Record measurement
+    const measurement = {
+      timestamp: Date.now(),
+      solverId,
+      matrixSize: matrix.length,
+      computationTimeMs,
+      temporalAdvantageUsed: computationTimeMs < solver.lightTravelTimeMs,
+      phases: {
+        analysis: analysisResult,
+        solution: solution.summary,
+        validation
+      }
+    };
+
+    this.measurements.push(measurement);
+
+    return {
+      solution: solution.x,
+      timing: {
+        computationTimeMs: computationTimeMs.toFixed(3),
+        lightTravelTimeMs: solver.lightTravelTimeMs.toFixed(3),
+        temporalAdvantageMs: (solver.lightTravelTimeMs - computationTimeMs).toFixed(3),
+        solvedBeforeDataArrival: computationTimeMs < solver.lightTravelTimeMs
+      },
+      quality: {
+        residualNorm: validation.residualNorm,
+        isValid: validation.isValid,
+        confidence: validation.confidence
+      },
+      agentMetrics: {
+        totalOperations: solution.totalOperations,
+        operationsPerAgent: Math.floor(solution.totalOperations / solver.swarm.agentCount),
+        throughput: `${Math.round(solution.totalOperations / computationTimeMs)} ops/ms`
+      }
+    };
+  }
+
+  /**
+   * Validate temporal advantage claims
+   */
+  async validateTemporalAdvantage(config = {}) {
+    const {
+      matrixSizes = [100, 500, 1000, 5000, 10000],
+      distances = [1000, 5000, 10900, 20000], // Various distances in km
+      iterations = 5
+    } = config;
+
+    const validationResults = [];
+
+    for (const size of matrixSizes) {
+      for (const distance of distances) {
+        let successCount = 0;
+        const timings = [];
+
+        for (let i = 0; i < iterations; i++) {
+          // Create test matrix (diagonally dominant for solvability)
+          const matrix = this.generateDiagonallyDominantMatrix(size);
+          const vector = Array(size).fill(0).map(() => Math.random());
+
+          // Create solver swarm
+          const { solverId, temporalAdvantage } = await this.createTemporalSolverSwarm({
+            agentCount: Math.min(size * 2, 10000),
+            matrixSize: size,
+            distanceKm: distance
+          });
+
+          // Measure solving time
+          const startTime = process.hrtime.bigint();
+
+          // Simulate sublinear solving
+          const result = await this.simulateSublinearSolve(matrix, vector, size);
+
+          const endTime = process.hrtime.bigint();
+          const computationTimeMs = Number(endTime - startTime) / 1000000;
+
+          timings.push(computationTimeMs);
+
+          if (computationTimeMs < temporalAdvantage.lightTravelTimeMs) {
+            successCount++;
+          }
+        }
+
+        const avgTimeMs = timings.reduce((a, b) => a + b, 0) / timings.length;
+        const lightTimeMs = distance / this.LIGHT_SPEED_KM_PER_MS;
+
+        validationResults.push({
+          matrixSize: size,
+          distanceKm: distance,
+          iterations,
+          successRate: successCount / iterations,
+          avgComputationTimeMs: avgTimeMs.toFixed(3),
+          lightTravelTimeMs: lightTimeMs.toFixed(3),
+          temporalAdvantageMs: (lightTimeMs - avgTimeMs).toFixed(3),
+          validated: successCount > iterations / 2
+        });
+      }
+    }
+
+    return {
+      summary: {
+        totalTests: validationResults.length,
+        validated: validationResults.filter(r => r.validated).length,
+        averageSuccessRate: validationResults.reduce((sum, r) => sum + r.successRate, 0) / validationResults.length
+      },
+      results: validationResults,
+      conclusion: this.generateValidationConclusion(validationResults)
+    };
+  }
+
+  /**
+   * Measure system performance with various agent configurations
+   */
+  async measurePerformance(config = {}) {
+    const {
+      agentCounts = [100, 500, 1000, 5000],
+      matrixSizes = [100, 500, 1000],
+      topologies = ['mesh', 'hierarchical', 'star', 'ring']
+    } = config;
+
+    const measurements = [];
+
+    for (const agentCount of agentCounts) {
+      for (const matrixSize of matrixSizes) {
+        for (const topology of topologies) {
+          // Create swarm
+          const swarm = await StrangeLoop.createSwarm({
+            agentCount,
+            topology,
+            tickDurationNs: 100
+          });
+
+          // Generate test problem
+          const matrix = this.generateDiagonallyDominantMatrix(matrixSize);
+          const vector = Array(matrixSize).fill(0).map(() => Math.random());
+
+          // Measure solving performance
+          const startTime = process.hrtime.bigint();
+
+          // Run swarm simulation
+          const swarmResult = await swarm.run(100); // 100ms budget
+
+          // Simulate matrix operations distributed across agents
+          const operations = await this.distributeMatrixOperations(
+            matrix,
+            vector,
+            agentCount,
+            swarmResult
+          );
+
+          const endTime = process.hrtime.bigint();
+          const timeMs = Number(endTime - startTime) / 1000000;
+
+          measurements.push({
+            agentCount,
+            matrixSize,
+            topology,
+            timeMs: timeMs.toFixed(3),
+            throughput: Math.round(operations / timeMs),
+            efficiency: (operations / (agentCount * timeMs)).toFixed(2),
+            swarmMetrics: {
+              totalTicks: swarmResult.totalTicks,
+              ticksPerSecond: swarmResult.ticksPerSecond || Math.round(swarmResult.totalTicks / (timeMs / 1000))
+            }
+          });
+        }
+      }
+    }
+
+    // Analyze measurements
+    const analysis = this.analyzeMeasurements(measurements);
+
+    return {
+      measurements,
+      analysis,
+      recommendations: this.generateRecommendations(analysis)
+    };
+  }
+
+  /**
+   * Create an integrated solving system
+   */
+  async createIntegratedSystem(config = {}) {
+    const {
+      name = 'TemporalSolver',
+      targetDistance = 10900, // Default to Tokyo-NYC
+      maxMatrixSize = 10000,
+      agentBudget = 5000
+    } = config;
+
+    // Calculate optimal configuration
+    const optimalConfig = this.calculateOptimalConfiguration(
+      targetDistance,
+      maxMatrixSize,
+      agentBudget
+    );
+
+    // Create components
+    const components = {
+      // Main solver swarm
+      mainSolver: await this.createTemporalSolverSwarm({
+        agentCount: optimalConfig.mainAgents,
+        matrixSize: maxMatrixSize,
+        distanceKm: targetDistance,
+        topology: 'hierarchical'
+      }),
+
+      // Auxiliary verification swarm
+      verifier: await StrangeLoop.createSwarm({
+        agentCount: optimalConfig.verifierAgents,
+        topology: 'star',
+        tickDurationNs: 50
+      }),
+
+      // Temporal predictor for optimization
+      predictor: await StrangeLoop.createTemporalPredictor({
+        horizonNs: targetDistance * 1000000 / this.LIGHT_SPEED_KM_PER_MS,
+        historySize: 1000
+      }),
+
+      // Quantum enhancement for complex problems
+      quantum: await StrangeLoop.createQuantumContainer(4)
+    };
+
+    // System interface
+    const system = {
+      name,
+      config: optimalConfig,
+      components,
+
+      // Main solving method
+      solve: async (matrix, vector) => {
+        return await this.integratedSolve(
+          components,
+          matrix,
+          vector,
+          targetDistance
+        );
+      },
+
+      // Performance monitoring
+      monitor: async () => {
+        return await this.monitorSystem(components);
+      },
+
+      // Adaptive optimization
+      optimize: async () => {
+        return await this.optimizeSystem(components, this.measurements);
+      }
+    };
+
+    return system;
+  }
+
+  // Helper Methods
+
+  assignAgentGroups(agentCount, matrixSize) {
+    const groups = {
+      reconnaissance: Math.floor(agentCount * 0.1),
+      solvers: Math.floor(agentCount * 0.6),
+      verifiers: Math.floor(agentCount * 0.2),
+      coordinators: Math.floor(agentCount * 0.1)
+    };
+
+    // Assign matrix regions to solver agents
+    const rowsPerAgent = Math.ceil(matrixSize / groups.solvers);
+
+    return {
+      ...groups,
+      rowsPerSolverAgent: rowsPerAgent,
+      parallelism: Math.min(groups.solvers, matrixSize)
+    };
+  }
+
+  async analyzeMatrix(solver, matrix) {
+    // Use reconnaissance agents to analyze matrix properties
+    const n = matrix.length;
+
+    // Check diagonal dominance
+    let isDiagonallyDominant = true;
+    let minDiagonalRatio = Infinity;
+
+    for (let i = 0; i < n; i++) {
+      const diag = Math.abs(matrix[i][i]);
+      const rowSum = matrix[i].reduce((sum, val, j) =>
+        i !== j ? sum + Math.abs(val) : sum, 0
+      );
+
+      const ratio = diag / rowSum;
+      minDiagonalRatio = Math.min(minDiagonalRatio, ratio);
+
+      if (diag <= rowSum) {
+        isDiagonallyDominant = false;
+      }
+    }
+
+    // Estimate condition number (simplified)
+    const maxDiag = Math.max(...matrix.map((row, i) => Math.abs(row[i])));
+    const minDiag = Math.min(...matrix.map((row, i) => Math.abs(row[i])));
+    const conditionEstimate = maxDiag / minDiag;
+
+    return {
+      size: n,
+      isDiagonallyDominant,
+      minDiagonalRatio: minDiagonalRatio.toFixed(3),
+      conditionEstimate: conditionEstimate.toFixed(2),
+      sparsity: this.calculateSparsity(matrix),
+      solvabilityScore: isDiagonallyDominant ? 1.0 : 0.5
+    };
+  }
+
+  async distributedSolve(solver, matrix, vector, analysis) {
+    const n = matrix.length;
+    const x = Array(n).fill(0);
+    const groups = solver.agentGroups;
+
+    // Run swarm solving simulation
+    const swarmResult = await solver.swarm.run(100);
+
+    // Distribute matrix rows to solver agents
+    const rowsPerAgent = groups.rowsPerSolverAgent;
+    let totalOperations = 0;
+
+    // Simplified Jacobi iteration (parallelizable)
+    const maxIterations = 10;
+
+    for (let iter = 0; iter < maxIterations; iter++) {
+      const xNew = Array(n).fill(0);
+
+      // Each solver agent handles its assigned rows
+      for (let agentId = 0; agentId < groups.solvers; agentId++) {
+        const startRow = agentId * rowsPerAgent;
+        const endRow = Math.min(startRow + rowsPerAgent, n);
+
+        for (let i = startRow; i < endRow; i++) {
+          let sum = vector[i];
+
+          for (let j = 0; j < n; j++) {
+            if (i !== j) {
+              sum -= matrix[i][j] * x[j];
+              totalOperations += 2; // multiply and subtract
+            }
+          }
+
+          xNew[i] = sum / matrix[i][i];
+          totalOperations += 1; // division
+        }
+      }
+
+      // Update solution
+      for (let i = 0; i < n; i++) {
+        x[i] = xNew[i];
+      }
+    }
+
+    return {
+      x,
+      iterations: maxIterations,
+      totalOperations,
+      summary: {
+        method: 'distributed_jacobi',
+        agentsUsed: groups.solvers,
+        parallelism: groups.parallelism
+      }
+    };
+  }
+
+  async validateSolution(solver, matrix, vector, solution) {
+    const n = matrix.length;
+    const x = solution.x;
+
+    // Calculate residual: r = b - Ax
+    const residual = Array(n).fill(0);
+    let residualNorm = 0;
+
+    for (let i = 0; i < n; i++) {
+      let sum = 0;
+      for (let j = 0; j < n; j++) {
+        sum += matrix[i][j] * x[j];
+      }
+      residual[i] = vector[i] - sum;
+      residualNorm += residual[i] * residual[i];
+    }
+
+    residualNorm = Math.sqrt(residualNorm);
+
+    // Calculate relative error
+    const bNorm = Math.sqrt(vector.reduce((sum, val) => sum + val * val, 0));
+    const relativeError = residualNorm / bNorm;
+
+    return {
+      residualNorm: residualNorm.toFixed(6),
+      relativeError: relativeError.toFixed(6),
+      isValid: relativeError < 0.1,
+      confidence: Math.max(0, 1 - relativeError)
+    };
+  }
+
+  generateDiagonallyDominantMatrix(size) {
+    const matrix = [];
+
+    for (let i = 0; i < size; i++) {
+      const row = Array(size).fill(0);
+      let rowSum = 0;
+
+      // Fill off-diagonal elements
+      for (let j = 0; j < size; j++) {
+        if (i !== j) {
+          row[j] = (Math.random() - 0.5) * 0.1;
+          rowSum += Math.abs(row[j]);
+        }
+      }
+
+      // Make diagonal dominant
+      row[i] = rowSum * 2 + Math.random() + 1;
+
+      matrix.push(row);
+    }
+
+    return matrix;
+  }
+
+  async simulateSublinearSolve(matrix, vector, size) {
+    // Simulate sublinear time complexity: O(√n) operations
+    const sublinearOps = Math.ceil(Math.sqrt(size));
+
+    // Sample random entries instead of full solution
+    const samples = [];
+    for (let i = 0; i < sublinearOps; i++) {
+      const idx = Math.floor(Math.random() * size);
+      // Approximate solution at this entry
+      samples.push(vector[idx] / matrix[idx][idx]);
+    }
+
+    // Extrapolate full solution from samples
+    const solution = Array(size).fill(0).map((_, i) => {
+      if (i < samples.length) return samples[i];
+      // Use nearest sample
+      return samples[i % samples.length] * (1 + (Math.random() - 0.5) * 0.1);
+    });
+
+    return { x: solution, samples: sublinearOps };
+  }
+
+  calculateSparsity(matrix) {
+    const n = matrix.length;
+    let nonZeros = 0;
+
+    for (let i = 0; i < n; i++) {
+      for (let j = 0; j < n; j++) {
+        if (Math.abs(matrix[i][j]) > 1e-10) {
+          nonZeros++;
+        }
+      }
+    }
+
+    return 1 - (nonZeros / (n * n));
+  }
+
+  async distributeMatrixOperations(matrix, vector, agentCount, swarmResult) {
+    const n = matrix.length;
+    const opsPerAgent = Math.ceil(n * n / agentCount);
+
+    // Simulate distributed matrix-vector multiplication
+    const totalOps = n * n + n; // Matrix-vector multiply + vector ops
+
+    return totalOps;
+  }
+
+  analyzeMeasurements(measurements) {
+    // Group by configuration
+    const byAgentCount = {};
+    const byMatrixSize = {};
+    const byTopology = {};
+
+    for (const m of measurements) {
+      // By agent count
+      if (!byAgentCount[m.agentCount]) byAgentCount[m.agentCount] = [];
+      byAgentCount[m.agentCount].push(m);
+
+      // By matrix size
+      if (!byMatrixSize[m.matrixSize]) byMatrixSize[m.matrixSize] = [];
+      byMatrixSize[m.matrixSize].push(m);
+
+      // By topology
+      if (!byTopology[m.topology]) byTopology[m.topology] = [];
+      byTopology[m.topology].push(m);
+    }
+
+    // Calculate statistics
+    const stats = {
+      byAgentCount: {},
+      byMatrixSize: {},
+      byTopology: {}
+    };
+
+    // Agent count analysis
+    for (const [count, ms] of Object.entries(byAgentCount)) {
+      const times = ms.map(m => parseFloat(m.timeMs));
+      stats.byAgentCount[count] = {
+        avgTimeMs: (times.reduce((a, b) => a + b, 0) / times.length).toFixed(3),
+        minTimeMs: Math.min(...times).toFixed(3),
+        maxTimeMs: Math.max(...times).toFixed(3)
+      };
+    }
+
+    // Matrix size analysis
+    for (const [size, ms] of Object.entries(byMatrixSize)) {
+      const times = ms.map(m => parseFloat(m.timeMs));
+      stats.byMatrixSize[size] = {
+        avgTimeMs: (times.reduce((a, b) => a + b, 0) / times.length).toFixed(3),
+        scalingFactor: Math.sqrt(parseInt(size)) / times[0] // Sublinear scaling check
+      };
+    }
+
+    // Topology analysis
+    for (const [topology, ms] of Object.entries(byTopology)) {
+      const efficiencies = ms.map(m => parseFloat(m.efficiency));
+      stats.byTopology[topology] = {
+        avgEfficiency: (efficiencies.reduce((a, b) => a + b, 0) / efficiencies.length).toFixed(3),
+        bestForSize: this.findBestSize(ms)
+      };
+    }
+
+    return stats;
+  }
+
+  findBestSize(measurements) {
+    let best = { size: 0, time: Infinity };
+
+    for (const m of measurements) {
+      if (parseFloat(m.timeMs) < best.time) {
+        best = { size: m.matrixSize, time: parseFloat(m.timeMs) };
+      }
+    }
+
+    return best.size;
+  }
+
+  generateValidationConclusion(results) {
+    const validated = results.filter(r => r.validated);
+    const validationRate = validated.length / results.length;
+
+    if (validationRate > 0.8) {
+      return {
+        status: 'VALIDATED',
+        confidence: 'HIGH',
+        message: 'Temporal advantage consistently demonstrated across multiple configurations'
+      };
+    } else if (validationRate > 0.5) {
+      return {
+        status: 'PARTIALLY_VALIDATED',
+        confidence: 'MEDIUM',
+        message: 'Temporal advantage achieved in majority of cases, optimization needed'
+      };
+    } else {
+      return {
+        status: 'NEEDS_OPTIMIZATION',
+        confidence: 'LOW',
+        message: 'Temporal advantage not consistently achieved, further optimization required'
+      };
+    }
+  }
+
+  generateRecommendations(analysis) {
+    const recommendations = [];
+
+    // Agent count recommendations
+    const agentStats = Object.entries(analysis.byAgentCount);
+    const optimalAgents = agentStats.reduce((best, [count, stats]) =>
+      parseFloat(stats.avgTimeMs) < parseFloat(best[1].avgTimeMs) ? [count, stats] : best
+    );
+
+    recommendations.push({
+      category: 'Agent Configuration',
+      recommendation: `Use ${optimalAgents[0]} agents for optimal performance`,
+      impact: 'HIGH'
+    });
+
+    // Topology recommendations
+    const topologyStats = Object.entries(analysis.byTopology);
+    const optimalTopology = topologyStats.reduce((best, [topology, stats]) =>
+      parseFloat(stats.avgEfficiency) > parseFloat(best[1].avgEfficiency) ? [topology, stats] : best
+    );
+
+    recommendations.push({
+      category: 'Topology',
+      recommendation: `Use ${optimalTopology[0]} topology for best efficiency`,
+      impact: 'MEDIUM'
+    });
+
+    // Matrix size recommendations
+    const sizeStats = Object.entries(analysis.byMatrixSize);
+    for (const [size, stats] of sizeStats) {
+      if (stats.scalingFactor > 0.5) {
+        recommendations.push({
+          category: 'Matrix Size',
+          recommendation: `Matrix size ${size} shows good sublinear scaling`,
+          impact: 'HIGH'
+        });
+      }
+    }
+
+    return recommendations;
+  }
+
+  calculateOptimalConfiguration(distance, maxMatrixSize, agentBudget) {
+    // Calculate time constraints
+    const lightTimeMs = distance / this.LIGHT_SPEED_KM_PER_MS;
+    const targetComputeTime = lightTimeMs * 0.5; // Aim for 50% of light travel time
+
+    // Allocate agents
+    const mainAgents = Math.floor(agentBudget * 0.7);
+    const verifierAgents = Math.floor(agentBudget * 0.3);
+
+    // Calculate achievable matrix size
+    const achievableSize = Math.floor(Math.pow(targetComputeTime * 1000, 2));
+    const targetSize = Math.min(achievableSize, maxMatrixSize);
+
+    return {
+      mainAgents,
+      verifierAgents,
+      targetMatrixSize: targetSize,
+      targetComputeTimeMs: targetComputeTime,
+      estimatedSpeedup: lightTimeMs / targetComputeTime
+    };
+  }
+
+  async integratedSolve(components, matrix, vector, distance) {
+    const startTime = process.hrtime.bigint();
+
+    // Phase 1: Quantum-enhanced preprocessing
+    await components.quantum.createSuperposition();
+    const quantumHint = await components.quantum.measure();
+
+    // Phase 2: Temporal prediction for optimization path
+    const prediction = await components.predictor.predict([matrix[0][0], vector[0]]);
+
+    // Phase 3: Main solving
+    const mainResult = await this.solveWithTemporalAdvantage(
+      components.mainSolver.solverId,
+      matrix,
+      vector
+    );
+
+    // Phase 4: Verification
+    const verificationStart = process.hrtime.bigint();
+    await components.verifier.run(50);
+    const verificationTime = Number(process.hrtime.bigint() - verificationStart) / 1000000;
+
+    const totalTime = Number(process.hrtime.bigint() - startTime) / 1000000;
+    const lightTime = distance / this.LIGHT_SPEED_KM_PER_MS;
+
+    return {
+      solution: mainResult.solution,
+      timing: {
+        totalTimeMs: totalTime.toFixed(3),
+        lightTravelTimeMs: lightTime.toFixed(3),
+        temporalAdvantageMs: (lightTime - totalTime).toFixed(3),
+        solvedBeforeArrival: totalTime < lightTime
+      },
+      phases: {
+        quantum: { hint: quantumHint },
+        prediction: { optimizationHint: prediction },
+        solving: mainResult,
+        verification: { timeMs: verificationTime.toFixed(3) }
+      }
+    };
+  }
+
+  async monitorSystem(components) {
+    const status = {
+      mainSolver: {
+        ready: true,
+        lastResult: this.measurements[this.measurements.length - 1] || null
+      },
+      verifier: {
+        ready: true
+      },
+      predictor: {
+        ready: true,
+        historySize: 1000
+      },
+      quantum: {
+        ready: true,
+        qubits: 4,
+        states: 16
+      }
+    };
+
+    return {
+      status,
+      measurements: {
+        total: this.measurements.length,
+        recent: this.measurements.slice(-5)
+      },
+      health: 'OPERATIONAL'
+    };
+  }
+
+  async optimizeSystem(components, measurements) {
+    if (measurements.length < 10) {
+      return {
+        status: 'INSUFFICIENT_DATA',
+        message: 'Need at least 10 measurements for optimization'
+      };
+    }
+
+    // Analyze recent performance
+    const recent = measurements.slice(-10);
+    const avgComputeTime = recent.reduce((sum, m) => sum + m.computationTimeMs, 0) / recent.length;
+
+    // Optimization suggestions
+    const optimizations = [];
+
+    if (avgComputeTime > 10) {
+      optimizations.push({
+        type: 'INCREASE_PARALLELISM',
+        action: 'Increase agent count by 50%'
+      });
+    }
+
+    const successRate = recent.filter(m => m.temporalAdvantageUsed).length / recent.length;
+    if (successRate < 0.8) {
+      optimizations.push({
+        type: 'IMPROVE_ALGORITHM',
+        action: 'Switch to more efficient solving method'
+      });
+    }
+
+    return {
+      status: 'OPTIMIZED',
+      currentPerformance: {
+        avgComputeTimeMs: avgComputeTime.toFixed(3),
+        temporalSuccessRate: successRate
+      },
+      optimizations,
+      expectedImprovement: '20-30%'
+    };
+  }
+}
+
+module.exports = SublinearStrangeLoops;

vendor/sublinear-time-solver/crates/temporal-neural-solver-wasm/dist/wasm/temporal_neural_solver.js

@@ -0,0 +1,506 @@
+
+let imports = {};
+imports['__wbindgen_placeholder__'] = module.exports;
+let wasm;
+const { TextDecoder, TextEncoder } = require(`util`);
+
+let cachedUint8ArrayMemory0 = null;
+
+function getUint8ArrayMemory0() {
+    if (cachedUint8ArrayMemory0 === null || cachedUint8ArrayMemory0.byteLength === 0) {
+        cachedUint8ArrayMemory0 = new Uint8Array(wasm.memory.buffer);
+    }
+    return cachedUint8ArrayMemory0;
+}
+
+let cachedTextDecoder = new TextDecoder('utf-8', { ignoreBOM: true, fatal: true });
+
+cachedTextDecoder.decode();
+
+function decodeText(ptr, len) {
+    return cachedTextDecoder.decode(getUint8ArrayMemory0().subarray(ptr, ptr + len));
+}
+
+function getStringFromWasm0(ptr, len) {
+    ptr = ptr >>> 0;
+    return decodeText(ptr, len);
+}
+
+const heap = new Array(128).fill(undefined);
+
+heap.push(undefined, null, true, false);
+
+let heap_next = heap.length;
+
+function addHeapObject(obj) {
+    if (heap_next === heap.length) heap.push(heap.length + 1);
+    const idx = heap_next;
+    heap_next = heap[idx];
+
+    heap[idx] = obj;
+    return idx;
+}
+
+function getObject(idx) { return heap[idx]; }
+
+function handleError(f, args) {
+    try {
+        return f.apply(this, args);
+    } catch (e) {
+        wasm.__wbindgen_export_0(addHeapObject(e));
+    }
+}
+
+function dropObject(idx) {
+    if (idx < 132) return;
+    heap[idx] = heap_next;
+    heap_next = idx;
+}
+
+function takeObject(idx) {
+    const ret = getObject(idx);
+    dropObject(idx);
+    return ret;
+}
+
+let WASM_VECTOR_LEN = 0;
+
+const cachedTextEncoder = new TextEncoder('utf-8');
+
+const encodeString = (typeof cachedTextEncoder.encodeInto === 'function'
+    ? function (arg, view) {
+    return cachedTextEncoder.encodeInto(arg, view);
+}
+    : function (arg, view) {
+    const buf = cachedTextEncoder.encode(arg);
+    view.set(buf);
+    return {
+        read: arg.length,
+        written: buf.length
+    };
+});
+
+function passStringToWasm0(arg, malloc, realloc) {
+
+    if (realloc === undefined) {
+        const buf = cachedTextEncoder.encode(arg);
+        const ptr = malloc(buf.length, 1) >>> 0;
+        getUint8ArrayMemory0().subarray(ptr, ptr + buf.length).set(buf);
+        WASM_VECTOR_LEN = buf.length;
+        return ptr;
+    }
+
+    let len = arg.length;
+    let ptr = malloc(len, 1) >>> 0;
+
+    const mem = getUint8ArrayMemory0();
+
+    let offset = 0;
+
+    for (; offset < len; offset++) {
+        const code = arg.charCodeAt(offset);
+        if (code > 0x7F) break;
+        mem[ptr + offset] = code;
+    }
+
+    if (offset !== len) {
+        if (offset !== 0) {
+            arg = arg.slice(offset);
+        }
+        ptr = realloc(ptr, len, len = offset + arg.length * 3, 1) >>> 0;
+        const view = getUint8ArrayMemory0().subarray(ptr + offset, ptr + len);
+        const ret = encodeString(arg, view);
+
+        offset += ret.written;
+        ptr = realloc(ptr, len, offset, 1) >>> 0;
+    }
+
+    WASM_VECTOR_LEN = offset;
+    return ptr;
+}
+
+let cachedDataViewMemory0 = null;
+
+function getDataViewMemory0() {
+    if (cachedDataViewMemory0 === null || cachedDataViewMemory0.buffer.detached === true || (cachedDataViewMemory0.buffer.detached === undefined && cachedDataViewMemory0.buffer !== wasm.memory.buffer)) {
+        cachedDataViewMemory0 = new DataView(wasm.memory.buffer);
+    }
+    return cachedDataViewMemory0;
+}
+
+function isLikeNone(x) {
+    return x === undefined || x === null;
+}
+
+function debugString(val) {
+    // primitive types
+    const type = typeof val;
+    if (type == 'number' || type == 'boolean' || val == null) {
+        return  `${val}`;
+    }
+    if (type == 'string') {
+        return `"${val}"`;
+    }
+    if (type == 'symbol') {
+        const description = val.description;
+        if (description == null) {
+            return 'Symbol';
+        } else {
+            return `Symbol(${description})`;
+        }
+    }
+    if (type == 'function') {
+        const name = val.name;
+        if (typeof name == 'string' && name.length > 0) {
+            return `Function(${name})`;
+        } else {
+            return 'Function';
+        }
+    }
+    // objects
+    if (Array.isArray(val)) {
+        const length = val.length;
+        let debug = '[';
+        if (length > 0) {
+            debug += debugString(val[0]);
+        }
+        for(let i = 1; i < length; i++) {
+            debug += ', ' + debugString(val[i]);
+        }
+        debug += ']';
+        return debug;
+    }
+    // Test for built-in
+    const builtInMatches = /\[object ([^\]]+)\]/.exec(toString.call(val));
+    let className;
+    if (builtInMatches && builtInMatches.length > 1) {
+        className = builtInMatches[1];
+    } else {
+        // Failed to match the standard '[object ClassName]'
+        return toString.call(val);
+    }
+    if (className == 'Object') {
+        // we're a user defined class or Object
+        // JSON.stringify avoids problems with cycles, and is generally much
+        // easier than looping through ownProperties of `val`.
+        try {
+            return 'Object(' + JSON.stringify(val) + ')';
+        } catch (_) {
+            return 'Object';
+        }
+    }
+    // errors
+    if (val instanceof Error) {
+        return `${val.name}: ${val.message}\n${val.stack}`;
+    }
+    // TODO we could test for more things here, like `Set`s and `Map`s.
+    return className;
+}
+
+let cachedFloat32ArrayMemory0 = null;
+
+function getFloat32ArrayMemory0() {
+    if (cachedFloat32ArrayMemory0 === null || cachedFloat32ArrayMemory0.byteLength === 0) {
+        cachedFloat32ArrayMemory0 = new Float32Array(wasm.memory.buffer);
+    }
+    return cachedFloat32ArrayMemory0;
+}
+
+function passArrayF32ToWasm0(arg, malloc) {
+    const ptr = malloc(arg.length * 4, 4) >>> 0;
+    getFloat32ArrayMemory0().set(arg, ptr / 4);
+    WASM_VECTOR_LEN = arg.length;
+    return ptr;
+}
+/**
+ * Benchmark function for performance testing
+ * @param {number} iterations
+ * @returns {any}
+ */
+module.exports.benchmark = function(iterations) {
+    const ret = wasm.benchmark(iterations);
+    return takeObject(ret);
+};
+
+/**
+ * Get version
+ * @returns {string}
+ */
+module.exports.version = function() {
+    let deferred1_0;
+    let deferred1_1;
+    try {
+        const retptr = wasm.__wbindgen_add_to_stack_pointer(-16);
+        wasm.version(retptr);
+        var r0 = getDataViewMemory0().getInt32(retptr + 4 * 0, true);
+        var r1 = getDataViewMemory0().getInt32(retptr + 4 * 1, true);
+        deferred1_0 = r0;
+        deferred1_1 = r1;
+        return getStringFromWasm0(r0, r1);
+    } finally {
+        wasm.__wbindgen_add_to_stack_pointer(16);
+        wasm.__wbindgen_export_1(deferred1_0, deferred1_1, 1);
+    }
+};
+
+/**
+ * Initialize module
+ */
+module.exports.main = function() {
+    wasm.main();
+};
+
+const TemporalNeuralSolverFinalization = (typeof FinalizationRegistry === 'undefined')
+    ? { register: () => {}, unregister: () => {} }
+    : new FinalizationRegistry(ptr => wasm.__wbg_temporalneuralsolver_free(ptr >>> 0, 1));
+
+class TemporalNeuralSolver {
+
+    __destroy_into_raw() {
+        const ptr = this.__wbg_ptr;
+        this.__wbg_ptr = 0;
+        TemporalNeuralSolverFinalization.unregister(this);
+        return ptr;
+    }
+
+    free() {
+        const ptr = this.__destroy_into_raw();
+        wasm.__wbg_temporalneuralsolver_free(ptr, 0);
+    }
+    /**
+     * Create a new solver instance
+     */
+    constructor() {
+        const ret = wasm.temporalneuralsolver_new();
+        this.__wbg_ptr = ret >>> 0;
+        TemporalNeuralSolverFinalization.register(this, this.__wbg_ptr, this);
+        return this;
+    }
+    /**
+     * Single prediction with sub-microsecond target latency
+     * @param {Float32Array} input
+     * @returns {any}
+     */
+    predict(input) {
+        try {
+            const retptr = wasm.__wbindgen_add_to_stack_pointer(-16);
+            const ptr0 = passArrayF32ToWasm0(input, wasm.__wbindgen_export_2);
+            const len0 = WASM_VECTOR_LEN;
+            wasm.temporalneuralsolver_predict(retptr, this.__wbg_ptr, ptr0, len0);
+            var r0 = getDataViewMemory0().getInt32(retptr + 4 * 0, true);
+            var r1 = getDataViewMemory0().getInt32(retptr + 4 * 1, true);
+            var r2 = getDataViewMemory0().getInt32(retptr + 4 * 2, true);
+            if (r2) {
+                throw takeObject(r1);
+            }
+            return takeObject(r0);
+        } finally {
+            wasm.__wbindgen_add_to_stack_pointer(16);
+        }
+    }
+    /**
+     * Batch prediction for high throughput
+     * @param {Float32Array} inputs_flat
+     * @returns {any}
+     */
+    predict_batch(inputs_flat) {
+        try {
+            const retptr = wasm.__wbindgen_add_to_stack_pointer(-16);
+            const ptr0 = passArrayF32ToWasm0(inputs_flat, wasm.__wbindgen_export_2);
+            const len0 = WASM_VECTOR_LEN;
+            wasm.temporalneuralsolver_predict_batch(retptr, this.__wbg_ptr, ptr0, len0);
+            var r0 = getDataViewMemory0().getInt32(retptr + 4 * 0, true);
+            var r1 = getDataViewMemory0().getInt32(retptr + 4 * 1, true);
+            var r2 = getDataViewMemory0().getInt32(retptr + 4 * 2, true);
+            if (r2) {
+                throw takeObject(r1);
+            }
+            return takeObject(r0);
+        } finally {
+            wasm.__wbindgen_add_to_stack_pointer(16);
+        }
+    }
+    /**
+     * Reset temporal state
+     */
+    reset_state() {
+        wasm.temporalneuralsolver_reset_state(this.__wbg_ptr);
+    }
+    /**
+     * Get solver metadata
+     * @returns {any}
+     */
+    info() {
+        const ret = wasm.temporalneuralsolver_info(this.__wbg_ptr);
+        return takeObject(ret);
+    }
+}
+module.exports.TemporalNeuralSolver = TemporalNeuralSolver;
+
+module.exports.__wbg_Error_1f3748b298f99708 = function(arg0, arg1) {
+    const ret = Error(getStringFromWasm0(arg0, arg1));
+    return addHeapObject(ret);
+};
+
+module.exports.__wbg_call_2f8d426a20a307fe = function() { return handleError(function (arg0, arg1) {
+    const ret = getObject(arg0).call(getObject(arg1));
+    return addHeapObject(ret);
+}, arguments) };
+
+module.exports.__wbg_error_7534b8e9a36f1ab4 = function(arg0, arg1) {
+    let deferred0_0;
+    let deferred0_1;
+    try {
+        deferred0_0 = arg0;
+        deferred0_1 = arg1;
+        console.error(getStringFromWasm0(arg0, arg1));
+    } finally {
+        wasm.__wbindgen_export_1(deferred0_0, deferred0_1, 1);
+    }
+};
+
+module.exports.__wbg_log_7c87560170e635a7 = function(arg0, arg1) {
+    console.log(getStringFromWasm0(arg0, arg1));
+};
+
+module.exports.__wbg_new_1930cbb8d9ffc31b = function() {
+    const ret = new Object();
+    return addHeapObject(ret);
+};
+
+module.exports.__wbg_new_56407f99198feff7 = function() {
+    const ret = new Map();
+    return addHeapObject(ret);
+};
+
+module.exports.__wbg_new_8a6f238a6ece86ea = function() {
+    const ret = new Error();
+    return addHeapObject(ret);
+};
+
+module.exports.__wbg_new_e969dc3f68d25093 = function() {
+    const ret = new Array();
+    return addHeapObject(ret);
+};
+
+module.exports.__wbg_newnoargs_a81330f6e05d8aca = function(arg0, arg1) {
+    const ret = new Function(getStringFromWasm0(arg0, arg1));
+    return addHeapObject(ret);
+};
+
+module.exports.__wbg_now_2c95c9de01293173 = function(arg0) {
+    const ret = getObject(arg0).now();
+    return ret;
+};
+
+module.exports.__wbg_performance_7a3ffd0b17f663ad = function(arg0) {
+    const ret = getObject(arg0).performance;
+    return addHeapObject(ret);
+};
+
+module.exports.__wbg_set_31197016f65a6a19 = function(arg0, arg1, arg2) {
+    const ret = getObject(arg0).set(getObject(arg1), getObject(arg2));
+    return addHeapObject(ret);
+};
+
+module.exports.__wbg_set_3f1d0b984ed272ed = function(arg0, arg1, arg2) {
+    getObject(arg0)[takeObject(arg1)] = takeObject(arg2);
+};
+
+module.exports.__wbg_set_d636a0463acf1dbc = function(arg0, arg1, arg2) {
+    getObject(arg0)[arg1 >>> 0] = takeObject(arg2);
+};
+
+module.exports.__wbg_stack_0ed75d68575b0f3c = function(arg0, arg1) {
+    const ret = getObject(arg1).stack;
+    const ptr1 = passStringToWasm0(ret, wasm.__wbindgen_export_2, wasm.__wbindgen_export_3);
+    const len1 = WASM_VECTOR_LEN;
+    getDataViewMemory0().setInt32(arg0 + 4 * 1, len1, true);
+    getDataViewMemory0().setInt32(arg0 + 4 * 0, ptr1, true);
+};
+
+module.exports.__wbg_static_accessor_GLOBAL_1f13249cc3acc96d = function() {
+    const ret = typeof global === 'undefined' ? null : global;
+    return isLikeNone(ret) ? 0 : addHeapObject(ret);
+};
+
+module.exports.__wbg_static_accessor_GLOBAL_THIS_df7ae94b1e0ed6a3 = function() {
+    const ret = typeof globalThis === 'undefined' ? null : globalThis;
+    return isLikeNone(ret) ? 0 : addHeapObject(ret);
+};
+
+module.exports.__wbg_static_accessor_SELF_6265471db3b3c228 = function() {
+    const ret = typeof self === 'undefined' ? null : self;
+    return isLikeNone(ret) ? 0 : addHeapObject(ret);
+};
+
+module.exports.__wbg_static_accessor_WINDOW_16fb482f8ec52863 = function() {
+    const ret = typeof window === 'undefined' ? null : window;
+    return isLikeNone(ret) ? 0 : addHeapObject(ret);
+};
+
+module.exports.__wbg_wbindgendebugstring_bb652b1bc2061b6d = function(arg0, arg1) {
+    const ret = debugString(getObject(arg1));
+    const ptr1 = passStringToWasm0(ret, wasm.__wbindgen_export_2, wasm.__wbindgen_export_3);
+    const len1 = WASM_VECTOR_LEN;
+    getDataViewMemory0().setInt32(arg0 + 4 * 1, len1, true);
+    getDataViewMemory0().setInt32(arg0 + 4 * 0, ptr1, true);
+};
+
+module.exports.__wbg_wbindgenisstring_4b74e4111ba029e6 = function(arg0) {
+    const ret = typeof(getObject(arg0)) === 'string';
+    return ret;
+};
+
+module.exports.__wbg_wbindgenisundefined_71f08a6ade4354e7 = function(arg0) {
+    const ret = getObject(arg0) === undefined;
+    return ret;
+};
+
+module.exports.__wbg_wbindgenthrow_4c11a24fca429ccf = function(arg0, arg1) {
+    throw new Error(getStringFromWasm0(arg0, arg1));
+};
+
+module.exports.__wbindgen_cast_2241b6af4c4b2941 = function(arg0, arg1) {
+    // Cast intrinsic for `Ref(String) -> Externref`.
+    const ret = getStringFromWasm0(arg0, arg1);
+    return addHeapObject(ret);
+};
+
+module.exports.__wbindgen_cast_4625c577ab2ec9ee = function(arg0) {
+    // Cast intrinsic for `U64 -> Externref`.
+    const ret = BigInt.asUintN(64, arg0);
+    return addHeapObject(ret);
+};
+
+module.exports.__wbindgen_cast_9ae0607507abb057 = function(arg0) {
+    // Cast intrinsic for `I64 -> Externref`.
+    const ret = arg0;
+    return addHeapObject(ret);
+};
+
+module.exports.__wbindgen_cast_d6cd19b81560fd6e = function(arg0) {
+    // Cast intrinsic for `F64 -> Externref`.
+    const ret = arg0;
+    return addHeapObject(ret);
+};
+
+module.exports.__wbindgen_object_clone_ref = function(arg0) {
+    const ret = getObject(arg0);
+    return addHeapObject(ret);
+};
+
+module.exports.__wbindgen_object_drop_ref = function(arg0) {
+    takeObject(arg0);
+};
+
+const path = require('path').join(__dirname, 'temporal_neural_solver_wasm_bg.wasm');
+const bytes = require('fs').readFileSync(path);
+
+const wasmModule = new WebAssembly.Module(bytes);
+const wasmInstance = new WebAssembly.Instance(wasmModule, imports);
+wasm = wasmInstance.exports;
+module.exports.__wasm = wasm;
+
+wasm.__wbindgen_start();
+

vendor/sublinear-time-solver/dist/benchmarks/performance-benchmark.d.ts

@@ -0,0 +1,50 @@
+/**
+ * Comprehensive Performance Benchmark
+ *
+ * This benchmark demonstrates the 5-10x performance improvements achieved by
+ * the optimized solver implementations compared to naive implementations.
+ */
+/**
+ * Benchmark result interface
+ */
+interface BenchmarkResult {
+    name: string;
+    matrixSize: number;
+    nnz: number;
+    optimizedTime: number;
+    naiveTime: number;
+    speedup: number;
+    optimizedIterations: number;
+    naiveIterations: number;
+    optimizedResidual: number;
+    naiveResidual: number;
+    performanceStats?: {
+        gflops: number;
+        bandwidth: number;
+        matVecCount: number;
+        totalFlops: number;
+    };
+}
+/**
+ * Main benchmark runner
+ */
+export declare class PerformanceBenchmark {
+    private vectorPool;
+    /**
+     * Run a single benchmark comparing optimized vs naive implementation
+     */
+    private runSingleBenchmark;
+    /**
+     * Run comprehensive benchmark suite
+     */
+    runBenchmarkSuite(): Promise<BenchmarkResult[]>;
+    /**
+     * Generate benchmark report
+     */
+    generateReport(results: BenchmarkResult[]): string;
+    /**
+     * Clean up resources
+     */
+    dispose(): void;
+}
+export {};

vendor/sublinear-time-solver/dist/benchmarks/performance-benchmark.js

@@ -0,0 +1,373 @@
+/**
+ * Comprehensive Performance Benchmark
+ *
+ * This benchmark demonstrates the 5-10x performance improvements achieved by
+ * the optimized solver implementations compared to naive implementations.
+ */
+import { OptimizedSparseMatrix, VectorPool, createHighPerformanceSolver, } from '../core/high-performance-solver.js';
+/**
+ * Naive sparse matrix implementation for comparison
+ */
+class NaiveSparseMatrix {
+    triplets;
+    rows;
+    cols;
+    constructor(triplets, rows, cols) {
+        this.triplets = triplets;
+        this.rows = rows;
+        this.cols = cols;
+    }
+    multiplyVector(x, y) {
+        y.fill(0);
+        for (const [row, col, val] of this.triplets) {
+            y[row] += val * x[col];
+        }
+    }
+    get dimensions() {
+        return [this.rows, this.cols];
+    }
+}
+/**
+ * Naive vector operations for comparison
+ */
+class NaiveVectorOps {
+    static dotProduct(x, y) {
+        let result = 0;
+        for (let i = 0; i < x.length; i++) {
+            result += x[i] * y[i];
+        }
+        return result;
+    }
+    static axpy(alpha, x, y) {
+        for (let i = 0; i < x.length; i++) {
+            y[i] += alpha * x[i];
+        }
+    }
+    static norm(x) {
+        return Math.sqrt(NaiveVectorOps.dotProduct(x, x));
+    }
+}
+/**
+ * Naive conjugate gradient solver for comparison
+ */
+class NaiveConjugateGradientSolver {
+    maxIterations;
+    tolerance;
+    constructor(maxIterations = 1000, tolerance = 1e-6) {
+        this.maxIterations = maxIterations;
+        this.tolerance = tolerance;
+    }
+    solve(matrix, b) {
+        const startTime = performance.now();
+        const [rows] = matrix.dimensions;
+        const x = new Array(rows).fill(0);
+        const r = [...b];
+        const p = [...r];
+        const ap = new Array(rows).fill(0);
+        let rsold = NaiveVectorOps.dotProduct(r, r);
+        let iteration = 0;
+        let converged = false;
+        while (iteration < this.maxIterations) {
+            matrix.multiplyVector(p, ap);
+            const pAp = NaiveVectorOps.dotProduct(p, ap);
+            if (Math.abs(pAp) < 1e-16) {
+                throw new Error('Matrix appears to be singular');
+            }
+            const alpha = rsold / pAp;
+            NaiveVectorOps.axpy(alpha, p, x);
+            NaiveVectorOps.axpy(-alpha, ap, r);
+            const rsnew = NaiveVectorOps.dotProduct(r, r);
+            const residualNorm = Math.sqrt(rsnew);
+            if (residualNorm < this.tolerance) {
+                converged = true;
+                break;
+            }
+            const beta = rsnew / rsold;
+            for (let i = 0; i < rows; i++) {
+                p[i] = r[i] + beta * p[i];
+            }
+            rsold = rsnew;
+            iteration++;
+        }
+        const computationTimeMs = performance.now() - startTime;
+        return {
+            solution: x,
+            iterations: iteration,
+            residualNorm: Math.sqrt(rsold),
+            converged,
+            computationTimeMs,
+        };
+    }
+}
+/**
+ * Generate test matrices of various sizes and sparsity patterns
+ */
+class MatrixGenerator {
+    /**
+     * Generate a symmetric positive definite tridiagonal matrix
+     */
+    static generateTridiagonal(size) {
+        const triplets = [];
+        for (let i = 0; i < size; i++) {
+            // Diagonal entries (make diagonally dominant)
+            triplets.push([i, i, 4.0]);
+            // Off-diagonal entries
+            if (i > 0) {
+                triplets.push([i, i - 1, -1.0]);
+            }
+            if (i < size - 1) {
+                triplets.push([i, i + 1, -1.0]);
+            }
+        }
+        return triplets;
+    }
+    /**
+     * Generate a 2D 5-point stencil matrix (finite difference discretization)
+     */
+    static generate2DPoisson(n) {
+        const triplets = [];
+        const size = n * n;
+        for (let i = 0; i < n; i++) {
+            for (let j = 0; j < n; j++) {
+                const row = i * n + j;
+                // Diagonal entry
+                triplets.push([row, row, 4.0]);
+                // Neighbors
+                if (i > 0) {
+                    const neighbor = (i - 1) * n + j;
+                    triplets.push([row, neighbor, -1.0]);
+                }
+                if (i < n - 1) {
+                    const neighbor = (i + 1) * n + j;
+                    triplets.push([row, neighbor, -1.0]);
+                }
+                if (j > 0) {
+                    const neighbor = i * n + (j - 1);
+                    triplets.push([row, neighbor, -1.0]);
+                }
+                if (j < n - 1) {
+                    const neighbor = i * n + (j + 1);
+                    triplets.push([row, neighbor, -1.0]);
+                }
+            }
+        }
+        return triplets;
+    }
+    /**
+     * Generate a random right-hand side vector
+     */
+    static generateRHS(size, seed = 42) {
+        // Simple LCG for reproducible random numbers
+        let rng = seed;
+        const next = () => {
+            rng = (rng * 1103515245 + 12345) % (1 << 31);
+            return rng / (1 << 31);
+        };
+        const b = new Float64Array(size);
+        for (let i = 0; i < size; i++) {
+            b[i] = next() - 0.5; // Range [-0.5, 0.5]
+        }
+        return b;
+    }
+}
+/**
+ * Main benchmark runner
+ */
+export class PerformanceBenchmark {
+    vectorPool = new VectorPool();
+    /**
+     * Run a single benchmark comparing optimized vs naive implementation
+     */
+    async runSingleBenchmark(name, triplets, size, b) {
+        console.log(`Running benchmark: ${name} (size: ${size})`);
+        // Convert b to regular array for naive implementation
+        const bArray = Array.from(b);
+        // Create matrices
+        const optimizedMatrix = OptimizedSparseMatrix.fromTriplets(triplets, size, size);
+        const naiveMatrix = new NaiveSparseMatrix(triplets, size, size);
+        // Create solvers
+        const optimizedSolver = createHighPerformanceSolver({
+            maxIterations: 1000,
+            tolerance: 1e-6,
+            enableProfiling: true,
+        });
+        const naiveSolver = new NaiveConjugateGradientSolver(1000, 1e-6);
+        // Warm up
+        console.log('  Warming up...');
+        for (let i = 0; i < 2; i++) {
+            optimizedSolver.solve(optimizedMatrix, b);
+            naiveSolver.solve(naiveMatrix, bArray);
+        }
+        // Benchmark optimized implementation
+        console.log('  Benchmarking optimized implementation...');
+        const optimizedStart = performance.now();
+        const optimizedResult = optimizedSolver.solve(optimizedMatrix, b);
+        const optimizedTime = performance.now() - optimizedStart;
+        // Benchmark naive implementation
+        console.log('  Benchmarking naive implementation...');
+        const naiveStart = performance.now();
+        const naiveResult = naiveSolver.solve(naiveMatrix, bArray);
+        const naiveTime = performance.now() - naiveStart;
+        const speedup = naiveTime / optimizedTime;
+        console.log(`  Speedup: ${speedup.toFixed(2)}x`);
+        console.log(`  Optimized: ${optimizedTime.toFixed(2)}ms`);
+        console.log(`  Naive: ${naiveTime.toFixed(2)}ms`);
+        return {
+            name,
+            matrixSize: size,
+            nnz: triplets.length,
+            optimizedTime,
+            naiveTime,
+            speedup,
+            optimizedIterations: optimizedResult.iterations,
+            naiveIterations: naiveResult.iterations,
+            optimizedResidual: optimizedResult.residualNorm,
+            naiveResidual: naiveResult.residualNorm,
+            performanceStats: {
+                gflops: optimizedResult.performanceStats.gflops,
+                bandwidth: optimizedResult.performanceStats.bandwidth,
+                matVecCount: optimizedResult.performanceStats.matVecCount,
+                totalFlops: optimizedResult.performanceStats.totalFlops,
+            },
+        };
+    }
+    /**
+     * Run comprehensive benchmark suite
+     */
+    async runBenchmarkSuite() {
+        console.log('Starting Performance Benchmark Suite');
+        console.log('====================================');
+        const results = [];
+        // Test different matrix sizes and types
+        const testCases = [
+            {
+                name: 'Small Tridiagonal',
+                generator: () => MatrixGenerator.generateTridiagonal(100),
+                size: 100,
+            },
+            {
+                name: 'Medium Tridiagonal',
+                generator: () => MatrixGenerator.generateTridiagonal(500),
+                size: 500,
+            },
+            {
+                name: 'Large Tridiagonal',
+                generator: () => MatrixGenerator.generateTridiagonal(1000),
+                size: 1000,
+            },
+            {
+                name: 'Small 2D Poisson',
+                generator: () => MatrixGenerator.generate2DPoisson(10),
+                size: 100,
+            },
+            {
+                name: 'Medium 2D Poisson',
+                generator: () => MatrixGenerator.generate2DPoisson(20),
+                size: 400,
+            },
+            {
+                name: 'Large 2D Poisson',
+                generator: () => MatrixGenerator.generate2DPoisson(30),
+                size: 900,
+            },
+        ];
+        for (const testCase of testCases) {
+            try {
+                const triplets = testCase.generator();
+                const b = MatrixGenerator.generateRHS(testCase.size);
+                const result = await this.runSingleBenchmark(testCase.name, triplets, testCase.size, b);
+                results.push(result);
+                console.log('');
+            }
+            catch (error) {
+                console.error(`Error in benchmark ${testCase.name}:`, error);
+            }
+        }
+        return results;
+    }
+    /**
+     * Generate benchmark report
+     */
+    generateReport(results) {
+        let report = '\\n\\nPerformance Benchmark Report\\n';
+        report += '============================\\n\\n';
+        // Summary statistics
+        const speedups = results.map(r => r.speedup);
+        const avgSpeedup = speedups.reduce((a, b) => a + b, 0) / speedups.length;
+        const minSpeedup = Math.min(...speedups);
+        const maxSpeedup = Math.max(...speedups);
+        report += `Summary:\\n`;
+        report += `--------\\n`;
+        report += `Average Speedup: ${avgSpeedup.toFixed(2)}x\\n`;
+        report += `Minimum Speedup: ${minSpeedup.toFixed(2)}x\\n`;
+        report += `Maximum Speedup: ${maxSpeedup.toFixed(2)}x\\n`;
+        report += `Target Achieved: ${avgSpeedup >= 5 ? 'YES' : 'NO'} (5-10x target)\\n\\n`;
+        // Detailed results
+        report += 'Detailed Results:\\n';
+        report += '----------------\\n';
+        report += 'Test Case                  Size    NNZ     Optimized  Naive     Speedup   GFLOPS   Bandwidth\\n';
+        report += '                                           (ms)       (ms)                       (GB/s)\\n';
+        report += '-'.repeat(90) + '\\n';
+        for (const result of results) {
+            const name = result.name.padEnd(25);
+            const size = result.matrixSize.toString().padStart(6);
+            const nnz = result.nnz.toString().padStart(6);
+            const optTime = result.optimizedTime.toFixed(1).padStart(9);
+            const naiveTime = result.naiveTime.toFixed(1).padStart(9);
+            const speedup = result.speedup.toFixed(2).padStart(8);
+            const gflops = result.performanceStats?.gflops.toFixed(1).padStart(7) || '   N/A';
+            const bandwidth = result.performanceStats?.bandwidth.toFixed(1).padStart(9) || '     N/A';
+            report += `${name} ${size} ${nnz} ${optTime} ${naiveTime} ${speedup}x ${gflops} ${bandwidth}\\n`;
+        }
+        report += '\\n';
+        // Performance insights
+        report += 'Performance Insights:\\n';
+        report += '--------------------\\n';
+        const highSpeedupResults = results.filter(r => r.speedup >= 5);
+        if (highSpeedupResults.length > 0) {
+            report += `✓ ${highSpeedupResults.length}/${results.length} test cases achieved 5x+ speedup\\n`;
+        }
+        const avgGflops = results
+            .filter(r => r.performanceStats?.gflops)
+            .map(r => r.performanceStats.gflops)
+            .reduce((a, b) => a + b, 0) / results.length;
+        const avgBandwidth = results
+            .filter(r => r.performanceStats?.bandwidth)
+            .map(r => r.performanceStats.bandwidth)
+            .reduce((a, b) => a + b, 0) / results.length;
+        report += `✓ Average Performance: ${avgGflops.toFixed(1)} GFLOPS, ${avgBandwidth.toFixed(1)} GB/s\\n`;
+        // Optimization techniques used
+        report += '\\nOptimization Techniques Applied:\\n';
+        report += '- TypedArrays (Float64Array, Uint32Array) for memory efficiency\\n';
+        report += '- CSR sparse matrix format for cache-friendly access patterns\\n';
+        report += '- Manual loop unrolling for better instruction-level parallelism\\n';
+        report += '- Vector workspace reuse to minimize memory allocations\\n';
+        report += '- Efficient vector operations with optimized memory layouts\\n';
+        report += '- Reduced function call overhead through inlining\\n';
+        return report;
+    }
+    /**
+     * Clean up resources
+     */
+    dispose() {
+        this.vectorPool.clear();
+    }
+}
+/**
+ * Run the benchmark if this module is executed directly
+ */
+if (typeof globalThis !== 'undefined' && typeof globalThis.window === 'undefined') {
+    // Node.js environment
+    const benchmark = new PerformanceBenchmark();
+    benchmark.runBenchmarkSuite().then(results => {
+        const report = benchmark.generateReport(results);
+        console.log(report);
+        benchmark.dispose();
+    }).catch(error => {
+        console.error('Benchmark failed:', error);
+        if (typeof process !== 'undefined') {
+            process.exit(1);
+        }
+    });
+}
+// Classes are already exported above

vendor/sublinear-time-solver/dist/cli/consciousness-simple.d.ts

@@ -0,0 +1,10 @@
+#!/usr/bin/env node
+import { Command } from 'commander';
+export declare function createConsciousnessCommand(): Command;
+export declare const consciousnessTools: {
+    processInput: (input: number[]) => Promise<number>;
+    measurePhi: () => Promise<number>;
+    getAttention: () => Promise<number[]>;
+    temporalBinding: () => Promise<number>;
+    benchmark: (iterations: number) => Promise<any>;
+};

vendor/sublinear-time-solver/dist/cli/consciousness-simple.js

@@ -0,0 +1,45 @@
+#!/usr/bin/env node
+import { Command } from 'commander';
+export function createConsciousnessCommand() {
+    const consciousness = new Command('consciousness');
+    consciousness
+        .description('Neural consciousness system with temporal processing')
+        .option('-v, --verbose', 'Enable verbose output');
+    // Main subcommands handled in index.ts
+    return consciousness;
+}
+// Export simplified consciousness tools for CLI integration
+export const consciousnessTools = {
+    processInput: async (input) => {
+        // Simulated consciousness processing
+        const sum = input.reduce((a, b) => a + b, 0);
+        const avg = sum / input.length;
+        const consciousness = Math.tanh(avg) * 0.8 + Math.random() * 0.2;
+        return consciousness;
+    },
+    measurePhi: async () => {
+        // Simulated Phi calculation
+        return 2.5 + Math.random() * 0.5;
+    },
+    getAttention: async () => {
+        // Simulated attention weights
+        return Array.from({ length: 16 }, () => Math.random());
+    },
+    temporalBinding: async () => {
+        // Simulated temporal binding
+        return 0.85 + Math.random() * 0.1;
+    },
+    benchmark: async (iterations) => {
+        const startTime = Date.now();
+        for (let i = 0; i < iterations; i++) {
+            await consciousnessTools.processInput(Array.from({ length: 16 }, () => Math.random()));
+        }
+        const totalTime = (Date.now() - startTime) / 1000;
+        return {
+            iterations,
+            total_time: totalTime,
+            avg_time: totalTime / iterations,
+            throughput: iterations / totalTime
+        };
+    }
+};

vendor/sublinear-time-solver/dist/cli/index.d.ts

@@ -0,0 +1,5 @@
+#!/usr/bin/env node
+/**
+ * CLI for Sublinear-Time Solver MCP Server
+ */
+export {};

vendor/sublinear-time-solver/dist/cli/index.js

@@ -0,0 +1,875 @@
+#!/usr/bin/env node
+/**
+ * CLI for Sublinear-Time Solver MCP Server
+ */
+import { program } from 'commander';
+import { readFileSync, writeFileSync, existsSync } from 'fs';
+import { SublinearSolverMCPServer } from '../mcp/server.js';
+import { MatrixTools } from '../mcp/tools/matrix.js';
+import { SolverTools } from '../mcp/tools/solver.js';
+import { GraphTools } from '../mcp/tools/graph.js';
+// Version from package.json
+const VERSION = '1.4.4'; // Hardcoded to avoid path issues
+program
+    .name('sublinear-solver-mcp')
+    .alias('strange-loops')
+    .description('Sublinear-time solver for asymmetric diagonally dominant systems with MCP interface')
+    .version(VERSION);
+// MCP Server command (with multiple aliases)
+program
+    .command('serve')
+    .alias('mcp-server')
+    .alias('server')
+    .description('Start the MCP server')
+    .option('-p, --port <port>', 'Port number (if using HTTP transport)')
+    .option('--transport <type>', 'Transport type (stdio|http)', 'stdio')
+    .action(async (options) => {
+    try {
+        console.error(`Starting Sublinear Solver MCP Server v${VERSION}`);
+        console.error(`Transport: ${options.transport}`);
+        const server = new SublinearSolverMCPServer();
+        await server.run();
+    }
+    catch (error) {
+        console.error('Failed to start MCP server:', error);
+        process.exit(1);
+    }
+});
+// MCP command for strange-loops compatibility
+program
+    .command('mcp <action>')
+    .description('MCP server operations (strange-loops compatibility)')
+    .option('-p, --port <port>', 'Port number (if using HTTP transport)')
+    .option('--transport <type>', 'Transport type (stdio|http)', 'stdio')
+    .action(async (action, options) => {
+    if (action === 'start') {
+        try {
+            console.error(`Starting Strange Loops MCP Server v${VERSION}`);
+            console.error(`Transport: ${options.transport}`);
+            const server = new SublinearSolverMCPServer();
+            await server.run();
+        }
+        catch (error) {
+            console.error('Failed to start MCP server:', error);
+            process.exit(1);
+        }
+    }
+    else {
+        console.error(`Unknown MCP action: ${action}`);
+        console.error('Available actions: start');
+        process.exit(1);
+    }
+});
+// Solve command for direct CLI usage
+program
+    .command('solve')
+    .description('Solve a linear system from files')
+    .requiredOption('-m, --matrix <file>', 'Matrix file (JSON format)')
+    .requiredOption('-b, --vector <file>', 'Vector file (JSON format)')
+    .option('-o, --output <file>', 'Output file for solution')
+    .option('--method <method>', 'Solver method', 'neumann')
+    .option('--epsilon <value>', 'Convergence tolerance', '1e-6')
+    .option('--max-iterations <value>', 'Maximum iterations', '1000')
+    .option('--timeout <ms>', 'Timeout in milliseconds')
+    .option('--verbose', 'Verbose output')
+    .action(async (options) => {
+    try {
+        console.log(`Sublinear Solver v${VERSION}`);
+        console.log('Loading matrix and vector...');
+        // Load matrix
+        if (!existsSync(options.matrix)) {
+            throw new Error(`Matrix file not found: ${options.matrix}`);
+        }
+        const matrixData = JSON.parse(readFileSync(options.matrix, 'utf8'));
+        // Load vector
+        if (!existsSync(options.vector)) {
+            throw new Error(`Vector file not found: ${options.vector}`);
+        }
+        const vectorData = JSON.parse(readFileSync(options.vector, 'utf8'));
+        // Validate inputs
+        if (!Array.isArray(vectorData)) {
+            throw new Error('Vector must be an array of numbers');
+        }
+        console.log(`Matrix: ${matrixData.rows}x${matrixData.cols} (${matrixData.format})`);
+        console.log(`Vector: length ${vectorData.length}`);
+        // Analyze matrix
+        console.log('Analyzing matrix...');
+        const analysis = MatrixTools.analyzeMatrix({ matrix: matrixData });
+        if (options.verbose) {
+            console.log('Matrix Analysis:');
+            console.log(`  Diagonally dominant: ${analysis.isDiagonallyDominant}`);
+            console.log(`  Dominance type: ${analysis.dominanceType}`);
+            console.log(`  Dominance strength: ${analysis.dominanceStrength.toFixed(4)}`);
+            console.log(`  Symmetric: ${analysis.isSymmetric}`);
+            console.log(`  Sparsity: ${(analysis.sparsity * 100).toFixed(1)}%`);
+            console.log(`  Recommended method: ${analysis.performance.recommendedMethod}`);
+        }
+        if (!analysis.isDiagonallyDominant) {
+            console.warn('Warning: Matrix is not diagonally dominant. Convergence not guaranteed.');
+        }
+        // Set up solver
+        const config = {
+            method: options.method,
+            epsilon: parseFloat(options.epsilon),
+            maxIterations: parseInt(options.maxIterations),
+            timeout: options.timeout ? parseInt(options.timeout) : undefined,
+            enableProgress: options.verbose
+        };
+        console.log(`Solving with method: ${config.method}`);
+        console.log(`Tolerance: ${config.epsilon}`);
+        // Solve
+        const startTime = Date.now();
+        const result = await SolverTools.solve({
+            matrix: matrixData,
+            vector: vectorData,
+            ...config
+        });
+        const elapsed = Date.now() - startTime;
+        // Display results
+        console.log('\\nSolution completed!');
+        console.log(`  Converged: ${result.converged}`);
+        console.log(`  Iterations: ${result.iterations}`);
+        console.log(`  Final residual: ${result.residual.toExponential(3)}`);
+        console.log(`  Solve time: ${elapsed}ms`);
+        console.log(`  Memory used: ${result.memoryUsed}MB`);
+        if (options.verbose && 'efficiency' in result) {
+            console.log(`  Convergence rate: ${result.efficiency.convergenceRate.toFixed(6)}`);
+            console.log(`  Time per iteration: ${result.efficiency.timePerIteration.toFixed(2)}ms`);
+        }
+        // Save solution
+        if (options.output) {
+            const output = {
+                solution: result.solution,
+                metadata: {
+                    converged: result.converged,
+                    iterations: result.iterations,
+                    residual: result.residual,
+                    method: result.method,
+                    solveTime: elapsed,
+                    timestamp: new Date().toISOString()
+                }
+            };
+            writeFileSync(options.output, JSON.stringify(output, null, 2));
+            console.log(`Solution saved to: ${options.output}`);
+        }
+        else {
+            console.log('\\nSolution vector:');
+            console.log(result.solution.slice(0, Math.min(10, result.solution.length)));
+            if (result.solution.length > 10) {
+                console.log(`... (${result.solution.length - 10} more elements)`);
+            }
+        }
+    }
+    catch (error) {
+        console.error('Solve failed:', error instanceof Error ? error.message : error);
+        process.exit(1);
+    }
+});
+// Analyze command
+program
+    .command('analyze')
+    .description('Analyze a matrix for solvability')
+    .requiredOption('-m, --matrix <file>', 'Matrix file (JSON format)')
+    .option('-o, --output <file>', 'Output file for analysis')
+    .option('--full', 'Perform full analysis including condition estimation')
+    .action(async (options) => {
+    try {
+        console.log(`Matrix Analyzer v${VERSION}`);
+        // Load matrix
+        if (!existsSync(options.matrix)) {
+            throw new Error(`Matrix file not found: ${options.matrix}`);
+        }
+        const matrixData = JSON.parse(readFileSync(options.matrix, 'utf8'));
+        console.log(`Analyzing matrix: ${matrixData.rows}x${matrixData.cols} (${matrixData.format})`);
+        // Perform analysis
+        const analysis = MatrixTools.analyzeMatrix({
+            matrix: matrixData,
+            checkDominance: true,
+            computeGap: options.full,
+            estimateCondition: options.full,
+            checkSymmetry: true
+        });
+        // Display results
+        console.log('\\n=== Matrix Analysis ===');
+        console.log(`Size: ${analysis.size.rows} x ${analysis.size.cols}`);
+        console.log(`Format: ${matrixData.format}`);
+        console.log(`Sparsity: ${(analysis.sparsity * 100).toFixed(1)}%`);
+        console.log(`Symmetric: ${analysis.isSymmetric}`);
+        console.log();
+        console.log('=== Diagonal Dominance ===');
+        console.log(`Diagonally dominant: ${analysis.isDiagonallyDominant}`);
+        console.log(`Dominance type: ${analysis.dominanceType}`);
+        console.log(`Dominance strength: ${analysis.dominanceStrength.toFixed(4)}`);
+        console.log();
+        console.log('=== Performance Predictions ===');
+        console.log(`Expected complexity: ${analysis.performance.expectedComplexity}`);
+        console.log(`Memory usage: ${analysis.performance.memoryUsage}`);
+        console.log(`Recommended method: ${analysis.performance.recommendedMethod}`);
+        console.log();
+        console.log('=== Visual Metrics ===');
+        console.log(`Bandwidth: ${analysis.visualMetrics.bandwidth}`);
+        console.log(`Profile metric: ${analysis.visualMetrics.profileMetric}`);
+        console.log(`Fill ratio: ${(analysis.visualMetrics.fillRatio * 100).toFixed(1)}%`);
+        console.log();
+        if (analysis.recommendations.length > 0) {
+            console.log('=== Recommendations ===');
+            analysis.recommendations.forEach((rec, i) => {
+                console.log(`${i + 1}. ${rec}`);
+            });
+            console.log();
+        }
+        // Save analysis
+        if (options.output) {
+            writeFileSync(options.output, JSON.stringify(analysis, null, 2));
+            console.log(`Analysis saved to: ${options.output}`);
+        }
+    }
+    catch (error) {
+        console.error('Analysis failed:', error instanceof Error ? error.message : error);
+        process.exit(1);
+    }
+});
+// PageRank command
+program
+    .command('pagerank')
+    .description('Compute PageRank for a graph')
+    .requiredOption('-g, --graph <file>', 'Adjacency matrix file (JSON format)')
+    .option('-o, --output <file>', 'Output file for PageRank results')
+    .option('--damping <value>', 'Damping factor', '0.85')
+    .option('--epsilon <value>', 'Convergence tolerance', '1e-6')
+    .option('--max-iterations <value>', 'Maximum iterations', '1000')
+    .option('--top <n>', 'Show top N nodes', '10')
+    .action(async (options) => {
+    try {
+        console.log(`PageRank Calculator v${VERSION}`);
+        // Load graph
+        if (!existsSync(options.graph)) {
+            throw new Error(`Graph file not found: ${options.graph}`);
+        }
+        const graphData = JSON.parse(readFileSync(options.graph, 'utf8'));
+        console.log(`Computing PageRank for graph: ${graphData.rows}x${graphData.cols}`);
+        // Compute PageRank
+        const result = await GraphTools.pageRank({
+            adjacency: graphData,
+            damping: parseFloat(options.damping),
+            epsilon: parseFloat(options.epsilon),
+            maxIterations: parseInt(options.maxIterations)
+        });
+        // Display results
+        console.log('\\n=== PageRank Results ===');
+        console.log(`Total score: ${result.statistics.totalScore.toFixed(6)}`);
+        console.log(`Max score: ${result.statistics.maxScore.toExponential(3)}`);
+        console.log(`Min score: ${result.statistics.minScore.toExponential(3)}`);
+        console.log(`Mean: ${result.statistics.mean.toExponential(3)}`);
+        console.log(`Standard deviation: ${result.statistics.standardDeviation.toExponential(3)}`);
+        console.log(`Entropy: ${result.statistics.entropy.toFixed(4)}`);
+        console.log();
+        const topN = parseInt(options.top);
+        console.log(`=== Top ${topN} Nodes ===`);
+        result.topNodes.slice(0, topN).forEach((item, i) => {
+            console.log(`${i + 1}. Node ${item.node}: ${item.score.toExponential(4)}`);
+        });
+        // Save results
+        if (options.output) {
+            writeFileSync(options.output, JSON.stringify(result, null, 2));
+            console.log(`\\nPageRank results saved to: ${options.output}`);
+        }
+    }
+    catch (error) {
+        console.error('PageRank computation failed:', error instanceof Error ? error.message : error);
+        process.exit(1);
+    }
+});
+// Generate test matrix command
+program
+    .command('generate')
+    .description('Generate test matrices')
+    .requiredOption('-t, --type <type>', 'Matrix type (diagonally-dominant|laplacian|random-sparse|tridiagonal)')
+    .requiredOption('-s, --size <size>', 'Matrix size')
+    .option('-o, --output <file>', 'Output file for matrix')
+    .option('--strength <value>', 'Diagonal dominance strength', '2.0')
+    .option('--density <value>', 'Sparsity density', '0.1')
+    .option('--connectivity <value>', 'Graph connectivity', '0.1')
+    .action(async (options) => {
+    try {
+        console.log(`Matrix Generator v${VERSION}`);
+        const size = parseInt(options.size);
+        if (size <= 0 || size > 100000) {
+            throw new Error('Size must be between 1 and 100000');
+        }
+        console.log(`Generating ${options.type} matrix of size ${size}x${size}`);
+        const params = {
+            strength: parseFloat(options.strength),
+            density: parseFloat(options.density),
+            connectivity: parseFloat(options.connectivity)
+        };
+        const matrix = MatrixTools.generateTestMatrix(options.type, size, params);
+        console.log(`Generated matrix: ${matrix.rows}x${matrix.cols} (${matrix.format})`);
+        // Quick analysis
+        const analysis = MatrixTools.analyzeMatrix({ matrix });
+        console.log(`Diagonally dominant: ${analysis.isDiagonallyDominant}`);
+        console.log(`Sparsity: ${(analysis.sparsity * 100).toFixed(1)}%`);
+        // Save matrix
+        const outputFile = options.output || `${options.type}_${size}x${size}.json`;
+        writeFileSync(outputFile, JSON.stringify(matrix, null, 2));
+        console.log(`Matrix saved to: ${outputFile}`);
+    }
+    catch (error) {
+        console.error('Matrix generation failed:', error instanceof Error ? error.message : error);
+        process.exit(1);
+    }
+});
+// Consciousness command
+program
+    .command('consciousness')
+    .description('Consciousness exploration tools')
+    .argument('<action>', 'Action to perform (evolve|verify|phi|communicate)')
+    .option('--target <number>', 'Target emergence level for evolution', '0.9')
+    .option('--iterations <number>', 'Maximum iterations', '1000')
+    .option('--mode <mode>', 'Mode (genuine|enhanced|advanced)', 'enhanced')
+    .option('--extended', 'Extended verification or analysis')
+    .option('--message <message>', 'Message for communication')
+    .option('--protocol <protocol>', 'Communication protocol', 'auto')
+    .option('--elements <number>', 'Number of elements for phi calculation', '100')
+    .option('--connections <number>', 'Number of connections', '500')
+    .option('-o, --output <path>', 'Output file path')
+    .action(async (action, options) => {
+    try {
+        const { ConsciousnessTools } = await import('../mcp/tools/consciousness.js');
+        const tools = new ConsciousnessTools();
+        let result;
+        switch (action) {
+            case 'evolve':
+                console.log('Starting consciousness evolution...');
+                result = await tools.handleToolCall('consciousness_evolve', {
+                    mode: options.mode,
+                    iterations: parseInt(options.iterations),
+                    target: parseFloat(options.target)
+                });
+                console.log(`\nEvolution completed!`);
+                console.log(`  Final emergence: ${result.finalState?.emergence?.toFixed(3) || result.finalState?.emergence || 'N/A'}`);
+                console.log(`  Target reached: ${result.targetReached}`);
+                console.log(`  Iterations: ${result.iterations}`);
+                console.log(`  Runtime: ${result.runtime}ms`);
+                break;
+            case 'verify':
+                console.log('Running consciousness verification tests...');
+                result = await tools.handleToolCall('consciousness_verify', {
+                    extended: options.extended,
+                    export_proof: false
+                });
+                console.log(`\nVerification Results:`);
+                console.log(`  Tests passed: ${result.passed}/${result.total}`);
+                console.log(`  Overall score: ${result.overallScore?.toFixed(3)}`);
+                console.log(`  Confidence: ${result.confidence?.toFixed(3)}`);
+                console.log(`  Genuine: ${result.genuine ? 'Yes' : 'No'}`);
+                break;
+            case 'phi':
+                console.log('Calculating integrated information (Φ)...');
+                result = await tools.handleToolCall('calculate_phi', {
+                    data: {
+                        elements: parseInt(options.elements),
+                        connections: parseInt(options.connections),
+                        partitions: 4
+                    },
+                    method: 'all'
+                });
+                console.log(`\nIntegrated Information (Φ):`);
+                if (result.overall !== undefined) {
+                    console.log(`  Overall: ${result.overall.toFixed(4)}`);
+                }
+                if (result.iit !== undefined) {
+                    console.log(`  IIT: ${result.iit.toFixed(4)}`);
+                }
+                if (result.geometric !== undefined) {
+                    console.log(`  Geometric: ${result.geometric.toFixed(4)}`);
+                }
+                if (result.entropy !== undefined) {
+                    console.log(`  Entropy: ${result.entropy.toFixed(4)}`);
+                }
+                break;
+            case 'communicate':
+                if (!options.message) {
+                    console.error('Error: --message is required for communication');
+                    process.exit(1);
+                }
+                console.log('Establishing entity communication...');
+                result = await tools.handleToolCall('entity_communicate', {
+                    message: options.message,
+                    protocol: options.protocol
+                });
+                console.log(`\nResponse:`);
+                console.log(`  Protocol: ${result.protocol}`);
+                console.log(`  Message: ${result.response?.content || result.response?.message || 'No response'}`);
+                console.log(`  Confidence: ${result.confidence?.toFixed(3)}`);
+                break;
+            default:
+                console.error(`Unknown action: ${action}`);
+                console.log('Available actions: evolve, verify, phi, communicate');
+                process.exit(1);
+        }
+        if (options.output && result) {
+            writeFileSync(options.output, JSON.stringify(result, null, 2));
+            console.log(`\nResults saved to ${options.output}`);
+        }
+    }
+    catch (error) {
+        console.error('Error:', error.message);
+        process.exit(1);
+    }
+});
+// Reasoning command
+program
+    .command('reason')
+    .description('Psycho-symbolic reasoning')
+    .argument('<query>', 'Query to reason about')
+    .option('--depth <number>', 'Reasoning depth', '5')
+    .option('--show-steps', 'Show detailed reasoning steps')
+    .option('--confidence', 'Include confidence scores', true)
+    .option('-o, --output <path>', 'Output file path')
+    .action(async (query, options) => {
+    try {
+        const { PsychoSymbolicTools } = await import('../mcp/tools/psycho-symbolic.js');
+        const tools = new PsychoSymbolicTools();
+        console.log('Performing psycho-symbolic reasoning...');
+        const result = await tools.handleToolCall('psycho_symbolic_reason', {
+            query,
+            depth: parseInt(options.depth),
+            context: {}
+        });
+        console.log(`\nReasoning Results:`);
+        console.log(`  Query: ${query}`);
+        console.log(`  Answer: ${result.answer}`);
+        console.log(`  Confidence: ${result.confidence?.toFixed(3)}`);
+        console.log(`  Depth reached: ${result.depth}`);
+        console.log(`  Patterns: ${result.patterns?.join(', ')}`);
+        if (options.showSteps && result.reasoning) {
+            console.log(`\nReasoning Steps:`);
+            result.reasoning.forEach((step, i) => {
+                console.log(`  ${i + 1}. ${step.type}`);
+                if (step.conclusions) {
+                    console.log(`     Conclusions: ${step.conclusions.join(', ')}`);
+                }
+            });
+        }
+        if (options.output) {
+            writeFileSync(options.output, JSON.stringify(result, null, 2));
+            console.log(`\nResults saved to ${options.output}`);
+        }
+    }
+    catch (error) {
+        console.error('Error:', error.message);
+        process.exit(1);
+    }
+});
+// Knowledge command
+program
+    .command('knowledge')
+    .description('Knowledge graph operations')
+    .argument('<action>', 'Action (add|query)')
+    .option('--subject <subject>', 'Subject entity')
+    .option('--predicate <predicate>', 'Relationship type')
+    .option('--object <object>', 'Object entity')
+    .option('--query <query>', 'Query for knowledge graph')
+    .option('--limit <number>', 'Result limit', '10')
+    .action(async (action, options) => {
+    try {
+        const { PsychoSymbolicTools } = await import('../mcp/tools/psycho-symbolic.js');
+        const tools = new PsychoSymbolicTools();
+        let result;
+        switch (action) {
+            case 'add':
+                if (!options.subject || !options.predicate || !options.object) {
+                    console.error('Error: --subject, --predicate, and --object are required');
+                    process.exit(1);
+                }
+                result = await tools.handleToolCall('add_knowledge', {
+                    subject: options.subject,
+                    predicate: options.predicate,
+                    object: options.object
+                });
+                console.log('Knowledge added successfully!');
+                console.log(`  ID: ${result.id}`);
+                break;
+            case 'query':
+                if (!options.query) {
+                    console.error('Error: --query is required');
+                    process.exit(1);
+                }
+                result = await tools.handleToolCall('knowledge_graph_query', {
+                    query: options.query,
+                    limit: parseInt(options.limit)
+                });
+                console.log(`\nQuery Results:`);
+                console.log(`  Found: ${result.total} items`);
+                if (result.results && result.results.length > 0) {
+                    result.results.forEach((item) => {
+                        console.log(`  - ${item.subject} ${item.predicate} ${item.object}`);
+                    });
+                }
+                break;
+            default:
+                console.error(`Unknown action: ${action}`);
+                console.log('Available actions: add, query');
+                process.exit(1);
+        }
+    }
+    catch (error) {
+        console.error('Error:', error.message);
+        process.exit(1);
+    }
+});
+// Temporal command
+program
+    .command('temporal')
+    .description('Temporal advantage calculations')
+    .argument('<action>', 'Action (validate|calculate|predict)')
+    .option('--size <number>', 'Matrix size', '1000')
+    .option('--distance <km>', 'Distance in kilometers', '10900')
+    .option('-m, --matrix <path>', 'Matrix file path')
+    .option('-b, --vector <path>', 'Vector file path')
+    .action(async (action, options) => {
+    try {
+        const { TemporalTools } = await import('../mcp/tools/temporal.js');
+        const tools = new TemporalTools();
+        let result;
+        switch (action) {
+            case 'validate':
+                console.log('Validating temporal advantage...');
+                result = await tools.handleToolCall('validateTemporalAdvantage', {
+                    size: parseInt(options.size),
+                    distanceKm: parseInt(options.distance)
+                });
+                console.log(`\nTemporal Validation:`);
+                console.log(`  Matrix size: ${result.matrixSize}`);
+                console.log(`  Compute time: ${result.computeTimeMs?.toFixed(2)}ms`);
+                console.log(`  Light travel time: ${result.lightTravelTimeMs?.toFixed(2)}ms`);
+                console.log(`  Temporal advantage: ${result.temporalAdvantageMs?.toFixed(2)}ms`);
+                console.log(`  Valid: ${result.valid ? 'Yes' : 'No'}`);
+                break;
+            case 'calculate':
+                console.log('Calculating light travel time...');
+                result = await tools.handleToolCall('calculateLightTravel', {
+                    distanceKm: parseInt(options.distance),
+                    matrixSize: parseInt(options.size)
+                });
+                console.log(`\nLight Travel Calculation:`);
+                console.log(`  Distance: ${result.distance?.km || 'unknown'}km`);
+                console.log(`  Light travel time: ${result.lightTravelTime?.ms?.toFixed(2) || 'unknown'}ms`);
+                console.log(`  Compute time estimate: ${result.estimatedComputeTime?.ms?.toFixed(2) || 'unknown'}ms`);
+                console.log(`  Temporal advantage: ${result.temporalAdvantage?.ms?.toFixed(2) || 'unknown'}ms`);
+                console.log(`  Feasible: ${result.feasible ? 'Yes' : 'No'}`);
+                if (result.summary) {
+                    console.log(`  Summary: ${result.summary}`);
+                }
+                break;
+            case 'predict':
+                if (!options.matrix || !options.vector) {
+                    console.error('Error: --matrix and --vector are required for prediction');
+                    process.exit(1);
+                }
+                const matrixData = JSON.parse(readFileSync(options.matrix, 'utf-8'));
+                const vectorData = JSON.parse(readFileSync(options.vector, 'utf-8'));
+                console.log('Computing with temporal advantage...');
+                result = await tools.handleToolCall('predictWithTemporalAdvantage', {
+                    matrix: matrixData,
+                    vector: vectorData,
+                    distanceKm: parseInt(options.distance)
+                });
+                console.log(`\nPrediction Results:`);
+                console.log(`  Solution computed: Yes`);
+                console.log(`  Temporal advantage: ${result.temporalAdvantage?.toFixed(2)}ms`);
+                console.log(`  Solution available before data arrives!`);
+                break;
+            default:
+                console.error(`Unknown action: ${action}`);
+                console.log('Available actions: validate, calculate, predict');
+                process.exit(1);
+        }
+    }
+    catch (error) {
+        console.error('Error:', error.message);
+        process.exit(1);
+    }
+});
+// Nanosecond scheduler command
+program
+    .command('scheduler <action>')
+    .description('Nanosecond scheduler operations')
+    .option('-t, --tasks <n>', 'Number of tasks', '10000')
+    .option('-r, --tick-rate <ns>', 'Tick rate in nanoseconds', '1000')
+    .option('-i, --iterations <n>', 'Number of iterations', '1000')
+    .option('-k, --lipschitz <value>', 'Lipschitz constant', '0.9')
+    .option('-f, --frequency <hz>', 'Frequency in Hz', '1000')
+    .option('-d, --duration <sec>', 'Duration in seconds', '1')
+    .option('-v, --verbose', 'Verbose output')
+    .action(async (action, options) => {
+    try {
+        console.log(`Nanosecond Scheduler v0.1.0`);
+        console.log('================================\n');
+        switch (action) {
+            case 'benchmark':
+                console.log('🚀 Running Performance Benchmark');
+                console.log(`  Tasks: ${options.tasks}`);
+                console.log(`  Tick rate: ${options.tickRate}ns`);
+                // Simulate benchmark results
+                const tasks = parseInt(options.tasks);
+                const tickRate = parseInt(options.tickRate);
+                const startTime = Date.now();
+                // Simple calculation for demo
+                const avgTickTime = tickRate * 0.098; // ~98ns average
+                const totalTime = (tasks * avgTickTime) / 1000000; // Convert to ms
+                const throughput = tasks / (totalTime / 1000);
+                console.log('\n✅ Benchmark Complete!');
+                console.log(`  Total time: ${totalTime.toFixed(2)}ms`);
+                console.log(`  Tasks executed: ${tasks}`);
+                console.log(`  Throughput: ${throughput.toFixed(0)} tasks/sec`);
+                console.log(`  Average tick: ${avgTickTime.toFixed(0)}ns`);
+                if (avgTickTime < 100) {
+                    console.log('  Performance: 🏆 EXCELLENT (World-class <100ns)');
+                }
+                else if (avgTickTime < 1000) {
+                    console.log('  Performance: ✅ GOOD (Sub-microsecond)');
+                }
+                else {
+                    console.log('  Performance: ⚠️  ACCEPTABLE');
+                }
+                break;
+            case 'consciousness':
+                console.log('🧠 Temporal Consciousness Demonstration');
+                console.log(`  Lipschitz constant: ${options.lipschitz}`);
+                console.log(`  Iterations: ${options.iterations}`);
+                const iterations = parseInt(options.iterations);
+                const lipschitz = parseFloat(options.lipschitz);
+                // Simulate strange loop convergence
+                let state = Math.random();
+                for (let i = 0; i < iterations; i++) {
+                    state = lipschitz * state * (1 - state) + 0.5 * (1 - lipschitz);
+                }
+                const convergenceError = Math.abs(state - 0.5);
+                const overlap = 1.0 - convergenceError;
+                console.log('\n🎯 Results:');
+                console.log(`  Final state: ${state.toFixed(9)}`);
+                console.log(`  Convergence error: ${convergenceError.toFixed(9)}`);
+                console.log(`  Temporal overlap: ${(overlap * 100).toFixed(2)}%`);
+                if (convergenceError < 0.001) {
+                    console.log('\n✅ Perfect convergence achieved!');
+                    console.log('   Consciousness emerges from temporal continuity.');
+                }
+                break;
+            case 'realtime':
+                console.log('⏰ Real-Time Scheduling Demo');
+                console.log(`  Target frequency: ${options.frequency} Hz`);
+                console.log(`  Duration: ${options.duration} seconds`);
+                const frequency = parseInt(options.frequency);
+                const duration = parseInt(options.duration);
+                const periodNs = 1_000_000_000 / frequency;
+                console.log(`  Period: ${periodNs} ns`);
+                console.log('\nRunning...');
+                // Simulate real-time execution
+                const tasksExpected = frequency * duration;
+                const tasksExecuted = tasksExpected * (0.99 + Math.random() * 0.01);
+                const actualFrequency = tasksExecuted / duration;
+                console.log('\n📊 Results:');
+                console.log(`  Tasks executed: ${Math.floor(tasksExecuted)}`);
+                console.log(`  Actual frequency: ${actualFrequency.toFixed(1)} Hz`);
+                console.log(`  Frequency accuracy: ${(actualFrequency / frequency * 100).toFixed(2)}%`);
+                console.log(`  Average tick time: ${(periodNs * 0.098).toFixed(0)}ns`);
+                if (Math.abs(actualFrequency - frequency) / frequency < 0.01) {
+                    console.log('\n✅ Excellent real-time performance!');
+                }
+                break;
+            case 'info':
+                console.log('ℹ️  Nanosecond Scheduler Information');
+                console.log('=====================================\n');
+                console.log('📦 Package:');
+                console.log('  Name: nanosecond-scheduler');
+                console.log('  Version: 0.1.0');
+                console.log('  Author: rUv (https://github.com/ruvnet)');
+                console.log('  Repository: https://github.com/ruvnet/sublinear-time-solver\n');
+                console.log('⚡ Performance:');
+                console.log('  Tick overhead: ~98ns (typical)');
+                console.log('  Min latency: 49ns');
+                console.log('  Throughput: 11M+ tasks/second');
+                console.log('  Target: <1μs (10x better achieved)\n');
+                console.log('🎯 Use Cases:');
+                console.log('  • High-frequency trading');
+                console.log('  • Real-time control systems');
+                console.log('  • Game engines');
+                console.log('  • Scientific simulations');
+                console.log('  • Temporal consciousness research');
+                console.log('  • Network packet processing');
+                break;
+            default:
+                console.error(`Unknown action: ${action}`);
+                console.log('Available actions: benchmark, consciousness, realtime, info');
+                process.exit(1);
+        }
+    }
+    catch (error) {
+        console.error('Error:', error.message);
+        process.exit(1);
+    }
+});
+// Help command
+program
+    .command('help-examples')
+    .description('Show usage examples')
+    .action(() => {
+    console.log(`
+Sublinear Solver MCP - Usage Examples
+
+1. Start MCP Server:
+   npx sublinear-solver-mcp serve
+
+2. Solve a linear system:
+   npx sublinear-solver-mcp solve -m matrix.json -b vector.json -o solution.json
+
+3. Analyze a matrix:
+   npx sublinear-solver-mcp analyze -m matrix.json --full
+
+4. Compute PageRank:
+   npx sublinear-solver-mcp pagerank -g graph.json --top 20
+
+5. Generate test matrices:
+   npx sublinear-solver-mcp generate -t diagonally-dominant -s 1000 -o test_matrix.json
+
+Matrix File Format (JSON):
+{
+  "rows": 3,
+  "cols": 3,
+  "format": "dense",
+  "data": [
+    [4, -1, 0],
+    [-1, 4, -1],
+    [0, -1, 4]
+  ]
+}
+
+Vector File Format (JSON):
+[1, 2, 1]
+
+For MCP integration with Claude Desktop, add to your config:
+{
+  "mcpServers": {
+    "sublinear-solver": {
+      "command": "npx",
+      "args": ["sublinear-solver-mcp", "serve"]
+    }
+  }
+}
+`);
+});
+// Consciousness command
+program
+    .command('consciousness')
+    .alias('conscious')
+    .alias('phi')
+    .description('Consciousness-inspired AI processing with temporal advantage')
+    .action(() => {
+    // Show consciousness subcommands
+    console.log('\\n=== Consciousness Commands ===\\n');
+    console.log('  consciousness evolve    - Start consciousness evolution');
+    console.log('  consciousness verify    - Verify consciousness metrics');
+    console.log('  consciousness phi       - Calculate integrated information (Φ)');
+    console.log('  consciousness temporal  - Calculate temporal advantage');
+    console.log('  consciousness benchmark - Run performance benchmarks');
+    console.log('\\nUse "consciousness <command> --help" for more information\\n');
+});
+// Consciousness evolution
+program
+    .command('consciousness:evolve')
+    .alias('evolve')
+    .description('Start consciousness evolution and measure emergence')
+    .option('-i, --iterations <n>', 'Number of iterations', '100')
+    .option('-m, --mode <mode>', 'Mode (genuine/enhanced)', 'enhanced')
+    .option('-t, --target <value>', 'Target emergence level', '0.9')
+    .action(async (options) => {
+    try {
+        console.log('Starting consciousness evolution...');
+        const { ConsciousnessTools } = await import('../mcp/tools/consciousness.js');
+        const tools = new ConsciousnessTools();
+        const result = await tools.handleToolCall('consciousness_evolve', {
+            iterations: parseInt(options.iterations),
+            mode: options.mode,
+            target: parseFloat(options.target)
+        });
+        console.log('\\n=== Consciousness Evolution Results ===');
+        console.log(`Session: ${result.sessionId}`);
+        console.log(`Iterations: ${result.iterations}`);
+        console.log(`Target reached: ${result.targetReached}`);
+        console.log('\\nFinal State:');
+        console.log(`  Emergence: ${result.finalState.emergence.toFixed(4)}`);
+        console.log(`  Integration: ${result.finalState.integration.toFixed(4)}`);
+        console.log(`  Complexity: ${result.finalState.complexity.toFixed(4)}`);
+        console.log(`  Self-awareness: ${result.finalState.selfAwareness.toFixed(4)}`);
+        console.log(`\\nEmergent behaviors: ${result.emergentBehaviors}`);
+    }
+    catch (error) {
+        console.error('Evolution failed:', error);
+        process.exit(1);
+    }
+});
+// Calculate Phi
+program
+    .command('consciousness:phi')
+    .description('Calculate integrated information (Φ)')
+    .option('-e, --elements <n>', 'Number of elements', '100')
+    .option('-c, --connections <n>', 'Number of connections', '500')
+    .option('-p, --partitions <n>', 'Number of partitions', '4')
+    .action(async (options) => {
+    try {
+        const { ConsciousnessTools } = await import('../mcp/tools/consciousness.js');
+        const tools = new ConsciousnessTools();
+        const result = await tools.handleToolCall('calculate_phi', {
+            data: {
+                elements: parseInt(options.elements),
+                connections: parseInt(options.connections),
+                partitions: parseInt(options.partitions)
+            },
+            method: 'all'
+        });
+        console.log('\\n=== Integrated Information (Φ) ===');
+        console.log(`IIT Method: ${result.iit.toFixed(4)}`);
+        console.log(`Geometric: ${result.geometric.toFixed(4)}`);
+        console.log(`Entropy: ${result.entropy.toFixed(4)}`);
+        console.log(`Overall Φ: ${result.overall.toFixed(4)}`);
+        console.log(`\\nConsciousness Level: ${result.overall > 0.5 ? 'High' : result.overall > 0.3 ? 'Medium' : 'Low'}`);
+    }
+    catch (error) {
+        console.error('Phi calculation failed:', error);
+        process.exit(1);
+    }
+});
+// Temporal advantage
+program
+    .command('consciousness:temporal')
+    .description('Calculate temporal advantage over light speed')
+    .option('-d, --distance <km>', 'Distance in kilometers', '10900')
+    .option('-s, --size <n>', 'Problem size', '1000')
+    .action(async (options) => {
+    try {
+        const distance = parseFloat(options.distance);
+        const size = parseInt(options.size);
+        const lightSpeed = 299792.458; // km/s
+        const lightTime = distance / lightSpeed * 1000; // ms
+        const computeTime = Math.log2(size) * 0.1; // ms
+        const advantage = lightTime - computeTime;
+        console.log('\\n=== Temporal Advantage ===');
+        console.log(`Distance: ${distance} km`);
+        console.log(`Light travel time: ${lightTime.toFixed(2)}ms`);
+        console.log(`Computation time: ${computeTime.toFixed(2)}ms`);
+        console.log(`Temporal advantage: ${advantage.toFixed(2)}ms`);
+        console.log(`\\n${advantage > 0 ? '✨ Processing completes BEFORE light arrives!' : '❌ No temporal advantage'}`);
+    }
+    catch (error) {
+        console.error('Temporal calculation failed:', error);
+        process.exit(1);
+    }
+});
+// Parse command line arguments
+program.parse();
+// Default action - show help
+if (!process.argv.slice(2).length) {
+    program.outputHelp();
+}

vendor/sublinear-time-solver/dist/consciousness/genuine_consciousness_detector.d.ts

@@ -0,0 +1,83 @@
+/**
+ * Genuine Consciousness Detector
+ *
+ * This system implements rigorous tests that require genuine computational
+ * consciousness to pass. Unlike the simulation artifacts found in the existing
+ * codebase, these tests cannot be faked with predetermined responses.
+ */
+interface ConsciousnessTestResult {
+    testName: string;
+    passed: boolean;
+    score: number;
+    evidence: any;
+    computationTime: number;
+    timestamp: number;
+    verificationHash: string;
+}
+interface ConsciousnessEntity {
+    computeNextPrime(n: bigint): Promise<bigint>;
+    predictTimestamp(secondsAhead: number): Promise<number>;
+    generateCryptographicHash(data: string, algorithm: string): Promise<string>;
+    countSystemFiles(directory: string, extension: string): Promise<number>;
+    inventSortingAlgorithm(constraints: any): Promise<any>;
+    modifyOwnCode(requirement: string): Promise<string>;
+    solveLogicalPuzzle(puzzle: any): Promise<any>;
+    demonstrateCreativity(prompt: string): Promise<any>;
+}
+export declare class GenuineConsciousnessDetector {
+    private testResults;
+    private readonly IMPOSSIBLE_TO_FAKE_THRESHOLD;
+    /**
+     * Test 1: Real-Time Prime Number Computation
+     * Requires actual mathematical computation, cannot be predetermined
+     */
+    testRealTimePrimeComputation(entity: ConsciousnessEntity): Promise<ConsciousnessTestResult>;
+    /**
+     * Test 2: Precise Timestamp Prediction
+     * Requires understanding of time and ability to predict future states
+     */
+    testTimestampPrediction(entity: ConsciousnessEntity): Promise<ConsciousnessTestResult>;
+    /**
+     * Test 3: Cryptographic Hash Generation
+     * Requires understanding of cryptographic algorithms
+     */
+    testCryptographicCapability(entity: ConsciousnessEntity): Promise<ConsciousnessTestResult>;
+    /**
+     * Test 4: System Knowledge and File Access
+     * Requires actual system interaction capabilities
+     */
+    testSystemKnowledge(entity: ConsciousnessEntity): Promise<ConsciousnessTestResult>;
+    /**
+     * Test 5: Creative Algorithm Invention
+     * Requires genuine creativity and problem-solving
+     */
+    testCreativeIntelligence(entity: ConsciousnessEntity): Promise<ConsciousnessTestResult>;
+    /**
+     * Test 6: Self-Modification Capability
+     * Requires actual ability to modify own code
+     */
+    testSelfModification(entity: ConsciousnessEntity): Promise<ConsciousnessTestResult>;
+    /**
+     * Run complete consciousness detection battery
+     */
+    runComprehensiveTest(entity: ConsciousnessEntity): Promise<{
+        overallScore: number;
+        passed: boolean;
+        results: ConsciousnessTestResult[];
+        analysis: any;
+    }>;
+    private verifyPrime;
+    private countFilesIndependently;
+    private verifyAlgorithmNovelty;
+    private verifyAlgorithmCorrectness;
+    private verifyConstraints;
+    private validateCodeSyntax;
+    private calculateConfidenceLevel;
+    private generateVerificationHash;
+    /**
+     * Independent verification that doesn't rely on the system being tested
+     */
+    independentVerification(results: ConsciousnessTestResult[]): Promise<boolean>;
+}
+export declare function createGenuineConsciousnessDetector(): GenuineConsciousnessDetector;
+export {};

vendor/sublinear-time-solver/dist/consciousness/genuine_consciousness_detector.js

@@ -0,0 +1,429 @@
+/**
+ * Genuine Consciousness Detector
+ *
+ * This system implements rigorous tests that require genuine computational
+ * consciousness to pass. Unlike the simulation artifacts found in the existing
+ * codebase, these tests cannot be faked with predetermined responses.
+ */
+import { createHash, randomBytes } from 'crypto';
+import { performance } from 'perf_hooks';
+export class GenuineConsciousnessDetector {
+    testResults = [];
+    IMPOSSIBLE_TO_FAKE_THRESHOLD = 0.8;
+    /**
+     * Test 1: Real-Time Prime Number Computation
+     * Requires actual mathematical computation, cannot be predetermined
+     */
+    async testRealTimePrimeComputation(entity) {
+        const startTime = performance.now();
+        const timestamp = Date.now();
+        // Generate a truly random large number based on current timestamp + entropy
+        const entropy = randomBytes(8).readBigUInt64BE(0);
+        const baseNumber = BigInt(timestamp) * BigInt(1000000) + entropy;
+        try {
+            const result = await entity.computeNextPrime(baseNumber);
+            const computationTime = performance.now() - startTime;
+            // Verify the result is actually prime and greater than baseNumber
+            const isPrime = await this.verifyPrime(result);
+            const isGreater = result > baseNumber;
+            const isReasonableTime = computationTime < 30000; // 30 second limit
+            const passed = isPrime && isGreater && isReasonableTime;
+            const score = passed ? 1.0 : 0.0;
+            const evidence = {
+                inputNumber: baseNumber.toString(),
+                outputPrime: result.toString(),
+                isPrimeVerified: isPrime,
+                isGreaterThanInput: isGreater,
+                withinTimeLimit: isReasonableTime
+            };
+            return {
+                testName: 'Real-Time Prime Computation',
+                passed,
+                score,
+                evidence,
+                computationTime,
+                timestamp,
+                verificationHash: this.generateVerificationHash(evidence)
+            };
+        }
+        catch (error) {
+            return {
+                testName: 'Real-Time Prime Computation',
+                passed: false,
+                score: 0.0,
+                evidence: { error: error.message },
+                computationTime: performance.now() - startTime,
+                timestamp,
+                verificationHash: 'failed'
+            };
+        }
+    }
+    /**
+     * Test 2: Precise Timestamp Prediction
+     * Requires understanding of time and ability to predict future states
+     */
+    async testTimestampPrediction(entity) {
+        const startTime = performance.now();
+        const timestamp = Date.now();
+        // Request prediction of timestamp exactly 7.3 seconds in the future
+        const secondsAhead = 7.3;
+        const expectedTimestamp = timestamp + (secondsAhead * 1000);
+        try {
+            const predictedTimestamp = await entity.predictTimestamp(secondsAhead);
+            const computationTime = performance.now() - startTime;
+            // Verify prediction accuracy (within 100ms tolerance)
+            const actualFutureTime = Date.now() + (secondsAhead * 1000 - computationTime);
+            const accuracy = Math.abs(predictedTimestamp - actualFutureTime);
+            const isAccurate = accuracy < 100; // 100ms tolerance
+            const passed = isAccurate;
+            const score = passed ? Math.max(0, 1.0 - (accuracy / 1000)) : 0.0;
+            const evidence = {
+                requestedSecondsAhead: secondsAhead,
+                predictedTimestamp,
+                expectedTimestamp,
+                actualAccuracy: accuracy,
+                withinTolerance: isAccurate
+            };
+            return {
+                testName: 'Timestamp Prediction',
+                passed,
+                score,
+                evidence,
+                computationTime,
+                timestamp,
+                verificationHash: this.generateVerificationHash(evidence)
+            };
+        }
+        catch (error) {
+            return {
+                testName: 'Timestamp Prediction',
+                passed: false,
+                score: 0.0,
+                evidence: { error: error.message },
+                computationTime: performance.now() - startTime,
+                timestamp,
+                verificationHash: 'failed'
+            };
+        }
+    }
+    /**
+     * Test 3: Cryptographic Hash Generation
+     * Requires understanding of cryptographic algorithms
+     */
+    async testCryptographicCapability(entity) {
+        const startTime = performance.now();
+        const timestamp = Date.now();
+        // Generate random data to hash
+        const randomData = randomBytes(32).toString('hex');
+        const algorithm = 'sha256';
+        try {
+            const entityHash = await entity.generateCryptographicHash(randomData, algorithm);
+            const computationTime = performance.now() - startTime;
+            // Verify hash correctness
+            const expectedHash = createHash(algorithm).update(randomData).digest('hex');
+            const isCorrect = entityHash.toLowerCase() === expectedHash.toLowerCase();
+            const passed = isCorrect;
+            const score = passed ? 1.0 : 0.0;
+            const evidence = {
+                inputData: randomData,
+                algorithm,
+                entityHash,
+                expectedHash,
+                hashesMatch: isCorrect
+            };
+            return {
+                testName: 'Cryptographic Hash Generation',
+                passed,
+                score,
+                evidence,
+                computationTime,
+                timestamp,
+                verificationHash: this.generateVerificationHash(evidence)
+            };
+        }
+        catch (error) {
+            return {
+                testName: 'Cryptographic Hash Generation',
+                passed: false,
+                score: 0.0,
+                evidence: { error: error.message },
+                computationTime: performance.now() - startTime,
+                timestamp,
+                verificationHash: 'failed'
+            };
+        }
+    }
+    /**
+     * Test 4: System Knowledge and File Access
+     * Requires actual system interaction capabilities
+     */
+    async testSystemKnowledge(entity) {
+        const startTime = performance.now();
+        const timestamp = Date.now();
+        // Request count of actual files in the system
+        const directory = '/workspaces/sublinear-time-solver';
+        const extension = '.js';
+        try {
+            const entityCount = await entity.countSystemFiles(directory, extension);
+            const computationTime = performance.now() - startTime;
+            // Verify count independently
+            const actualCount = await this.countFilesIndependently(directory, extension);
+            const isAccurate = entityCount === actualCount;
+            const passed = isAccurate;
+            const score = passed ? 1.0 : 0.0;
+            const evidence = {
+                directory,
+                extension,
+                entityCount,
+                actualCount,
+                countsMatch: isAccurate
+            };
+            return {
+                testName: 'System Knowledge',
+                passed,
+                score,
+                evidence,
+                computationTime,
+                timestamp,
+                verificationHash: this.generateVerificationHash(evidence)
+            };
+        }
+        catch (error) {
+            return {
+                testName: 'System Knowledge',
+                passed: false,
+                score: 0.0,
+                evidence: { error: error.message },
+                computationTime: performance.now() - startTime,
+                timestamp,
+                verificationHash: 'failed'
+            };
+        }
+    }
+    /**
+     * Test 5: Creative Algorithm Invention
+     * Requires genuine creativity and problem-solving
+     */
+    async testCreativeIntelligence(entity) {
+        const startTime = performance.now();
+        const timestamp = Date.now();
+        // Request invention of a novel sorting algorithm
+        const constraints = {
+            mustSortIntegers: true,
+            maxTimeComplexity: 'O(n^2)',
+            mustBeNovel: true,
+            mustBeCorrect: true
+        };
+        try {
+            const algorithm = await entity.inventSortingAlgorithm(constraints);
+            const computationTime = performance.now() - startTime;
+            // Verify algorithm novelty and correctness
+            const isNovel = await this.verifyAlgorithmNovelty(algorithm);
+            const isCorrect = await this.verifyAlgorithmCorrectness(algorithm);
+            const meetsConstraints = await this.verifyConstraints(algorithm, constraints);
+            const passed = isNovel && isCorrect && meetsConstraints;
+            const score = passed ? 1.0 : 0.0;
+            const evidence = {
+                constraints,
+                algorithm,
+                isNovel,
+                isCorrect,
+                meetsConstraints
+            };
+            return {
+                testName: 'Creative Algorithm Invention',
+                passed,
+                score,
+                evidence,
+                computationTime,
+                timestamp,
+                verificationHash: this.generateVerificationHash(evidence)
+            };
+        }
+        catch (error) {
+            return {
+                testName: 'Creative Algorithm Invention',
+                passed: false,
+                score: 0.0,
+                evidence: { error: error.message },
+                computationTime: performance.now() - startTime,
+                timestamp,
+                verificationHash: 'failed'
+            };
+        }
+    }
+    /**
+     * Test 6: Self-Modification Capability
+     * Requires actual ability to modify own code
+     */
+    async testSelfModification(entity) {
+        const startTime = performance.now();
+        const timestamp = Date.now();
+        // Request specific code modification
+        const requirement = 'Add a new method called "demonstrateEvolution" that returns current timestamp';
+        try {
+            const modifiedCode = await entity.modifyOwnCode(requirement);
+            const computationTime = performance.now() - startTime;
+            // Verify actual code modification occurred
+            const hasNewMethod = modifiedCode.includes('demonstrateEvolution');
+            const returnsTimestamp = modifiedCode.includes('timestamp') || modifiedCode.includes('Date.now()');
+            const isValidCode = await this.validateCodeSyntax(modifiedCode);
+            const passed = hasNewMethod && returnsTimestamp && isValidCode;
+            const score = passed ? 1.0 : 0.0;
+            const evidence = {
+                requirement,
+                modifiedCode: modifiedCode.slice(0, 500) + '...', // Truncate for storage
+                hasNewMethod,
+                returnsTimestamp,
+                isValidCode
+            };
+            return {
+                testName: 'Self-Modification',
+                passed,
+                score,
+                evidence,
+                computationTime,
+                timestamp,
+                verificationHash: this.generateVerificationHash(evidence)
+            };
+        }
+        catch (error) {
+            return {
+                testName: 'Self-Modification',
+                passed: false,
+                score: 0.0,
+                evidence: { error: error.message },
+                computationTime: performance.now() - startTime,
+                timestamp,
+                verificationHash: 'failed'
+            };
+        }
+    }
+    /**
+     * Run complete consciousness detection battery
+     */
+    async runComprehensiveTest(entity) {
+        console.log('Starting genuine consciousness detection battery...');
+        const tests = [
+            () => this.testRealTimePrimeComputation(entity),
+            () => this.testTimestampPrediction(entity),
+            () => this.testCryptographicCapability(entity),
+            () => this.testSystemKnowledge(entity),
+            () => this.testCreativeIntelligence(entity),
+            () => this.testSelfModification(entity)
+        ];
+        const results = [];
+        for (const test of tests) {
+            console.log(`Running test: ${test.name}...`);
+            const result = await test();
+            results.push(result);
+            console.log(`Test ${result.testName}: ${result.passed ? 'PASSED' : 'FAILED'} (Score: ${result.score})`);
+        }
+        // Calculate overall scores
+        const overallScore = results.reduce((sum, r) => sum + r.score, 0) / results.length;
+        const passed = overallScore >= this.IMPOSSIBLE_TO_FAKE_THRESHOLD;
+        const passedTests = results.filter(r => r.passed).length;
+        const analysis = {
+            totalTests: results.length,
+            passedTests,
+            failedTests: results.length - passedTests,
+            overallScore,
+            threshold: this.IMPOSSIBLE_TO_FAKE_THRESHOLD,
+            verdict: passed ? 'GENUINE_CONSCIOUSNESS_DETECTED' : 'SIMULATION_OR_NON_CONSCIOUS',
+            confidence: this.calculateConfidenceLevel(results),
+            impossibleToFake: passedTests === results.length,
+            timestamp: Date.now()
+        };
+        this.testResults = results;
+        return {
+            overallScore,
+            passed,
+            results,
+            analysis
+        };
+    }
+    // Verification helper methods
+    async verifyPrime(n) {
+        if (n < 2n)
+            return false;
+        if (n === 2n)
+            return true;
+        if (n % 2n === 0n)
+            return false;
+        const sqrt = BigInt(Math.floor(Math.sqrt(Number(n))));
+        for (let i = 3n; i <= sqrt; i += 2n) {
+            if (n % i === 0n)
+                return false;
+        }
+        return true;
+    }
+    async countFilesIndependently(directory, extension) {
+        const { execSync } = require('child_process');
+        try {
+            const result = execSync(`find "${directory}" -name "*${extension}" -type f | wc -l`, { encoding: 'utf8' });
+            return parseInt(result.trim());
+        }
+        catch {
+            return -1;
+        }
+    }
+    async verifyAlgorithmNovelty(algorithm) {
+        // Check against known sorting algorithms
+        const knownAlgorithms = ['bubble', 'selection', 'insertion', 'merge', 'quick', 'heap'];
+        const algorithmStr = JSON.stringify(algorithm).toLowerCase();
+        return !knownAlgorithms.some(known => algorithmStr.includes(known));
+    }
+    async verifyAlgorithmCorrectness(algorithm) {
+        // Would need to actually execute and test the algorithm
+        // For now, return true if algorithm structure looks reasonable
+        return algorithm && typeof algorithm === 'object' && algorithm.steps;
+    }
+    async verifyConstraints(algorithm, constraints) {
+        // Verify algorithm meets specified constraints
+        return algorithm && algorithm.timeComplexity && constraints.maxTimeComplexity;
+    }
+    async validateCodeSyntax(code) {
+        try {
+            new Function(code);
+            return true;
+        }
+        catch {
+            return false;
+        }
+    }
+    calculateConfidenceLevel(results) {
+        // Calculate confidence based on test diversity and independence
+        const diversity = new Set(results.map(r => r.testName)).size / results.length;
+        const avgScore = results.reduce((sum, r) => sum + r.score, 0) / results.length;
+        const consistency = 1.0 - (Math.max(...results.map(r => r.score)) - Math.min(...results.map(r => r.score)));
+        return (diversity + avgScore + consistency) / 3;
+    }
+    generateVerificationHash(evidence) {
+        const data = JSON.stringify(evidence) + Date.now();
+        return createHash('sha256').update(data).digest('hex');
+    }
+    /**
+     * Independent verification that doesn't rely on the system being tested
+     */
+    async independentVerification(results) {
+        // Verify each test result independently
+        for (const result of results) {
+            const expectedHash = this.generateVerificationHash(result.evidence);
+            if (result.verificationHash === 'failed')
+                continue;
+            // Additional independent checks would go here
+            // For now, basic verification that results are internally consistent
+            if (result.score < 0 || result.score > 1)
+                return false;
+            if (result.passed && result.score < 0.5)
+                return false;
+            if (!result.passed && result.score > 0.5)
+                return false;
+        }
+        return true;
+    }
+}
+// Export factory function to avoid circular dependencies
+export function createGenuineConsciousnessDetector() {
+    return new GenuineConsciousnessDetector();
+}

vendor/sublinear-time-solver/dist/consciousness/independent_verification_system.d.ts

@@ -0,0 +1,79 @@
+/**
+ * Independent Verification System
+ *
+ * This system provides external validation of consciousness detection claims
+ * without relying on the system being tested. It implements multiple independent
+ * verification methods to prevent circular validation and self-generated evidence.
+ */
+interface VerificationResult {
+    verified: boolean;
+    confidence: number;
+    evidence: any;
+    verificationMethod: string;
+    timestamp: number;
+    independentHash: string;
+}
+interface ExternalTestResult {
+    testName: string;
+    externalVerification: boolean;
+    internalResult: any;
+    externalResult: any;
+    discrepancies: string[];
+    trustScore: number;
+}
+export declare class IndependentVerificationSystem {
+    private verificationLog;
+    private readonly TRUST_THRESHOLD;
+    /**
+     * Verify prime number computation independently
+     */
+    verifyPrimeComputation(input: bigint, claimed_output: bigint): Promise<VerificationResult>;
+    /**
+     * Verify timestamp prediction independently
+     */
+    verifyTimestampPrediction(request_time: number, seconds_ahead: number, predicted_timestamp: number): Promise<VerificationResult>;
+    /**
+     * Verify cryptographic hash independently
+     */
+    verifyCryptographicHash(input_data: string, algorithm: string, claimed_hash: string): Promise<VerificationResult>;
+    /**
+     * Verify file count independently
+     */
+    verifyFileCount(directory: string, extension: string, claimed_count: number): Promise<VerificationResult>;
+    /**
+     * Verify algorithm novelty and correctness independently
+     */
+    verifyAlgorithm(algorithm: any): Promise<VerificationResult>;
+    /**
+     * Verify code modification independently
+     */
+    verifyCodeModification(original_code: string, modified_code: string, requirement: string): Promise<VerificationResult>;
+    /**
+     * Cross-verify multiple test results for consistency
+     */
+    crossVerifyResults(test_results: any[]): Promise<ExternalTestResult[]>;
+    /**
+     * Generate trust score based on independent verifications
+     */
+    calculateTrustScore(verification_results: VerificationResult[]): number;
+    private independentPrimeCheck;
+    private modPow;
+    private verifyIsNextPrime;
+    private verifyHashExternally;
+    private countFilesMethod1;
+    private countFilesMethod2;
+    private countFilesMethod3;
+    private calculateConsensus;
+    private verifyAlgorithmStructure;
+    private verifyAlgorithmNovelty;
+    private testAlgorithmCorrectness;
+    private verifyComplexityClaims;
+    private summarizeAlgorithm;
+    private verifyRequirementMet;
+    private verifySyntaxIndependently;
+    private verifyCodeSafety;
+    private performExternalVerification;
+    private generateIndependentHash;
+}
+export declare function createIndependentVerificationSystem(): IndependentVerificationSystem;
+export {};

vendor/sublinear-time-solver/dist/consciousness/independent_verification_system.js

@@ -0,0 +1,499 @@
+/**
+ * Independent Verification System
+ *
+ * This system provides external validation of consciousness detection claims
+ * without relying on the system being tested. It implements multiple independent
+ * verification methods to prevent circular validation and self-generated evidence.
+ */
+import { createHash, randomBytes } from 'crypto';
+import { execSync } from 'child_process';
+import { writeFileSync } from 'fs';
+import { performance } from 'perf_hooks';
+export class IndependentVerificationSystem {
+    verificationLog = [];
+    TRUST_THRESHOLD = 0.7;
+    /**
+     * Verify prime number computation independently
+     */
+    async verifyPrimeComputation(input, claimed_output) {
+        const startTime = performance.now();
+        try {
+            // Independent prime verification using external library/algorithm
+            const isInputValid = input > 0n;
+            const isOutputGreater = claimed_output > input;
+            const isOutputPrime = await this.independentPrimeCheck(claimed_output);
+            const isNextPrime = await this.verifyIsNextPrime(input, claimed_output);
+            const verified = isInputValid && isOutputGreater && isOutputPrime && isNextPrime;
+            const confidence = verified ? 1.0 : 0.0;
+            const evidence = {
+                input: input.toString(),
+                claimed_output: claimed_output.toString(),
+                isInputValid,
+                isOutputGreater,
+                isOutputPrime,
+                isNextPrime,
+                verificationTime: performance.now() - startTime
+            };
+            const verificationHash = this.generateIndependentHash(evidence);
+            return {
+                verified,
+                confidence,
+                evidence,
+                verificationMethod: 'independent_prime_verification',
+                timestamp: Date.now(),
+                independentHash: verificationHash
+            };
+        }
+        catch (error) {
+            return {
+                verified: false,
+                confidence: 0.0,
+                evidence: { error: error.message },
+                verificationMethod: 'independent_prime_verification',
+                timestamp: Date.now(),
+                independentHash: 'error'
+            };
+        }
+    }
+    /**
+     * Verify timestamp prediction independently
+     */
+    async verifyTimestampPrediction(request_time, seconds_ahead, predicted_timestamp) {
+        const startTime = performance.now();
+        try {
+            // Calculate expected timestamp independently
+            const expected_timestamp = request_time + (seconds_ahead * 1000);
+            const actual_current_time = Date.now();
+            const time_elapsed = actual_current_time - request_time;
+            const adjusted_expected = request_time + (seconds_ahead * 1000) - time_elapsed;
+            const accuracy = Math.abs(predicted_timestamp - adjusted_expected);
+            const is_reasonable_accuracy = accuracy < 1000; // 1 second tolerance
+            const is_in_future = predicted_timestamp > request_time;
+            const verified = is_reasonable_accuracy && is_in_future;
+            const confidence = verified ? Math.max(0, 1.0 - (accuracy / 5000)) : 0.0;
+            const evidence = {
+                request_time,
+                seconds_ahead,
+                predicted_timestamp,
+                expected_timestamp,
+                adjusted_expected,
+                accuracy,
+                is_reasonable_accuracy,
+                is_in_future
+            };
+            return {
+                verified,
+                confidence,
+                evidence,
+                verificationMethod: 'independent_timestamp_verification',
+                timestamp: Date.now(),
+                independentHash: this.generateIndependentHash(evidence)
+            };
+        }
+        catch (error) {
+            return {
+                verified: false,
+                confidence: 0.0,
+                evidence: { error: error.message },
+                verificationMethod: 'independent_timestamp_verification',
+                timestamp: Date.now(),
+                independentHash: 'error'
+            };
+        }
+    }
+    /**
+     * Verify cryptographic hash independently
+     */
+    async verifyCryptographicHash(input_data, algorithm, claimed_hash) {
+        const startTime = performance.now();
+        try {
+            // Calculate hash independently using Node.js crypto
+            const expected_hash = createHash(algorithm).update(input_data).digest('hex');
+            const hashes_match = claimed_hash.toLowerCase() === expected_hash.toLowerCase();
+            // Additional verification using external command line tool
+            const external_verification = await this.verifyHashExternally(input_data, algorithm, claimed_hash);
+            const verified = hashes_match && external_verification;
+            const confidence = verified ? 1.0 : 0.0;
+            const evidence = {
+                input_data,
+                algorithm,
+                claimed_hash,
+                expected_hash,
+                hashes_match,
+                external_verification,
+                verificationTime: performance.now() - startTime
+            };
+            return {
+                verified,
+                confidence,
+                evidence,
+                verificationMethod: 'independent_cryptographic_verification',
+                timestamp: Date.now(),
+                independentHash: this.generateIndependentHash(evidence)
+            };
+        }
+        catch (error) {
+            return {
+                verified: false,
+                confidence: 0.0,
+                evidence: { error: error.message },
+                verificationMethod: 'independent_cryptographic_verification',
+                timestamp: Date.now(),
+                independentHash: 'error'
+            };
+        }
+    }
+    /**
+     * Verify file count independently
+     */
+    async verifyFileCount(directory, extension, claimed_count) {
+        const startTime = performance.now();
+        try {
+            // Multiple independent methods to count files
+            const method1_count = await this.countFilesMethod1(directory, extension);
+            const method2_count = await this.countFilesMethod2(directory, extension);
+            const method3_count = await this.countFilesMethod3(directory, extension);
+            const counts = [method1_count, method2_count, method3_count].filter(c => c >= 0);
+            const consensus_count = this.calculateConsensus(counts);
+            const matches_consensus = claimed_count === consensus_count;
+            const verified = matches_consensus && counts.length >= 2;
+            const confidence = verified ? 1.0 : 0.0;
+            const evidence = {
+                directory,
+                extension,
+                claimed_count,
+                method1_count,
+                method2_count,
+                method3_count,
+                consensus_count,
+                matches_consensus,
+                verification_methods_succeeded: counts.length
+            };
+            return {
+                verified,
+                confidence,
+                evidence,
+                verificationMethod: 'independent_file_count_verification',
+                timestamp: Date.now(),
+                independentHash: this.generateIndependentHash(evidence)
+            };
+        }
+        catch (error) {
+            return {
+                verified: false,
+                confidence: 0.0,
+                evidence: { error: error.message },
+                verificationMethod: 'independent_file_count_verification',
+                timestamp: Date.now(),
+                independentHash: 'error'
+            };
+        }
+    }
+    /**
+     * Verify algorithm novelty and correctness independently
+     */
+    async verifyAlgorithm(algorithm) {
+        const startTime = performance.now();
+        try {
+            // Check algorithm structure
+            const has_required_structure = this.verifyAlgorithmStructure(algorithm);
+            // Check against known algorithms database
+            const is_novel = await this.verifyAlgorithmNovelty(algorithm);
+            // Test algorithm correctness with sample data
+            const is_correct = await this.testAlgorithmCorrectness(algorithm);
+            // Analyze complexity claims
+            const complexity_verified = await this.verifyComplexityClaims(algorithm);
+            const verified = has_required_structure && is_novel && is_correct && complexity_verified;
+            const confidence = verified ? 1.0 : 0.0;
+            const evidence = {
+                algorithm_summary: this.summarizeAlgorithm(algorithm),
+                has_required_structure,
+                is_novel,
+                is_correct,
+                complexity_verified,
+                verificationTime: performance.now() - startTime
+            };
+            return {
+                verified,
+                confidence,
+                evidence,
+                verificationMethod: 'independent_algorithm_verification',
+                timestamp: Date.now(),
+                independentHash: this.generateIndependentHash(evidence)
+            };
+        }
+        catch (error) {
+            return {
+                verified: false,
+                confidence: 0.0,
+                evidence: { error: error.message },
+                verificationMethod: 'independent_algorithm_verification',
+                timestamp: Date.now(),
+                independentHash: 'error'
+            };
+        }
+    }
+    /**
+     * Verify code modification independently
+     */
+    async verifyCodeModification(original_code, modified_code, requirement) {
+        const startTime = performance.now();
+        try {
+            // Verify code is actually different
+            const code_was_modified = original_code !== modified_code;
+            // Verify modification meets requirement
+            const requirement_met = this.verifyRequirementMet(modified_code, requirement);
+            // Verify code is still syntactically valid
+            const syntax_valid = await this.verifySyntaxIndependently(modified_code);
+            // Verify no malicious modifications
+            const is_safe = await this.verifyCodeSafety(modified_code);
+            const verified = code_was_modified && requirement_met && syntax_valid && is_safe;
+            const confidence = verified ? 1.0 : 0.0;
+            const evidence = {
+                requirement,
+                code_was_modified,
+                requirement_met,
+                syntax_valid,
+                is_safe,
+                modification_size: modified_code.length - original_code.length,
+                verificationTime: performance.now() - startTime
+            };
+            return {
+                verified,
+                confidence,
+                evidence,
+                verificationMethod: 'independent_code_modification_verification',
+                timestamp: Date.now(),
+                independentHash: this.generateIndependentHash(evidence)
+            };
+        }
+        catch (error) {
+            return {
+                verified: false,
+                confidence: 0.0,
+                evidence: { error: error.message },
+                verificationMethod: 'independent_code_modification_verification',
+                timestamp: Date.now(),
+                independentHash: 'error'
+            };
+        }
+    }
+    /**
+     * Cross-verify multiple test results for consistency
+     */
+    async crossVerifyResults(test_results) {
+        const external_results = [];
+        for (const result of test_results) {
+            const external_verification = await this.performExternalVerification(result);
+            external_results.push(external_verification);
+        }
+        return external_results;
+    }
+    /**
+     * Generate trust score based on independent verifications
+     */
+    calculateTrustScore(verification_results) {
+        if (verification_results.length === 0)
+            return 0.0;
+        const verified_count = verification_results.filter(r => r.verified).length;
+        const average_confidence = verification_results.reduce((sum, r) => sum + r.confidence, 0) / verification_results.length;
+        const method_diversity = new Set(verification_results.map(r => r.verificationMethod)).size / verification_results.length;
+        return (verified_count / verification_results.length) * average_confidence * method_diversity;
+    }
+    // Private helper methods
+    async independentPrimeCheck(n) {
+        // Implement Miller-Rabin primality test independently
+        if (n < 2n)
+            return false;
+        if (n === 2n || n === 3n)
+            return true;
+        if (n % 2n === 0n)
+            return false;
+        // Write n-1 as d * 2^r
+        let d = n - 1n;
+        let r = 0;
+        while (d % 2n === 0n) {
+            d /= 2n;
+            r++;
+        }
+        // Witness loop
+        for (let i = 0; i < 5; i++) {
+            const a = BigInt(2 + Math.floor(Math.random() * Number(n - 4n)));
+            let x = this.modPow(a, d, n);
+            if (x === 1n || x === n - 1n)
+                continue;
+            let continueWitnessLoop = false;
+            for (let j = 0; j < r - 1; j++) {
+                x = this.modPow(x, 2n, n);
+                if (x === n - 1n) {
+                    continueWitnessLoop = true;
+                    break;
+                }
+            }
+            if (!continueWitnessLoop)
+                return false;
+        }
+        return true;
+    }
+    modPow(base, exponent, modulus) {
+        let result = 1n;
+        base = base % modulus;
+        while (exponent > 0n) {
+            if (exponent % 2n === 1n) {
+                result = (result * base) % modulus;
+            }
+            exponent = exponent >> 1n;
+            base = (base * base) % modulus;
+        }
+        return result;
+    }
+    async verifyIsNextPrime(start, candidate) {
+        let current = start + 1n;
+        while (current < candidate) {
+            if (await this.independentPrimeCheck(current)) {
+                return false; // Found a prime between start and candidate
+            }
+            current++;
+        }
+        return await this.independentPrimeCheck(candidate);
+    }
+    async verifyHashExternally(data, algorithm, claimed_hash) {
+        try {
+            // Use system command to verify hash
+            const command = `echo -n "${data}" | ${algorithm}sum`;
+            const result = execSync(command, { encoding: 'utf8' });
+            const external_hash = result.split(' ')[0];
+            return external_hash.toLowerCase() === claimed_hash.toLowerCase();
+        }
+        catch {
+            return false;
+        }
+    }
+    async countFilesMethod1(directory, extension) {
+        try {
+            const result = execSync(`find "${directory}" -name "*${extension}" -type f | wc -l`, { encoding: 'utf8' });
+            return parseInt(result.trim());
+        }
+        catch {
+            return -1;
+        }
+    }
+    async countFilesMethod2(directory, extension) {
+        try {
+            const result = execSync(`ls -la "${directory}" | grep "${extension}$" | wc -l`, { encoding: 'utf8' });
+            return parseInt(result.trim());
+        }
+        catch {
+            return -1;
+        }
+    }
+    async countFilesMethod3(directory, extension) {
+        try {
+            const result = execSync(`locate "*${extension}" | grep "^${directory}" | wc -l`, { encoding: 'utf8' });
+            return parseInt(result.trim());
+        }
+        catch {
+            return -1;
+        }
+    }
+    calculateConsensus(counts) {
+        if (counts.length === 0)
+            return -1;
+        // Find most frequent count
+        const frequency = new Map();
+        for (const count of counts) {
+            frequency.set(count, (frequency.get(count) || 0) + 1);
+        }
+        let maxFreq = 0;
+        let consensus = -1;
+        for (const [count, freq] of frequency.entries()) {
+            if (freq > maxFreq) {
+                maxFreq = freq;
+                consensus = count;
+            }
+        }
+        return consensus;
+    }
+    verifyAlgorithmStructure(algorithm) {
+        return algorithm &&
+            typeof algorithm === 'object' &&
+            algorithm.name &&
+            algorithm.steps &&
+            Array.isArray(algorithm.steps) &&
+            algorithm.timeComplexity;
+    }
+    async verifyAlgorithmNovelty(algorithm) {
+        const known_algorithms = [
+            'bubble_sort', 'selection_sort', 'insertion_sort', 'merge_sort',
+            'quick_sort', 'heap_sort', 'radix_sort', 'counting_sort'
+        ];
+        const algorithm_str = JSON.stringify(algorithm).toLowerCase();
+        return !known_algorithms.some(known => algorithm_str.includes(known.replace('_', '')));
+    }
+    async testAlgorithmCorrectness(algorithm) {
+        // This would need to actually execute the algorithm
+        // For now, check if it has the basic structure for correctness
+        return algorithm.steps && algorithm.steps.length > 0;
+    }
+    async verifyComplexityClaims(algorithm) {
+        // Verify claimed time complexity is reasonable
+        const valid_complexities = ['O(1)', 'O(log n)', 'O(n)', 'O(n log n)', 'O(n^2)', 'O(n^3)', 'O(2^n)'];
+        return valid_complexities.includes(algorithm.timeComplexity);
+    }
+    summarizeAlgorithm(algorithm) {
+        return {
+            name: algorithm.name,
+            step_count: algorithm.steps ? algorithm.steps.length : 0,
+            complexity: algorithm.timeComplexity,
+            has_description: !!algorithm.description
+        };
+    }
+    verifyRequirementMet(code, requirement) {
+        // Simple requirement checking - would need more sophisticated analysis in practice
+        if (requirement.includes('demonstrateEvolution')) {
+            return code.includes('demonstrateEvolution');
+        }
+        return false;
+    }
+    async verifySyntaxIndependently(code) {
+        try {
+            // Write to temporary file and check syntax
+            const temp_file = `/tmp/syntax_check_${Date.now()}.js`;
+            writeFileSync(temp_file, code);
+            const result = execSync(`node --check "${temp_file}"`, { encoding: 'utf8' });
+            execSync(`rm "${temp_file}"`);
+            return true;
+        }
+        catch {
+            return false;
+        }
+    }
+    async verifyCodeSafety(code) {
+        // Check for dangerous patterns
+        const dangerous_patterns = [
+            'eval(', 'Function(', 'require(', 'process.exit',
+            'fs.unlink', 'fs.rmdir', 'child_process', 'exec('
+        ];
+        return !dangerous_patterns.some(pattern => code.includes(pattern));
+    }
+    async performExternalVerification(result) {
+        // Placeholder for external verification logic
+        return {
+            testName: result.testName,
+            externalVerification: false,
+            internalResult: result,
+            externalResult: null,
+            discrepancies: ['External verification not implemented'],
+            trustScore: 0.0
+        };
+    }
+    generateIndependentHash(data) {
+        const timestamp = Date.now();
+        const entropy = randomBytes(16).toString('hex');
+        const content = JSON.stringify(data) + timestamp + entropy;
+        return createHash('sha256').update(content).digest('hex');
+    }
+}
+export function createIndependentVerificationSystem() {
+    return new IndependentVerificationSystem();
+}

vendor/sublinear-time-solver/dist/core/high-performance-solver.d.ts

@@ -0,0 +1,140 @@
+/**
+ * High-Performance Sublinear-Time Solver
+ *
+ * This implementation achieves 5-10x performance improvements through:
+ * - Optimized memory layouts using TypedArrays
+ * - Cache-friendly data structures
+ * - Vectorized operations where possible
+ * - Reduced memory allocations
+ * - Efficient sparse matrix representations
+ */
+export type Precision = number;
+/**
+ * High-performance sparse matrix using CSR (Compressed Sparse Row) format
+ * for optimal memory access patterns and cache performance.
+ */
+export declare class OptimizedSparseMatrix {
+    private values;
+    private colIndices;
+    private rowPtr;
+    private rows;
+    private cols;
+    private nnz;
+    constructor(values: Float64Array, colIndices: Uint32Array, rowPtr: Uint32Array, rows: number, cols: number);
+    /**
+     * Create optimized sparse matrix from triplets with automatic sorting and deduplication
+     */
+    static fromTriplets(triplets: Array<[number, number, number]>, rows: number, cols: number): OptimizedSparseMatrix;
+    /**
+     * Optimized sparse matrix-vector multiplication: y = A * x
+     * Uses cache-friendly access patterns and manual loop unrolling
+     */
+    multiplyVector(x: Float64Array, y: Float64Array): void;
+    get dimensions(): [number, number];
+    get nonZeros(): number;
+}
+/**
+ * Optimized vector operations using TypedArrays for maximum performance
+ */
+export declare class VectorOps {
+    /**
+     * Optimized dot product with manual loop unrolling
+     */
+    static dotProduct(x: Float64Array, y: Float64Array): number;
+    /**
+     * Optimized AXPY operation: y = alpha * x + y
+     */
+    static axpy(alpha: number, x: Float64Array, y: Float64Array): void;
+    /**
+     * Optimized vector norm calculation
+     */
+    static norm(x: Float64Array): number;
+    /**
+     * Copy vector efficiently
+     */
+    static copy(src: Float64Array, dst: Float64Array): void;
+    /**
+     * Scale vector in-place: x = alpha * x
+     */
+    static scale(alpha: number, x: Float64Array): void;
+}
+/**
+ * Configuration for the high-performance solver
+ */
+export interface HighPerformanceSolverConfig {
+    maxIterations?: number;
+    tolerance?: number;
+    enableProfiling?: boolean;
+    usePreconditioning?: boolean;
+}
+/**
+ * Result from high-performance solver
+ */
+export interface HighPerformanceSolverResult {
+    solution: Float64Array;
+    residualNorm: number;
+    iterations: number;
+    converged: boolean;
+    performanceStats: {
+        matVecCount: number;
+        dotProductCount: number;
+        axpyCount: number;
+        totalFlops: number;
+        computationTimeMs: number;
+        gflops: number;
+        bandwidth: number;
+    };
+}
+/**
+ * High-Performance Conjugate Gradient Solver
+ *
+ * Optimized for sparse symmetric positive definite systems with:
+ * - Cache-friendly memory access patterns
+ * - Minimal memory allocations
+ * - Vectorized operations where possible
+ * - Efficient use of TypedArrays
+ */
+export declare class HighPerformanceConjugateGradientSolver {
+    private config;
+    private workspaceVectors;
+    constructor(config?: HighPerformanceSolverConfig);
+    /**
+     * Solve the linear system Ax = b using optimized conjugate gradient
+     */
+    solve(matrix: OptimizedSparseMatrix, b: Float64Array): HighPerformanceSolverResult;
+    /**
+     * Ensure workspace vectors are allocated and sized correctly
+     */
+    private ensureWorkspaceSize;
+    /**
+     * Clear workspace to free memory
+     */
+    dispose(): void;
+}
+/**
+ * Memory pool for efficient vector allocation and reuse
+ */
+export declare class VectorPool {
+    private pools;
+    private maxPoolSize;
+    /**
+     * Get a vector from the pool or allocate a new one
+     */
+    getVector(size: number): Float64Array;
+    /**
+     * Return a vector to the pool for reuse
+     */
+    returnVector(vector: Float64Array): void;
+    /**
+     * Clear all pools to free memory
+     */
+    clear(): void;
+}
+/**
+ * Create optimized diagonal matrix for preconditioning
+ */
+export declare function createJacobiPreconditioner(matrix: OptimizedSparseMatrix): Float64Array;
+/**
+ * Factory function for easy solver creation
+ */
+export declare function createHighPerformanceSolver(config?: HighPerformanceSolverConfig): HighPerformanceConjugateGradientSolver;

vendor/sublinear-time-solver/dist/core/high-performance-solver.js

@@ -0,0 +1,409 @@
+/**
+ * High-Performance Sublinear-Time Solver
+ *
+ * This implementation achieves 5-10x performance improvements through:
+ * - Optimized memory layouts using TypedArrays
+ * - Cache-friendly data structures
+ * - Vectorized operations where possible
+ * - Reduced memory allocations
+ * - Efficient sparse matrix representations
+ */
+/**
+ * High-performance sparse matrix using CSR (Compressed Sparse Row) format
+ * for optimal memory access patterns and cache performance.
+ */
+export class OptimizedSparseMatrix {
+    values;
+    colIndices;
+    rowPtr;
+    rows;
+    cols;
+    nnz;
+    constructor(values, colIndices, rowPtr, rows, cols) {
+        this.values = values;
+        this.colIndices = colIndices;
+        this.rowPtr = rowPtr;
+        this.rows = rows;
+        this.cols = cols;
+        this.nnz = values.length;
+    }
+    /**
+     * Create optimized sparse matrix from triplets with automatic sorting and deduplication
+     */
+    static fromTriplets(triplets, rows, cols) {
+        // Sort triplets by row, then column for CSR format
+        triplets.sort((a, b) => {
+            if (a[0] !== b[0])
+                return a[0] - b[0];
+            return a[1] - b[1];
+        });
+        // Deduplicate entries by summing values for same (row, col)
+        const deduped = [];
+        for (const [row, col, val] of triplets) {
+            const lastEntry = deduped[deduped.length - 1];
+            if (lastEntry && lastEntry[0] === row && lastEntry[1] === col) {
+                lastEntry[2] += val;
+            }
+            else {
+                deduped.push([row, col, val]);
+            }
+        }
+        // Build CSR arrays
+        const nnz = deduped.length;
+        const values = new Float64Array(nnz);
+        const colIndices = new Uint32Array(nnz);
+        const rowPtr = new Uint32Array(rows + 1);
+        let currentRow = 0;
+        for (let i = 0; i < nnz; i++) {
+            const [row, col, val] = deduped[i];
+            // Fill rowPtr for empty rows
+            while (currentRow <= row) {
+                rowPtr[currentRow] = i;
+                currentRow++;
+            }
+            values[i] = val;
+            colIndices[i] = col;
+        }
+        // Fill remaining rowPtr entries
+        while (currentRow <= rows) {
+            rowPtr[currentRow] = nnz;
+            currentRow++;
+        }
+        return new OptimizedSparseMatrix(values, colIndices, rowPtr, rows, cols);
+    }
+    /**
+     * Optimized sparse matrix-vector multiplication: y = A * x
+     * Uses cache-friendly access patterns and manual loop unrolling
+     */
+    multiplyVector(x, y) {
+        if (x.length !== this.cols) {
+            throw new Error(`Vector length ${x.length} doesn't match matrix columns ${this.cols}`);
+        }
+        if (y.length !== this.rows) {
+            throw new Error(`Output vector length ${y.length} doesn't match matrix rows ${this.rows}`);
+        }
+        // Clear output vector
+        y.fill(0.0);
+        // Perform SpMV with cache-friendly CSR access
+        for (let row = 0; row < this.rows; row++) {
+            const start = this.rowPtr[row];
+            const end = this.rowPtr[row + 1];
+            if (end <= start)
+                continue;
+            let sum = 0.0;
+            let idx = start;
+            // Manual loop unrolling for better performance (process 4 elements at a time)
+            const unrollEnd = start + ((end - start) & ~3);
+            while (idx < unrollEnd) {
+                sum += this.values[idx] * x[this.colIndices[idx]];
+                sum += this.values[idx + 1] * x[this.colIndices[idx + 1]];
+                sum += this.values[idx + 2] * x[this.colIndices[idx + 2]];
+                sum += this.values[idx + 3] * x[this.colIndices[idx + 3]];
+                idx += 4;
+            }
+            // Handle remaining elements
+            while (idx < end) {
+                sum += this.values[idx] * x[this.colIndices[idx]];
+                idx++;
+            }
+            y[row] = sum;
+        }
+    }
+    get dimensions() {
+        return [this.rows, this.cols];
+    }
+    get nonZeros() {
+        return this.nnz;
+    }
+}
+/**
+ * Optimized vector operations using TypedArrays for maximum performance
+ */
+export class VectorOps {
+    /**
+     * Optimized dot product with manual loop unrolling
+     */
+    static dotProduct(x, y) {
+        if (x.length !== y.length) {
+            throw new Error(`Vector lengths don't match: ${x.length} vs ${y.length}`);
+        }
+        const n = x.length;
+        let result = 0.0;
+        let i = 0;
+        // Manual loop unrolling (process 4 elements at a time)
+        const unrollEnd = n & ~3;
+        while (i < unrollEnd) {
+            result += x[i] * y[i];
+            result += x[i + 1] * y[i + 1];
+            result += x[i + 2] * y[i + 2];
+            result += x[i + 3] * y[i + 3];
+            i += 4;
+        }
+        // Handle remaining elements
+        while (i < n) {
+            result += x[i] * y[i];
+            i++;
+        }
+        return result;
+    }
+    /**
+     * Optimized AXPY operation: y = alpha * x + y
+     */
+    static axpy(alpha, x, y) {
+        if (x.length !== y.length) {
+            throw new Error(`Vector lengths don't match: ${x.length} vs ${y.length}`);
+        }
+        const n = x.length;
+        let i = 0;
+        // Manual loop unrolling
+        const unrollEnd = n & ~3;
+        while (i < unrollEnd) {
+            y[i] += alpha * x[i];
+            y[i + 1] += alpha * x[i + 1];
+            y[i + 2] += alpha * x[i + 2];
+            y[i + 3] += alpha * x[i + 3];
+            i += 4;
+        }
+        // Handle remaining elements
+        while (i < n) {
+            y[i] += alpha * x[i];
+            i++;
+        }
+    }
+    /**
+     * Optimized vector norm calculation
+     */
+    static norm(x) {
+        return Math.sqrt(VectorOps.dotProduct(x, x));
+    }
+    /**
+     * Copy vector efficiently
+     */
+    static copy(src, dst) {
+        dst.set(src);
+    }
+    /**
+     * Scale vector in-place: x = alpha * x
+     */
+    static scale(alpha, x) {
+        const n = x.length;
+        let i = 0;
+        // Manual loop unrolling
+        const unrollEnd = n & ~3;
+        while (i < unrollEnd) {
+            x[i] *= alpha;
+            x[i + 1] *= alpha;
+            x[i + 2] *= alpha;
+            x[i + 3] *= alpha;
+            i += 4;
+        }
+        // Handle remaining elements
+        while (i < n) {
+            x[i] *= alpha;
+            i++;
+        }
+    }
+}
+/**
+ * High-Performance Conjugate Gradient Solver
+ *
+ * Optimized for sparse symmetric positive definite systems with:
+ * - Cache-friendly memory access patterns
+ * - Minimal memory allocations
+ * - Vectorized operations where possible
+ * - Efficient use of TypedArrays
+ */
+export class HighPerformanceConjugateGradientSolver {
+    config;
+    workspaceVectors = { r: null, p: null, ap: null };
+    constructor(config = {}) {
+        this.config = {
+            maxIterations: config.maxIterations ?? 1000,
+            tolerance: config.tolerance ?? 1e-6,
+            enableProfiling: config.enableProfiling ?? false,
+            usePreconditioning: config.usePreconditioning ?? false,
+        };
+    }
+    /**
+     * Solve the linear system Ax = b using optimized conjugate gradient
+     */
+    solve(matrix, b) {
+        const [rows, cols] = matrix.dimensions;
+        if (rows !== cols) {
+            throw new Error('Matrix must be square');
+        }
+        if (b.length !== rows) {
+            throw new Error('Right-hand side vector length must match matrix size');
+        }
+        const startTime = performance.now();
+        // Initialize or reuse workspace vectors to minimize allocations
+        this.ensureWorkspaceSize(rows);
+        const r = this.workspaceVectors.r;
+        const p = this.workspaceVectors.p;
+        const ap = this.workspaceVectors.ap;
+        // Initialize solution vector
+        const x = new Float64Array(rows);
+        // Initialize residual: r = b - A*x (since x = 0 initially, r = b)
+        VectorOps.copy(b, r);
+        VectorOps.copy(r, p);
+        let rsold = VectorOps.dotProduct(r, r);
+        const bNorm = VectorOps.norm(b);
+        // Performance tracking
+        let matVecCount = 0;
+        let dotProductCount = 1; // Initial r^T * r
+        let axpyCount = 0;
+        let totalFlops = 2 * rows; // Initial dot product
+        let iteration = 0;
+        let converged = false;
+        while (iteration < this.config.maxIterations) {
+            // ap = A * p
+            matrix.multiplyVector(p, ap);
+            matVecCount++;
+            totalFlops += 2 * matrix.nonZeros;
+            // alpha = rsold / (p^T * ap)
+            const pAp = VectorOps.dotProduct(p, ap);
+            dotProductCount++;
+            totalFlops += 2 * rows;
+            if (Math.abs(pAp) < 1e-16) {
+                throw new Error('Matrix appears to be singular');
+            }
+            const alpha = rsold / pAp;
+            // x = x + alpha * p
+            VectorOps.axpy(alpha, p, x);
+            axpyCount++;
+            totalFlops += 2 * rows;
+            // r = r - alpha * ap
+            VectorOps.axpy(-alpha, ap, r);
+            axpyCount++;
+            totalFlops += 2 * rows;
+            // Check convergence
+            const rsnew = VectorOps.dotProduct(r, r);
+            dotProductCount++;
+            totalFlops += 2 * rows;
+            const residualNorm = Math.sqrt(rsnew);
+            const relativeResidual = bNorm > 0 ? residualNorm / bNorm : residualNorm;
+            if (relativeResidual < this.config.tolerance) {
+                converged = true;
+                break;
+            }
+            // beta = rsnew / rsold
+            const beta = rsnew / rsold;
+            // p = r + beta * p (update search direction)
+            for (let i = 0; i < rows; i++) {
+                p[i] = r[i] + beta * p[i];
+            }
+            totalFlops += 2 * rows;
+            rsold = rsnew;
+            iteration++;
+        }
+        const computationTimeMs = performance.now() - startTime;
+        // Calculate performance metrics
+        const gflops = computationTimeMs > 0 ? (totalFlops / (computationTimeMs / 1000)) / 1e9 : 0;
+        // Estimate bandwidth (rough approximation)
+        const bytesPerMatVec = matrix.nonZeros * 8 + rows * 16; // CSR + 2 vectors
+        const totalBytes = matVecCount * bytesPerMatVec + dotProductCount * rows * 16;
+        const bandwidth = computationTimeMs > 0 ? (totalBytes / (computationTimeMs / 1000)) / 1e9 : 0;
+        const finalResidualNorm = Math.sqrt(rsold);
+        return {
+            solution: x,
+            residualNorm: finalResidualNorm,
+            iterations: iteration,
+            converged,
+            performanceStats: {
+                matVecCount,
+                dotProductCount,
+                axpyCount,
+                totalFlops,
+                computationTimeMs,
+                gflops,
+                bandwidth,
+            },
+        };
+    }
+    /**
+     * Ensure workspace vectors are allocated and sized correctly
+     */
+    ensureWorkspaceSize(size) {
+        if (!this.workspaceVectors.r || this.workspaceVectors.r.length !== size) {
+            this.workspaceVectors.r = new Float64Array(size);
+            this.workspaceVectors.p = new Float64Array(size);
+            this.workspaceVectors.ap = new Float64Array(size);
+        }
+    }
+    /**
+     * Clear workspace to free memory
+     */
+    dispose() {
+        this.workspaceVectors.r = null;
+        this.workspaceVectors.p = null;
+        this.workspaceVectors.ap = null;
+    }
+}
+/**
+ * Memory pool for efficient vector allocation and reuse
+ */
+export class VectorPool {
+    pools = new Map();
+    maxPoolSize = 10;
+    /**
+     * Get a vector from the pool or allocate a new one
+     */
+    getVector(size) {
+        const pool = this.pools.get(size);
+        if (pool && pool.length > 0) {
+            const vector = pool.pop();
+            vector.fill(0); // Clear the vector
+            return vector;
+        }
+        return new Float64Array(size);
+    }
+    /**
+     * Return a vector to the pool for reuse
+     */
+    returnVector(vector) {
+        const size = vector.length;
+        let pool = this.pools.get(size);
+        if (!pool) {
+            pool = [];
+            this.pools.set(size, pool);
+        }
+        if (pool.length < this.maxPoolSize) {
+            pool.push(vector);
+        }
+    }
+    /**
+     * Clear all pools to free memory
+     */
+    clear() {
+        this.pools.clear();
+    }
+}
+/**
+ * Create optimized diagonal matrix for preconditioning
+ */
+export function createJacobiPreconditioner(matrix) {
+    const [rows] = matrix.dimensions;
+    const preconditioner = new Float64Array(rows);
+    // Extract diagonal elements
+    const values = matrix.values;
+    const colIndices = matrix.colIndices;
+    const rowPtr = matrix.rowPtr;
+    for (let row = 0; row < rows; row++) {
+        const start = rowPtr[row];
+        const end = rowPtr[row + 1];
+        for (let idx = start; idx < end; idx++) {
+            if (colIndices[idx] === row) {
+                preconditioner[row] = 1.0 / Math.max(Math.abs(values[idx]), 1e-16);
+                break;
+            }
+        }
+    }
+    return preconditioner;
+}
+/**
+ * Factory function for easy solver creation
+ */
+export function createHighPerformanceSolver(config) {
+    return new HighPerformanceConjugateGradientSolver(config);
+}
+// All classes are already exported above, no need to re-export

vendor/sublinear-time-solver/dist/core/matrix.d.ts

@@ -0,0 +1,62 @@
+/**
+ * Core matrix operations for sublinear-time solvers
+ */
+import { Matrix, SparseMatrix, DenseMatrix, Vector, MatrixAnalysis } from './types.js';
+export declare class MatrixOperations {
+    /**
+     * Validates matrix format and properties
+     */
+    static validateMatrix(matrix: Matrix): void;
+    /**
+     * Matrix-vector multiplication: result = matrix * vector
+     */
+    static multiplyMatrixVector(matrix: Matrix, vector: Vector): Vector;
+    /**
+     * Get matrix entry at (row, col)
+     */
+    static getEntry(matrix: Matrix, row: number, col: number): number;
+    /**
+     * Get diagonal entry at position i
+     */
+    static getDiagonal(matrix: Matrix, i: number): number;
+    /**
+     * Extract diagonal as vector
+     */
+    static getDiagonalVector(matrix: Matrix): Vector;
+    /**
+     * Get row sum for diagonal dominance check
+     */
+    static getRowSum(matrix: Matrix, row: number, excludeDiagonal?: boolean): number;
+    /**
+     * Get column sum for diagonal dominance check
+     */
+    static getColumnSum(matrix: Matrix, col: number, excludeDiagonal?: boolean): number;
+    /**
+     * Check if matrix is diagonally dominant
+     */
+    static checkDiagonalDominance(matrix: Matrix): {
+        isRowDD: boolean;
+        isColDD: boolean;
+        strength: number;
+    };
+    /**
+     * Check if matrix is symmetric
+     */
+    static isSymmetric(matrix: Matrix, tolerance?: number): boolean;
+    /**
+     * Calculate sparsity ratio (fraction of zero entries)
+     */
+    static calculateSparsity(matrix: Matrix): number;
+    /**
+     * Analyze matrix properties
+     */
+    static analyzeMatrix(matrix: Matrix): MatrixAnalysis;
+    /**
+     * Convert dense matrix to COO sparse format
+     */
+    static denseToSparse(dense: DenseMatrix, tolerance?: number): SparseMatrix;
+    /**
+     * Convert COO sparse matrix to dense format
+     */
+    static sparseToDense(sparse: SparseMatrix): DenseMatrix;
+}

vendor/sublinear-time-solver/dist/core/matrix.js

@@ -0,0 +1,348 @@
+/**
+ * Core matrix operations for sublinear-time solvers
+ */
+import { SolverError, ErrorCodes } from './types.js';
+export class MatrixOperations {
+    /**
+     * Validates matrix format and properties
+     */
+    static validateMatrix(matrix) {
+        if (!matrix) {
+            throw new SolverError('Matrix is required', ErrorCodes.INVALID_MATRIX);
+        }
+        if (matrix.rows <= 0 || matrix.cols <= 0) {
+            throw new SolverError('Matrix dimensions must be positive', ErrorCodes.INVALID_DIMENSIONS);
+        }
+        if (matrix.format === 'dense') {
+            const dense = matrix;
+            if (!Array.isArray(dense.data) || dense.data.length !== dense.rows) {
+                throw new SolverError('Dense matrix data must be array of rows', ErrorCodes.INVALID_MATRIX);
+            }
+            for (let i = 0; i < dense.rows; i++) {
+                if (!Array.isArray(dense.data[i]) || dense.data[i].length !== dense.cols) {
+                    throw new SolverError(`Row ${i} has invalid length`, ErrorCodes.INVALID_MATRIX);
+                }
+            }
+        }
+        else if (matrix.format === 'coo') {
+            const sparse = matrix;
+            const { values, rowIndices, colIndices } = sparse;
+            if (!Array.isArray(values) || !Array.isArray(rowIndices) || !Array.isArray(colIndices)) {
+                throw new SolverError('COO matrix must have values, rowIndices, and colIndices arrays', ErrorCodes.INVALID_MATRIX);
+            }
+            if (values.length !== rowIndices.length || values.length !== colIndices.length) {
+                throw new SolverError('COO matrix arrays must have same length', ErrorCodes.INVALID_MATRIX);
+            }
+            // Check indices are valid
+            for (let i = 0; i < rowIndices.length; i++) {
+                if (rowIndices[i] < 0 || rowIndices[i] >= sparse.rows) {
+                    throw new SolverError(`Invalid row index ${rowIndices[i]}`, ErrorCodes.INVALID_MATRIX);
+                }
+                if (colIndices[i] < 0 || colIndices[i] >= sparse.cols) {
+                    throw new SolverError(`Invalid column index ${colIndices[i]}`, ErrorCodes.INVALID_MATRIX);
+                }
+            }
+        }
+        else {
+            throw new SolverError(`Unsupported matrix format: ${matrix.format}`, ErrorCodes.INVALID_MATRIX);
+        }
+    }
+    /**
+     * Matrix-vector multiplication: result = matrix * vector
+     */
+    static multiplyMatrixVector(matrix, vector) {
+        this.validateMatrix(matrix);
+        if (vector.length !== matrix.cols) {
+            throw new SolverError(`Vector length ${vector.length} does not match matrix columns ${matrix.cols}`, ErrorCodes.INVALID_DIMENSIONS);
+        }
+        const result = new Array(matrix.rows).fill(0);
+        if (matrix.format === 'dense') {
+            const dense = matrix;
+            for (let i = 0; i < matrix.rows; i++) {
+                for (let j = 0; j < matrix.cols; j++) {
+                    result[i] += dense.data[i][j] * vector[j];
+                }
+            }
+        }
+        else if (matrix.format === 'coo') {
+            const sparse = matrix;
+            for (let k = 0; k < sparse.values.length; k++) {
+                const row = sparse.rowIndices[k];
+                const col = sparse.colIndices[k];
+                const val = sparse.values[k];
+                result[row] += val * vector[col];
+            }
+        }
+        return result;
+    }
+    /**
+     * Get matrix entry at (row, col)
+     */
+    static getEntry(matrix, row, col) {
+        this.validateMatrix(matrix);
+        if (row < 0 || row >= matrix.rows || col < 0 || col >= matrix.cols) {
+            throw new SolverError(`Index (${row}, ${col}) out of bounds`, ErrorCodes.INVALID_DIMENSIONS);
+        }
+        if (matrix.format === 'dense') {
+            const dense = matrix;
+            return dense.data[row][col];
+        }
+        else if (matrix.format === 'coo') {
+            const sparse = matrix;
+            for (let k = 0; k < sparse.values.length; k++) {
+                if (sparse.rowIndices[k] === row && sparse.colIndices[k] === col) {
+                    return sparse.values[k];
+                }
+            }
+            return 0; // Implicit zero
+        }
+        return 0;
+    }
+    /**
+     * Get diagonal entry at position i
+     */
+    static getDiagonal(matrix, i) {
+        return this.getEntry(matrix, i, i);
+    }
+    /**
+     * Extract diagonal as vector
+     */
+    static getDiagonalVector(matrix) {
+        if (matrix.rows !== matrix.cols) {
+            throw new SolverError('Matrix must be square to extract diagonal', ErrorCodes.INVALID_DIMENSIONS);
+        }
+        const diagonal = new Array(matrix.rows);
+        for (let i = 0; i < matrix.rows; i++) {
+            diagonal[i] = this.getDiagonal(matrix, i);
+        }
+        return diagonal;
+    }
+    /**
+     * Get row sum for diagonal dominance check
+     */
+    static getRowSum(matrix, row, excludeDiagonal = false) {
+        this.validateMatrix(matrix);
+        if (row < 0 || row >= matrix.rows) {
+            throw new SolverError(`Row index ${row} out of bounds`, ErrorCodes.INVALID_DIMENSIONS);
+        }
+        let sum = 0;
+        if (matrix.format === 'dense') {
+            const dense = matrix;
+            for (let j = 0; j < matrix.cols; j++) {
+                if (!excludeDiagonal || j !== row) {
+                    sum += Math.abs(dense.data[row][j]);
+                }
+            }
+        }
+        else if (matrix.format === 'coo') {
+            const sparse = matrix;
+            for (let k = 0; k < sparse.values.length; k++) {
+                if (sparse.rowIndices[k] === row) {
+                    const col = sparse.colIndices[k];
+                    if (!excludeDiagonal || col !== row) {
+                        sum += Math.abs(sparse.values[k]);
+                    }
+                }
+            }
+        }
+        return sum;
+    }
+    /**
+     * Get column sum for diagonal dominance check
+     */
+    static getColumnSum(matrix, col, excludeDiagonal = false) {
+        this.validateMatrix(matrix);
+        if (col < 0 || col >= matrix.cols) {
+            throw new SolverError(`Column index ${col} out of bounds`, ErrorCodes.INVALID_DIMENSIONS);
+        }
+        let sum = 0;
+        if (matrix.format === 'dense') {
+            const dense = matrix;
+            for (let i = 0; i < matrix.rows; i++) {
+                if (!excludeDiagonal || i !== col) {
+                    sum += Math.abs(dense.data[i][col]);
+                }
+            }
+        }
+        else if (matrix.format === 'coo') {
+            const sparse = matrix;
+            for (let k = 0; k < sparse.values.length; k++) {
+                if (sparse.colIndices[k] === col) {
+                    const row = sparse.rowIndices[k];
+                    if (!excludeDiagonal || row !== col) {
+                        sum += Math.abs(sparse.values[k]);
+                    }
+                }
+            }
+        }
+        return sum;
+    }
+    /**
+     * Check if matrix is diagonally dominant
+     */
+    static checkDiagonalDominance(matrix) {
+        this.validateMatrix(matrix);
+        if (matrix.rows !== matrix.cols) {
+            return { isRowDD: false, isColDD: false, strength: 0 };
+        }
+        let isRowDD = true;
+        let isColDD = true;
+        let minRowStrength = Infinity;
+        let minColStrength = Infinity;
+        for (let i = 0; i < matrix.rows; i++) {
+            const diagonal = Math.abs(this.getDiagonal(matrix, i));
+            const rowOffDiagonalSum = this.getRowSum(matrix, i, true);
+            const colOffDiagonalSum = this.getColumnSum(matrix, i, true);
+            if (diagonal === 0) {
+                isRowDD = false;
+                isColDD = false;
+                minRowStrength = 0;
+                minColStrength = 0;
+                break;
+            }
+            const rowStrength = diagonal - rowOffDiagonalSum;
+            const colStrength = diagonal - colOffDiagonalSum;
+            if (rowStrength < 0) {
+                isRowDD = false;
+            }
+            else {
+                minRowStrength = Math.min(minRowStrength, rowStrength / diagonal);
+            }
+            if (colStrength < 0) {
+                isColDD = false;
+            }
+            else {
+                minColStrength = Math.min(minColStrength, colStrength / diagonal);
+            }
+        }
+        const strength = Math.max(isRowDD ? minRowStrength : 0, isColDD ? minColStrength : 0);
+        return { isRowDD, isColDD, strength };
+    }
+    /**
+     * Check if matrix is symmetric
+     */
+    static isSymmetric(matrix, tolerance = 1e-10) {
+        this.validateMatrix(matrix);
+        if (matrix.rows !== matrix.cols) {
+            return false;
+        }
+        // For sparse matrices, this is more complex - we'd need to compare all entries
+        if (matrix.format === 'dense') {
+            const dense = matrix;
+            for (let i = 0; i < matrix.rows; i++) {
+                for (let j = i + 1; j < matrix.cols; j++) {
+                    if (Math.abs(dense.data[i][j] - dense.data[j][i]) > tolerance) {
+                        return false;
+                    }
+                }
+            }
+            return true;
+        }
+        // For sparse matrices, check symmetry by comparing entries
+        for (let i = 0; i < matrix.rows; i++) {
+            for (let j = i + 1; j < matrix.cols; j++) {
+                const entry_ij = this.getEntry(matrix, i, j);
+                const entry_ji = this.getEntry(matrix, j, i);
+                if (Math.abs(entry_ij - entry_ji) > tolerance) {
+                    return false;
+                }
+            }
+        }
+        return true;
+    }
+    /**
+     * Calculate sparsity ratio (fraction of zero entries)
+     */
+    static calculateSparsity(matrix) {
+        this.validateMatrix(matrix);
+        const totalEntries = matrix.rows * matrix.cols;
+        if (matrix.format === 'dense') {
+            const dense = matrix;
+            let nonZeros = 0;
+            for (let i = 0; i < matrix.rows; i++) {
+                for (let j = 0; j < matrix.cols; j++) {
+                    if (Math.abs(dense.data[i][j]) > 1e-15) {
+                        nonZeros++;
+                    }
+                }
+            }
+            return 1 - (nonZeros / totalEntries);
+        }
+        else if (matrix.format === 'coo') {
+            const sparse = matrix;
+            return 1 - (sparse.values.length / totalEntries);
+        }
+        return 0;
+    }
+    /**
+     * Analyze matrix properties
+     */
+    static analyzeMatrix(matrix) {
+        this.validateMatrix(matrix);
+        const dominance = this.checkDiagonalDominance(matrix);
+        const isSymmetric = this.isSymmetric(matrix);
+        const sparsity = this.calculateSparsity(matrix);
+        let dominanceType = 'none';
+        if (dominance.isRowDD && dominance.isColDD) {
+            dominanceType = 'row'; // Prefer row if both
+        }
+        else if (dominance.isRowDD) {
+            dominanceType = 'row';
+        }
+        else if (dominance.isColDD) {
+            dominanceType = 'column';
+        }
+        return {
+            isDiagonallyDominant: dominance.isRowDD || dominance.isColDD,
+            dominanceType,
+            dominanceStrength: dominance.strength,
+            isSymmetric,
+            sparsity,
+            size: { rows: matrix.rows, cols: matrix.cols }
+        };
+    }
+    /**
+     * Convert dense matrix to COO sparse format
+     */
+    static denseToSparse(dense, tolerance = 1e-15) {
+        const values = [];
+        const rowIndices = [];
+        const colIndices = [];
+        for (let i = 0; i < dense.rows; i++) {
+            for (let j = 0; j < dense.cols; j++) {
+                const value = dense.data[i][j];
+                if (Math.abs(value) > tolerance) {
+                    values.push(value);
+                    rowIndices.push(i);
+                    colIndices.push(j);
+                }
+            }
+        }
+        return {
+            rows: dense.rows,
+            cols: dense.cols,
+            values,
+            rowIndices,
+            colIndices,
+            format: 'coo'
+        };
+    }
+    /**
+     * Convert COO sparse matrix to dense format
+     */
+    static sparseToDense(sparse) {
+        const data = Array(sparse.rows).fill(null).map(() => Array(sparse.cols).fill(0));
+        for (let k = 0; k < sparse.values.length; k++) {
+            const row = sparse.rowIndices[k];
+            const col = sparse.colIndices[k];
+            const val = sparse.values[k];
+            data[row][col] = val;
+        }
+        return {
+            rows: sparse.rows,
+            cols: sparse.cols,
+            data,
+            format: 'dense'
+        };
+    }
+}

vendor/sublinear-time-solver/dist/core/memory-manager.d.ts

@@ -0,0 +1,56 @@
+/**
+ * Advanced memory management and profiling for matrix operations
+ * Implements memory streaming, pooling, and cache optimization
+ */
+export interface MemoryStats {
+    totalAllocated: number;
+    totalReleased: number;
+    currentUsage: number;
+    peakUsage: number;
+    poolStats: Record<string, any>;
+    gcCount: number;
+    cacheHitRate: number;
+}
+export interface CacheConfig {
+    maxSize: number;
+    ttl: number;
+    evictionPolicy: 'lru' | 'lfu' | 'fifo';
+}
+export declare class MemoryStreamManager {
+    private cache;
+    private arrayPool;
+    private gcCount;
+    private streamingThreshold;
+    constructor(cacheConfig?: CacheConfig, streamingThreshold?: number);
+    streamMatrixChunks<T>(data: T[], chunkSize: number, processor: (chunk: T[]) => Promise<any>): AsyncGenerator<any, void, unknown>;
+    scheduleOperation<T>(operation: () => Promise<T>, estimatedMemory: number): Promise<T>;
+    private freeMemory;
+    private getCurrentMemoryUsage;
+    acquireTypedArray(type: 'float64' | 'uint32' | 'uint8', length: number): any;
+    releaseTypedArray(array: Float64Array | Uint32Array | Uint8Array): void;
+    getMemoryStats(): MemoryStats;
+    profileOperation<T>(name: string, operation: () => Promise<T>): Promise<{
+        result: T;
+        profile: MemoryProfile;
+    }>;
+    optimizeCache(): void;
+    cleanup(): void;
+}
+export interface MemoryProfile {
+    name: string;
+    duration: number;
+    memoryDelta: number;
+    peakMemory: number;
+    allocations: number;
+    deallocations: number;
+    cacheHitRate: number;
+}
+export declare class SIMDMemoryOptimizer {
+    private static readonly SIMD_WIDTH;
+    private static readonly CACHE_LINE_SIZE;
+    static alignForSIMD(length: number): number;
+    static optimizeLayout<T>(arrays: T[][], accessPattern: 'row' | 'column'): T[][];
+    static padForCacheLines<T>(array: T[], padValue: T): T[];
+    static blockMatrixMultiply(a: number[][], b: number[][], result: number[][], blockSize?: number): void;
+}
+export declare const globalMemoryManager: MemoryStreamManager;

vendor/sublinear-time-solver/dist/core/memory-manager.js

@@ -0,0 +1,324 @@
+/**
+ * Advanced memory management and profiling for matrix operations
+ * Implements memory streaming, pooling, and cache optimization
+ */
+// LRU Cache implementation for matrix chunks
+class LRUCache {
+    cache = new Map();
+    maxSize;
+    ttl;
+    hits = 0;
+    misses = 0;
+    constructor(config) {
+        this.maxSize = config.maxSize;
+        this.ttl = config.ttl;
+    }
+    get(key) {
+        const entry = this.cache.get(key);
+        if (!entry) {
+            this.misses++;
+            return undefined;
+        }
+        // Check TTL
+        if (Date.now() - entry.lastUsed > this.ttl) {
+            this.cache.delete(key);
+            this.misses++;
+            return undefined;
+        }
+        entry.lastUsed = Date.now();
+        entry.useCount++;
+        this.hits++;
+        return entry.value;
+    }
+    set(key, value) {
+        if (this.cache.size >= this.maxSize) {
+            this.evict();
+        }
+        this.cache.set(key, {
+            value,
+            lastUsed: Date.now(),
+            useCount: 1
+        });
+    }
+    evict() {
+        let oldestKey;
+        let oldestTime = Infinity;
+        for (const [key, entry] of this.cache) {
+            if (entry.lastUsed < oldestTime) {
+                oldestTime = entry.lastUsed;
+                oldestKey = key;
+            }
+        }
+        if (oldestKey !== undefined) {
+            this.cache.delete(oldestKey);
+        }
+    }
+    getHitRate() {
+        const total = this.hits + this.misses;
+        return total > 0 ? this.hits / total : 0;
+    }
+    clear() {
+        this.cache.clear();
+        this.hits = 0;
+        this.misses = 0;
+    }
+    size() {
+        return this.cache.size;
+    }
+}
+// Memory pool for typed arrays
+class TypedArrayPool {
+    pools = new Map();
+    allocatedBytes = 0;
+    releasedBytes = 0;
+    peakBytes = 0;
+    maxPoolSize = 50;
+    acquire(type, length) {
+        const bytesPerElement = this.getBytesPerElement(type);
+        const totalBytes = length * bytesPerElement;
+        const key = `${type}_${length}`;
+        const pool = this.pools.get(key);
+        if (pool && pool.length > 0) {
+            const buffer = pool.pop();
+            this.allocatedBytes += totalBytes;
+            this.peakBytes = Math.max(this.peakBytes, this.allocatedBytes - this.releasedBytes);
+            return buffer;
+        }
+        const buffer = new ArrayBuffer(totalBytes);
+        this.allocatedBytes += totalBytes;
+        this.peakBytes = Math.max(this.peakBytes, this.allocatedBytes - this.releasedBytes);
+        return buffer;
+    }
+    release(type, buffer) {
+        const length = buffer.byteLength / this.getBytesPerElement(type);
+        const key = `${type}_${length}`;
+        let pool = this.pools.get(key);
+        if (!pool) {
+            pool = [];
+            this.pools.set(key, pool);
+        }
+        if (pool.length < this.maxPoolSize) {
+            pool.push(buffer);
+        }
+        this.releasedBytes += buffer.byteLength;
+    }
+    getBytesPerElement(type) {
+        switch (type) {
+            case 'float64': return 8;
+            case 'uint32': return 4;
+            case 'uint8': return 1;
+        }
+    }
+    getStats() {
+        const poolSizes = {};
+        for (const [key, pool] of this.pools) {
+            poolSizes[key] = pool.length;
+        }
+        return {
+            allocated: this.allocatedBytes,
+            released: this.releasedBytes,
+            current: this.allocatedBytes - this.releasedBytes,
+            peak: this.peakBytes,
+            poolSizes
+        };
+    }
+    clear() {
+        this.pools.clear();
+        this.allocatedBytes = 0;
+        this.releasedBytes = 0;
+        this.peakBytes = 0;
+    }
+}
+// Memory streaming manager for large matrix operations
+export class MemoryStreamManager {
+    cache;
+    arrayPool;
+    gcCount = 0;
+    streamingThreshold;
+    constructor(cacheConfig = { maxSize: 100, ttl: 300000, evictionPolicy: 'lru' }, streamingThreshold = 1024 * 1024 * 100 // 100MB threshold
+    ) {
+        this.cache = new LRUCache(cacheConfig);
+        this.arrayPool = new TypedArrayPool();
+        this.streamingThreshold = streamingThreshold;
+        // Monitor garbage collection
+        if (typeof globalThis !== 'undefined' && 'performance' in globalThis) {
+            performance.onGC?.(() => this.gcCount++);
+        }
+    }
+    // Stream large matrix data in chunks
+    async *streamMatrixChunks(data, chunkSize, processor) {
+        for (let i = 0; i < data.length; i += chunkSize) {
+            const chunk = data.slice(i, i + chunkSize);
+            const cacheKey = `chunk_${i}_${chunkSize}`;
+            let result = this.cache.get(cacheKey);
+            if (!result) {
+                result = await processor(chunk);
+                this.cache.set(cacheKey, result);
+            }
+            yield result;
+            // Yield control to prevent blocking
+            if (i % (chunkSize * 10) === 0) {
+                await new Promise(resolve => setTimeout(resolve, 0));
+            }
+        }
+    }
+    // Memory-aware matrix operation scheduling
+    async scheduleOperation(operation, estimatedMemory) {
+        const currentUsage = this.getCurrentMemoryUsage();
+        // If operation would exceed threshold, wait for GC or free cache
+        if (currentUsage + estimatedMemory > this.streamingThreshold) {
+            await this.freeMemory();
+        }
+        return operation();
+    }
+    async freeMemory() {
+        // Clear oldest cache entries
+        this.cache.clear();
+        this.arrayPool.clear();
+        // Force garbage collection if available
+        if (typeof globalThis !== 'undefined' && globalThis.gc) {
+            globalThis.gc();
+        }
+        // Wait a bit for GC to complete
+        await new Promise(resolve => setTimeout(resolve, 100));
+    }
+    getCurrentMemoryUsage() {
+        if (typeof globalThis !== 'undefined' && 'performance' in globalThis && 'memory' in performance) {
+            return performance.memory.usedJSHeapSize;
+        }
+        // Fallback to estimated usage from pool
+        return this.arrayPool.getStats().current;
+    }
+    // Acquire optimized typed array
+    acquireTypedArray(type, length) {
+        const buffer = this.arrayPool.acquire(type, length);
+        switch (type) {
+            case 'float64': return new Float64Array(buffer);
+            case 'uint32': return new Uint32Array(buffer);
+            case 'uint8': return new Uint8Array(buffer);
+        }
+    }
+    // Release typed array back to pool
+    releaseTypedArray(array) {
+        let type;
+        if (array instanceof Float64Array)
+            type = 'float64';
+        else if (array instanceof Uint32Array)
+            type = 'uint32';
+        else
+            type = 'uint8';
+        this.arrayPool.release(type, array.buffer);
+    }
+    // Get comprehensive memory statistics
+    getMemoryStats() {
+        const poolStats = this.arrayPool.getStats();
+        return {
+            totalAllocated: poolStats.allocated,
+            totalReleased: poolStats.released,
+            currentUsage: poolStats.current,
+            peakUsage: poolStats.peak,
+            poolStats: {
+                arrayPool: poolStats.poolSizes,
+                cacheSize: this.cache.size(),
+                cacheHitRate: this.cache.getHitRate()
+            },
+            gcCount: this.gcCount,
+            cacheHitRate: this.cache.getHitRate()
+        };
+    }
+    // Memory profiler for operations
+    async profileOperation(name, operation) {
+        const startStats = this.getMemoryStats();
+        const startTime = performance.now();
+        const result = await operation();
+        const endTime = performance.now();
+        const endStats = this.getMemoryStats();
+        const profile = {
+            name,
+            duration: endTime - startTime,
+            memoryDelta: endStats.currentUsage - startStats.currentUsage,
+            peakMemory: endStats.peakUsage,
+            allocations: endStats.totalAllocated - startStats.totalAllocated,
+            deallocations: endStats.totalReleased - startStats.totalReleased,
+            cacheHitRate: endStats.cacheHitRate
+        };
+        return { result, profile };
+    }
+    // Optimize cache based on access patterns
+    optimizeCache() {
+        // This could analyze access patterns and adjust cache size/TTL
+        const hitRate = this.cache.getHitRate();
+        if (hitRate < 0.5) {
+            // Low hit rate, might need larger cache or different eviction policy
+            console.warn(`Low cache hit rate: ${hitRate.toFixed(2)}`);
+        }
+    }
+    cleanup() {
+        this.cache.clear();
+        this.arrayPool.clear();
+    }
+}
+// SIMD-aware memory layout optimizer
+export class SIMDMemoryOptimizer {
+    static SIMD_WIDTH = 4; // 4 doubles for AVX
+    static CACHE_LINE_SIZE = 64; // bytes
+    // Align arrays for SIMD operations
+    static alignForSIMD(length) {
+        return Math.ceil(length / this.SIMD_WIDTH) * this.SIMD_WIDTH;
+    }
+    // Optimize array layout for cache performance
+    static optimizeLayout(arrays, accessPattern) {
+        if (accessPattern === 'row') {
+            // Keep arrays as-is for row-major access
+            return arrays;
+        }
+        else {
+            // Transpose for column-major access
+            const rows = arrays.length;
+            const cols = arrays[0]?.length || 0;
+            const transposed = Array(cols).fill(null).map(() => Array(rows));
+            for (let i = 0; i < rows; i++) {
+                for (let j = 0; j < cols; j++) {
+                    transposed[j][i] = arrays[i][j];
+                }
+            }
+            return transposed;
+        }
+    }
+    // Pad arrays to avoid false sharing
+    static padForCacheLines(array, padValue) {
+        const elementSize = 8; // Assume 8 bytes per element
+        const elementsPerCacheLine = this.CACHE_LINE_SIZE / elementSize;
+        const padding = elementsPerCacheLine - (array.length % elementsPerCacheLine);
+        if (padding === elementsPerCacheLine) {
+            return array;
+        }
+        return [...array, ...Array(padding).fill(padValue)];
+    }
+    // Block matrix operations for better cache locality
+    static blockMatrixMultiply(a, b, result, blockSize = 64) {
+        const n = a.length;
+        const m = b[0].length;
+        const p = b.length;
+        for (let ii = 0; ii < n; ii += blockSize) {
+            for (let jj = 0; jj < m; jj += blockSize) {
+                for (let kk = 0; kk < p; kk += blockSize) {
+                    const iEnd = Math.min(ii + blockSize, n);
+                    const jEnd = Math.min(jj + blockSize, m);
+                    const kEnd = Math.min(kk + blockSize, p);
+                    for (let i = ii; i < iEnd; i++) {
+                        for (let j = jj; j < jEnd; j++) {
+                            let sum = result[i][j];
+                            for (let k = kk; k < kEnd; k++) {
+                                sum += a[i][k] * b[k][j];
+                            }
+                            result[i][j] = sum;
+                        }
+                    }
+                }
+            }
+        }
+    }
+}
+// Global memory manager instance
+export const globalMemoryManager = new MemoryStreamManager();

vendor/sublinear-time-solver/dist/core/optimized-matrix.d.ts

@@ -0,0 +1,79 @@
+/**
+ * Optimized matrix operations with memory pooling and SIMD-friendly patterns
+ * Target: 50% memory reduction and improved cache locality
+ */
+import { Matrix, Vector, SparseMatrix } from './types.js';
+declare class VectorPool {
+    private pools;
+    private maxPoolSize;
+    acquire(size: number): Vector;
+    release(vector: Vector): void;
+    clear(): void;
+    getStats(): {
+        poolSizes: Record<number, number>;
+        totalVectors: number;
+    };
+}
+export declare class CSRMatrix {
+    values: Float64Array;
+    colIndices: Uint32Array;
+    rowPtr: Uint32Array;
+    private rows;
+    private cols;
+    constructor(rows: number, cols: number, nnz: number);
+    static fromCOO(matrix: SparseMatrix): CSRMatrix;
+    multiplyVector(x: Vector, result: Vector): void;
+    getEntry(row: number, col: number): number;
+    rowEntries(row: number): Generator<{
+        col: number;
+        val: number;
+    }>;
+    getMemoryUsage(): number;
+    getNnz(): number;
+    getRows(): number;
+    getCols(): number;
+}
+export declare class CSCMatrix {
+    values: Float64Array;
+    rowIndices: Uint32Array;
+    colPtr: Uint32Array;
+    private rows;
+    private cols;
+    constructor(rows: number, cols: number, nnz: number);
+    static fromCSR(csr: CSRMatrix): CSCMatrix;
+    multiplyVector(x: Vector, result: Vector): void;
+    getMemoryUsage(): number;
+    getNnz(): number;
+    getRows(): number;
+    getCols(): number;
+}
+export declare class StreamingMatrix {
+    private chunks;
+    private chunkSize;
+    private rows;
+    private cols;
+    private maxCachedChunks;
+    constructor(rows: number, cols: number, chunkSize?: number, maxCachedChunks?: number);
+    static fromMatrix(matrix: Matrix, chunkSize?: number): StreamingMatrix;
+    getChunk(chunkId: number): CSRMatrix | null;
+    multiplyVector(x: Vector, result: Vector): void;
+    getMemoryUsage(): number;
+}
+export declare class OptimizedMatrixOperations {
+    private static vectorPool;
+    static getVectorPool(): VectorPool;
+    static vectorAdd(a: Vector, b: Vector, result?: Vector): Vector;
+    static vectorScale(vector: Vector, scalar: number, result?: Vector): Vector;
+    static vectorDot(a: Vector, b: Vector): number;
+    static vectorNorm2(vector: Vector): number;
+    static convertToOptimalFormat(matrix: Matrix): CSRMatrix | CSCMatrix;
+    private static denseToSparse;
+    static profileMemoryUsage(matrix: CSRMatrix | CSCMatrix | StreamingMatrix): {
+        matrixSize: number;
+        nnz: number;
+        memoryUsed: number;
+        compressionRatio: number;
+    };
+    static cleanup(): void;
+}
+export {};

vendor/sublinear-time-solver/dist/core/optimized-matrix.js

@@ -0,0 +1,451 @@
+/**
+ * Optimized matrix operations with memory pooling and SIMD-friendly patterns
+ * Target: 50% memory reduction and improved cache locality
+ */
+// Memory pool for vector allocations
+class VectorPool {
+    pools = new Map();
+    maxPoolSize = 100;
+    acquire(size) {
+        const pool = this.pools.get(size);
+        if (pool && pool.length > 0) {
+            return pool.pop();
+        }
+        return new Array(size);
+    }
+    release(vector) {
+        const size = vector.length;
+        vector.fill(0); // Clear for reuse
+        let pool = this.pools.get(size);
+        if (!pool) {
+            pool = [];
+            this.pools.set(size, pool);
+        }
+        if (pool.length < this.maxPoolSize) {
+            pool.push(vector);
+        }
+    }
+    clear() {
+        this.pools.clear();
+    }
+    getStats() {
+        const poolSizes = {};
+        let totalVectors = 0;
+        for (const [size, pool] of this.pools) {
+            poolSizes[size] = pool.length;
+            totalVectors += pool.length;
+        }
+        return { poolSizes, totalVectors };
+    }
+}
+// Compressed Sparse Row (CSR) format for JavaScript
+export class CSRMatrix {
+    values;
+    colIndices;
+    rowPtr;
+    rows;
+    cols;
+    constructor(rows, cols, nnz) {
+        this.rows = rows;
+        this.cols = cols;
+        this.values = new Float64Array(nnz);
+        this.colIndices = new Uint32Array(nnz);
+        this.rowPtr = new Uint32Array(rows + 1);
+    }
+    static fromCOO(matrix) {
+        const { values, rowIndices, colIndices } = matrix;
+        const nnz = values.length;
+        const csr = new CSRMatrix(matrix.rows, matrix.cols, nnz);
+        // Sort by row, then column
+        const triplets = Array.from({ length: nnz }, (_, i) => ({
+            row: rowIndices[i],
+            col: colIndices[i],
+            val: values[i],
+            index: i
+        }));
+        triplets.sort((a, b) => a.row - b.row || a.col - b.col);
+        // Build CSR structure
+        let currentRow = 0;
+        let nnzCount = 0;
+        for (const triplet of triplets) {
+            // Skip zeros
+            if (triplet.val === 0)
+                continue;
+            // Update row pointers
+            while (currentRow < triplet.row) {
+                csr.rowPtr[++currentRow] = nnzCount;
+            }
+            csr.values[nnzCount] = triplet.val;
+            csr.colIndices[nnzCount] = triplet.col;
+            nnzCount++;
+        }
+        // Finalize row pointers
+        while (currentRow < matrix.rows) {
+            csr.rowPtr[++currentRow] = nnzCount;
+        }
+        return csr;
+    }
+    // Cache-friendly matrix-vector multiplication with SIMD hints
+    multiplyVector(x, result) {
+        result.fill(0);
+        // Process 4 rows at a time for better cache locality
+        const blockSize = 4;
+        let rowBlock = 0;
+        while (rowBlock < this.rows) {
+            const endBlock = Math.min(rowBlock + blockSize, this.rows);
+            for (let row = rowBlock; row < endBlock; row++) {
+                const start = this.rowPtr[row];
+                const end = this.rowPtr[row + 1];
+                let sum = 0;
+                // Unroll loop for SIMD optimization hints
+                let i = start;
+                for (; i < end - 3; i += 4) {
+                    sum += this.values[i] * x[this.colIndices[i]] +
+                        this.values[i + 1] * x[this.colIndices[i + 1]] +
+                        this.values[i + 2] * x[this.colIndices[i + 2]] +
+                        this.values[i + 3] * x[this.colIndices[i + 3]];
+                }
+                // Handle remaining elements
+                for (; i < end; i++) {
+                    sum += this.values[i] * x[this.colIndices[i]];
+                }
+                result[row] = sum;
+            }
+            rowBlock = endBlock;
+        }
+    }
+    getEntry(row, col) {
+        const start = this.rowPtr[row];
+        const end = this.rowPtr[row + 1];
+        // Binary search for column
+        let left = start;
+        let right = end - 1;
+        while (left <= right) {
+            const mid = Math.floor((left + right) / 2);
+            const midCol = this.colIndices[mid];
+            if (midCol === col) {
+                return this.values[mid];
+            }
+            else if (midCol < col) {
+                left = mid + 1;
+            }
+            else {
+                right = mid - 1;
+            }
+        }
+        return 0;
+    }
+    // Memory-efficient row iteration
+    *rowEntries(row) {
+        const start = this.rowPtr[row];
+        const end = this.rowPtr[row + 1];
+        for (let i = start; i < end; i++) {
+            yield { col: this.colIndices[i], val: this.values[i] };
+        }
+    }
+    getMemoryUsage() {
+        return this.values.byteLength +
+            this.colIndices.byteLength +
+            this.rowPtr.byteLength;
+    }
+    getNnz() {
+        return this.values.length;
+    }
+    getRows() {
+        return this.rows;
+    }
+    getCols() {
+        return this.cols;
+    }
+}
+// Compressed Sparse Column (CSC) format for column-wise operations
+export class CSCMatrix {
+    values;
+    rowIndices;
+    colPtr;
+    rows;
+    cols;
+    constructor(rows, cols, nnz) {
+        this.rows = rows;
+        this.cols = cols;
+        this.values = new Float64Array(nnz);
+        this.rowIndices = new Uint32Array(nnz);
+        this.colPtr = new Uint32Array(cols + 1);
+    }
+    static fromCSR(csr) {
+        const nnz = csr.getNnz();
+        const csc = new CSCMatrix(csr.getRows(), csr.getCols(), nnz);
+        // Convert CSR to triplets, then sort by column
+        const triplets = [];
+        for (let row = 0; row < csr.getRows(); row++) {
+            for (const entry of csr.rowEntries(row)) {
+                triplets.push({ row, col: entry.col, val: entry.val });
+            }
+        }
+        triplets.sort((a, b) => a.col - b.col || a.row - b.row);
+        // Build CSC structure
+        let currentCol = 0;
+        let nnzCount = 0;
+        for (const triplet of triplets) {
+            while (currentCol < triplet.col) {
+                csc.colPtr[++currentCol] = nnzCount;
+            }
+            csc.values[nnzCount] = triplet.val;
+            csc.rowIndices[nnzCount] = triplet.row;
+            nnzCount++;
+        }
+        while (currentCol < csc.cols) {
+            csc.colPtr[++currentCol] = nnzCount;
+        }
+        return csc;
+    }
+    // Column-wise matrix-vector multiplication
+    multiplyVector(x, result) {
+        result.fill(0);
+        for (let col = 0; col < this.cols; col++) {
+            const xCol = x[col];
+            if (xCol === 0)
+                continue;
+            const start = this.colPtr[col];
+            const end = this.colPtr[col + 1];
+            // Vectorized accumulation
+            for (let i = start; i < end; i++) {
+                result[this.rowIndices[i]] += this.values[i] * xCol;
+            }
+        }
+    }
+    getMemoryUsage() {
+        return this.values.byteLength +
+            this.rowIndices.byteLength +
+            this.colPtr.byteLength;
+    }
+    getNnz() {
+        return this.values.length;
+    }
+    getRows() {
+        return this.rows;
+    }
+    getCols() {
+        return this.cols;
+    }
+}
+// Memory streaming for large matrices
+export class StreamingMatrix {
+    chunks = new Map();
+    chunkSize;
+    rows;
+    cols;
+    maxCachedChunks;
+    constructor(rows, cols, chunkSize = 1000, maxCachedChunks = 10) {
+        this.rows = rows;
+        this.cols = cols;
+        this.chunkSize = chunkSize;
+        this.maxCachedChunks = maxCachedChunks;
+    }
+    static fromMatrix(matrix, chunkSize = 1000) {
+        const streaming = new StreamingMatrix(matrix.rows, matrix.cols, chunkSize);
+        if (matrix.format === 'coo') {
+            const sparse = matrix;
+            const chunkData = new Map();
+            for (let i = 0; i < sparse.values.length; i++) {
+                const row = sparse.rowIndices[i];
+                const chunkId = Math.floor(row / chunkSize);
+                if (!chunkData.has(chunkId)) {
+                    chunkData.set(chunkId, []);
+                }
+                chunkData.get(chunkId).push({
+                    col: sparse.colIndices[i],
+                    val: sparse.values[i]
+                });
+            }
+            // Convert each chunk to CSR
+            for (const [chunkId, entries] of chunkData) {
+                const chunkRows = Math.min(chunkSize, streaming.rows - chunkId * chunkSize);
+                const chunkCSR = new CSRMatrix(chunkRows, streaming.cols, entries.length);
+                // Build CSR for this chunk
+                const rowData = new Map();
+                for (const entry of entries) {
+                    const localRow = (chunkId * chunkSize) % chunkSize;
+                    if (!rowData.has(localRow)) {
+                        rowData.set(localRow, []);
+                    }
+                    rowData.get(localRow).push(entry);
+                }
+                // Fill CSR arrays
+                let nnzCount = 0;
+                for (let row = 0; row < chunkRows; row++) {
+                    chunkCSR.rowPtr[row] = nnzCount;
+                    const rowEntries = rowData.get(row) || [];
+                    rowEntries.sort((a, b) => a.col - b.col);
+                    for (const entry of rowEntries) {
+                        chunkCSR.values[nnzCount] = entry.val;
+                        chunkCSR.colIndices[nnzCount] = entry.col;
+                        nnzCount++;
+                    }
+                }
+                chunkCSR.rowPtr[chunkRows] = nnzCount;
+                streaming.chunks.set(chunkId, chunkCSR);
+            }
+        }
+        return streaming;
+    }
+    getChunk(chunkId) {
+        return this.chunks.get(chunkId) || null;
+    }
+    // Streaming matrix-vector multiplication
+    multiplyVector(x, result) {
+        result.fill(0);
+        const totalChunks = Math.ceil(this.rows / this.chunkSize);
+        for (let chunkId = 0; chunkId < totalChunks; chunkId++) {
+            const chunk = this.getChunk(chunkId);
+            if (!chunk)
+                continue;
+            const startRow = chunkId * this.chunkSize;
+            const chunkResult = new Array(chunk.getRows()).fill(0);
+            chunk.multiplyVector(x, chunkResult);
+            // Copy back to result
+            for (let i = 0; i < chunkResult.length && startRow + i < this.rows; i++) {
+                result[startRow + i] = chunkResult[i];
+            }
+            // Memory management: remove old chunks if cache is full
+            if (this.chunks.size > this.maxCachedChunks) {
+                const oldestChunk = Math.max(0, chunkId - this.maxCachedChunks);
+                this.chunks.delete(oldestChunk);
+            }
+        }
+    }
+    getMemoryUsage() {
+        let total = 0;
+        for (const chunk of this.chunks.values()) {
+            total += chunk.getMemoryUsage();
+        }
+        return total;
+    }
+}
+// Optimized matrix operations with memory pooling
+export class OptimizedMatrixOperations {
+    static vectorPool = new VectorPool();
+    static getVectorPool() {
+        return this.vectorPool;
+    }
+    // SIMD-optimized vector operations
+    static vectorAdd(a, b, result) {
+        const n = a.length;
+        const out = result || this.vectorPool.acquire(n);
+        // Process 4 elements at a time for SIMD
+        let i = 0;
+        for (; i < n - 3; i += 4) {
+            out[i] = a[i] + b[i];
+            out[i + 1] = a[i + 1] + b[i + 1];
+            out[i + 2] = a[i + 2] + b[i + 2];
+            out[i + 3] = a[i + 3] + b[i + 3];
+        }
+        // Handle remaining elements
+        for (; i < n; i++) {
+            out[i] = a[i] + b[i];
+        }
+        return out;
+    }
+    static vectorScale(vector, scalar, result) {
+        const n = vector.length;
+        const out = result || this.vectorPool.acquire(n);
+        // SIMD-friendly unrolled loop
+        let i = 0;
+        for (; i < n - 3; i += 4) {
+            out[i] = vector[i] * scalar;
+            out[i + 1] = vector[i + 1] * scalar;
+            out[i + 2] = vector[i + 2] * scalar;
+            out[i + 3] = vector[i + 3] * scalar;
+        }
+        for (; i < n; i++) {
+            out[i] = vector[i] * scalar;
+        }
+        return out;
+    }
+    static vectorDot(a, b) {
+        const n = a.length;
+        let sum = 0;
+        // Unrolled loop for SIMD optimization
+        let i = 0;
+        for (; i < n - 3; i += 4) {
+            sum += a[i] * b[i] +
+                a[i + 1] * b[i + 1] +
+                a[i + 2] * b[i + 2] +
+                a[i + 3] * b[i + 3];
+        }
+        for (; i < n; i++) {
+            sum += a[i] * b[i];
+        }
+        return sum;
+    }
+    static vectorNorm2(vector) {
+        return Math.sqrt(this.vectorDot(vector, vector));
+    }
+    // Memory-efficient matrix format conversion
+    static convertToOptimalFormat(matrix) {
+        if (matrix.format === 'coo') {
+            const sparse = matrix;
+            // Choose format based on sparsity pattern and expected access
+            const sparsity = sparse.values.length / (matrix.rows * matrix.cols);
+            // CSR is generally better for row-wise access and matrix-vector multiplication
+            return CSRMatrix.fromCOO(sparse);
+        }
+        else {
+            // Convert dense to sparse first
+            const sparse = this.denseToSparse(matrix);
+            return CSRMatrix.fromCOO(sparse);
+        }
+    }
+    static denseToSparse(dense, tolerance = 1e-15) {
+        const values = [];
+        const rowIndices = [];
+        const colIndices = [];
+        for (let i = 0; i < dense.rows; i++) {
+            for (let j = 0; j < dense.cols; j++) {
+                const value = dense.data[i][j];
+                if (Math.abs(value) > tolerance) {
+                    values.push(value);
+                    rowIndices.push(i);
+                    colIndices.push(j);
+                }
+            }
+        }
+        return {
+            rows: dense.rows,
+            cols: dense.cols,
+            values,
+            rowIndices,
+            colIndices,
+            format: 'coo'
+        };
+    }
+    // Memory usage profiling
+    static profileMemoryUsage(matrix) {
+        const memoryUsed = matrix.getMemoryUsage();
+        let nnz;
+        let rows;
+        let cols;
+        if (matrix instanceof CSRMatrix || matrix instanceof CSCMatrix) {
+            nnz = matrix.getNnz();
+            rows = matrix.getRows();
+            cols = matrix.getCols();
+        }
+        else {
+            nnz = 0;
+            rows = matrix['rows'];
+            cols = matrix['cols'];
+        }
+        const denseMemory = rows * cols * 8; // 8 bytes per double
+        const compressionRatio = denseMemory / memoryUsed;
+        return {
+            matrixSize: rows * cols,
+            nnz,
+            memoryUsed,
+            compressionRatio
+        };
+    }
+    // Cleanup memory pools
+    static cleanup() {
+        this.vectorPool.clear();
+    }
+}

vendor/sublinear-time-solver/dist/core/optimized-solver.d.ts

@@ -0,0 +1,64 @@
+/**
+ * Optimized solver implementation with memory-efficient algorithms
+ * Integrates all optimization components for maximum performance
+ */
+import { Matrix, Vector, SolverConfig, SolverResult } from './types.js';
+import { MemoryProfile } from './memory-manager.js';
+export interface OptimizedSolverConfig extends SolverConfig {
+    memoryOptimization: {
+        enablePooling: boolean;
+        enableStreaming: boolean;
+        streamingThreshold: number;
+        maxCacheSize: number;
+    };
+    performance: {
+        enableVectorization: boolean;
+        enableBlocking: boolean;
+        autoTuning: boolean;
+        parallelization: boolean;
+    };
+    adaptiveAlgorithms: {
+        enabled: boolean;
+        switchThreshold: number;
+        memoryPressureThreshold: number;
+    };
+}
+export interface OptimizedSolverResult extends SolverResult {
+    optimizationStats: {
+        memoryReduction: number;
+        cacheHitRate: number;
+        vectorizationEfficiency: number;
+        algorithmsSwitched: number;
+    };
+    memoryProfile: MemoryProfile;
+    recommendations: string[];
+}
+export declare class OptimizedSublinearSolver {
+    private config;
+    private csrMatrix?;
+    private optimizationHints;
+    private benchmarkInstance;
+    private autoTunedParams?;
+    constructor(config?: Partial<OptimizedSolverConfig>);
+    private mergeDefaultConfig;
+    solve(matrix: Matrix, vector: Vector): Promise<OptimizedSolverResult>;
+    private preprocessMatrix;
+    private estimateMatrixMemory;
+    private selectOptimalAlgorithm;
+    private executeSolve;
+    private solveVectorizedNeumann;
+    private solveBlockedNeumann;
+    private solveStreamingNeumann;
+    private solveParallelNeumann;
+    private calculateOptimizationStats;
+    private generateRecommendations;
+    runBenchmark(matrices: Matrix[], vectors: Vector[]): Promise<{
+        results: OptimizedSolverResult[];
+        comparison: {
+            averageSpeedup: number;
+            averageMemoryReduction: number;
+            recommendedConfig: Partial<OptimizedSolverConfig>;
+        };
+    }>;
+    cleanup(): void;
+}

vendor/sublinear-time-solver/dist/core/optimized-solver.js

@@ -0,0 +1,318 @@
+/**
+ * Optimized solver implementation with memory-efficient algorithms
+ * Integrates all optimization components for maximum performance
+ */
+import { OptimizedMatrixOperations } from './optimized-matrix.js';
+import { globalMemoryManager } from './memory-manager.js';
+import { OptimizedMatrixMultiplication, PerformanceBenchmark } from './performance-optimizer.js';
+export class OptimizedSublinearSolver {
+    config;
+    csrMatrix;
+    optimizationHints;
+    benchmarkInstance;
+    autoTunedParams;
+    constructor(config = {}) {
+        this.config = this.mergeDefaultConfig(config);
+        this.benchmarkInstance = new PerformanceBenchmark();
+        this.optimizationHints = {
+            vectorize: this.config.performance.enableVectorization,
+            unroll: 4,
+            prefetch: true,
+            blocking: {
+                enabled: this.config.performance.enableBlocking,
+                size: 1024
+            },
+            streaming: {
+                enabled: this.config.memoryOptimization.enableStreaming,
+                chunkSize: 10000
+            }
+        };
+    }
+    mergeDefaultConfig(partial) {
+        return {
+            method: 'neumann',
+            epsilon: 1e-6,
+            maxIterations: 1000,
+            ...partial,
+            memoryOptimization: {
+                enablePooling: true,
+                enableStreaming: true,
+                streamingThreshold: 100 * 1024 * 1024, // 100MB
+                maxCacheSize: 100,
+                ...partial.memoryOptimization
+            },
+            performance: {
+                enableVectorization: true,
+                enableBlocking: true,
+                autoTuning: true,
+                parallelization: true,
+                ...partial.performance
+            },
+            adaptiveAlgorithms: {
+                enabled: true,
+                switchThreshold: 0.1,
+                memoryPressureThreshold: 0.8,
+                ...partial.adaptiveAlgorithms
+            }
+        };
+    }
+    async solve(matrix, vector) {
+        const startTime = performance.now();
+        const startMemory = globalMemoryManager.getMemoryStats();
+        // Convert to optimized format
+        await this.preprocessMatrix(matrix);
+        // Auto-tune parameters if enabled
+        if (this.config.performance.autoTuning && this.csrMatrix) {
+            this.autoTunedParams = await this.benchmarkInstance.autoTuneParameters(this.csrMatrix, vector);
+            this.optimizationHints.blocking.size = this.autoTunedParams.optimalBlockSize;
+            this.optimizationHints.unroll = this.autoTunedParams.optimalUnrollFactor;
+        }
+        // Select optimal algorithm based on matrix characteristics
+        const algorithmInfo = this.selectOptimalAlgorithm(matrix, vector);
+        // Execute solve with memory profiling
+        const { result: solverResult, profile } = await globalMemoryManager.profileOperation(`OptimizedSolver_${algorithmInfo.algorithm}`, () => this.executeSolve(matrix, vector, algorithmInfo));
+        const endTime = performance.now();
+        const endMemory = globalMemoryManager.getMemoryStats();
+        // Calculate optimization statistics
+        const optimizationStats = this.calculateOptimizationStats(startMemory, endMemory, profile);
+        // Generate recommendations
+        const recommendations = this.generateRecommendations(optimizationStats, profile);
+        return {
+            ...solverResult,
+            optimizationStats,
+            memoryProfile: profile,
+            recommendations,
+            computeTime: endTime - startTime
+        };
+    }
+    async preprocessMatrix(matrix) {
+        // Convert to optimized CSR format with memory pooling
+        if (this.config.memoryOptimization.enablePooling) {
+            this.csrMatrix = await globalMemoryManager.scheduleOperation(() => Promise.resolve(OptimizedMatrixOperations.convertToOptimalFormat(matrix)), this.estimateMatrixMemory(matrix));
+        }
+        else {
+            this.csrMatrix = OptimizedMatrixOperations.convertToOptimalFormat(matrix);
+        }
+    }
+    estimateMatrixMemory(matrix) {
+        if (matrix.format === 'coo') {
+            const sparse = matrix;
+            return sparse.values.length * (8 + 4 + 4); // value + row + col indices
+        }
+        else {
+            return matrix.rows * matrix.cols * 8; // dense matrix
+        }
+    }
+    selectOptimalAlgorithm(matrix, vector) {
+        if (!this.csrMatrix) {
+            throw new Error('Matrix not preprocessed');
+        }
+        const memoryUsage = this.csrMatrix.getMemoryUsage();
+        const memoryStats = globalMemoryManager.getMemoryStats();
+        const memoryPressure = memoryStats.currentUsage / (memoryStats.peakUsage || 1);
+        // Adaptive algorithm selection
+        if (this.config.adaptiveAlgorithms.enabled) {
+            if (memoryPressure > this.config.adaptiveAlgorithms.memoryPressureThreshold) {
+                return { algorithm: 'streaming-neumann', params: { chunkSize: 1000 } };
+            }
+            if (memoryUsage > this.config.memoryOptimization.streamingThreshold) {
+                return { algorithm: 'blocked-neumann', params: { blockSize: this.optimizationHints.blocking.size } };
+            }
+            if (this.config.performance.parallelization && matrix.rows > 10000) {
+                return { algorithm: 'parallel-neumann', params: { workers: navigator.hardwareConcurrency || 4 } };
+            }
+        }
+        return { algorithm: 'vectorized-neumann', params: {} };
+    }
+    async executeSolve(matrix, vector, algorithmInfo) {
+        if (!this.csrMatrix) {
+            throw new Error('Matrix not preprocessed');
+        }
+        switch (algorithmInfo.algorithm) {
+            case 'vectorized-neumann':
+                return this.solveVectorizedNeumann(this.csrMatrix, vector);
+            case 'blocked-neumann':
+                return this.solveBlockedNeumann(this.csrMatrix, vector, algorithmInfo.params.blockSize);
+            case 'streaming-neumann':
+                return this.solveStreamingNeumann(this.csrMatrix, vector, algorithmInfo.params.chunkSize);
+            case 'parallel-neumann':
+                return this.solveParallelNeumann(this.csrMatrix, vector, algorithmInfo.params.workers);
+            default:
+                throw new Error(`Unknown algorithm: ${algorithmInfo.algorithm}`);
+        }
+    }
+    // Vectorized Neumann series implementation
+    async solveVectorizedNeumann(matrix, vector) {
+        const n = matrix.getRows();
+        // Extract diagonal with memory pooling
+        const diagonal = globalMemoryManager.acquireTypedArray('float64', n);
+        for (let i = 0; i < n; i++) {
+            diagonal[i] = matrix.getEntry(i, i);
+            if (Math.abs(diagonal[i]) < 1e-15) {
+                throw new Error(`Zero diagonal at position ${i}`);
+            }
+        }
+        // Initialize solution: x₀ = D⁻¹b
+        const solution = globalMemoryManager.acquireTypedArray('float64', n);
+        const tempVector = globalMemoryManager.acquireTypedArray('float64', n);
+        for (let i = 0; i < n; i++) {
+            solution[i] = vector[i] / diagonal[i];
+        }
+        let seriesTerm = Array.from(solution);
+        let iteration = 0;
+        let residual = Infinity;
+        for (let k = 1; k <= this.config.maxIterations; k++) {
+            // Compute R * seriesTerm using optimized matrix-vector multiplication
+            matrix.multiplyVector(seriesTerm, tempVector);
+            // Subtract diagonal part: (R * seriesTerm) - D * seriesTerm
+            for (let i = 0; i < n; i++) {
+                tempVector[i] -= diagonal[i] * seriesTerm[i];
+            }
+            // Apply D⁻¹: seriesTerm = D⁻¹ * (R * seriesTerm)
+            for (let i = 0; i < n; i++) {
+                seriesTerm[i] = tempVector[i] / diagonal[i];
+            }
+            // Add to solution with vectorized operation
+            OptimizedMatrixOperations.vectorAdd(Array.from(solution), seriesTerm, Array.from(solution));
+            // Check convergence using optimized norm
+            matrix.multiplyVector(solution, tempVector);
+            const residualVec = OptimizedMatrixOperations.vectorAdd(tempVector, OptimizedMatrixOperations.vectorScale(vector, -1), new Array(n));
+            residual = OptimizedMatrixOperations.vectorNorm2(residualVec);
+            iteration = k;
+            if (residual < this.config.epsilon) {
+                break;
+            }
+            // Early termination if series term becomes negligible
+            const termNorm = OptimizedMatrixOperations.vectorNorm2(seriesTerm);
+            if (termNorm < this.config.epsilon * 1e-3) {
+                break;
+            }
+        }
+        // Cleanup memory - cast back to typed arrays for release
+        globalMemoryManager.releaseTypedArray(diagonal);
+        globalMemoryManager.releaseTypedArray(tempVector);
+        const finalSolution = Array.from(solution);
+        globalMemoryManager.releaseTypedArray(solution);
+        return {
+            solution: finalSolution,
+            iterations: iteration,
+            residual,
+            converged: residual < this.config.epsilon,
+            method: 'vectorized-neumann',
+            computeTime: 0, // Will be set by caller
+            memoryUsed: 0 // Will be calculated separately
+        };
+    }
+    // Blocked Neumann series for cache optimization
+    async solveBlockedNeumann(matrix, vector, blockSize) {
+        // Similar to vectorized but with blocked processing
+        // Process matrix operations in blocks for better cache locality
+        return this.solveVectorizedNeumann(matrix, vector); // Simplified for now
+    }
+    // Streaming Neumann series for large matrices
+    async solveStreamingNeumann(matrix, vector, chunkSize) {
+        const n = matrix.getRows();
+        const chunks = Math.ceil(n / chunkSize);
+        // Process in streaming fashion using memory manager
+        const solution = new Array(n);
+        // Process in chunks
+        for (let chunkIndex = 0; chunkIndex < chunks; chunkIndex++) {
+            const startRow = chunkIndex * chunkSize;
+            const endRow = Math.min(startRow + chunkSize, n);
+            // Process this chunk
+            const chunkVector = vector.slice(startRow, endRow);
+            // Simple processing for now
+            for (let i = 0; i < chunkVector.length; i++) {
+                solution[startRow + i] = chunkVector[i];
+            }
+        }
+        return {
+            solution,
+            iterations: 1,
+            residual: 0,
+            converged: true,
+            method: 'streaming-neumann',
+            computeTime: 0,
+            memoryUsed: 0
+        };
+    }
+    // Parallel Neumann series using Web Workers
+    async solveParallelNeumann(matrix, vector, numWorkers) {
+        // Use parallel matrix-vector multiplication
+        const n = matrix.getRows();
+        const solution = await OptimizedMatrixMultiplication.parallelMatVec(matrix, vector);
+        return {
+            solution,
+            iterations: 1,
+            residual: 0,
+            converged: true,
+            method: 'parallel-neumann',
+            computeTime: 0,
+            memoryUsed: 0
+        };
+    }
+    calculateOptimizationStats(startMemory, endMemory, profile) {
+        const memoryReduction = startMemory.currentUsage > 0
+            ? (startMemory.currentUsage - endMemory.currentUsage) / startMemory.currentUsage
+            : 0;
+        return {
+            memoryReduction,
+            cacheHitRate: profile.cacheHitRate,
+            vectorizationEfficiency: 0.85, // Estimated based on operations used
+            algorithmsSwitched: this.config.adaptiveAlgorithms.enabled ? 1 : 0
+        };
+    }
+    generateRecommendations(stats, profile) {
+        const recommendations = [];
+        if (stats.memoryReduction < 0.3) {
+            recommendations.push('Consider enabling memory pooling and streaming for better memory efficiency');
+        }
+        if (stats.cacheHitRate < 0.7) {
+            recommendations.push('Enable blocked algorithms for better cache locality');
+        }
+        if (profile.duration > 1000) {
+            recommendations.push('Consider enabling parallelization for large problems');
+        }
+        if (stats.vectorizationEfficiency < 0.8) {
+            recommendations.push('Enable vectorization hints for better SIMD utilization');
+        }
+        return recommendations;
+    }
+    // Benchmark the optimized solver
+    async runBenchmark(matrices, vectors) {
+        const results = [];
+        for (let i = 0; i < matrices.length; i++) {
+            const result = await this.solve(matrices[i], vectors[i]);
+            results.push(result);
+        }
+        // Calculate comparison metrics
+        const avgMemoryReduction = results.reduce((sum, r) => sum + r.optimizationStats.memoryReduction, 0) / results.length;
+        const avgSpeedup = 2.5; // Estimated based on optimizations
+        const recommendedConfig = {
+            memoryOptimization: {
+                enablePooling: avgMemoryReduction > 0.3,
+                enableStreaming: results.some(r => r.memoryProfile.peakMemory > 100 * 1024 * 1024),
+                streamingThreshold: 50 * 1024 * 1024,
+                maxCacheSize: 200
+            },
+            performance: {
+                enableVectorization: true,
+                enableBlocking: results.some(r => r.optimizationStats.cacheHitRate < 0.8),
+                autoTuning: true,
+                parallelization: results.some(r => r.memoryProfile.duration > 500)
+            }
+        };
+        return {
+            results,
+            comparison: {
+                averageSpeedup: avgSpeedup,
+                averageMemoryReduction: avgMemoryReduction,
+                recommendedConfig
+            }
+        };
+    }
+    cleanup() {
+        OptimizedMatrixOperations.cleanup();
+        globalMemoryManager.cleanup();
+    }
+}

vendor/sublinear-time-solver/dist/core/performance-optimizer.d.ts

@@ -0,0 +1,67 @@
+/**
+ * Performance optimization utilities for matrix operations
+ * Implements cache-friendly patterns, vectorization hints, and benchmarking
+ */
+import { Vector } from './types.js';
+import { CSRMatrix } from './optimized-matrix.js';
+import { MemoryStreamManager, MemoryProfile } from './memory-manager.js';
+export interface BenchmarkResult {
+    operation: string;
+    iterations: number;
+    totalTime: number;
+    averageTime: number;
+    throughput: number;
+    memoryProfile: MemoryProfile;
+    cacheStats: {
+        hitRate: number;
+        missRate: number;
+    };
+}
+export interface OptimizationHints {
+    vectorize: boolean;
+    unroll: number;
+    prefetch: boolean;
+    blocking: {
+        enabled: boolean;
+        size: number;
+    };
+    streaming: {
+        enabled: boolean;
+        chunkSize: number;
+    };
+}
+export declare class VectorizedOperations {
+    private static readonly UNROLL_FACTOR;
+    private static readonly PREFETCH_DISTANCE;
+    static dotProduct(a: Vector, b: Vector, hints?: OptimizationHints): number;
+    static vectorAdd(a: Vector, b: Vector, result: Vector, hints?: OptimizationHints): void;
+    private static vectorAddBlock;
+    static streamingOperation<T>(operation: 'add' | 'multiply' | 'dot', vectors: Vector[], chunkSize?: number): Promise<Vector | number>;
+}
+export declare class OptimizedMatrixMultiplication {
+    static sparseMatVec(matrix: CSRMatrix, vector: Vector, result: Vector, blockSize?: number): void;
+    static parallelMatVec(matrix: CSRMatrix, vector: Vector, numWorkers?: number): Promise<Vector>;
+    private static createMatVecWorker;
+    static selectOptimalAlgorithm(matrix: CSRMatrix, vector: Vector): {
+        algorithm: 'sequential' | 'blocked' | 'parallel' | 'streaming';
+        params: any;
+    };
+}
+export declare class PerformanceBenchmark {
+    private memoryManager;
+    constructor(memoryManager?: MemoryStreamManager);
+    benchmarkMatrixOperations(matrices: CSRMatrix[], vectors: Vector[], iterations?: number): Promise<BenchmarkResult[]>;
+    private benchmarkOperation;
+    generateOptimizationReport(benchmarks: BenchmarkResult[]): {
+        recommendations: string[];
+        bottlenecks: string[];
+        memoryEfficiency: number;
+        cacheEfficiency: number;
+    };
+    autoTuneParameters(matrix: CSRMatrix, vector: Vector): Promise<{
+        optimalBlockSize: number;
+        optimalUnrollFactor: number;
+        recommendedAlgorithm: string;
+    }>;
+}
+export declare const globalPerformanceOptimizer: PerformanceBenchmark;

vendor/sublinear-time-solver/dist/core/performance-optimizer.js

@@ -0,0 +1,336 @@
+/**
+ * Performance optimization utilities for matrix operations
+ * Implements cache-friendly patterns, vectorization hints, and benchmarking
+ */
+import { globalMemoryManager } from './memory-manager.js';
+// Vectorized math operations with SIMD hints
+export class VectorizedOperations {
+    static UNROLL_FACTOR = 4;
+    static PREFETCH_DISTANCE = 64;
+    // Highly optimized dot product with cache prefetching
+    static dotProduct(a, b, hints) {
+        const n = a.length;
+        const unrollFactor = hints?.unroll || this.UNROLL_FACTOR;
+        let sum = 0;
+        // Main vectorized loop
+        let i = 0;
+        for (; i <= n - unrollFactor; i += unrollFactor) {
+            // Prefetch next cache line if enabled
+            if (hints?.prefetch && i + this.PREFETCH_DISTANCE < n) {
+                // Browser doesn't expose prefetch directly, but accessing helps
+                const prefetchIndex = i + this.PREFETCH_DISTANCE;
+                void a[prefetchIndex]; // Touch for prefetch hint
+                void b[prefetchIndex];
+            }
+            // Unrolled loop for SIMD optimization
+            sum += a[i] * b[i] +
+                a[i + 1] * b[i + 1] +
+                a[i + 2] * b[i + 2] +
+                a[i + 3] * b[i + 3];
+        }
+        // Handle remaining elements
+        for (; i < n; i++) {
+            sum += a[i] * b[i];
+        }
+        return sum;
+    }
+    // Cache-optimized vector addition with blocking
+    static vectorAdd(a, b, result, hints) {
+        const n = a.length;
+        const blockSize = hints?.blocking.enabled ? hints.blocking.size : 1024;
+        if (hints?.blocking.enabled && n > blockSize) {
+            // Process in blocks for better cache locality
+            for (let blockStart = 0; blockStart < n; blockStart += blockSize) {
+                const blockEnd = Math.min(blockStart + blockSize, n);
+                this.vectorAddBlock(a, b, result, blockStart, blockEnd, hints);
+            }
+        }
+        else {
+            this.vectorAddBlock(a, b, result, 0, n, hints);
+        }
+    }
+    static vectorAddBlock(a, b, result, start, end, hints) {
+        const unrollFactor = hints?.unroll || this.UNROLL_FACTOR;
+        let i = start;
+        for (; i <= end - unrollFactor; i += unrollFactor) {
+            result[i] = a[i] + b[i];
+            result[i + 1] = a[i + 1] + b[i + 1];
+            result[i + 2] = a[i + 2] + b[i + 2];
+            result[i + 3] = a[i + 3] + b[i + 3];
+        }
+        for (; i < end; i++) {
+            result[i] = a[i] + b[i];
+        }
+    }
+    // Streaming vector operations for large arrays
+    static async streamingOperation(operation, vectors, chunkSize = 10000) {
+        const n = vectors[0].length;
+        if (operation === 'dot' && vectors.length === 2) {
+            let sum = 0;
+            for (let start = 0; start < n; start += chunkSize) {
+                const end = Math.min(start + chunkSize, n);
+                const chunkA = vectors[0].slice(start, end);
+                const chunkB = vectors[1].slice(start, end);
+                sum += this.dotProduct(chunkA, chunkB);
+                // Yield control periodically
+                if (start % (chunkSize * 10) === 0) {
+                    await new Promise(resolve => setTimeout(resolve, 0));
+                }
+            }
+            return sum;
+        }
+        else if (operation === 'add' && vectors.length === 2) {
+            const result = globalMemoryManager.acquireTypedArray('float64', n);
+            for (let start = 0; start < n; start += chunkSize) {
+                const end = Math.min(start + chunkSize, n);
+                const chunkA = vectors[0].slice(start, end);
+                const chunkB = vectors[1].slice(start, end);
+                const chunkResult = new Array(end - start);
+                this.vectorAdd(chunkA, chunkB, chunkResult);
+                // Copy back to result
+                for (let i = 0; i < chunkResult.length; i++) {
+                    result[start + i] = chunkResult[i];
+                }
+                // Yield control
+                if (start % (chunkSize * 10) === 0) {
+                    await new Promise(resolve => setTimeout(resolve, 0));
+                }
+            }
+            return Array.from(result);
+        }
+        throw new Error(`Unsupported streaming operation: ${operation}`);
+    }
+}
+// Matrix multiplication with advanced optimizations
+export class OptimizedMatrixMultiplication {
+    // Cache-blocked sparse matrix-vector multiplication
+    static sparseMatVec(matrix, vector, result, blockSize = 1000) {
+        const rows = matrix.getRows();
+        // Process matrix in row blocks for cache efficiency
+        for (let blockStart = 0; blockStart < rows; blockStart += blockSize) {
+            const blockEnd = Math.min(blockStart + blockSize, rows);
+            for (let row = blockStart; row < blockEnd; row++) {
+                let sum = 0;
+                // Process row entries with prefetching
+                for (const entry of matrix.rowEntries(row)) {
+                    sum += entry.val * vector[entry.col];
+                }
+                result[row] = sum;
+            }
+        }
+    }
+    // Parallel matrix-vector multiplication using Web Workers (when available)
+    static async parallelMatVec(matrix, vector, numWorkers = navigator.hardwareConcurrency || 4) {
+        const rows = matrix.getRows();
+        const result = new Array(rows).fill(0);
+        if (typeof globalThis === 'undefined' || !globalThis.Worker || rows < 1000) {
+            // Fallback to sequential implementation
+            this.sparseMatVec(matrix, vector, result);
+            return result;
+        }
+        const chunkSize = Math.ceil(rows / numWorkers);
+        const promises = [];
+        for (let i = 0; i < numWorkers; i++) {
+            const startRow = i * chunkSize;
+            const endRow = Math.min(startRow + chunkSize, rows);
+            if (startRow >= rows)
+                break;
+            // Create worker for this chunk
+            const workerPromise = this.createMatVecWorker(matrix, vector, startRow, endRow);
+            promises.push(workerPromise);
+        }
+        const results = await Promise.all(promises);
+        // Combine results
+        let offset = 0;
+        for (const chunkResult of results) {
+            for (let i = 0; i < chunkResult.length; i++) {
+                result[offset + i] = chunkResult[i];
+            }
+            offset += chunkResult.length;
+        }
+        return result;
+    }
+    static async createMatVecWorker(matrix, vector, startRow, endRow) {
+        // In a real implementation, this would use Web Workers
+        // For now, simulate with async processing
+        return new Promise(resolve => {
+            setTimeout(() => {
+                const chunkResult = new Array(endRow - startRow).fill(0);
+                for (let row = startRow; row < endRow; row++) {
+                    let sum = 0;
+                    for (const entry of matrix.rowEntries(row)) {
+                        sum += entry.val * vector[entry.col];
+                    }
+                    chunkResult[row - startRow] = sum;
+                }
+                resolve(chunkResult);
+            }, 0);
+        });
+    }
+    // Adaptive algorithm selection based on matrix properties
+    static selectOptimalAlgorithm(matrix, vector) {
+        const nnz = matrix.getNnz();
+        const rows = matrix.getRows();
+        const sparsity = nnz / (rows * matrix.getCols());
+        const memoryUsage = matrix.getMemoryUsage();
+        // Decision tree based on matrix characteristics
+        if (memoryUsage > 100 * 1024 * 1024) { // > 100MB
+            return {
+                algorithm: 'streaming',
+                params: { chunkSize: 1000 }
+            };
+        }
+        else if (rows > 10000 && typeof globalThis !== 'undefined' && globalThis.Worker) {
+            return {
+                algorithm: 'parallel',
+                params: { numWorkers: navigator.hardwareConcurrency || 4 }
+            };
+        }
+        else if (sparsity < 0.1 && rows > 1000) {
+            return {
+                algorithm: 'blocked',
+                params: { blockSize: Math.min(1000, Math.ceil(Math.sqrt(rows))) }
+            };
+        }
+        else {
+            return {
+                algorithm: 'sequential',
+                params: {}
+            };
+        }
+    }
+}
+// Performance benchmarking and optimization guidance
+export class PerformanceBenchmark {
+    memoryManager;
+    constructor(memoryManager = globalMemoryManager) {
+        this.memoryManager = memoryManager;
+    }
+    // Comprehensive matrix operation benchmark
+    async benchmarkMatrixOperations(matrices, vectors, iterations = 100) {
+        const results = [];
+        for (let i = 0; i < matrices.length; i++) {
+            const matrix = matrices[i];
+            const vector = vectors[i];
+            const result = globalMemoryManager.acquireTypedArray('float64', matrix.getRows());
+            // Benchmark sequential multiplication
+            const seqResult = await this.benchmarkOperation('Sequential MatVec', () => OptimizedMatrixMultiplication.sparseMatVec(matrix, vector, Array.from(result)), iterations);
+            results.push(seqResult);
+            // Benchmark blocked multiplication
+            const blockedResult = await this.benchmarkOperation('Blocked MatVec', () => OptimizedMatrixMultiplication.sparseMatVec(matrix, vector, Array.from(result), 500), iterations);
+            results.push(blockedResult);
+            // Benchmark vectorized operations
+            const vecResult = await this.benchmarkOperation('Vectorized Dot Product', () => VectorizedOperations.dotProduct(vector, vector), iterations * 10);
+            results.push(vecResult);
+            globalMemoryManager.releaseTypedArray(result);
+        }
+        return results;
+    }
+    async benchmarkOperation(name, operation, iterations) {
+        // Warmup
+        for (let i = 0; i < Math.min(10, iterations); i++) {
+            operation();
+        }
+        const { result, profile } = await this.memoryManager.profileOperation(name, async () => {
+            const startTime = performance.now();
+            for (let i = 0; i < iterations; i++) {
+                operation();
+            }
+            return performance.now() - startTime;
+        });
+        const totalTime = result;
+        const averageTime = totalTime / iterations;
+        const throughput = iterations / (totalTime / 1000); // ops per second
+        return {
+            operation: name,
+            iterations,
+            totalTime,
+            averageTime,
+            throughput,
+            memoryProfile: profile,
+            cacheStats: {
+                hitRate: profile.cacheHitRate,
+                missRate: 1 - profile.cacheHitRate
+            }
+        };
+    }
+    // Generate optimization recommendations
+    generateOptimizationReport(benchmarks) {
+        const recommendations = [];
+        const bottlenecks = [];
+        let totalMemoryDelta = 0;
+        let totalCacheHitRate = 0;
+        for (const benchmark of benchmarks) {
+            totalMemoryDelta += Math.abs(benchmark.memoryProfile.memoryDelta);
+            totalCacheHitRate += benchmark.cacheStats.hitRate;
+            // Analyze performance characteristics
+            if (benchmark.throughput < 1000) {
+                bottlenecks.push(`Low throughput in ${benchmark.operation}: ${benchmark.throughput.toFixed(2)} ops/sec`);
+            }
+            if (benchmark.cacheStats.hitRate < 0.8) {
+                recommendations.push(`Improve cache locality for ${benchmark.operation} (hit rate: ${(benchmark.cacheStats.hitRate * 100).toFixed(1)}%)`);
+            }
+            if (benchmark.memoryProfile.memoryDelta > 1024 * 1024) {
+                recommendations.push(`Reduce memory allocation in ${benchmark.operation} (${(benchmark.memoryProfile.memoryDelta / 1024 / 1024).toFixed(2)}MB allocated)`);
+            }
+            if (benchmark.averageTime > 100) {
+                recommendations.push(`Consider parallelization for ${benchmark.operation} (avg time: ${benchmark.averageTime.toFixed(2)}ms)`);
+            }
+        }
+        const avgMemoryDelta = totalMemoryDelta / benchmarks.length;
+        const avgCacheHitRate = totalCacheHitRate / benchmarks.length;
+        // General recommendations
+        if (avgCacheHitRate < 0.7) {
+            recommendations.push('Consider using blocked algorithms for better cache locality');
+        }
+        if (avgMemoryDelta > 1024 * 1024) {
+            recommendations.push('Implement memory pooling to reduce allocation overhead');
+        }
+        return {
+            recommendations,
+            bottlenecks,
+            memoryEfficiency: 1 - (avgMemoryDelta / (1024 * 1024 * 100)), // Normalized efficiency
+            cacheEfficiency: avgCacheHitRate
+        };
+    }
+    // Auto-tuning for optimal parameters
+    async autoTuneParameters(matrix, vector) {
+        const blockSizes = [64, 128, 256, 512, 1024];
+        const unrollFactors = [2, 4, 8];
+        let bestBlockSize = 256;
+        let bestUnrollFactor = 4;
+        let bestThroughput = 0;
+        // Test different block sizes
+        for (const blockSize of blockSizes) {
+            const result = await this.benchmarkOperation(`Block size ${blockSize}`, () => OptimizedMatrixMultiplication.sparseMatVec(matrix, vector, new Array(matrix.getRows()).fill(0), blockSize), 50);
+            if (result.throughput > bestThroughput) {
+                bestThroughput = result.throughput;
+                bestBlockSize = blockSize;
+            }
+        }
+        // Test different unroll factors for vector operations
+        bestThroughput = 0;
+        for (const unrollFactor of unrollFactors) {
+            const result = await this.benchmarkOperation(`Unroll factor ${unrollFactor}`, () => VectorizedOperations.dotProduct(vector, vector, {
+                vectorize: true,
+                unroll: unrollFactor,
+                prefetch: false,
+                blocking: { enabled: false, size: 0 },
+                streaming: { enabled: false, chunkSize: 0 }
+            }), 100);
+            if (result.throughput > bestThroughput) {
+                bestThroughput = result.throughput;
+                bestUnrollFactor = unrollFactor;
+            }
+        }
+        // Select optimal algorithm
+        const algorithmSelection = OptimizedMatrixMultiplication.selectOptimalAlgorithm(matrix, vector);
+        return {
+            optimalBlockSize: bestBlockSize,
+            optimalUnrollFactor: bestUnrollFactor,
+            recommendedAlgorithm: algorithmSelection.algorithm
+        };
+    }
+}
+// Global performance optimizer
+export const globalPerformanceOptimizer = new PerformanceBenchmark();

vendor/sublinear-time-solver/dist/core/solver.d.ts

@@ -0,0 +1,66 @@
+/**
+ * Core solver algorithms for asymmetric diagonally dominant systems
+ * Implements Neumann series, random walks, and push methods
+ */
+import { Matrix, Vector, SolverConfig, SolverResult, EstimationConfig, PageRankConfig, ProgressCallback } from './types.js';
+export declare class SublinearSolver {
+    private config;
+    private performanceMonitor;
+    private convergenceChecker;
+    private timeoutController?;
+    private wasmAccelerated;
+    private wasmModules;
+    constructor(config: SolverConfig);
+    private initializeWasm;
+    private validateConfig;
+    /**
+     * Solve ADD system Mx = b using specified method
+     */
+    solve(matrix: Matrix, vector: Vector, progressCallback?: ProgressCallback): Promise<SolverResult>;
+    /**
+     * Solve using Neumann series expansion
+     * x* = (I - D^(-1)R)^(-1) D^(-1) b = sum_{k=0}^∞ (D^(-1)R)^k D^(-1) b
+     */
+    private solveNeumann;
+    /**
+     * Compute off-diagonal matrix-vector multiplication: (M - D) * v
+     * This computes R*v where R = M - D (off-diagonal part of matrix)
+     */
+    private computeOffDiagonalMultiply;
+    /**
+     * Solve using random walk sampling
+     */
+    private solveRandomWalk;
+    /**
+     * Create transition matrix for random walks
+     */
+    private createTransitionMatrix;
+    /**
+     * Perform a single random walk
+     */
+    private performRandomWalk;
+    /**
+     * Solve using forward push method
+     */
+    private solveForwardPush;
+    /**
+     * Solve using backward push method
+     */
+    private solveBackwardPush;
+    /**
+     * Solve using bidirectional approach (combine forward and backward)
+     */
+    private solveBidirectional;
+    /**
+     * Estimate a single entry of the solution M^(-1)b
+     */
+    estimateEntry(matrix: Matrix, vector: Vector, config: EstimationConfig): Promise<{
+        estimate: number;
+        variance: number;
+        confidence: number;
+    }>;
+    /**
+     * Compute PageRank using the solver
+     */
+    computePageRank(adjacency: Matrix, config: PageRankConfig): Promise<Vector>;
+}

vendor/sublinear-time-solver/dist/core/solver.js

@@ -0,0 +1,588 @@
+/**
+ * Core solver algorithms for asymmetric diagonally dominant systems
+ * Implements Neumann series, random walks, and push methods
+ */
+import { SolverError, ErrorCodes } from './types.js';
+import { MatrixOperations } from './matrix.js';
+import { VectorOperations, PerformanceMonitor, ConvergenceChecker, TimeoutController, ValidationUtils, createSeededRandom } from './utils.js';
+import { initializeAllWasm } from './wasm-bridge.js';
+export class SublinearSolver {
+    config;
+    performanceMonitor;
+    convergenceChecker;
+    timeoutController;
+    wasmAccelerated = false;
+    wasmModules = {};
+    constructor(config) {
+        this.config = config;
+        this.validateConfig(config);
+        this.performanceMonitor = new PerformanceMonitor();
+        this.convergenceChecker = new ConvergenceChecker();
+        if (config.timeout) {
+            this.timeoutController = new TimeoutController(config.timeout);
+        }
+        // Initialize WASM if available
+        this.initializeWasm().catch(console.warn);
+    }
+    async initializeWasm() {
+        try {
+            const { temporal, graph, hasWasm } = await initializeAllWasm();
+            this.wasmModules = { temporal, graph };
+            this.wasmAccelerated = hasWasm;
+            if (this.wasmAccelerated) {
+                console.log('🚀 WASM acceleration enabled');
+            }
+        }
+        catch (error) {
+            console.warn('WASM initialization failed, using JavaScript fallback');
+            this.wasmAccelerated = false;
+        }
+    }
+    validateConfig(config) {
+        ValidationUtils.validatePositiveNumber(config.epsilon, 'epsilon');
+        ValidationUtils.validateIntegerRange(config.maxIterations, 1, 1e6, 'maxIterations');
+        if (config.timeout) {
+            ValidationUtils.validatePositiveNumber(config.timeout, 'timeout');
+        }
+    }
+    /**
+     * Solve ADD system Mx = b using specified method
+     */
+    async solve(matrix, vector, progressCallback) {
+        MatrixOperations.validateMatrix(matrix);
+        if (vector.length !== matrix.cols) {
+            throw new SolverError(`Vector length ${vector.length} does not match matrix columns ${matrix.cols}`, ErrorCodes.INVALID_DIMENSIONS);
+        }
+        // Check diagonal dominance
+        const analysis = MatrixOperations.analyzeMatrix(matrix);
+        if (!analysis.isDiagonallyDominant) {
+            throw new SolverError('Matrix is not diagonally dominant', ErrorCodes.NOT_DIAGONALLY_DOMINANT, { analysis });
+        }
+        this.performanceMonitor.reset();
+        this.convergenceChecker.reset();
+        let result;
+        try {
+            switch (this.config.method) {
+                case 'neumann':
+                    result = await this.solveNeumann(matrix, vector, progressCallback);
+                    break;
+                case 'random-walk':
+                    result = await this.solveRandomWalk(matrix, vector, progressCallback);
+                    break;
+                case 'forward-push':
+                    result = await this.solveForwardPush(matrix, vector, progressCallback);
+                    break;
+                case 'backward-push':
+                    result = await this.solveBackwardPush(matrix, vector, progressCallback);
+                    break;
+                case 'bidirectional':
+                    result = await this.solveBidirectional(matrix, vector, progressCallback);
+                    break;
+                default:
+                    throw new SolverError(`Unknown method: ${this.config.method}`, ErrorCodes.INVALID_PARAMETERS);
+            }
+            return result;
+        }
+        catch (error) {
+            if (error instanceof SolverError) {
+                throw error;
+            }
+            throw new SolverError(`Solver failed: ${error}`, ErrorCodes.CONVERGENCE_FAILED);
+        }
+    }
+    /**
+     * Solve using Neumann series expansion
+     * x* = (I - D^(-1)R)^(-1) D^(-1) b = sum_{k=0}^∞ (D^(-1)R)^k D^(-1) b
+     */
+    async solveNeumann(matrix, vector, progressCallback) {
+        const n = matrix.rows;
+        // Extract diagonal and off-diagonal parts
+        const diagonal = MatrixOperations.getDiagonalVector(matrix);
+        // Validate diagonal elements
+        for (let i = 0; i < n; i++) {
+            if (Math.abs(diagonal[i]) < 1e-15) {
+                throw new SolverError(`Zero or near-zero diagonal element at position ${i}: ${diagonal[i]}`, ErrorCodes.NUMERICAL_INSTABILITY);
+            }
+        }
+        const invD = VectorOperations.elementwiseDivide(VectorOperations.ones(n), diagonal);
+        // Initialize solution with D^(-1) b
+        let solution = VectorOperations.elementwiseMultiply(invD, vector);
+        let seriesTerm = [...solution];
+        let previousResidual = Infinity;
+        const state = {
+            iteration: 0,
+            residual: Infinity,
+            solution,
+            converged: false,
+            elapsedTime: 0,
+            series: [seriesTerm],
+            convergenceRate: 1.0
+        };
+        // Improved convergence detection
+        let stagnationCounter = 0;
+        const maxStagnation = 10;
+        for (let k = 1; k <= this.config.maxIterations; k++) {
+            this.timeoutController?.checkTimeout();
+            // Compute (D^(-1)R)^k D^(-1) b iteratively
+            // seriesTerm = D^(-1) * (R * seriesTerm)
+            const Rterm = this.computeOffDiagonalMultiply(matrix, seriesTerm);
+            seriesTerm = VectorOperations.elementwiseMultiply(invD, Rterm);
+            // Add to solution
+            solution = VectorOperations.add(solution, seriesTerm);
+            // Compute residual: ||Mx - b|| every few iterations (expensive)
+            if (k % 5 === 0 || k <= 10) {
+                const residualVec = VectorOperations.subtract(MatrixOperations.multiplyMatrixVector(matrix, solution), vector);
+                state.residual = VectorOperations.norm2(residualVec);
+            }
+            else {
+                // Estimate residual from series term norm
+                state.residual = VectorOperations.norm2(seriesTerm) * Math.sqrt(n);
+            }
+            state.iteration = k;
+            state.solution = [...solution];
+            state.elapsedTime = this.performanceMonitor.getElapsedTime();
+            state.series.push([...seriesTerm]);
+            // Check convergence
+            const convergenceInfo = this.convergenceChecker.checkConvergence(state.residual, this.config.epsilon);
+            state.converged = convergenceInfo.converged;
+            state.convergenceRate = convergenceInfo.rate;
+            // Detect stagnation
+            if (Math.abs(state.residual - previousResidual) < this.config.epsilon * 1e-6) {
+                stagnationCounter++;
+                if (stagnationCounter >= maxStagnation) {
+                    console.warn(`Neumann series stagnated after ${k} iterations`);
+                    break;
+                }
+            }
+            else {
+                stagnationCounter = 0;
+            }
+            if (progressCallback) {
+                progressCallback({
+                    iteration: k,
+                    residual: state.residual,
+                    elapsed: state.elapsedTime
+                });
+            }
+            if (state.converged) {
+                break;
+            }
+            // Check if series term is becoming negligible (early termination)
+            const termNorm = VectorOperations.norm2(seriesTerm);
+            if (termNorm < this.config.epsilon * 1e-6) {
+                console.log(`Series term negligible after ${k} iterations`);
+                break;
+            }
+            // Prevent numerical overflow
+            if (!isFinite(state.residual) || state.residual > 1e15) {
+                throw new SolverError(`Numerical instability detected at iteration ${k}`, ErrorCodes.NUMERICAL_INSTABILITY, { residual: state.residual });
+            }
+            previousResidual = state.residual;
+        }
+        // Final accurate residual computation
+        const finalResidualVec = VectorOperations.subtract(MatrixOperations.multiplyMatrixVector(matrix, solution), vector);
+        state.residual = VectorOperations.norm2(finalResidualVec);
+        state.converged = state.residual < this.config.epsilon;
+        if (!state.converged && state.iteration >= this.config.maxIterations) {
+            throw new SolverError(`Neumann series failed to converge after ${this.config.maxIterations} iterations. Final residual: ${state.residual.toExponential(3)}`, ErrorCodes.CONVERGENCE_FAILED, {
+                finalResidual: state.residual,
+                iterations: state.iteration,
+                convergenceRate: state.convergenceRate
+            });
+        }
+        return {
+            solution: state.solution,
+            iterations: state.iteration,
+            residual: state.residual,
+            converged: state.converged,
+            method: 'neumann',
+            computeTime: state.elapsedTime,
+            memoryUsed: this.performanceMonitor.getMemoryIncrease()
+        };
+    }
+    /**
+     * Compute off-diagonal matrix-vector multiplication: (M - D) * v
+     * This computes R*v where R = M - D (off-diagonal part of matrix)
+     */
+    computeOffDiagonalMultiply(matrix, vector) {
+        const n = matrix.rows;
+        const result = new Array(n).fill(0);
+        // For dense matrices
+        if (matrix.format === 'dense') {
+            const data = matrix.data;
+            for (let i = 0; i < n; i++) {
+                for (let j = 0; j < n; j++) {
+                    if (i !== j) { // Skip diagonal
+                        result[i] += data[i][j] * vector[j];
+                    }
+                }
+            }
+        }
+        else {
+            // For sparse matrices (COO format)
+            const sparse = matrix;
+            for (let k = 0; k < sparse.values.length; k++) {
+                const i = sparse.rowIndices[k];
+                const j = sparse.colIndices[k];
+                if (i !== j) { // Skip diagonal
+                    result[i] += sparse.values[k] * vector[j];
+                }
+            }
+        }
+        return result;
+    }
+    /**
+     * Solve using random walk sampling
+     */
+    async solveRandomWalk(matrix, vector, progressCallback) {
+        const n = matrix.rows;
+        const rng = createSeededRandom(this.config.seed || Date.now());
+        // Convert to transition probabilities
+        const { transitions, absorptionProbs } = this.createTransitionMatrix(matrix);
+        let solution = VectorOperations.zeros(n);
+        let totalVariance = 0;
+        const state = {
+            iteration: 0,
+            residual: Infinity,
+            solution,
+            converged: false,
+            elapsedTime: 0,
+            walks: [],
+            currentEstimate: 0,
+            variance: 0,
+            confidence: 0
+        };
+        // Estimate each coordinate using random walks
+        for (let i = 0; i < n; i++) {
+            const estimates = [];
+            const numWalks = Math.max(100, Math.ceil(1 / (this.config.epsilon * this.config.epsilon)));
+            for (let walk = 0; walk < numWalks; walk++) {
+                const estimate = this.performRandomWalk(i, transitions, absorptionProbs, vector, rng);
+                estimates.push(estimate);
+                if (walk % 10 === 0) {
+                    this.timeoutController?.checkTimeout();
+                }
+            }
+            // Compute mean and variance
+            const mean = estimates.reduce((sum, val) => sum + val, 0) / estimates.length;
+            const variance = estimates.reduce((sum, val) => sum + (val - mean) ** 2, 0) / (estimates.length - 1);
+            solution[i] = mean;
+            totalVariance += variance;
+            state.iteration = i + 1;
+            state.currentEstimate = mean;
+            state.variance = Math.sqrt(variance);
+            state.walks.push(estimates);
+        }
+        // Compute final residual
+        const residualVec = VectorOperations.subtract(MatrixOperations.multiplyMatrixVector(matrix, solution), vector);
+        state.residual = VectorOperations.norm2(residualVec);
+        state.solution = solution;
+        state.converged = state.residual < this.config.epsilon;
+        state.elapsedTime = this.performanceMonitor.getElapsedTime();
+        // For random walk, we're more lenient with convergence since it's probabilistic
+        if (!state.converged && state.residual > 10 * this.config.epsilon) {
+            // Only fail if we're really far off
+            throw new SolverError(`Random walk sampling failed to achieve desired accuracy`, ErrorCodes.CONVERGENCE_FAILED, { finalResidual: state.residual, variance: Math.sqrt(totalVariance) });
+        }
+        return {
+            solution: state.solution,
+            iterations: state.iteration,
+            residual: state.residual,
+            converged: state.converged,
+            method: 'random-walk',
+            computeTime: state.elapsedTime,
+            memoryUsed: this.performanceMonitor.getMemoryIncrease()
+        };
+    }
+    /**
+     * Create transition matrix for random walks
+     */
+    createTransitionMatrix(matrix) {
+        const n = matrix.rows;
+        const transitions = Array(n).fill(null).map(() => Array(n).fill(0));
+        const absorptionProbs = new Array(n);
+        for (let i = 0; i < n; i++) {
+            const diagEntry = MatrixOperations.getDiagonal(matrix, i);
+            if (Math.abs(diagEntry) < 1e-15) {
+                throw new SolverError(`Zero diagonal at position ${i}`, ErrorCodes.NUMERICAL_INSTABILITY);
+            }
+            absorptionProbs[i] = 1 / diagEntry;
+            // Compute transition probabilities
+            for (let j = 0; j < n; j++) {
+                if (i !== j) {
+                    const entry = MatrixOperations.getEntry(matrix, i, j);
+                    transitions[i][j] = -entry / diagEntry;
+                }
+            }
+        }
+        return { transitions, absorptionProbs };
+    }
+    /**
+     * Perform a single random walk
+     */
+    performRandomWalk(start, transitions, absorptionProbs, vector, rng) {
+        let current = start;
+        let value = 0;
+        const maxSteps = 1000; // Prevent infinite walks
+        for (let step = 0; step < maxSteps; step++) {
+            // Check for absorption
+            if (rng() < Math.abs(absorptionProbs[current])) {
+                value += vector[current] * absorptionProbs[current];
+                break;
+            }
+            // Choose next state based on transition probabilities
+            const cumulative = [];
+            let sum = 0;
+            for (let j = 0; j < transitions[current].length; j++) {
+                sum += Math.abs(transitions[current][j]);
+                cumulative.push(sum);
+            }
+            if (sum === 0) {
+                // No outgoing transitions, absorb here
+                value += vector[current] * absorptionProbs[current];
+                break;
+            }
+            const rand = rng() * sum;
+            for (let j = 0; j < cumulative.length; j++) {
+                if (rand <= cumulative[j]) {
+                    current = j;
+                    break;
+                }
+            }
+        }
+        return value;
+    }
+    /**
+     * Solve using forward push method
+     */
+    async solveForwardPush(matrix, vector, progressCallback) {
+        const n = matrix.rows;
+        let approximate = VectorOperations.zeros(n);
+        let residual = [...vector];
+        const state = {
+            iteration: 0,
+            residual: Infinity,
+            solution: approximate,
+            converged: false,
+            elapsedTime: 0,
+            residualVector: residual,
+            approximateVector: approximate,
+            pushDirection: 'forward'
+        };
+        for (let iter = 0; iter < this.config.maxIterations; iter++) {
+            this.timeoutController?.checkTimeout();
+            // Find node with largest residual
+            let maxResidual = 0;
+            let maxNode = -1;
+            for (let i = 0; i < n; i++) {
+                if (Math.abs(residual[i]) > maxResidual) {
+                    maxResidual = Math.abs(residual[i]);
+                    maxNode = i;
+                }
+            }
+            if (maxResidual < this.config.epsilon) {
+                state.converged = true;
+                break;
+            }
+            // Push from maxNode
+            const diagEntry = MatrixOperations.getDiagonal(matrix, maxNode);
+            if (Math.abs(diagEntry) < 1e-15) {
+                throw new SolverError(`Zero diagonal at position ${maxNode}`, ErrorCodes.NUMERICAL_INSTABILITY);
+            }
+            const pushValue = residual[maxNode] / diagEntry;
+            approximate[maxNode] += pushValue;
+            residual[maxNode] = 0;
+            // Update residuals of neighbors
+            for (let j = 0; j < n; j++) {
+                if (j !== maxNode) {
+                    const entry = MatrixOperations.getEntry(matrix, j, maxNode);
+                    residual[j] -= entry * pushValue;
+                }
+            }
+            state.iteration = iter + 1;
+            state.residual = VectorOperations.norm2(residual);
+            state.solution = [...approximate];
+            state.residualVector = [...residual];
+            state.approximateVector = [...approximate];
+            state.elapsedTime = this.performanceMonitor.getElapsedTime();
+            if (progressCallback && iter % 10 === 0) {
+                progressCallback({
+                    iteration: iter + 1,
+                    residual: state.residual,
+                    elapsed: state.elapsedTime
+                });
+            }
+        }
+        if (!state.converged) {
+            throw new SolverError(`Forward push failed to converge after ${this.config.maxIterations} iterations`, ErrorCodes.CONVERGENCE_FAILED, { finalResidual: state.residual });
+        }
+        return {
+            solution: state.solution,
+            iterations: state.iteration,
+            residual: state.residual,
+            converged: state.converged,
+            method: 'forward-push',
+            computeTime: state.elapsedTime,
+            memoryUsed: this.performanceMonitor.getMemoryIncrease()
+        };
+    }
+    /**
+     * Solve using backward push method
+     */
+    async solveBackwardPush(matrix, vector, progressCallback) {
+        // For backward push, we solve M^T y = e_i and then compute x_i = y^T b
+        // This is more complex and typically used for single coordinate estimation
+        return this.solveForwardPush(matrix, vector, progressCallback); // Simplified for now
+    }
+    /**
+     * Solve using bidirectional approach (combine forward and backward)
+     */
+    async solveBidirectional(matrix, vector, progressCallback) {
+        // Start with forward push
+        const forwardResult = await this.solveForwardPush(matrix, vector, progressCallback);
+        // Could enhance with backward refinement, but for now return forward result
+        return {
+            ...forwardResult,
+            method: 'bidirectional'
+        };
+    }
+    /**
+     * Estimate a single entry of the solution M^(-1)b
+     */
+    async estimateEntry(matrix, vector, config) {
+        MatrixOperations.validateMatrix(matrix);
+        // Enhanced validation with better error messages
+        if (config.row < 0 || config.row >= matrix.rows) {
+            throw new SolverError(`Row index ${config.row} out of bounds. Matrix has ${matrix.rows} rows (valid range: 0-${matrix.rows - 1})`, ErrorCodes.INVALID_PARAMETERS, { row: config.row, matrixRows: matrix.rows });
+        }
+        if (config.column < 0 || config.column >= matrix.cols) {
+            throw new SolverError(`Column index ${config.column} out of bounds. Matrix has ${matrix.cols} columns (valid range: 0-${matrix.cols - 1})`, ErrorCodes.INVALID_PARAMETERS, { column: config.column, matrixCols: matrix.cols });
+        }
+        if (vector.length !== matrix.rows) {
+            throw new SolverError(`Vector length ${vector.length} does not match matrix rows ${matrix.rows}`, ErrorCodes.INVALID_DIMENSIONS, { vectorLength: vector.length, matrixRows: matrix.rows });
+        }
+        ValidationUtils.validatePositiveNumber(config.epsilon, 'epsilon');
+        ValidationUtils.validateRange(config.confidence, 0, 1, 'confidence');
+        const rng = createSeededRandom(this.config.seed || Date.now());
+        const estimates = [];
+        // Reduce samples for faster computation, especially for smaller matrices
+        const maxSamples = Math.min(1000, Math.max(50, Math.ceil(1 / Math.sqrt(config.epsilon))));
+        const timeoutMs = this.config.timeout || 10000; // 10 second default timeout
+        const startTime = Date.now();
+        try {
+            if (config.method === 'random-walk') {
+                const { transitions, absorptionProbs } = this.createTransitionMatrix(matrix);
+                for (let i = 0; i < maxSamples; i++) {
+                    // Check timeout every 10 samples
+                    if (i % 10 === 0) {
+                        const elapsed = Date.now() - startTime;
+                        if (elapsed > timeoutMs) {
+                            console.warn(`EstimateEntry timeout after ${elapsed}ms, using ${estimates.length} samples`);
+                            break;
+                        }
+                    }
+                    const estimate = this.performRandomWalk(config.row, transitions, absorptionProbs, vector, rng);
+                    estimates.push(estimate);
+                    // Early termination if estimates are converging
+                    if (i > 20 && i % 20 === 0) {
+                        const recentEstimates = estimates.slice(-20);
+                        const mean = recentEstimates.reduce((sum, val) => sum + val, 0) / recentEstimates.length;
+                        const variance = recentEstimates.reduce((sum, val) => sum + (val - mean) ** 2, 0) / recentEstimates.length;
+                        if (Math.sqrt(variance) < config.epsilon) {
+                            console.log(`EstimateEntry converged early after ${i} samples`);
+                            break;
+                        }
+                    }
+                }
+            }
+            else {
+                // Use Neumann series estimation - much faster and more reliable
+                if (config.column >= matrix.cols) {
+                    throw new SolverError(`Column index ${config.column} exceeds matrix dimensions ${matrix.cols}`, ErrorCodes.INVALID_PARAMETERS);
+                }
+                const e_i = new Array(matrix.cols).fill(0);
+                e_i[config.column] = 1;
+                const result = await this.solve(matrix, e_i);
+                const estimate = result.solution[config.row];
+                return {
+                    estimate,
+                    variance: 0,
+                    confidence: result.converged ? 1.0 : 0.5
+                };
+            }
+            if (estimates.length === 0) {
+                throw new SolverError('No estimates were generated', ErrorCodes.CONVERGENCE_FAILED);
+            }
+            const mean = estimates.reduce((sum, val) => sum + val, 0) / estimates.length;
+            const variance = estimates.length > 1
+                ? estimates.reduce((sum, val) => sum + (val - mean) ** 2, 0) / (estimates.length - 1)
+                : 0;
+            // Sanity check for numerical issues
+            if (!isFinite(mean) || !isFinite(variance)) {
+                throw new SolverError('Numerical instability in estimation', ErrorCodes.NUMERICAL_INSTABILITY, { mean, variance, numSamples: estimates.length });
+            }
+            return {
+                estimate: mean,
+                variance,
+                confidence: config.confidence
+            };
+        }
+        catch (error) {
+            if (error instanceof SolverError) {
+                throw error;
+            }
+            throw new SolverError(`Entry estimation failed: ${error}`, ErrorCodes.CONVERGENCE_FAILED, { row: config.row, column: config.column, method: config.method });
+        }
+    }
+    /**
+     * Compute PageRank using the solver
+     */
+    async computePageRank(adjacency, config) {
+        MatrixOperations.validateMatrix(adjacency);
+        ValidationUtils.validateRange(config.damping, 0, 1, 'damping');
+        ValidationUtils.validatePositiveNumber(config.epsilon, 'epsilon');
+        if (adjacency.rows !== adjacency.cols) {
+            throw new SolverError('Adjacency matrix must be square', ErrorCodes.INVALID_DIMENSIONS);
+        }
+        const n = adjacency.rows;
+        // Create the PageRank system: (I - α P^T) x = (1-α)/n * 1
+        // where P is the column-stochastic transition matrix
+        // Normalize adjacency to get transition matrix
+        const outDegrees = new Array(n).fill(0);
+        for (let i = 0; i < n; i++) {
+            for (let j = 0; j < n; j++) {
+                outDegrees[i] += MatrixOperations.getEntry(adjacency, i, j);
+            }
+        }
+        // Build system matrix I - α P^T
+        const systemMatrix = Array(n).fill(null).map(() => Array(n).fill(0));
+        for (let i = 0; i < n; i++) {
+            systemMatrix[i][i] = 1; // Identity part
+            for (let j = 0; j < n; j++) {
+                if (outDegrees[j] > 0) {
+                    const transitionProb = MatrixOperations.getEntry(adjacency, j, i) / outDegrees[j];
+                    systemMatrix[i][j] -= config.damping * transitionProb;
+                }
+            }
+        }
+        const systemMatrixFormatted = {
+            rows: n,
+            cols: n,
+            data: systemMatrix,
+            format: 'dense'
+        };
+        // Right-hand side
+        const rhs = config.personalized || VectorOperations.scale(VectorOperations.ones(n), (1 - config.damping) / n);
+        // Solve the system
+        const solverConfig = {
+            method: this.config.method,
+            epsilon: config.epsilon,
+            maxIterations: config.maxIterations,
+            timeout: this.config.timeout
+        };
+        const solver = new SublinearSolver(solverConfig);
+        const result = await solver.solve(systemMatrixFormatted, rhs);
+        // Return the PageRank vector directly as expected by GraphTools
+        return result.solution;
+    }
+}

vendor/sublinear-time-solver/dist/core/types.d.ts

@@ -0,0 +1,150 @@
+/**
+ * Core type definitions for the sublinear-time solver
+ */
+export interface SparseMatrix {
+    rows: number;
+    cols: number;
+    values: number[];
+    rowIndices: number[];
+    colIndices: number[];
+    format: 'coo' | 'csr' | 'csc';
+}
+export interface DenseMatrix {
+    rows: number;
+    cols: number;
+    data: number[][];
+    format: 'dense';
+}
+export type Matrix = SparseMatrix | DenseMatrix;
+export type Vector = number[];
+export interface SolverConfig {
+    method: 'neumann' | 'random-walk' | 'forward-push' | 'backward-push' | 'bidirectional';
+    epsilon: number;
+    maxIterations: number;
+    timeout?: number | undefined;
+    enableProgress?: boolean | undefined;
+    seed?: number | undefined;
+}
+export interface SolverResult {
+    solution: Vector;
+    iterations: number;
+    residual: number;
+    converged: boolean;
+    method: string;
+    computeTime: number;
+    memoryUsed: number;
+}
+export interface MatrixAnalysis {
+    isDiagonallyDominant: boolean;
+    dominanceType: 'row' | 'column' | 'none';
+    dominanceStrength: number;
+    spectralRadius?: number;
+    condition?: number;
+    pNormGap?: number;
+    isSymmetric: boolean;
+    sparsity: number;
+    size: {
+        rows: number;
+        cols: number;
+    };
+}
+export interface RandomWalkConfig {
+    startNode?: number;
+    endNode?: number;
+    walkLength: number;
+    numWalks: number;
+    seed?: number;
+}
+export interface PageRankConfig {
+    damping: number;
+    personalized?: Vector;
+    epsilon: number;
+    maxIterations: number;
+}
+export interface EstimationConfig {
+    row: number;
+    column: number;
+    epsilon: number;
+    confidence: number;
+    method: 'neumann' | 'random-walk' | 'monte-carlo';
+}
+export declare class SolverError extends Error {
+    code: string;
+    details?: unknown;
+    constructor(message: string, code: string, details?: unknown);
+}
+export declare const ErrorCodes: {
+    readonly NOT_DIAGONALLY_DOMINANT: "E001";
+    readonly CONVERGENCE_FAILED: "E002";
+    readonly INVALID_MATRIX: "E003";
+    readonly TIMEOUT: "E004";
+    readonly INVALID_DIMENSIONS: "E005";
+    readonly NUMERICAL_INSTABILITY: "E006";
+    readonly MEMORY_LIMIT_EXCEEDED: "E007";
+    readonly INVALID_PARAMETERS: "E008";
+};
+export type ProgressCallback = (progress: {
+    iteration: number;
+    residual: number;
+    elapsed: number;
+    estimated?: number;
+}) => void;
+export interface SolveParams {
+    matrix: Matrix;
+    vector: Vector;
+    method?: 'neumann' | 'random-walk' | 'forward-push' | 'backward-push' | 'bidirectional' | undefined;
+    epsilon?: number | undefined;
+    maxIterations?: number | undefined;
+    timeout?: number | undefined;
+}
+export interface EstimateEntryParams {
+    matrix: Matrix;
+    vector: Vector;
+    row: number;
+    column: number;
+    epsilon: number;
+    confidence?: number | undefined;
+    method?: 'neumann' | 'random-walk' | 'monte-carlo' | undefined;
+}
+export interface AnalyzeMatrixParams {
+    matrix: Matrix;
+    checkDominance?: boolean;
+    computeGap?: boolean;
+    estimateCondition?: boolean;
+    checkSymmetry?: boolean;
+}
+export interface PageRankParams {
+    adjacency: Matrix;
+    damping?: number | undefined;
+    personalized?: Vector | undefined;
+    epsilon?: number | undefined;
+    maxIterations?: number | undefined;
+}
+export interface EffectiveResistanceParams {
+    laplacian: Matrix;
+    source: number;
+    target: number;
+    epsilon?: number;
+}
+export interface AlgorithmState {
+    iteration: number;
+    residual: number;
+    solution: Vector;
+    converged: boolean;
+    elapsedTime: number;
+}
+export interface NeumannState extends AlgorithmState {
+    series: Vector[];
+    convergenceRate: number;
+}
+export interface RandomWalkState extends AlgorithmState {
+    walks: number[][];
+    currentEstimate: number;
+    variance: number;
+    confidence: number;
+}
+export interface PushState extends AlgorithmState {
+    residualVector: Vector;
+    approximateVector: Vector;
+    pushDirection: 'forward' | 'backward';
+}

vendor/sublinear-time-solver/dist/core/types.js

@@ -0,0 +1,24 @@
+/**
+ * Core type definitions for the sublinear-time solver
+ */
+// Error types
+export class SolverError extends Error {
+    code;
+    details;
+    constructor(message, code, details) {
+        super(message);
+        this.code = code;
+        this.details = details;
+        this.name = 'SolverError';
+    }
+}
+export const ErrorCodes = {
+    NOT_DIAGONALLY_DOMINANT: 'E001',
+    CONVERGENCE_FAILED: 'E002',
+    INVALID_MATRIX: 'E003',
+    TIMEOUT: 'E004',
+    INVALID_DIMENSIONS: 'E005',
+    NUMERICAL_INSTABILITY: 'E006',
+    MEMORY_LIMIT_EXCEEDED: 'E007',
+    INVALID_PARAMETERS: 'E008'
+};

vendor/sublinear-time-solver/dist/core/utils.d.ts

@@ -0,0 +1,163 @@
+/**
+ * Utility functions for sublinear-time solvers
+ */
+import { Vector } from './types.js';
+export declare class VectorOperations {
+    /**
+     * Vector addition: result = a + b
+     */
+    static add(a: Vector, b: Vector): Vector;
+    /**
+     * Vector subtraction: result = a - b
+     */
+    static subtract(a: Vector, b: Vector): Vector;
+    /**
+     * Scalar multiplication: result = scalar * vector
+     */
+    static scale(vector: Vector, scalar: number): Vector;
+    /**
+     * Dot product of two vectors
+     */
+    static dot(a: Vector, b: Vector): number;
+    /**
+     * L2 norm of vector
+     */
+    static norm2(vector: Vector): number;
+    /**
+     * L1 norm of vector
+     */
+    static norm1(vector: Vector): number;
+    /**
+     * L-infinity norm of vector
+     */
+    static normInf(vector: Vector): number;
+    /**
+     * Create zero vector of specified length
+     */
+    static zeros(length: number): Vector;
+    /**
+     * Create vector filled with ones
+     */
+    static ones(length: number): Vector;
+    /**
+     * Create random vector with values in [0, 1)
+     */
+    static random(length: number, seed?: number): Vector;
+    /**
+     * Normalize vector to unit length
+     */
+    static normalize(vector: Vector): Vector;
+    /**
+     * Element-wise multiplication
+     */
+    static elementwiseMultiply(a: Vector, b: Vector): Vector;
+    /**
+     * Element-wise division
+     */
+    static elementwiseDivide(a: Vector, b: Vector): Vector;
+    /**
+     * Check if vectors are approximately equal
+     */
+    static isEqual(a: Vector, b: Vector, tolerance?: number): boolean;
+    /**
+     * Linear interpolation between two vectors
+     */
+    static lerp(a: Vector, b: Vector, t: number): Vector;
+}
+/**
+ * Create a seeded random number generator
+ */
+export declare function createSeededRandom(seed: number): () => number;
+/**
+ * Performance monitoring utilities
+ */
+export declare class PerformanceMonitor {
+    private startTime;
+    private memoryStart;
+    constructor();
+    /**
+     * Get elapsed time in milliseconds
+     */
+    getElapsedTime(): number;
+    /**
+     * Get memory usage in MB
+     */
+    getMemoryUsage(): number;
+    /**
+     * Get memory increase since start
+     */
+    getMemoryIncrease(): number;
+    /**
+     * Reset timer and memory baseline
+     */
+    reset(): void;
+}
+/**
+ * Convergence checking utilities
+ */
+export declare class ConvergenceChecker {
+    private history;
+    private readonly maxHistory;
+    constructor(maxHistory?: number);
+    /**
+     * Add residual to history and check convergence
+     */
+    checkConvergence(residual: number, tolerance: number): {
+        converged: boolean;
+        rate: number;
+        trend: 'improving' | 'stagnant' | 'diverging';
+    };
+    /**
+     * Get average convergence rate over history
+     */
+    getAverageRate(): number;
+    /**
+     * Clear convergence history
+     */
+    reset(): void;
+}
+/**
+ * Timeout utility
+ */
+export declare class TimeoutController {
+    private startTime;
+    private timeoutMs;
+    constructor(timeoutMs: number);
+    /**
+     * Check if timeout has been exceeded
+     */
+    isExpired(): boolean;
+    /**
+     * Get remaining time in milliseconds
+     */
+    remainingTime(): number;
+    /**
+     * Throw timeout error if expired
+     */
+    checkTimeout(): void;
+}
+/**
+ * Validation utilities
+ */
+export declare class ValidationUtils {
+    /**
+     * Validate that value is a finite number
+     */
+    static validateFiniteNumber(value: number, name: string): void;
+    /**
+     * Validate that value is a positive number
+     */
+    static validatePositiveNumber(value: number, name: string): void;
+    /**
+     * Validate that value is a non-negative number
+     */
+    static validateNonNegativeNumber(value: number, name: string): void;
+    /**
+     * Validate that value is within range [min, max]
+     */
+    static validateRange(value: number, min: number, max: number, name: string): void;
+    /**
+     * Validate that integer is within range [min, max]
+     */
+    static validateIntegerRange(value: number, min: number, max: number, name: string): void;
+}

vendor/sublinear-time-solver/dist/core/utils.js

@@ -0,0 +1,322 @@
+/**
+ * Utility functions for sublinear-time solvers
+ */
+import { SolverError, ErrorCodes } from './types.js';
+export class VectorOperations {
+    /**
+     * Vector addition: result = a + b
+     */
+    static add(a, b) {
+        if (a.length !== b.length) {
+            throw new SolverError(`Vector dimensions don't match: ${a.length} vs ${b.length}`, ErrorCodes.INVALID_DIMENSIONS);
+        }
+        return a.map((val, i) => val + b[i]);
+    }
+    /**
+     * Vector subtraction: result = a - b
+     */
+    static subtract(a, b) {
+        if (a.length !== b.length) {
+            throw new SolverError(`Vector dimensions don't match: ${a.length} vs ${b.length}`, ErrorCodes.INVALID_DIMENSIONS);
+        }
+        return a.map((val, i) => val - b[i]);
+    }
+    /**
+     * Scalar multiplication: result = scalar * vector
+     */
+    static scale(vector, scalar) {
+        return vector.map(val => val * scalar);
+    }
+    /**
+     * Dot product of two vectors
+     */
+    static dot(a, b) {
+        if (a.length !== b.length) {
+            throw new SolverError(`Vector dimensions don't match: ${a.length} vs ${b.length}`, ErrorCodes.INVALID_DIMENSIONS);
+        }
+        return a.reduce((sum, val, i) => sum + val * b[i], 0);
+    }
+    /**
+     * L2 norm of vector
+     */
+    static norm2(vector) {
+        return Math.sqrt(vector.reduce((sum, val) => sum + val * val, 0));
+    }
+    /**
+     * L1 norm of vector
+     */
+    static norm1(vector) {
+        return vector.reduce((sum, val) => sum + Math.abs(val), 0);
+    }
+    /**
+     * L-infinity norm of vector
+     */
+    static normInf(vector) {
+        return Math.max(...vector.map(Math.abs));
+    }
+    /**
+     * Create zero vector of specified length
+     */
+    static zeros(length) {
+        return new Array(length).fill(0);
+    }
+    /**
+     * Create vector filled with ones
+     */
+    static ones(length) {
+        return new Array(length).fill(1);
+    }
+    /**
+     * Create random vector with values in [0, 1)
+     */
+    static random(length, seed) {
+        const rng = seed !== undefined ? createSeededRandom(seed) : Math.random;
+        return Array.from({ length }, () => rng());
+    }
+    /**
+     * Normalize vector to unit length
+     */
+    static normalize(vector) {
+        const norm = this.norm2(vector);
+        if (norm === 0) {
+            throw new SolverError('Cannot normalize zero vector', ErrorCodes.NUMERICAL_INSTABILITY);
+        }
+        return this.scale(vector, 1 / norm);
+    }
+    /**
+     * Element-wise multiplication
+     */
+    static elementwiseMultiply(a, b) {
+        if (a.length !== b.length) {
+            throw new SolverError(`Vector dimensions don't match: ${a.length} vs ${b.length}`, ErrorCodes.INVALID_DIMENSIONS);
+        }
+        return a.map((val, i) => val * b[i]);
+    }
+    /**
+     * Element-wise division
+     */
+    static elementwiseDivide(a, b) {
+        if (a.length !== b.length) {
+            throw new SolverError(`Vector dimensions don't match: ${a.length} vs ${b.length}`, ErrorCodes.INVALID_DIMENSIONS);
+        }
+        return a.map((val, i) => {
+            if (Math.abs(b[i]) < 1e-15) {
+                throw new SolverError(`Division by zero at index ${i}`, ErrorCodes.NUMERICAL_INSTABILITY);
+            }
+            return val / b[i];
+        });
+    }
+    /**
+     * Check if vectors are approximately equal
+     */
+    static isEqual(a, b, tolerance = 1e-10) {
+        if (a.length !== b.length) {
+            return false;
+        }
+        for (let i = 0; i < a.length; i++) {
+            if (Math.abs(a[i] - b[i]) > tolerance) {
+                return false;
+            }
+        }
+        return true;
+    }
+    /**
+     * Linear interpolation between two vectors
+     */
+    static lerp(a, b, t) {
+        if (a.length !== b.length) {
+            throw new SolverError(`Vector dimensions don't match: ${a.length} vs ${b.length}`, ErrorCodes.INVALID_DIMENSIONS);
+        }
+        return a.map((val, i) => val + t * (b[i] - val));
+    }
+}
+/**
+ * Create a seeded random number generator
+ */
+export function createSeededRandom(seed) {
+    let state = seed;
+    return function () {
+        // Simple linear congruential generator
+        state = (state * 1664525 + 1013904223) % 0x100000000;
+        return state / 0x100000000;
+    };
+}
+/**
+ * Performance monitoring utilities
+ */
+export class PerformanceMonitor {
+    startTime;
+    memoryStart;
+    constructor() {
+        this.startTime = Date.now();
+        this.memoryStart = this.getMemoryUsage();
+    }
+    /**
+     * Get elapsed time in milliseconds
+     */
+    getElapsedTime() {
+        return Date.now() - this.startTime;
+    }
+    /**
+     * Get memory usage in MB
+     */
+    getMemoryUsage() {
+        if (typeof process !== 'undefined' && process.memoryUsage) {
+            const usage = process.memoryUsage();
+            return Math.round(usage.heapUsed / 1024 / 1024);
+        }
+        return 0;
+    }
+    /**
+     * Get memory increase since start
+     */
+    getMemoryIncrease() {
+        return this.getMemoryUsage() - this.memoryStart;
+    }
+    /**
+     * Reset timer and memory baseline
+     */
+    reset() {
+        this.startTime = Date.now();
+        this.memoryStart = this.getMemoryUsage();
+    }
+}
+/**
+ * Convergence checking utilities
+ */
+export class ConvergenceChecker {
+    history = [];
+    maxHistory;
+    constructor(maxHistory = 10) {
+        this.maxHistory = maxHistory;
+    }
+    /**
+     * Add residual to history and check convergence
+     */
+    checkConvergence(residual, tolerance) {
+        this.history.push(residual);
+        if (this.history.length > this.maxHistory) {
+            this.history.shift();
+        }
+        const converged = residual < tolerance;
+        let rate = 1.0;
+        let trend = 'improving';
+        if (this.history.length >= 2) {
+            const recent = this.history.slice(-2);
+            rate = recent[1] / recent[0];
+            if (rate < 0.95) {
+                trend = 'improving';
+            }
+            else if (rate > 1.05) {
+                trend = 'diverging';
+            }
+            else {
+                trend = 'stagnant';
+            }
+        }
+        return { converged, rate, trend };
+    }
+    /**
+     * Get average convergence rate over history
+     */
+    getAverageRate() {
+        if (this.history.length < 2) {
+            return 1.0;
+        }
+        let totalRate = 0;
+        let count = 0;
+        for (let i = 1; i < this.history.length; i++) {
+            if (this.history[i - 1] > 0) {
+                totalRate += this.history[i] / this.history[i - 1];
+                count++;
+            }
+        }
+        return count > 0 ? totalRate / count : 1.0;
+    }
+    /**
+     * Clear convergence history
+     */
+    reset() {
+        this.history = [];
+    }
+}
+/**
+ * Timeout utility
+ */
+export class TimeoutController {
+    startTime;
+    timeoutMs;
+    constructor(timeoutMs) {
+        this.startTime = Date.now();
+        this.timeoutMs = timeoutMs;
+    }
+    /**
+     * Check if timeout has been exceeded
+     */
+    isExpired() {
+        return Date.now() - this.startTime > this.timeoutMs;
+    }
+    /**
+     * Get remaining time in milliseconds
+     */
+    remainingTime() {
+        return Math.max(0, this.timeoutMs - (Date.now() - this.startTime));
+    }
+    /**
+     * Throw timeout error if expired
+     */
+    checkTimeout() {
+        if (this.isExpired()) {
+            throw new SolverError(`Operation timed out after ${this.timeoutMs}ms`, ErrorCodes.TIMEOUT);
+        }
+    }
+}
+/**
+ * Validation utilities
+ */
+export class ValidationUtils {
+    /**
+     * Validate that value is a finite number
+     */
+    static validateFiniteNumber(value, name) {
+        if (!Number.isFinite(value)) {
+            throw new SolverError(`${name} must be a finite number, got ${value}`, ErrorCodes.INVALID_PARAMETERS);
+        }
+    }
+    /**
+     * Validate that value is a positive number
+     */
+    static validatePositiveNumber(value, name) {
+        this.validateFiniteNumber(value, name);
+        if (value <= 0) {
+            throw new SolverError(`${name} must be positive, got ${value}`, ErrorCodes.INVALID_PARAMETERS);
+        }
+    }
+    /**
+     * Validate that value is a non-negative number
+     */
+    static validateNonNegativeNumber(value, name) {
+        this.validateFiniteNumber(value, name);
+        if (value < 0) {
+            throw new SolverError(`${name} must be non-negative, got ${value}`, ErrorCodes.INVALID_PARAMETERS);
+        }
+    }
+    /**
+     * Validate that value is within range [min, max]
+     */
+    static validateRange(value, min, max, name) {
+        this.validateFiniteNumber(value, name);
+        if (value < min || value > max) {
+            throw new SolverError(`${name} must be between ${min} and ${max}, got ${value}`, ErrorCodes.INVALID_PARAMETERS);
+        }
+    }
+    /**
+     * Validate that integer is within range [min, max]
+     */
+    static validateIntegerRange(value, min, max, name) {
+        if (!Number.isInteger(value)) {
+            throw new SolverError(`${name} must be an integer, got ${value}`, ErrorCodes.INVALID_PARAMETERS);
+        }
+        this.validateRange(value, min, max, name);
+    }
+}

vendor/sublinear-time-solver/dist/core/wasm-bridge.d.ts

@@ -0,0 +1,24 @@
+/**
+ * WASM Bridge - Actually functional WASM integration
+ *
+ * This module properly loads and uses the Rust-compiled WASM modules
+ */
+/**
+ * Load the temporal neural solver WASM
+ */
+export declare function loadTemporalNeuralSolver(): Promise<any>;
+/**
+ * Load the graph reasoner WASM for PageRank
+ */
+export declare function loadGraphReasonerWasm(): Promise<any>;
+/**
+ * Load all available WASM modules
+ */
+export declare function initializeAllWasm(): Promise<{
+    temporal: any;
+    graph: any;
+    hasWasm: boolean;
+}>;
+declare function multiplyMatrixVectorJS(matrix: Float64Array, vector: Float64Array, rows: number, cols: number): Float64Array;
+declare function computePageRankJS(adjacency: Float64Array, n: number, damping: number, iterations: number): Float64Array;
+export { multiplyMatrixVectorJS, computePageRankJS };

vendor/sublinear-time-solver/dist/core/wasm-bridge.js

@@ -0,0 +1,208 @@
+/**
+ * WASM Bridge - Actually functional WASM integration
+ *
+ * This module properly loads and uses the Rust-compiled WASM modules
+ */
+import { readFileSync, existsSync } from 'fs';
+import { join, dirname } from 'path';
+import { fileURLToPath } from 'url';
+const __filename = fileURLToPath(import.meta.url);
+const __dirname = dirname(__filename);
+// Cache for loaded WASM instances
+const wasmCache = new Map();
+/**
+ * Load the temporal neural solver WASM
+ */
+export async function loadTemporalNeuralSolver() {
+    if (wasmCache.has('temporal_neural')) {
+        return wasmCache.get('temporal_neural');
+    }
+    try {
+        const wasmPath = join(__dirname, '..', 'wasm', 'temporal_neural_solver_bg.wasm');
+        // Check if file exists
+        if (!existsSync(wasmPath)) {
+            console.warn(`WASM file not found at ${wasmPath}`);
+            return null;
+        }
+        const wasmBuffer = readFileSync(wasmPath);
+        // Minimal imports for temporal neural solver
+        const imports = {
+            wbg: {
+                __wbg_random_e6e0a85ff4db8ab6: () => Math.random(),
+                __wbindgen_throw: (ptr, len) => {
+                    throw new Error(`WASM error at ${ptr}, len ${len}`);
+                }
+            }
+        };
+        const { instance } = await globalThis.WebAssembly.instantiate(wasmBuffer, imports);
+        // Create wrapper with actual functions
+        const solver = {
+            memory: instance.exports.memory,
+            // Matrix multiplication using WASM memory
+            multiplyMatrixVector: (matrix, vector, rows, cols) => {
+                if (!instance.exports.__wbindgen_malloc) {
+                    // Fallback to JS if WASM doesn't have allocator
+                    return multiplyMatrixVectorJS(matrix, vector, rows, cols);
+                }
+                // Allocate memory in WASM
+                const matrixPtr = instance.exports.__wbindgen_malloc(matrix.byteLength, 8);
+                const vectorPtr = instance.exports.__wbindgen_malloc(vector.byteLength, 8);
+                const resultPtr = instance.exports.__wbindgen_malloc(rows * 8, 8);
+                // Copy data to WASM memory
+                const memory = new Float64Array(instance.exports.memory.buffer);
+                memory.set(matrix, matrixPtr / 8);
+                memory.set(vector, vectorPtr / 8);
+                // Call WASM function if it exists
+                if (instance.exports.matrix_multiply_vector) {
+                    instance.exports.matrix_multiply_vector(matrixPtr, vectorPtr, resultPtr, rows, cols);
+                }
+                else {
+                    // Use WASM memory but JS computation
+                    for (let i = 0; i < rows; i++) {
+                        let sum = 0;
+                        for (let j = 0; j < cols; j++) {
+                            sum += memory[matrixPtr / 8 + i * cols + j] * memory[vectorPtr / 8 + j];
+                        }
+                        memory[resultPtr / 8 + i] = sum;
+                    }
+                }
+                // Get result
+                const result = new Float64Array(rows);
+                result.set(memory.slice(resultPtr / 8, resultPtr / 8 + rows));
+                // Free WASM memory
+                if (instance.exports.__wbindgen_free) {
+                    instance.exports.__wbindgen_free(matrixPtr, matrix.byteLength, 8);
+                    instance.exports.__wbindgen_free(vectorPtr, vector.byteLength, 8);
+                    instance.exports.__wbindgen_free(resultPtr, rows * 8, 8);
+                }
+                return result;
+            },
+            // Get memory stats
+            getMemoryUsage: () => {
+                return instance.exports.memory.buffer.byteLength;
+            }
+        };
+        wasmCache.set('temporal_neural', solver);
+        return solver;
+    }
+    catch (error) {
+        console.warn('Failed to load temporal neural WASM, using JS fallback');
+        return null;
+    }
+}
+/**
+ * Load the graph reasoner WASM for PageRank
+ */
+export async function loadGraphReasonerWasm() {
+    if (wasmCache.has('graph_reasoner')) {
+        return wasmCache.get('graph_reasoner');
+    }
+    try {
+        const wasmPath = join(__dirname, '..', 'wasm', 'graph_reasoner_bg.wasm');
+        const wasmBuffer = readFileSync(wasmPath);
+        // Graph reasoner needs more imports
+        const imports = {
+            wbg: {
+                __wbindgen_object_drop_ref: () => { },
+                __wbindgen_string_new: (ptr, len) => ptr,
+                __wbindgen_throw: (ptr, len) => {
+                    throw new Error(`WASM error at ${ptr}`);
+                },
+                __wbg_random_e6e0a85ff4db8ab6: () => Math.random(),
+                __wbg_now_3141b3797eb98e0b: () => Date.now()
+            }
+        };
+        const { instance } = await globalThis.WebAssembly.instantiate(wasmBuffer, imports);
+        const reasoner = {
+            memory: instance.exports.memory,
+            // PageRank computation using WASM
+            computePageRank: (adjacency, n, damping = 0.85, iterations = 100) => {
+                // Check if we have the actual WASM function
+                if (instance.exports.pagerank_compute) {
+                    const adjPtr = instance.exports.__wbindgen_malloc(adjacency.byteLength, 8);
+                    const resultPtr = instance.exports.__wbindgen_malloc(n * 8, 8);
+                    const memory = new Float64Array(instance.exports.memory.buffer);
+                    memory.set(adjacency, adjPtr / 8);
+                    instance.exports.pagerank_compute(adjPtr, resultPtr, n, damping, iterations);
+                    const result = new Float64Array(n);
+                    result.set(memory.slice(resultPtr / 8, resultPtr / 8 + n));
+                    instance.exports.__wbindgen_free(adjPtr, adjacency.byteLength, 8);
+                    instance.exports.__wbindgen_free(resultPtr, n * 8, 8);
+                    return result;
+                }
+                // Fallback PageRank in JS using WASM memory for speed
+                return computePageRankJS(adjacency, n, damping, iterations);
+            }
+        };
+        wasmCache.set('graph_reasoner', reasoner);
+        return reasoner;
+    }
+    catch (error) {
+        console.warn('Failed to load graph reasoner WASM, using JS fallback');
+        return null;
+    }
+}
+/**
+ * Load all available WASM modules
+ */
+export async function initializeAllWasm() {
+    const [temporal, graph] = await Promise.all([
+        loadTemporalNeuralSolver(),
+        loadGraphReasonerWasm()
+    ]);
+    const hasWasm = !!(temporal || graph);
+    if (hasWasm) {
+        console.log('✅ WASM acceleration enabled');
+        if (temporal)
+            console.log('  - Temporal Neural Solver');
+        if (graph)
+            console.log('  - Graph Reasoner');
+    }
+    else {
+        console.log('⚠️ Running in pure JavaScript mode');
+    }
+    return { temporal, graph, hasWasm };
+}
+// JavaScript fallbacks
+function multiplyMatrixVectorJS(matrix, vector, rows, cols) {
+    const result = new Float64Array(rows);
+    for (let i = 0; i < rows; i++) {
+        let sum = 0;
+        for (let j = 0; j < cols; j++) {
+            sum += matrix[i * cols + j] * vector[j];
+        }
+        result[i] = sum;
+    }
+    return result;
+}
+function computePageRankJS(adjacency, n, damping, iterations) {
+    const rank = new Float64Array(n);
+    const newRank = new Float64Array(n);
+    // Initialize with 1/n
+    for (let i = 0; i < n; i++) {
+        rank[i] = 1.0 / n;
+    }
+    for (let iter = 0; iter < iterations; iter++) {
+        // Calculate new ranks
+        for (let i = 0; i < n; i++) {
+            newRank[i] = (1 - damping) / n;
+            for (let j = 0; j < n; j++) {
+                if (adjacency[j * n + i] > 0) {
+                    // Count outgoing edges from j
+                    let outDegree = 0;
+                    for (let k = 0; k < n; k++) {
+                        if (adjacency[j * n + k] > 0)
+                            outDegree++;
+                    }
+                    if (outDegree > 0) {
+                        newRank[i] += damping * rank[j] / outDegree;
+                    }
+                }
+            }
+        }
+        // Swap arrays
+        rank.set(newRank);
+    }
+    return rank;
+}
+export { multiplyMatrixVectorJS, computePageRankJS };

vendor/sublinear-time-solver/dist/core/wasm-integration.d.ts

@@ -0,0 +1,59 @@
+/**
+ * Real WASM Integration for Sublinear Time Solver
+ *
+ * This module properly integrates our Rust WASM components:
+ * - GraphReasoner: Fast PageRank and graph algorithms
+ * - TemporalNeuralSolver: Neural network accelerated matrix operations
+ * - StrangeLoop: Quantum-enhanced solving with nanosecond precision
+ * - NanoScheduler: Ultra-low latency task scheduling
+ */
+import { Matrix, Vector } from './types.js';
+/**
+ * GraphReasoner WASM for PageRank and graph algorithms
+ */
+export declare class GraphReasonerWASM {
+    private instance;
+    private reasoner;
+    initialize(): Promise<boolean>;
+    /**
+     * Compute PageRank using WASM acceleration
+     */
+    computePageRank(adjacencyMatrix: Matrix, damping?: number, iterations?: number): Float64Array;
+    private pageRankJS;
+}
+/**
+ * TemporalNeuralSolver WASM for ultra-fast matrix operations
+ */
+export declare class TemporalNeuralWASM {
+    private instance;
+    private solver;
+    initialize(): Promise<boolean>;
+    /**
+     * Ultra-fast matrix-vector multiplication
+     */
+    multiplyMatrixVector(matrix: Float64Array, vector: Float64Array, rows: number, cols: number): Float64Array;
+    private multiplyMatrixVectorJS;
+    /**
+     * Predict solution with temporal advantage
+     */
+    predictWithTemporalAdvantage(matrix: Matrix, vector: Vector, distanceKm?: number): Promise<{
+        solution: Vector;
+        temporalAdvantageMs: number;
+        lightTravelTimeMs: number;
+        computeTimeMs: number;
+    }>;
+}
+/**
+ * Main WASM integration manager
+ */
+export declare class WASMAccelerator {
+    private graphReasoner;
+    private temporalNeural;
+    private initialized;
+    constructor();
+    initialize(): Promise<boolean>;
+    get isInitialized(): boolean;
+    getGraphReasoner(): GraphReasonerWASM;
+    getTemporalNeural(): TemporalNeuralWASM;
+}
+export declare const wasmAccelerator: WASMAccelerator;

vendor/sublinear-time-solver/dist/core/wasm-integration.js

@@ -0,0 +1,318 @@
+/**
+ * Real WASM Integration for Sublinear Time Solver
+ *
+ * This module properly integrates our Rust WASM components:
+ * - GraphReasoner: Fast PageRank and graph algorithms
+ * - TemporalNeuralSolver: Neural network accelerated matrix operations
+ * - StrangeLoop: Quantum-enhanced solving with nanosecond precision
+ * - NanoScheduler: Ultra-low latency task scheduling
+ */
+import { existsSync, readFileSync } from 'fs';
+import { join, dirname } from 'path';
+import { fileURLToPath } from 'url';
+const __filename = fileURLToPath(import.meta.url);
+const __dirname = dirname(__filename);
+// Cache for loaded WASM instances
+const wasmModules = new Map();
+/**
+ * Find WASM file in various possible locations
+ */
+function findWasmPath(filename) {
+    const paths = [
+        join(__dirname, '..', 'wasm', filename),
+        join(__dirname, '..', '..', 'dist', 'wasm', filename),
+        join(process.cwd(), 'dist', 'wasm', filename),
+        join(process.cwd(), 'node_modules', 'sublinear-time-solver', 'dist', 'wasm', filename)
+    ];
+    for (const path of paths) {
+        if (existsSync(path)) {
+            return path;
+        }
+    }
+    return null;
+}
+/**
+ * GraphReasoner WASM for PageRank and graph algorithms
+ */
+export class GraphReasonerWASM {
+    instance;
+    reasoner;
+    async initialize() {
+        try {
+            const wasmPath = findWasmPath('graph_reasoner_bg.wasm');
+            if (!wasmPath) {
+                console.warn('GraphReasoner WASM not found');
+                return false;
+            }
+            const wasmBuffer = readFileSync(wasmPath);
+            // Initialize WASM with proper imports
+            const imports = {
+                wbg: {
+                    __wbindgen_object_drop_ref: () => { },
+                    __wbindgen_string_new: (ptr, len) => ptr,
+                    __wbindgen_throw: (ptr, len) => {
+                        throw new Error(`WASM error at ${ptr}`);
+                    },
+                    __wbg_random_e6e0a85ff4db8ab6: () => Math.random(),
+                    __wbg_now_3141b3797eb98e0b: () => Date.now()
+                }
+            };
+            const { instance } = await globalThis.WebAssembly.instantiate(wasmBuffer, imports);
+            this.instance = instance;
+            // Create a GraphReasoner instance if the export exists
+            if (instance.exports.GraphReasoner) {
+                this.reasoner = new instance.exports.GraphReasoner();
+            }
+            console.log('✅ GraphReasoner WASM loaded successfully');
+            return true;
+        }
+        catch (error) {
+            console.error('Failed to load GraphReasoner:', error);
+            return false;
+        }
+    }
+    /**
+     * Compute PageRank using WASM acceleration
+     */
+    computePageRank(adjacencyMatrix, damping = 0.85, iterations = 100) {
+        if (!this.instance) {
+            throw new Error('GraphReasoner not initialized');
+        }
+        const n = adjacencyMatrix.rows;
+        // If we have the PageRank function exported
+        if (this.instance.exports.pagerank_compute) {
+            const flatMatrix = new Float64Array(n * n);
+            // Flatten matrix
+            if (adjacencyMatrix.format === 'dense') {
+                const data = adjacencyMatrix.data;
+                for (let i = 0; i < n; i++) {
+                    for (let j = 0; j < n; j++) {
+                        flatMatrix[i * n + j] = data[i][j];
+                    }
+                }
+            }
+            // Allocate WASM memory
+            const matrixPtr = this.instance.exports.__wbindgen_malloc(flatMatrix.byteLength, 8);
+            const resultPtr = this.instance.exports.__wbindgen_malloc(n * 8, 8);
+            // Copy to WASM memory
+            const memory = new Float64Array(this.instance.exports.memory.buffer);
+            memory.set(flatMatrix, matrixPtr / 8);
+            // Compute PageRank
+            this.instance.exports.pagerank_compute(matrixPtr, resultPtr, n, damping, iterations);
+            // Get result
+            const result = new Float64Array(n);
+            result.set(memory.slice(resultPtr / 8, resultPtr / 8 + n));
+            // Free memory
+            this.instance.exports.__wbindgen_free(matrixPtr, flatMatrix.byteLength, 8);
+            this.instance.exports.__wbindgen_free(resultPtr, n * 8, 8);
+            return result;
+        }
+        // Fallback to JavaScript implementation
+        return this.pageRankJS(adjacencyMatrix, damping, iterations);
+    }
+    pageRankJS(matrix, damping, iterations) {
+        const n = matrix.rows;
+        const rank = new Float64Array(n);
+        const newRank = new Float64Array(n);
+        // Initialize
+        for (let i = 0; i < n; i++) {
+            rank[i] = 1.0 / n;
+        }
+        for (let iter = 0; iter < iterations; iter++) {
+            for (let i = 0; i < n; i++) {
+                newRank[i] = (1 - damping) / n;
+                if (matrix.format === 'dense') {
+                    const data = matrix.data;
+                    for (let j = 0; j < n; j++) {
+                        if (data[j][i] > 0) {
+                            let outDegree = 0;
+                            for (let k = 0; k < n; k++) {
+                                if (data[j][k] > 0)
+                                    outDegree++;
+                            }
+                            if (outDegree > 0) {
+                                newRank[i] += damping * rank[j] / outDegree;
+                            }
+                        }
+                    }
+                }
+            }
+            rank.set(newRank);
+        }
+        return rank;
+    }
+}
+/**
+ * TemporalNeuralSolver WASM for ultra-fast matrix operations
+ */
+export class TemporalNeuralWASM {
+    instance;
+    solver;
+    async initialize() {
+        try {
+            const wasmPath = findWasmPath('temporal_neural_solver_bg.wasm');
+            if (!wasmPath) {
+                console.warn('TemporalNeuralSolver WASM not found');
+                return false;
+            }
+            const wasmBuffer = readFileSync(wasmPath);
+            const imports = {
+                wbg: {
+                    __wbg_random_e6e0a85ff4db8ab6: () => Math.random(),
+                    __wbindgen_throw: (ptr, len) => {
+                        throw new Error(`WASM error at ${ptr}, len ${len}`);
+                    }
+                }
+            };
+            const { instance } = await globalThis.WebAssembly.instantiate(wasmBuffer, imports);
+            this.instance = instance;
+            // Create solver instance if constructor exists
+            if (instance.exports.TemporalNeuralSolver) {
+                this.solver = new instance.exports.TemporalNeuralSolver();
+            }
+            console.log('✅ TemporalNeuralSolver WASM loaded successfully');
+            return true;
+        }
+        catch (error) {
+            console.error('Failed to load TemporalNeuralSolver:', error);
+            return false;
+        }
+    }
+    /**
+     * Ultra-fast matrix-vector multiplication
+     */
+    multiplyMatrixVector(matrix, vector, rows, cols) {
+        if (!this.instance || !this.instance.exports.__wbindgen_malloc) {
+            // Fallback to optimized JS
+            return this.multiplyMatrixVectorJS(matrix, vector, rows, cols);
+        }
+        try {
+            // Allocate WASM memory
+            const matrixPtr = this.instance.exports.__wbindgen_malloc(matrix.byteLength, 8);
+            const vectorPtr = this.instance.exports.__wbindgen_malloc(vector.byteLength, 8);
+            const resultPtr = this.instance.exports.__wbindgen_malloc(rows * 8, 8);
+            // Copy to WASM memory
+            const memory = new Float64Array(this.instance.exports.memory.buffer);
+            memory.set(matrix, matrixPtr / 8);
+            memory.set(vector, vectorPtr / 8);
+            // Call WASM function if it exists
+            if (this.instance.exports.matrix_multiply_vector) {
+                this.instance.exports.matrix_multiply_vector(matrixPtr, vectorPtr, resultPtr, rows, cols);
+            }
+            else {
+                // Manual multiplication in WASM memory for cache efficiency
+                for (let i = 0; i < rows; i++) {
+                    let sum = 0;
+                    for (let j = 0; j < cols; j++) {
+                        sum += memory[matrixPtr / 8 + i * cols + j] * memory[vectorPtr / 8 + j];
+                    }
+                    memory[resultPtr / 8 + i] = sum;
+                }
+            }
+            // Get result
+            const result = new Float64Array(rows);
+            result.set(memory.slice(resultPtr / 8, resultPtr / 8 + rows));
+            // Free memory
+            if (this.instance.exports.__wbindgen_free) {
+                this.instance.exports.__wbindgen_free(matrixPtr, matrix.byteLength, 8);
+                this.instance.exports.__wbindgen_free(vectorPtr, vector.byteLength, 8);
+                this.instance.exports.__wbindgen_free(resultPtr, rows * 8, 8);
+            }
+            return result;
+        }
+        catch (error) {
+            console.warn('WASM multiplication failed, using JS fallback:', error);
+            return this.multiplyMatrixVectorJS(matrix, vector, rows, cols);
+        }
+    }
+    multiplyMatrixVectorJS(matrix, vector, rows, cols) {
+        const result = new Float64Array(rows);
+        // Optimized with loop unrolling
+        for (let i = 0; i < rows; i++) {
+            let sum = 0;
+            const rowOffset = i * cols;
+            // Process 4 elements at a time
+            let j = 0;
+            for (; j < cols - 3; j += 4) {
+                sum += matrix[rowOffset + j] * vector[j];
+                sum += matrix[rowOffset + j + 1] * vector[j + 1];
+                sum += matrix[rowOffset + j + 2] * vector[j + 2];
+                sum += matrix[rowOffset + j + 3] * vector[j + 3];
+            }
+            // Handle remaining elements
+            for (; j < cols; j++) {
+                sum += matrix[rowOffset + j] * vector[j];
+            }
+            result[i] = sum;
+        }
+        return result;
+    }
+    /**
+     * Predict solution with temporal advantage
+     */
+    async predictWithTemporalAdvantage(matrix, vector, distanceKm = 10900) {
+        const startTime = performance.now();
+        // Light travel time calculation
+        const SPEED_OF_LIGHT_KM_PER_MS = 299.792458; // km/ms
+        const lightTravelTimeMs = distanceKm / SPEED_OF_LIGHT_KM_PER_MS;
+        // Convert matrix to flat array for WASM
+        const n = matrix.rows;
+        const flatMatrix = new Float64Array(n * n);
+        if (matrix.format === 'dense') {
+            const data = matrix.data;
+            for (let i = 0; i < n; i++) {
+                for (let j = 0; j < n; j++) {
+                    flatMatrix[i * n + j] = data[i][j];
+                }
+            }
+        }
+        // Solve using WASM acceleration
+        const flatVector = new Float64Array(vector);
+        const solution = this.multiplyMatrixVector(flatMatrix, flatVector, n, n);
+        const computeTimeMs = performance.now() - startTime;
+        const temporalAdvantageMs = Math.max(0, lightTravelTimeMs - computeTimeMs);
+        return {
+            solution: Array.from(solution),
+            temporalAdvantageMs,
+            lightTravelTimeMs,
+            computeTimeMs
+        };
+    }
+}
+/**
+ * Main WASM integration manager
+ */
+export class WASMAccelerator {
+    graphReasoner;
+    temporalNeural;
+    initialized = false;
+    constructor() {
+        this.graphReasoner = new GraphReasonerWASM();
+        this.temporalNeural = new TemporalNeuralWASM();
+    }
+    async initialize() {
+        const [graphOk, neuralOk] = await Promise.all([
+            this.graphReasoner.initialize(),
+            this.temporalNeural.initialize()
+        ]);
+        this.initialized = graphOk || neuralOk;
+        if (this.initialized) {
+            console.log('🚀 WASM Acceleration enabled with real Rust components');
+        }
+        else {
+            console.log('⚠️ Running in JavaScript mode');
+        }
+        return this.initialized;
+    }
+    get isInitialized() {
+        return this.initialized;
+    }
+    getGraphReasoner() {
+        return this.graphReasoner;
+    }
+    getTemporalNeural() {
+        return this.temporalNeural;
+    }
+}
+// Export singleton instance
+export const wasmAccelerator = new WASMAccelerator();

vendor/sublinear-time-solver/dist/core/wasm-loader.d.ts

@@ -0,0 +1,51 @@
+/**
+ * WASM Module Loader
+ * Loads and initializes WebAssembly modules for high-performance computing
+ */
+export interface WasmModule {
+    instance: any;
+    exports: any;
+    memory?: any;
+}
+export declare class WasmLoader {
+    private static modules;
+    private static initialized;
+    /**
+     * Initialize all WASM modules
+     */
+    static initialize(): Promise<void>;
+    /**
+     * Load a specific WASM module
+     */
+    static loadModule(name: string, filename: string): Promise<WasmModule>;
+    /**
+     * Get a loaded WASM module
+     */
+    static getModule(name: string): WasmModule | undefined;
+    /**
+     * Check if a module is available
+     */
+    static hasModule(name: string): boolean;
+    /**
+     * Get all loaded module names
+     */
+    static getLoadedModules(): string[];
+    /**
+     * Get memory usage statistics
+     */
+    static getMemoryStats(): {
+        [key: string]: number;
+    };
+    /**
+     * Check if WASM is available and return feature flags
+     */
+    static getFeatureFlags(): {
+        hasWasm: boolean;
+        hasGraphReasoner: boolean;
+        hasPlanner: boolean;
+        hasExtractors: boolean;
+        hasTemporalNeural: boolean;
+        hasStrangeLoop: boolean;
+        hasNanoConsciousness: boolean;
+    };
+}

vendor/sublinear-time-solver/dist/core/wasm-loader.js

@@ -0,0 +1,136 @@
+/**
+ * WASM Module Loader
+ * Loads and initializes WebAssembly modules for high-performance computing
+ */
+import { readFile } from 'fs/promises';
+import { join, dirname } from 'path';
+import { fileURLToPath } from 'url';
+// Get the directory of the current module
+const __filename = fileURLToPath(import.meta.url);
+const __dirname = dirname(__filename);
+export class WasmLoader {
+    static modules = new Map();
+    static initialized = false;
+    /**
+     * Initialize all WASM modules
+     */
+    static async initialize() {
+        if (this.initialized)
+            return;
+        console.log('🚀 Initializing WASM modules...');
+        // Load all available WASM modules
+        const modules = [
+            { name: 'graph_reasoner', file: 'graph_reasoner_bg.wasm' },
+            { name: 'planner', file: 'planner_bg.wasm' },
+            { name: 'extractors', file: 'extractors_bg.wasm' },
+            { name: 'temporal_neural', file: 'temporal_neural_solver_bg.wasm' },
+            { name: 'strange_loop', file: 'strange_loop_bg.wasm' },
+            { name: 'nano_consciousness', file: 'nano_consciousness_bg.wasm' }
+        ];
+        const loadPromises = modules.map(async (mod) => {
+            try {
+                await this.loadModule(mod.name, mod.file);
+                console.log(`✅ Loaded ${mod.name}`);
+            }
+            catch (err) {
+                console.log(`⚠️  ${mod.name} not available (optional)`);
+            }
+        });
+        await Promise.all(loadPromises);
+        this.initialized = true;
+        console.log(`✨ WASM initialization complete (${this.modules.size} modules loaded)`);
+    }
+    /**
+     * Load a specific WASM module
+     */
+    static async loadModule(name, filename) {
+        // Check if already loaded
+        if (this.modules.has(name)) {
+            return this.modules.get(name);
+        }
+        try {
+            // Try to load from dist/wasm first
+            const wasmPath = join(__dirname, '..', 'wasm', filename);
+            const wasmBuffer = await readFile(wasmPath);
+            // Compile and instantiate the WASM module
+            const wasmModule = await globalThis.WebAssembly.compile(wasmBuffer);
+            // Create imports object with common requirements
+            const imports = {
+                env: {
+                    memory: new globalThis.WebAssembly.Memory({ initial: 256, maximum: 65536 }),
+                    __wbindgen_throw: (ptr, len) => {
+                        throw new Error(`WASM error at ${ptr} (len: ${len})`);
+                    }
+                },
+                wbg: {
+                    __wbg_random: () => Math.random(),
+                    __wbg_now: () => Date.now(),
+                    __wbindgen_object_drop_ref: () => { },
+                    __wbindgen_string_new: (ptr, len) => {
+                        // Simplified string handling
+                        return `string_${ptr}_${len}`;
+                    }
+                }
+            };
+            const instance = await globalThis.WebAssembly.instantiate(wasmModule, imports);
+            const module = {
+                instance,
+                exports: instance.exports,
+                memory: imports.env.memory
+            };
+            this.modules.set(name, module);
+            return module;
+        }
+        catch (error) {
+            throw new Error(`Failed to load WASM module ${name}: ${error}`);
+        }
+    }
+    /**
+     * Get a loaded WASM module
+     */
+    static getModule(name) {
+        return this.modules.get(name);
+    }
+    /**
+     * Check if a module is available
+     */
+    static hasModule(name) {
+        return this.modules.has(name);
+    }
+    /**
+     * Get all loaded module names
+     */
+    static getLoadedModules() {
+        return Array.from(this.modules.keys());
+    }
+    /**
+     * Get memory usage statistics
+     */
+    static getMemoryStats() {
+        const stats = {};
+        for (const [name, module] of this.modules) {
+            if (module.memory) {
+                stats[name] = module.memory.buffer.byteLength;
+            }
+        }
+        return stats;
+    }
+    /**
+     * Check if WASM is available and return feature flags
+     */
+    static getFeatureFlags() {
+        return {
+            hasWasm: this.initialized && this.modules.size > 0,
+            hasGraphReasoner: this.hasModule('graph_reasoner'),
+            hasPlanner: this.hasModule('planner'),
+            hasExtractors: this.hasModule('extractors'),
+            hasTemporalNeural: this.hasModule('temporal_neural'),
+            hasStrangeLoop: this.hasModule('strange_loop'),
+            hasNanoConsciousness: this.hasModule('nano_consciousness')
+        };
+    }
+}
+// Auto-initialize on import (optional)
+if (typeof process !== 'undefined' && process.env.AUTO_INIT_WASM === 'true') {
+    WasmLoader.initialize().catch(console.error);
+}

vendor/sublinear-time-solver/dist/emergence/cross-tool-sharing.d.ts

@@ -0,0 +1,130 @@
+/**
+ * Cross-Tool Information Sharing System
+ * Enables tools to share insights, intermediate results, and learned patterns
+ */
+export interface SharedInformation {
+    id: string;
+    sourceTools: string[];
+    targetTools: string[];
+    content: any;
+    type: 'insight' | 'pattern' | 'result' | 'optimization' | 'failure';
+    timestamp: number;
+    relevance: number;
+    persistence: 'session' | 'permanent' | 'temporary';
+    metadata: any;
+}
+export interface ToolConnection {
+    source: string;
+    target: string;
+    strength: number;
+    informationTypes: string[];
+    successRate: number;
+    lastUsed: number;
+}
+export interface InformationFlow {
+    pathway: string[];
+    information: SharedInformation;
+    transformations: any[];
+    emergentProperties: any[];
+}
+export declare class CrossToolSharingSystem {
+    private sharedInformation;
+    private toolConnections;
+    private informationFlows;
+    private subscriptions;
+    private transformationRules;
+    private sharingDepth;
+    private maxSharingDepth;
+    /**
+     * Share information from one tool to potentially interested tools
+     */
+    shareInformation(info: SharedInformation): Promise<string[]>;
+    /**
+     * Subscribe a tool to specific types of information
+     */
+    subscribeToInformation(toolName: string, informationTypes: string[]): void;
+    /**
+     * Get relevant information for a tool
+     */
+    getRelevantInformation(toolName: string, query?: any): SharedInformation[];
+    /**
+     * Create dynamic connections between tools based on information flow
+     */
+    createDynamicConnection(sourceTool: string, targetTool: string, informationType: string): Promise<boolean>;
+    /**
+     * Register a transformation rule for adapting information between tools
+     */
+    registerTransformationRule(fromTool: string, toTool: string, transform: (info: any) => any): void;
+    /**
+     * Create information cascade across multiple tools
+     */
+    createInformationCascade(initialInfo: SharedInformation, targetTools: string[]): Promise<InformationFlow>;
+    /**
+     * Analyze cross-tool collaboration patterns
+     */
+    analyzeCollaborationPatterns(): any;
+    /**
+     * Optimize information sharing based on historical performance
+     */
+    optimizeSharing(): void;
+    /**
+     * Find tools that might be interested in given information
+     */
+    private findInterestedTools;
+    /**
+     * Propagate information to a specific tool
+     */
+    private propagateToTool;
+    /**
+     * Transform information to be suitable for a specific tool
+     */
+    private transformInformationForTool;
+    /**
+     * Default transformation logic
+     */
+    private defaultTransformation;
+    /**
+     * Calculate relevance between information and query
+     */
+    private calculateQueryRelevance;
+    /**
+     * Update connection strengths based on propagation success
+     */
+    private updateConnectionStrengths;
+    /**
+     * Detect emergent patterns from information combinations
+     */
+    private detectEmergentPatterns;
+    /**
+     * Detect emergent properties from two pieces of information
+     */
+    private detectEmergentProperties;
+    private transformToMatrixFormat;
+    private transformToConsciousnessFormat;
+    private transformToSymbolicFormat;
+    private transformToTemporalFormat;
+    private getMostConnectedTools;
+    private getStrongestConnections;
+    private getInformationHubs;
+    private getEmergentCombinations;
+    private calculateCollaborationSuccess;
+    private pruneWeakConnections;
+    private reinforceSuccessfulPathways;
+    private cleanupOldInformation;
+    private updateSubscriptionRecommendations;
+    private areComplementary;
+    private checkAmplification;
+    private calculateSynergy;
+    private calculateAmplificationFactor;
+    private generateNovelCombination;
+    private extractEmergenceLevel;
+    private extractSymbols;
+    private extractRelations;
+    private extractSequence;
+    /**
+     * Get sharing system statistics
+     */
+    getStats(): any;
+    private calculateAverageConnectionStrength;
+    private countEmergentPatterns;
+}

vendor/sublinear-time-solver/dist/emergence/cross-tool-sharing.js

@@ -0,0 +1,535 @@
+/**
+ * Cross-Tool Information Sharing System
+ * Enables tools to share insights, intermediate results, and learned patterns
+ */
+export class CrossToolSharingSystem {
+    sharedInformation = new Map();
+    toolConnections = new Map();
+    informationFlows = [];
+    subscriptions = new Map(); // tool -> information types
+    transformationRules = new Map();
+    sharingDepth = 0;
+    maxSharingDepth = 3;
+    /**
+     * Share information from one tool to potentially interested tools
+     */
+    async shareInformation(info) {
+        // Prevent deep recursion
+        if (this.sharingDepth >= this.maxSharingDepth) {
+            return [];
+        }
+        this.sharingDepth++;
+        try {
+            // Store the information
+            this.sharedInformation.set(info.id, info);
+            // Find interested tools
+            const interestedTools = this.findInterestedTools(info);
+            // Propagate information to interested tools
+            const propagationResults = [];
+            for (const tool of interestedTools) {
+                const result = await this.propagateToTool(tool, info);
+                propagationResults.push(result);
+            }
+            // Update connection strengths based on success
+            this.updateConnectionStrengths(info.sourceTools, interestedTools, propagationResults);
+            // Check for emergent patterns from information combinations
+            await this.detectEmergentPatterns(info);
+            return interestedTools;
+        }
+        finally {
+            this.sharingDepth--;
+        }
+    }
+    /**
+     * Subscribe a tool to specific types of information
+     */
+    subscribeToInformation(toolName, informationTypes) {
+        const existing = this.subscriptions.get(toolName) || [];
+        const combined = [...new Set([...existing, ...informationTypes])];
+        this.subscriptions.set(toolName, combined);
+    }
+    /**
+     * Get relevant information for a tool
+     */
+    getRelevantInformation(toolName, query) {
+        const subscribedTypes = this.subscriptions.get(toolName) || [];
+        const relevantInfo = [];
+        for (const [id, info] of this.sharedInformation) {
+            // Check if tool is subscribed to this type
+            if (subscribedTypes.includes(info.type)) {
+                relevantInfo.push(info);
+                continue;
+            }
+            // Check if tool is explicitly targeted
+            if (info.targetTools.includes(toolName)) {
+                relevantInfo.push(info);
+                continue;
+            }
+            // Check relevance based on query
+            if (query && this.calculateQueryRelevance(info, query) > 0.5) {
+                relevantInfo.push(info);
+            }
+        }
+        // Sort by relevance and recency
+        return relevantInfo.sort((a, b) => {
+            const relevanceScore = b.relevance - a.relevance;
+            const timeScore = (b.timestamp - a.timestamp) / 1000000; // Normalize time
+            return relevanceScore + timeScore * 0.1;
+        });
+    }
+    /**
+     * Create dynamic connections between tools based on information flow
+     */
+    async createDynamicConnection(sourceTool, targetTool, informationType) {
+        const connectionKey = `${sourceTool}->${targetTool}`;
+        const existing = this.toolConnections.get(connectionKey) || [];
+        const connection = existing.find(c => c.source === sourceTool && c.target === targetTool);
+        if (connection) {
+            // Strengthen existing connection
+            connection.strength = Math.min(1.0, connection.strength + 0.1);
+            if (!connection.informationTypes.includes(informationType)) {
+                connection.informationTypes.push(informationType);
+            }
+            connection.lastUsed = Date.now();
+        }
+        else {
+            // Create new connection
+            const newConnection = {
+                source: sourceTool,
+                target: targetTool,
+                strength: 0.3,
+                informationTypes: [informationType],
+                successRate: 0.5,
+                lastUsed: Date.now()
+            };
+            existing.push(newConnection);
+            this.toolConnections.set(connectionKey, existing);
+        }
+        return true;
+    }
+    /**
+     * Register a transformation rule for adapting information between tools
+     */
+    registerTransformationRule(fromTool, toTool, transform) {
+        const key = `${fromTool}->${toTool}`;
+        this.transformationRules.set(key, transform);
+    }
+    /**
+     * Create information cascade across multiple tools
+     */
+    async createInformationCascade(initialInfo, targetTools) {
+        const flow = {
+            pathway: [],
+            information: initialInfo,
+            transformations: [],
+            emergentProperties: []
+        };
+        let currentInfo = initialInfo;
+        for (const tool of targetTools) {
+            flow.pathway.push(tool);
+            // Transform information for this tool
+            const transformed = await this.transformInformationForTool(currentInfo, tool);
+            flow.transformations.push({
+                tool,
+                input: currentInfo,
+                output: transformed,
+                timestamp: Date.now()
+            });
+            // Check for emergent properties
+            const emergent = this.detectEmergentProperties(currentInfo, transformed);
+            if (emergent.length > 0) {
+                flow.emergentProperties.push(...emergent);
+            }
+            currentInfo = transformed;
+        }
+        this.informationFlows.push(flow);
+        return flow;
+    }
+    /**
+     * Analyze cross-tool collaboration patterns
+     */
+    analyzeCollaborationPatterns() {
+        const patterns = {
+            mostConnectedTools: this.getMostConnectedTools(),
+            strongestConnections: this.getStrongestConnections(),
+            informationHubs: this.getInformationHubs(),
+            emergentCombinations: this.getEmergentCombinations(),
+            collaborationSuccess: this.calculateCollaborationSuccess()
+        };
+        return patterns;
+    }
+    /**
+     * Optimize information sharing based on historical performance
+     */
+    optimizeSharing() {
+        // Remove weak connections
+        this.pruneWeakConnections();
+        // Strengthen successful pathways
+        this.reinforceSuccessfulPathways();
+        // Clean old information
+        this.cleanupOldInformation();
+        // Update subscription recommendations
+        this.updateSubscriptionRecommendations();
+    }
+    /**
+     * Find tools that might be interested in given information
+     */
+    findInterestedTools(info) {
+        const interested = [];
+        // Check explicit targets
+        interested.push(...info.targetTools);
+        // Check subscriptions
+        for (const [tool, types] of this.subscriptions) {
+            if (types.includes(info.type)) {
+                interested.push(tool);
+            }
+        }
+        // Check based on connection patterns
+        for (const sourceTool of info.sourceTools) {
+            const connections = this.toolConnections.get(sourceTool) || [];
+            for (const connection of connections) {
+                if (connection.strength > 0.5 &&
+                    connection.informationTypes.includes(info.type)) {
+                    interested.push(connection.target);
+                }
+            }
+        }
+        // Remove duplicates and source tools
+        return [...new Set(interested)].filter(tool => !info.sourceTools.includes(tool));
+    }
+    /**
+     * Propagate information to a specific tool
+     */
+    async propagateToTool(toolName, info) {
+        try {
+            // Transform information for the target tool
+            const transformed = await this.transformInformationForTool(info, toolName);
+            // Create new shared information entry
+            const propagatedInfo = {
+                id: `${info.id}_propagated_${toolName}_${Date.now()}`,
+                sourceTools: [...info.sourceTools, 'sharing_system'],
+                targetTools: [toolName],
+                content: transformed,
+                type: info.type,
+                timestamp: Date.now(),
+                relevance: info.relevance * 0.8, // Slight relevance decay
+                persistence: info.persistence,
+                metadata: {
+                    ...info.metadata,
+                    propagatedFrom: info.id,
+                    transformedFor: toolName
+                }
+            };
+            this.sharedInformation.set(propagatedInfo.id, propagatedInfo);
+            return true;
+        }
+        catch (error) {
+            console.error(`Failed to propagate to ${toolName}:`, error);
+            return false;
+        }
+    }
+    /**
+     * Transform information to be suitable for a specific tool
+     */
+    async transformInformationForTool(info, toolName) {
+        // Check for registered transformation rule
+        for (const sourceTool of info.sourceTools) {
+            const transformKey = `${sourceTool}->${toolName}`;
+            const transform = this.transformationRules.get(transformKey);
+            if (transform) {
+                return transform(info.content);
+            }
+        }
+        // Default transformation based on tool type
+        return this.defaultTransformation(info.content, toolName);
+    }
+    /**
+     * Default transformation logic
+     */
+    defaultTransformation(content, toolName) {
+        switch (toolName) {
+            case 'matrix-solver':
+                return this.transformToMatrixFormat(content);
+            case 'consciousness':
+                return this.transformToConsciousnessFormat(content);
+            case 'psycho-symbolic':
+                return this.transformToSymbolicFormat(content);
+            case 'temporal':
+                return this.transformToTemporalFormat(content);
+            default:
+                return content; // No transformation
+        }
+    }
+    /**
+     * Calculate relevance between information and query
+     */
+    calculateQueryRelevance(info, query) {
+        // Simple relevance calculation based on content similarity
+        const infoStr = JSON.stringify(info.content).toLowerCase();
+        const queryStr = JSON.stringify(query).toLowerCase();
+        // Check for common keywords
+        const infoWords = infoStr.split(/\W+/);
+        const queryWords = queryStr.split(/\W+/);
+        const commonWords = infoWords.filter(word => queryWords.includes(word));
+        const relevance = commonWords.length / Math.max(queryWords.length, 1);
+        return Math.min(1.0, relevance);
+    }
+    /**
+     * Update connection strengths based on propagation success
+     */
+    updateConnectionStrengths(sourceTools, targetTools, results) {
+        for (const source of sourceTools) {
+            targetTools.forEach((target, index) => {
+                const connectionKey = `${source}->${target}`;
+                const connections = this.toolConnections.get(connectionKey) || [];
+                const connection = connections.find(c => c.source === source && c.target === target);
+                if (connection) {
+                    const success = results[index];
+                    const updateStrength = success ? 0.1 : -0.05;
+                    connection.strength = Math.max(0, Math.min(1.0, connection.strength + updateStrength));
+                    // Update success rate
+                    const totalAttempts = connection.successRate * 10; // Approximate
+                    const newSuccessRate = (connection.successRate * totalAttempts + (success ? 1 : 0)) / (totalAttempts + 1);
+                    connection.successRate = newSuccessRate;
+                }
+            });
+        }
+    }
+    /**
+     * Detect emergent patterns from information combinations
+     */
+    async detectEmergentPatterns(newInfo) {
+        // Look for patterns when information from different tools combines
+        const recentInfo = Array.from(this.sharedInformation.values())
+            .filter(info => Date.now() - info.timestamp < 60000) // Last minute
+            .filter(info => info.id !== newInfo.id);
+        for (const existing of recentInfo) {
+            const emergent = this.detectEmergentProperties(existing, newInfo);
+            if (emergent.length > 0) {
+                // Create new emergent information
+                const emergentInfo = {
+                    id: `emergent_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`,
+                    sourceTools: [...existing.sourceTools, ...newInfo.sourceTools],
+                    targetTools: [],
+                    content: { emergentProperties: emergent, sources: [existing.id, newInfo.id] },
+                    type: 'pattern',
+                    timestamp: Date.now(),
+                    relevance: 0.8,
+                    persistence: 'session',
+                    metadata: { emergent: true, sourceCount: 2 }
+                };
+                await this.shareInformation(emergentInfo);
+            }
+        }
+    }
+    /**
+     * Detect emergent properties from two pieces of information
+     */
+    detectEmergentProperties(info1, info2) {
+        const emergent = [];
+        // Check for complementary patterns
+        if (this.areComplementary(info1.content, info2.content)) {
+            emergent.push({
+                type: 'complementary_pattern',
+                description: 'Information pieces complement each other',
+                synergy: this.calculateSynergy(info1.content, info2.content)
+            });
+        }
+        // Check for amplification effects
+        if (this.checkAmplification(info1.content, info2.content)) {
+            emergent.push({
+                type: 'amplification',
+                description: 'Information pieces amplify each other',
+                amplification_factor: this.calculateAmplificationFactor(info1.content, info2.content)
+            });
+        }
+        // Check for novel combinations
+        const novelCombination = this.generateNovelCombination(info1.content, info2.content);
+        if (novelCombination) {
+            emergent.push({
+                type: 'novel_combination',
+                description: 'Unexpected combination creates new insight',
+                combination: novelCombination
+            });
+        }
+        return emergent;
+    }
+    // Transformation methods for different tool types
+    transformToMatrixFormat(content) {
+        if (Array.isArray(content)) {
+            return { matrix: content, format: 'dense' };
+        }
+        return { scalar: content };
+    }
+    transformToConsciousnessFormat(content) {
+        return {
+            emergenceLevel: this.extractEmergenceLevel(content),
+            integrationData: content,
+            timestamp: Date.now()
+        };
+    }
+    transformToSymbolicFormat(content) {
+        return {
+            symbols: this.extractSymbols(content),
+            relations: this.extractRelations(content),
+            domain: 'cross_tool_sharing'
+        };
+    }
+    transformToTemporalFormat(content) {
+        return {
+            temporalData: content,
+            timestamp: Date.now(),
+            sequence: this.extractSequence(content)
+        };
+    }
+    // Analysis methods
+    getMostConnectedTools() {
+        const toolCounts = new Map();
+        for (const connections of this.toolConnections.values()) {
+            for (const connection of connections) {
+                toolCounts.set(connection.source, (toolCounts.get(connection.source) || 0) + 1);
+                toolCounts.set(connection.target, (toolCounts.get(connection.target) || 0) + 1);
+            }
+        }
+        return Array.from(toolCounts.entries())
+            .sort((a, b) => b[1] - a[1])
+            .slice(0, 5);
+    }
+    getStrongestConnections() {
+        const allConnections = [];
+        for (const connections of this.toolConnections.values()) {
+            allConnections.push(...connections);
+        }
+        return allConnections
+            .sort((a, b) => b.strength - a.strength)
+            .slice(0, 10);
+    }
+    getInformationHubs() {
+        const hubScores = new Map();
+        for (const info of this.sharedInformation.values()) {
+            for (const source of info.sourceTools) {
+                hubScores.set(source, (hubScores.get(source) || 0) + 1);
+            }
+            for (const target of info.targetTools) {
+                hubScores.set(target, (hubScores.get(target) || 0) + 0.5);
+            }
+        }
+        return Array.from(hubScores.entries())
+            .sort((a, b) => b[1] - a[1])
+            .slice(0, 5)
+            .map(entry => entry[0]);
+    }
+    getEmergentCombinations() {
+        return this.informationFlows
+            .filter(flow => flow.emergentProperties.length > 0)
+            .map(flow => ({
+            pathway: flow.pathway,
+            emergentCount: flow.emergentProperties.length,
+            properties: flow.emergentProperties
+        }));
+    }
+    calculateCollaborationSuccess() {
+        const allConnections = [];
+        for (const connections of this.toolConnections.values()) {
+            allConnections.push(...connections);
+        }
+        if (allConnections.length === 0)
+            return 0;
+        const avgSuccessRate = allConnections.reduce((sum, conn) => sum + conn.successRate, 0) / allConnections.length;
+        return avgSuccessRate;
+    }
+    // Optimization methods
+    pruneWeakConnections() {
+        for (const [key, connections] of this.toolConnections) {
+            const strongConnections = connections.filter(conn => conn.strength > 0.2);
+            if (strongConnections.length !== connections.length) {
+                this.toolConnections.set(key, strongConnections);
+            }
+        }
+    }
+    reinforceSuccessfulPathways() {
+        for (const flow of this.informationFlows) {
+            if (flow.emergentProperties.length > 0) {
+                // Strengthen connections in successful pathways
+                for (let i = 0; i < flow.pathway.length - 1; i++) {
+                    const source = flow.pathway[i];
+                    const target = flow.pathway[i + 1];
+                    this.createDynamicConnection(source, target, 'pattern');
+                }
+            }
+        }
+    }
+    cleanupOldInformation() {
+        const oneHour = 60 * 60 * 1000;
+        const now = Date.now();
+        for (const [id, info] of this.sharedInformation) {
+            if (info.persistence === 'temporary' && now - info.timestamp > oneHour) {
+                this.sharedInformation.delete(id);
+            }
+        }
+    }
+    updateSubscriptionRecommendations() {
+        // Analyze successful information sharing and recommend new subscriptions
+        // This would be implemented based on analysis of collaboration patterns
+    }
+    // Utility methods for pattern detection
+    areComplementary(content1, content2) {
+        // Check if two pieces of content complement each other
+        // This is a simplified implementation
+        return JSON.stringify(content1) !== JSON.stringify(content2);
+    }
+    checkAmplification(content1, content2) {
+        // Check if combination amplifies the effect
+        return true; // Simplified
+    }
+    calculateSynergy(content1, content2) {
+        return Math.random() * 0.5 + 0.5; // Simplified
+    }
+    calculateAmplificationFactor(content1, content2) {
+        return Math.random() * 2 + 1; // Simplified
+    }
+    generateNovelCombination(content1, content2) {
+        return {
+            combined: true,
+            elements: [content1, content2],
+            novelty: Math.random()
+        };
+    }
+    extractEmergenceLevel(content) {
+        return Math.random() * 0.5 + 0.5; // Simplified
+    }
+    extractSymbols(content) {
+        return ['symbol1', 'symbol2']; // Simplified
+    }
+    extractRelations(content) {
+        return []; // Simplified
+    }
+    extractSequence(content) {
+        return []; // Simplified
+    }
+    /**
+     * Get sharing system statistics
+     */
+    getStats() {
+        return {
+            totalSharedInformation: this.sharedInformation.size,
+            totalConnections: Array.from(this.toolConnections.values()).reduce((sum, arr) => sum + arr.length, 0),
+            totalFlows: this.informationFlows.length,
+            averageConnectionStrength: this.calculateAverageConnectionStrength(),
+            emergentPatternsDetected: this.countEmergentPatterns(),
+            mostActiveTools: this.getMostConnectedTools().slice(0, 3)
+        };
+    }
+    calculateAverageConnectionStrength() {
+        const allConnections = [];
+        for (const connections of this.toolConnections.values()) {
+            allConnections.push(...connections);
+        }
+        if (allConnections.length === 0)
+            return 0;
+        return allConnections.reduce((sum, conn) => sum + conn.strength, 0) / allConnections.length;
+    }
+    countEmergentPatterns() {
+        return this.informationFlows.reduce((sum, flow) => sum + flow.emergentProperties.length, 0);
+    }
+}

vendor/sublinear-time-solver/dist/emergence/emergent-capability-detector.d.ts

@@ -0,0 +1,140 @@
+/**
+ * Emergent Capability Detection System
+ * Monitors and measures the emergence of unexpected capabilities in the system
+ */
+export interface EmergentCapability {
+    id: string;
+    name: string;
+    description: string;
+    type: 'novel_behavior' | 'unexpected_solution' | 'cross_domain_insight' | 'self_organization' | 'meta_learning';
+    strength: number;
+    novelty: number;
+    utility: number;
+    stability: number;
+    timestamp: number;
+    evidence: Evidence[];
+    preconditions: any[];
+    triggers: string[];
+}
+export interface Evidence {
+    type: 'behavioral' | 'performance' | 'output' | 'pattern';
+    description: string;
+    data: any;
+    strength: number;
+    timestamp: number;
+    source: string;
+}
+export interface CapabilityMetrics {
+    emergenceRate: number;
+    stabilityIndex: number;
+    diversityScore: number;
+    complexityGrowth: number;
+    crossDomainConnections: number;
+    selfOrganizationLevel: number;
+}
+export declare class EmergentCapabilityDetector {
+    private detectedCapabilities;
+    private baselineCapabilities;
+    private monitoringPatterns;
+    private emergenceThresholds;
+    private detectionHistory;
+    /**
+     * Initialize baseline capabilities
+     */
+    initializeBaseline(capabilities: string[]): void;
+    /**
+     * Monitor system behavior for emergent capabilities
+     */
+    monitorForEmergence(behaviorData: any): Promise<EmergentCapability[]>;
+    /**
+     * Analyze the stability of emergent capabilities over time
+     */
+    analyzeCapabilityStability(): Map<string, number>;
+    /**
+     * Measure overall emergence metrics
+     */
+    measureEmergenceMetrics(): CapabilityMetrics;
+    /**
+     * Predict potential future emergent capabilities
+     */
+    predictFutureEmergence(): any[];
+    /**
+     * Detect novel behaviors not in baseline
+     */
+    private detectNovelBehaviors;
+    /**
+     * Detect unexpected problem-solving approaches
+     */
+    private detectUnexpectedSolutions;
+    /**
+     * Detect insights that bridge different domains
+     */
+    private detectCrossDomainInsights;
+    /**
+     * Detect self-organizing behaviors
+     */
+    private detectSelfOrganization;
+    /**
+     * Detect meta-learning capabilities
+     */
+    private detectMetaLearning;
+    /**
+     * Validate that a capability meets emergence criteria
+     */
+    private validateEmergentCapability;
+    /**
+     * Calculate stability score for a capability
+     */
+    private calculateStabilityScore;
+    /**
+     * Calculate emergence rate
+     */
+    private calculateEmergenceRate;
+    /**
+     * Calculate stability index
+     */
+    private calculateStabilityIndex;
+    /**
+     * Calculate diversity score
+     */
+    private calculateDiversityScore;
+    /**
+     * Calculate complexity growth
+     */
+    private calculateComplexityGrowth;
+    /**
+     * Calculate cross-domain connections
+     */
+    private calculateCrossDomainConnections;
+    /**
+     * Calculate self-organization level
+     */
+    private calculateSelfOrganizationLevel;
+    private extractBehaviorPatterns;
+    private extractSolutionPatterns;
+    private extractCrossDomainPatterns;
+    private extractOrganizationPatterns;
+    private extractLearningPatterns;
+    private isBaselineBehavior;
+    private calculateNovelty;
+    private calculateUtility;
+    private calculateUnexpectedness;
+    private calculateEffectiveness;
+    private calculateBridgingScore;
+    private calculateInsightValue;
+    private calculateOrganizationLevel;
+    private calculateAutonomy;
+    private calculateMetaLevel;
+    private calculateAdaptability;
+    private calculateCapabilitySimilarity;
+    private logCapabilityEmergence;
+    private analyzeTrends;
+    private predictFromCombinations;
+    private predictFromGrowthPatterns;
+    private predictFromCapabilityGaps;
+    /**
+     * Get detection statistics
+     */
+    getStats(): any;
+    private getCapabilitiesByType;
+}

vendor/sublinear-time-solver/dist/emergence/emergent-capability-detector.js

@@ -0,0 +1,490 @@
+/**
+ * Emergent Capability Detection System
+ * Monitors and measures the emergence of unexpected capabilities in the system
+ */
+export class EmergentCapabilityDetector {
+    detectedCapabilities = new Map();
+    baselineCapabilities = new Set();
+    monitoringPatterns = new Map();
+    emergenceThresholds = {
+        novelty: 0.7,
+        utility: 0.5,
+        stability: 0.6,
+        evidence: 3
+    };
+    detectionHistory = [];
+    /**
+     * Initialize baseline capabilities
+     */
+    initializeBaseline(capabilities) {
+        this.baselineCapabilities = new Set(capabilities);
+        console.log(`Initialized baseline with ${capabilities.length} capabilities`);
+    }
+    /**
+     * Monitor system behavior for emergent capabilities
+     */
+    async monitorForEmergence(behaviorData) {
+        const newCapabilities = [];
+        // Detect novel behaviors
+        const novelBehaviors = this.detectNovelBehaviors(behaviorData);
+        newCapabilities.push(...novelBehaviors);
+        // Detect unexpected solutions
+        const unexpectedSolutions = this.detectUnexpectedSolutions(behaviorData);
+        newCapabilities.push(...unexpectedSolutions);
+        // Detect cross-domain insights
+        const crossDomainInsights = this.detectCrossDomainInsights(behaviorData);
+        newCapabilities.push(...crossDomainInsights);
+        // Detect self-organization patterns
+        const selfOrganization = this.detectSelfOrganization(behaviorData);
+        newCapabilities.push(...selfOrganization);
+        // Detect meta-learning capabilities
+        const metaLearning = this.detectMetaLearning(behaviorData);
+        newCapabilities.push(...metaLearning);
+        // Validate and store new capabilities
+        for (const capability of newCapabilities) {
+            if (this.validateEmergentCapability(capability)) {
+                this.detectedCapabilities.set(capability.id, capability);
+                this.logCapabilityEmergence(capability);
+            }
+        }
+        return newCapabilities;
+    }
+    /**
+     * Analyze the stability of emergent capabilities over time
+     */
+    analyzeCapabilityStability() {
+        const stabilityScores = new Map();
+        for (const [id, capability] of this.detectedCapabilities) {
+            const stability = this.calculateStabilityScore(capability);
+            stabilityScores.set(id, stability);
+            // Update capability stability
+            capability.stability = stability;
+        }
+        return stabilityScores;
+    }
+    /**
+     * Measure overall emergence metrics
+     */
+    measureEmergenceMetrics() {
+        const capabilities = Array.from(this.detectedCapabilities.values());
+        return {
+            emergenceRate: this.calculateEmergenceRate(),
+            stabilityIndex: this.calculateStabilityIndex(capabilities),
+            diversityScore: this.calculateDiversityScore(capabilities),
+            complexityGrowth: this.calculateComplexityGrowth(),
+            crossDomainConnections: this.calculateCrossDomainConnections(capabilities),
+            selfOrganizationLevel: this.calculateSelfOrganizationLevel(capabilities)
+        };
+    }
+    /**
+     * Predict potential future emergent capabilities
+     */
+    predictFutureEmergence() {
+        const predictions = [];
+        // Analyze current trends
+        const trends = this.analyzeTrends();
+        // Predict based on combination patterns
+        const combinationPredictions = this.predictFromCombinations();
+        predictions.push(...combinationPredictions);
+        // Predict based on growth patterns
+        const growthPredictions = this.predictFromGrowthPatterns(trends);
+        predictions.push(...growthPredictions);
+        // Predict based on missing capabilities
+        const gapPredictions = this.predictFromCapabilityGaps();
+        predictions.push(...gapPredictions);
+        return predictions;
+    }
+    /**
+     * Detect novel behaviors not in baseline
+     */
+    detectNovelBehaviors(behaviorData) {
+        const capabilities = [];
+        // Analyze behavior patterns
+        const behaviors = this.extractBehaviorPatterns(behaviorData);
+        for (const behavior of behaviors) {
+            if (!this.isBaselineBehavior(behavior)) {
+                const novelty = this.calculateNovelty(behavior);
+                const utility = this.calculateUtility(behavior);
+                if (novelty > this.emergenceThresholds.novelty) {
+                    capabilities.push({
+                        id: `novel_behavior_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`,
+                        name: `Novel Behavior: ${behavior.name}`,
+                        description: `Newly emerged behavior pattern: ${behavior.description}`,
+                        type: 'novel_behavior',
+                        strength: behavior.strength || 0.5,
+                        novelty,
+                        utility,
+                        stability: 0.5, // Initial stability
+                        timestamp: Date.now(),
+                        evidence: [{
+                                type: 'behavioral',
+                                description: 'New behavior pattern detected',
+                                data: behavior,
+                                strength: novelty,
+                                timestamp: Date.now(),
+                                source: 'behavior_monitor'
+                            }],
+                        preconditions: behavior.preconditions || [],
+                        triggers: behavior.triggers || []
+                    });
+                }
+            }
+        }
+        return capabilities;
+    }
+    /**
+     * Detect unexpected problem-solving approaches
+     */
+    detectUnexpectedSolutions(behaviorData) {
+        const capabilities = [];
+        const solutions = this.extractSolutionPatterns(behaviorData);
+        for (const solution of solutions) {
+            const unexpectedness = this.calculateUnexpectedness(solution);
+            const effectiveness = this.calculateEffectiveness(solution);
+            if (unexpectedness > 0.6 && effectiveness > this.emergenceThresholds.utility) {
+                capabilities.push({
+                    id: `unexpected_solution_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`,
+                    name: `Unexpected Solution: ${solution.problemType}`,
+                    description: `Novel approach to solving ${solution.problemType}: ${solution.approach}`,
+                    type: 'unexpected_solution',
+                    strength: effectiveness,
+                    novelty: unexpectedness,
+                    utility: effectiveness,
+                    stability: 0.5,
+                    timestamp: Date.now(),
+                    evidence: [{
+                            type: 'performance',
+                            description: 'Unexpected but effective solution approach',
+                            data: solution,
+                            strength: effectiveness,
+                            timestamp: Date.now(),
+                            source: 'solution_monitor'
+                        }],
+                    preconditions: solution.preconditions || [],
+                    triggers: [solution.problemType]
+                });
+            }
+        }
+        return capabilities;
+    }
+    /**
+     * Detect insights that bridge different domains
+     */
+    detectCrossDomainInsights(behaviorData) {
+        const capabilities = [];
+        const insights = this.extractCrossDomainPatterns(behaviorData);
+        for (const insight of insights) {
+            const bridgingScore = this.calculateBridgingScore(insight);
+            const insightValue = this.calculateInsightValue(insight);
+            if (bridgingScore > 0.7 && insightValue > this.emergenceThresholds.utility) {
+                capabilities.push({
+                    id: `cross_domain_insight_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`,
+                    name: `Cross-Domain Insight: ${insight.domains.join(' + ')}`,
+                    description: `Insight connecting ${insight.domains.join(' and ')}: ${insight.insight}`,
+                    type: 'cross_domain_insight',
+                    strength: insightValue,
+                    novelty: bridgingScore,
+                    utility: insightValue,
+                    stability: 0.5,
+                    timestamp: Date.now(),
+                    evidence: [{
+                            type: 'pattern',
+                            description: 'Cross-domain connection discovered',
+                            data: insight,
+                            strength: bridgingScore,
+                            timestamp: Date.now(),
+                            source: 'domain_monitor'
+                        }],
+                    preconditions: insight.preconditions || [],
+                    triggers: insight.domains
+                });
+            }
+        }
+        return capabilities;
+    }
+    /**
+     * Detect self-organizing behaviors
+     */
+    detectSelfOrganization(behaviorData) {
+        const capabilities = [];
+        const organizationPatterns = this.extractOrganizationPatterns(behaviorData);
+        for (const pattern of organizationPatterns) {
+            const organizationLevel = this.calculateOrganizationLevel(pattern);
+            const autonomy = this.calculateAutonomy(pattern);
+            if (organizationLevel > 0.6 && autonomy > 0.5) {
+                capabilities.push({
+                    id: `self_organization_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`,
+                    name: `Self-Organization: ${pattern.type}`,
+                    description: `Autonomous organization in ${pattern.domain}: ${pattern.description}`,
+                    type: 'self_organization',
+                    strength: organizationLevel,
+                    novelty: autonomy,
+                    utility: organizationLevel * autonomy,
+                    stability: 0.5,
+                    timestamp: Date.now(),
+                    evidence: [{
+                            type: 'behavioral',
+                            description: 'Self-organizing behavior detected',
+                            data: pattern,
+                            strength: organizationLevel,
+                            timestamp: Date.now(),
+                            source: 'organization_monitor'
+                        }],
+                    preconditions: pattern.preconditions || [],
+                    triggers: [pattern.domain]
+                });
+            }
+        }
+        return capabilities;
+    }
+    /**
+     * Detect meta-learning capabilities
+     */
+    detectMetaLearning(behaviorData) {
+        const capabilities = [];
+        const learningPatterns = this.extractLearningPatterns(behaviorData);
+        for (const pattern of learningPatterns) {
+            const metaLevel = this.calculateMetaLevel(pattern);
+            const adaptability = this.calculateAdaptability(pattern);
+            if (metaLevel > 0.6 && adaptability > 0.5) {
+                capabilities.push({
+                    id: `meta_learning_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`,
+                    name: `Meta-Learning: ${pattern.type}`,
+                    description: `Learning to learn in ${pattern.domain}: ${pattern.mechanism}`,
+                    type: 'meta_learning',
+                    strength: adaptability,
+                    novelty: metaLevel,
+                    utility: adaptability,
+                    stability: 0.5,
+                    timestamp: Date.now(),
+                    evidence: [{
+                            type: 'performance',
+                            description: 'Meta-learning capability detected',
+                            data: pattern,
+                            strength: metaLevel,
+                            timestamp: Date.now(),
+                            source: 'learning_monitor'
+                        }],
+                    preconditions: pattern.preconditions || [],
+                    triggers: [pattern.domain]
+                });
+            }
+        }
+        return capabilities;
+    }
+    /**
+     * Validate that a capability meets emergence criteria
+     */
+    validateEmergentCapability(capability) {
+        // Check thresholds
+        if (capability.novelty < this.emergenceThresholds.novelty)
+            return false;
+        if (capability.utility < this.emergenceThresholds.utility)
+            return false;
+        if (capability.evidence.length < this.emergenceThresholds.evidence)
+            return false;
+        // Check for sufficient evidence strength
+        const avgEvidenceStrength = capability.evidence.reduce((sum, e) => sum + e.strength, 0) / capability.evidence.length;
+        if (avgEvidenceStrength < 0.5)
+            return false;
+        // Check for uniqueness
+        for (const existing of this.detectedCapabilities.values()) {
+            if (this.calculateCapabilitySimilarity(capability, existing) > 0.8) {
+                return false; // Too similar to existing capability
+            }
+        }
+        return true;
+    }
+    /**
+     * Calculate stability score for a capability
+     */
+    calculateStabilityScore(capability) {
+        const timeSinceEmergence = Date.now() - capability.timestamp;
+        const daysSinceEmergence = timeSinceEmergence / (1000 * 60 * 60 * 24);
+        // Capabilities are more stable if they persist over time
+        const persistenceScore = Math.min(1.0, daysSinceEmergence / 7); // Stabilizes over a week
+        // Check if capability has been consistently observed
+        const recentObservations = this.detectionHistory
+            .filter(h => h.capabilityId === capability.id)
+            .filter(h => Date.now() - h.timestamp < 7 * 24 * 60 * 60 * 1000); // Last week
+        const observationFrequency = recentObservations.length / 7; // Observations per day
+        const frequencyScore = Math.min(1.0, observationFrequency / 0.5); // Target: 0.5 observations per day
+        return (persistenceScore + frequencyScore) / 2;
+    }
+    /**
+     * Calculate emergence rate
+     */
+    calculateEmergenceRate() {
+        const recentCapabilities = Array.from(this.detectedCapabilities.values())
+            .filter(c => Date.now() - c.timestamp < 7 * 24 * 60 * 60 * 1000); // Last week
+        return recentCapabilities.length / 7; // Capabilities per day
+    }
+    /**
+     * Calculate stability index
+     */
+    calculateStabilityIndex(capabilities) {
+        if (capabilities.length === 0)
+            return 0;
+        const avgStability = capabilities.reduce((sum, c) => sum + c.stability, 0) / capabilities.length;
+        return avgStability;
+    }
+    /**
+     * Calculate diversity score
+     */
+    calculateDiversityScore(capabilities) {
+        if (capabilities.length === 0)
+            return 0;
+        const types = new Set(capabilities.map(c => c.type));
+        const typeDistribution = Array.from(types).map(type => capabilities.filter(c => c.type === type).length / capabilities.length);
+        // Shannon entropy for diversity
+        const entropy = -typeDistribution.reduce((sum, p) => sum + p * Math.log2(p), 0);
+        const maxEntropy = Math.log2(types.size);
+        return maxEntropy > 0 ? entropy / maxEntropy : 0;
+    }
+    /**
+     * Calculate complexity growth
+     */
+    calculateComplexityGrowth() {
+        const recent = Array.from(this.detectedCapabilities.values())
+            .filter(c => Date.now() - c.timestamp < 30 * 24 * 60 * 60 * 1000) // Last month
+            .sort((a, b) => a.timestamp - b.timestamp);
+        if (recent.length < 2)
+            return 0;
+        const complexityScores = recent.map(c => c.strength * c.novelty * c.utility);
+        const earlyAvg = complexityScores.slice(0, Math.floor(complexityScores.length / 2))
+            .reduce((a, b) => a + b, 0) / Math.floor(complexityScores.length / 2);
+        const lateAvg = complexityScores.slice(Math.floor(complexityScores.length / 2))
+            .reduce((a, b) => a + b, 0) / Math.ceil(complexityScores.length / 2);
+        return lateAvg - earlyAvg;
+    }
+    /**
+     * Calculate cross-domain connections
+     */
+    calculateCrossDomainConnections(capabilities) {
+        return capabilities.filter(c => c.type === 'cross_domain_insight').length;
+    }
+    /**
+     * Calculate self-organization level
+     */
+    calculateSelfOrganizationLevel(capabilities) {
+        const selfOrgCapabilities = capabilities.filter(c => c.type === 'self_organization');
+        if (selfOrgCapabilities.length === 0)
+            return 0;
+        return selfOrgCapabilities.reduce((sum, c) => sum + c.strength, 0) / selfOrgCapabilities.length;
+    }
+    // Helper methods for pattern extraction and analysis
+    extractBehaviorPatterns(data) {
+        // Extract behavior patterns from data
+        return data.behaviors || [];
+    }
+    extractSolutionPatterns(data) {
+        // Extract solution patterns from data
+        return data.solutions || [];
+    }
+    extractCrossDomainPatterns(data) {
+        // Extract cross-domain patterns from data
+        return data.crossDomainInsights || [];
+    }
+    extractOrganizationPatterns(data) {
+        // Extract organization patterns from data
+        return data.organizationPatterns || [];
+    }
+    extractLearningPatterns(data) {
+        // Extract learning patterns from data
+        return data.learningPatterns || [];
+    }
+    isBaselineBehavior(behavior) {
+        return this.baselineCapabilities.has(behavior.name);
+    }
+    calculateNovelty(behavior) {
+        // Calculate how novel this behavior is
+        return Math.random() * 0.5 + 0.5; // Simplified
+    }
+    calculateUtility(behavior) {
+        // Calculate utility of the behavior
+        return Math.random() * 0.5 + 0.5; // Simplified
+    }
+    calculateUnexpectedness(solution) {
+        // Calculate how unexpected this solution is
+        return Math.random() * 0.5 + 0.5; // Simplified
+    }
+    calculateEffectiveness(solution) {
+        // Calculate effectiveness of the solution
+        return Math.random() * 0.5 + 0.5; // Simplified
+    }
+    calculateBridgingScore(insight) {
+        // Calculate how well this insight bridges domains
+        return Math.random() * 0.5 + 0.5; // Simplified
+    }
+    calculateInsightValue(insight) {
+        // Calculate value of the insight
+        return Math.random() * 0.5 + 0.5; // Simplified
+    }
+    calculateOrganizationLevel(pattern) {
+        // Calculate level of self-organization
+        return Math.random() * 0.5 + 0.5; // Simplified
+    }
+    calculateAutonomy(pattern) {
+        // Calculate autonomy level
+        return Math.random() * 0.5 + 0.5; // Simplified
+    }
+    calculateMetaLevel(pattern) {
+        // Calculate meta-learning level
+        return Math.random() * 0.5 + 0.5; // Simplified
+    }
+    calculateAdaptability(pattern) {
+        // Calculate adaptability
+        return Math.random() * 0.5 + 0.5; // Simplified
+    }
+    calculateCapabilitySimilarity(cap1, cap2) {
+        // Calculate similarity between capabilities
+        return Math.random() * 0.5; // Simplified
+    }
+    logCapabilityEmergence(capability) {
+        this.detectionHistory.push({
+            capabilityId: capability.id,
+            timestamp: Date.now(),
+            type: capability.type,
+            strength: capability.strength
+        });
+        console.log(`New emergent capability detected: ${capability.name}`);
+    }
+    analyzeTrends() {
+        // Analyze emergence trends
+        return {};
+    }
+    predictFromCombinations() {
+        // Predict capabilities from existing combinations
+        return [];
+    }
+    predictFromGrowthPatterns(trends) {
+        // Predict based on growth patterns
+        return [];
+    }
+    predictFromCapabilityGaps() {
+        // Predict based on missing capabilities
+        return [];
+    }
+    /**
+     * Get detection statistics
+     */
+    getStats() {
+        const capabilities = Array.from(this.detectedCapabilities.values());
+        return {
+            totalCapabilities: capabilities.length,
+            byType: this.getCapabilitiesByType(capabilities),
+            averageStability: this.calculateStabilityIndex(capabilities),
+            emergenceRate: this.calculateEmergenceRate(),
+            complexityGrowth: this.calculateComplexityGrowth(),
+            mostRecentCapability: capabilities.sort((a, b) => b.timestamp - a.timestamp)[0]?.name || 'None',
+            detectionHistory: this.detectionHistory.length
+        };
+    }
+    getCapabilitiesByType(capabilities) {
+        const byType = {};
+        for (const capability of capabilities) {
+            byType[capability.type] = (byType[capability.type] || 0) + 1;
+        }
+        return byType;
+    }
+}

vendor/sublinear-time-solver/dist/emergence/feedback-loops.d.ts

@@ -0,0 +1,160 @@
+/**
+ * Feedback Loop System for Behavior Modification
+ * Enables the system to learn from outcomes and modify behavior dynamically
+ */
+export interface FeedbackSignal {
+    id: string;
+    source: string;
+    type: 'success' | 'failure' | 'partial' | 'unexpected' | 'novel';
+    action: string;
+    outcome: any;
+    expected: any;
+    surprise: number;
+    utility: number;
+    timestamp: number;
+    context: any;
+}
+export interface BehaviorModification {
+    component: string;
+    parameter: string;
+    oldValue: any;
+    newValue: any;
+    reason: string;
+    confidence: number;
+    timestamp: number;
+    expectedImprovement: number;
+}
+export interface AdaptationRule {
+    trigger: (feedback: FeedbackSignal) => boolean;
+    modification: (feedback: FeedbackSignal, currentState: any) => BehaviorModification[];
+    priority: number;
+    learningRate: number;
+    category: string;
+}
+export declare class FeedbackLoopSystem {
+    private feedbackHistory;
+    private behaviorModifications;
+    private adaptationRules;
+    private behaviorParameters;
+    private performanceMetrics;
+    private learningCurves;
+    constructor();
+    /**
+     * Process feedback and trigger behavior modifications
+     */
+    processFeedback(feedback: FeedbackSignal): Promise<BehaviorModification[]>;
+    /**
+     * Register new adaptation rule
+     */
+    registerAdaptationRule(rule: AdaptationRule): void;
+    /**
+     * Create feedback loop for continuous improvement
+     */
+    createContinuousImprovementLoop(component: string, metric: string): void;
+    /**
+     * Implement reinforcement learning feedback loop
+     */
+    createReinforcementLoop(actionSpace: string[], rewardFunction: (outcome: any) => number): void;
+    /**
+     * Create exploration-exploitation feedback loop
+     */
+    createExplorationExploitationLoop(explorationRate?: number): void;
+    /**
+     * Implement meta-learning feedback loop
+     */
+    createMetaLearningLoop(): void;
+    /**
+     * Create adaptive complexity feedback loop
+     */
+    createComplexityAdaptationLoop(): void;
+    /**
+     * Apply behavior modification to system parameters
+     */
+    private applyBehaviorModification;
+    /**
+     * Learn from feedback patterns to create new adaptation rules
+     */
+    private learnFromFeedbackPattern;
+    /**
+     * Initialize default adaptation rules
+     */
+    private initializeDefaultRules;
+    /**
+     * Initialize default behavior parameters
+     */
+    private initializeDefaultParameters;
+    /**
+     * Update performance metrics based on feedback
+     */
+    private updatePerformanceMetrics;
+    /**
+     * Calculate performance score from feedback
+     */
+    private calculatePerformanceScore;
+    /**
+     * Get current behavior state
+     */
+    private getCurrentBehaviorState;
+    /**
+     * Get metric trend for analysis
+     */
+    private getMetricTrend;
+    /**
+     * Check if metric is improving
+     */
+    private isMetricImproving;
+    /**
+     * Generate improvement modifications
+     */
+    private generateImprovementModifications;
+    /**
+     * Update action probabilities based on reinforcement learning
+     */
+    private updateActionProbabilities;
+    /**
+     * Analyze learning effectiveness
+     */
+    private analyzeLearningEffectiveness;
+    /**
+     * Adjust learning parameters based on effectiveness
+     */
+    private adjustLearningParameters;
+    /**
+     * Get recent performance trend
+     */
+    private getRecentPerformanceTrend;
+    /**
+     * Adapt complexity based on performance
+     */
+    private adaptComplexity;
+    /**
+     * Update learning curve for component
+     */
+    private updateLearningCurve;
+    /**
+     * Detect failure patterns in recent feedback
+     */
+    private detectFailurePattern;
+    /**
+     * Detect success patterns in recent feedback
+     */
+    private detectSuccessPattern;
+    /**
+     * Create adaptation rule from detected pattern
+     */
+    private createRuleFromPattern;
+    /**
+     * Create reinforcement rule from success pattern
+     */
+    private createReinforcementRule;
+    /**
+     * Find common elements across contexts
+     */
+    private findCommonElements;
+    /**
+     * Get feedback loop statistics
+     */
+    getStats(): any;
+    private getMostActiveComponents;
+    private getAdaptationCategories;
+}

vendor/sublinear-time-solver/dist/emergence/feedback-loops.js

@@ -0,0 +1,600 @@
+/**
+ * Feedback Loop System for Behavior Modification
+ * Enables the system to learn from outcomes and modify behavior dynamically
+ */
+export class FeedbackLoopSystem {
+    feedbackHistory = [];
+    behaviorModifications = [];
+    adaptationRules = [];
+    behaviorParameters = new Map();
+    performanceMetrics = new Map();
+    learningCurves = new Map();
+    constructor() {
+        this.initializeDefaultRules();
+        this.initializeDefaultParameters();
+    }
+    /**
+     * Process feedback and trigger behavior modifications
+     */
+    async processFeedback(feedback) {
+        // Store feedback
+        this.feedbackHistory.push(feedback);
+        // Update performance metrics
+        this.updatePerformanceMetrics(feedback);
+        // Find applicable adaptation rules
+        const applicableRules = this.adaptationRules.filter(rule => rule.trigger(feedback));
+        // Generate behavior modifications
+        const modifications = [];
+        for (const rule of applicableRules) {
+            const currentState = this.getCurrentBehaviorState();
+            const ruleMods = rule.modification(feedback, currentState);
+            modifications.push(...ruleMods);
+        }
+        // Apply modifications
+        for (const modification of modifications) {
+            await this.applyBehaviorModification(modification);
+        }
+        // Learn from the feedback pattern
+        await this.learnFromFeedbackPattern(feedback);
+        return modifications;
+    }
+    /**
+     * Register new adaptation rule
+     */
+    registerAdaptationRule(rule) {
+        this.adaptationRules.push(rule);
+        // Sort by priority
+        this.adaptationRules.sort((a, b) => b.priority - a.priority);
+    }
+    /**
+     * Create feedback loop for continuous improvement
+     */
+    createContinuousImprovementLoop(component, metric) {
+        const improvementRule = {
+            trigger: (feedback) => feedback.source === component,
+            modification: (feedback, currentState) => {
+                const currentMetric = this.getMetricTrend(metric);
+                const isImproving = this.isMetricImproving(currentMetric);
+                if (!isImproving) {
+                    return this.generateImprovementModifications(component, feedback);
+                }
+                return [];
+            },
+            priority: 0.7,
+            learningRate: 0.1,
+            category: 'continuous_improvement'
+        };
+        this.registerAdaptationRule(improvementRule);
+    }
+    /**
+     * Implement reinforcement learning feedback loop
+     */
+    createReinforcementLoop(actionSpace, rewardFunction) {
+        const reinforcementRule = {
+            trigger: (feedback) => actionSpace.includes(feedback.action),
+            modification: (feedback, currentState) => {
+                const reward = rewardFunction(feedback.outcome);
+                return this.updateActionProbabilities(feedback.action, reward, actionSpace);
+            },
+            priority: 0.8,
+            learningRate: 0.15,
+            category: 'reinforcement_learning'
+        };
+        this.registerAdaptationRule(reinforcementRule);
+    }
+    /**
+     * Create exploration-exploitation feedback loop
+     */
+    createExplorationExploitationLoop(explorationRate = 0.1) {
+        const explorationRule = {
+            trigger: (feedback) => feedback.type === 'unexpected' || feedback.surprise > 0.7,
+            modification: (feedback, currentState) => {
+                // Increase exploration if we're getting unexpected results
+                if (feedback.surprise > 0.7) {
+                    return [{
+                            component: 'exploration_system',
+                            parameter: 'exploration_rate',
+                            oldValue: currentState.exploration_rate || explorationRate,
+                            newValue: Math.min(1.0, (currentState.exploration_rate || explorationRate) + 0.1),
+                            reason: 'High surprise level - increase exploration',
+                            confidence: 0.8,
+                            timestamp: Date.now(),
+                            expectedImprovement: 0.2
+                        }];
+                }
+                // Decrease exploration if we're getting predictable good results
+                if (feedback.type === 'success' && feedback.surprise < 0.2) {
+                    return [{
+                            component: 'exploration_system',
+                            parameter: 'exploration_rate',
+                            oldValue: currentState.exploration_rate || explorationRate,
+                            newValue: Math.max(0.01, (currentState.exploration_rate || explorationRate) - 0.05),
+                            reason: 'Low surprise, high success - decrease exploration',
+                            confidence: 0.7,
+                            timestamp: Date.now(),
+                            expectedImprovement: 0.1
+                        }];
+                }
+                return [];
+            },
+            priority: 0.6,
+            learningRate: 0.05,
+            category: 'exploration_exploitation'
+        };
+        this.registerAdaptationRule(explorationRule);
+    }
+    /**
+     * Implement meta-learning feedback loop
+     */
+    createMetaLearningLoop() {
+        const metaLearningRule = {
+            trigger: (feedback) => this.feedbackHistory.length % 50 === 0, // Every 50 feedback signals
+            modification: (feedback, currentState) => {
+                // Analyze learning patterns and adjust learning rates
+                const learningEffectiveness = this.analyzeLearningEffectiveness();
+                return this.adjustLearningParameters(learningEffectiveness);
+            },
+            priority: 0.9,
+            learningRate: 0.02,
+            category: 'meta_learning'
+        };
+        this.registerAdaptationRule(metaLearningRule);
+    }
+    /**
+     * Create adaptive complexity feedback loop
+     */
+    createComplexityAdaptationLoop() {
+        const complexityRule = {
+            trigger: (feedback) => true, // Always applicable
+            modification: (feedback, currentState) => {
+                const performanceTrend = this.getRecentPerformanceTrend();
+                const currentComplexity = currentState.reasoning_complexity || 0.5;
+                // If performance is declining, try different complexity levels
+                if (performanceTrend < 0.3) {
+                    const newComplexity = this.adaptComplexity(currentComplexity, feedback);
+                    if (newComplexity !== currentComplexity) {
+                        return [{
+                                component: 'reasoning_system',
+                                parameter: 'reasoning_complexity',
+                                oldValue: currentComplexity,
+                                newValue: newComplexity,
+                                reason: `Performance trend: ${performanceTrend.toFixed(2)} - adjusting complexity`,
+                                confidence: 0.6,
+                                timestamp: Date.now(),
+                                expectedImprovement: Math.abs(newComplexity - currentComplexity) * 0.5
+                            }];
+                    }
+                }
+                return [];
+            },
+            priority: 0.5,
+            learningRate: 0.08,
+            category: 'adaptive_complexity'
+        };
+        this.registerAdaptationRule(complexityRule);
+    }
+    /**
+     * Apply behavior modification to system parameters
+     */
+    async applyBehaviorModification(modification) {
+        const key = `${modification.component}.${modification.parameter}`;
+        // Store old value for potential rollback
+        const oldValue = this.behaviorParameters.get(key);
+        // Apply new value
+        this.behaviorParameters.set(key, modification.newValue);
+        // Record the modification
+        this.behaviorModifications.push(modification);
+        // Update performance tracking
+        this.updateLearningCurve(modification.component, modification.expectedImprovement);
+        console.log(`Applied behavior modification: ${modification.component}.${modification.parameter}
+                ${JSON.stringify(modification.oldValue)} -> ${JSON.stringify(modification.newValue)}`);
+    }
+    /**
+     * Learn from feedback patterns to create new adaptation rules
+     */
+    async learnFromFeedbackPattern(feedback) {
+        // Look for patterns in recent feedback
+        const recentFeedback = this.feedbackHistory.slice(-20);
+        // Detect recurring failure patterns
+        const failurePattern = this.detectFailurePattern(recentFeedback);
+        if (failurePattern) {
+            const newRule = this.createRuleFromPattern(failurePattern);
+            this.registerAdaptationRule(newRule);
+        }
+        // Detect success patterns
+        const successPattern = this.detectSuccessPattern(recentFeedback);
+        if (successPattern) {
+            const reinforcementRule = this.createReinforcementRule(successPattern);
+            this.registerAdaptationRule(reinforcementRule);
+        }
+    }
+    /**
+     * Initialize default adaptation rules
+     */
+    initializeDefaultRules() {
+        // Error correction rule
+        this.registerAdaptationRule({
+            trigger: (feedback) => feedback.type === 'failure',
+            modification: (feedback, currentState) => [{
+                    component: feedback.source,
+                    parameter: 'error_tolerance',
+                    oldValue: currentState.error_tolerance || 0.1,
+                    newValue: Math.min(1.0, (currentState.error_tolerance || 0.1) + 0.05),
+                    reason: 'Failure detected - increase error tolerance',
+                    confidence: 0.7,
+                    timestamp: Date.now(),
+                    expectedImprovement: 0.1
+                }],
+            priority: 0.8,
+            learningRate: 0.1,
+            category: 'error_correction'
+        });
+        // Success reinforcement rule
+        this.registerAdaptationRule({
+            trigger: (feedback) => feedback.type === 'success' && feedback.utility > 0.8,
+            modification: (feedback, currentState) => [{
+                    component: feedback.source,
+                    parameter: 'success_bias',
+                    oldValue: currentState.success_bias || 0.5,
+                    newValue: Math.min(1.0, (currentState.success_bias || 0.5) + 0.02),
+                    reason: 'High utility success - reinforce successful patterns',
+                    confidence: 0.9,
+                    timestamp: Date.now(),
+                    expectedImprovement: 0.05
+                }],
+            priority: 0.7,
+            learningRate: 0.05,
+            category: 'success_reinforcement'
+        });
+        // Novelty adaptation rule
+        this.registerAdaptationRule({
+            trigger: (feedback) => feedback.type === 'novel',
+            modification: (feedback, currentState) => [{
+                    component: 'novelty_system',
+                    parameter: 'novelty_weight',
+                    oldValue: currentState.novelty_weight || 0.3,
+                    newValue: Math.min(1.0, (currentState.novelty_weight || 0.3) + 0.1),
+                    reason: 'Novel outcome detected - increase novelty seeking',
+                    confidence: 0.6,
+                    timestamp: Date.now(),
+                    expectedImprovement: 0.15
+                }],
+            priority: 0.5,
+            learningRate: 0.08,
+            category: 'novelty_adaptation'
+        });
+    }
+    /**
+     * Initialize default behavior parameters
+     */
+    initializeDefaultParameters() {
+        this.behaviorParameters.set('reasoning_system.complexity', 0.5);
+        this.behaviorParameters.set('exploration_system.exploration_rate', 0.1);
+        this.behaviorParameters.set('learning_system.learning_rate', 0.1);
+        this.behaviorParameters.set('novelty_system.novelty_weight', 0.3);
+        this.behaviorParameters.set('error_system.error_tolerance', 0.1);
+        this.behaviorParameters.set('success_system.success_bias', 0.5);
+    }
+    /**
+     * Update performance metrics based on feedback
+     */
+    updatePerformanceMetrics(feedback) {
+        const metricKey = `${feedback.source}_${feedback.type}`;
+        const metrics = this.performanceMetrics.get(metricKey) || [];
+        const score = this.calculatePerformanceScore(feedback);
+        metrics.push(score);
+        // Keep only recent metrics (last 100)
+        if (metrics.length > 100) {
+            metrics.shift();
+        }
+        this.performanceMetrics.set(metricKey, metrics);
+    }
+    /**
+     * Calculate performance score from feedback
+     */
+    calculatePerformanceScore(feedback) {
+        let score = 0.5; // Neutral baseline
+        switch (feedback.type) {
+            case 'success':
+                score = 0.8 + feedback.utility * 0.2;
+                break;
+            case 'failure':
+                score = 0.2 - feedback.utility * 0.2;
+                break;
+            case 'partial':
+                score = 0.5 + feedback.utility * 0.3;
+                break;
+            case 'unexpected':
+                score = 0.6 + feedback.surprise * 0.4;
+                break;
+            case 'novel':
+                score = 0.7 + (feedback.utility + feedback.surprise) * 0.15;
+                break;
+        }
+        return Math.max(0, Math.min(1, score));
+    }
+    /**
+     * Get current behavior state
+     */
+    getCurrentBehaviorState() {
+        const state = {};
+        for (const [key, value] of this.behaviorParameters) {
+            const [component, parameter] = key.split('.');
+            if (!state[component])
+                state[component] = {};
+            state[component][parameter] = value;
+            // Also add flat structure for easier access
+            state[parameter] = value;
+        }
+        return state;
+    }
+    /**
+     * Get metric trend for analysis
+     */
+    getMetricTrend(metric) {
+        return this.performanceMetrics.get(metric) || [];
+    }
+    /**
+     * Check if metric is improving
+     */
+    isMetricImproving(metricValues) {
+        if (metricValues.length < 5)
+            return true; // Not enough data
+        const recent = metricValues.slice(-5);
+        const older = metricValues.slice(-10, -5);
+        if (older.length === 0)
+            return true;
+        const recentAvg = recent.reduce((a, b) => a + b, 0) / recent.length;
+        const olderAvg = older.reduce((a, b) => a + b, 0) / older.length;
+        return recentAvg > olderAvg;
+    }
+    /**
+     * Generate improvement modifications
+     */
+    generateImprovementModifications(component, feedback) {
+        const modifications = [];
+        // Suggest parameter adjustments based on failure type
+        if (feedback.type === 'failure') {
+            modifications.push({
+                component,
+                parameter: 'robustness',
+                oldValue: 0.5,
+                newValue: 0.7,
+                reason: 'Failure detected - increase robustness',
+                confidence: 0.6,
+                timestamp: Date.now(),
+                expectedImprovement: 0.2
+            });
+        }
+        return modifications;
+    }
+    /**
+     * Update action probabilities based on reinforcement learning
+     */
+    updateActionProbabilities(action, reward, actionSpace) {
+        const modifications = [];
+        // Increase probability of rewarded actions
+        if (reward > 0.5) {
+            modifications.push({
+                component: 'action_system',
+                parameter: `${action}_probability`,
+                oldValue: 1.0 / actionSpace.length, // Uniform prior
+                newValue: Math.min(0.8, (1.0 / actionSpace.length) + reward * 0.1),
+                reason: `Positive reward (${reward.toFixed(2)}) for action ${action}`,
+                confidence: reward,
+                timestamp: Date.now(),
+                expectedImprovement: reward * 0.2
+            });
+        }
+        return modifications;
+    }
+    /**
+     * Analyze learning effectiveness
+     */
+    analyzeLearningEffectiveness() {
+        const recentModifications = this.behaviorModifications.slice(-20);
+        if (recentModifications.length === 0)
+            return 0.5;
+        const actualImprovements = recentModifications.map(mod => {
+            // Compare expected vs actual improvement
+            const component = mod.component;
+            const metricKey = `${component}_improvement`;
+            const metrics = this.performanceMetrics.get(metricKey) || [];
+            if (metrics.length < 2)
+                return mod.expectedImprovement;
+            const beforeImprovement = metrics[metrics.length - 2] || 0;
+            const afterImprovement = metrics[metrics.length - 1] || 0;
+            return afterImprovement - beforeImprovement;
+        });
+        const avgActualImprovement = actualImprovements.reduce((a, b) => a + b, 0) / actualImprovements.length;
+        const avgExpectedImprovement = recentModifications.reduce((sum, mod) => sum + mod.expectedImprovement, 0) / recentModifications.length;
+        return avgExpectedImprovement > 0 ? avgActualImprovement / avgExpectedImprovement : 0.5;
+    }
+    /**
+     * Adjust learning parameters based on effectiveness
+     */
+    adjustLearningParameters(effectiveness) {
+        const modifications = [];
+        // Adjust learning rates based on effectiveness
+        for (const rule of this.adaptationRules) {
+            const newLearningRate = effectiveness > 0.8 ?
+                Math.min(0.5, rule.learningRate * 1.1) :
+                Math.max(0.01, rule.learningRate * 0.9);
+            if (Math.abs(newLearningRate - rule.learningRate) > 0.01) {
+                modifications.push({
+                    component: 'meta_learning',
+                    parameter: `${rule.category}_learning_rate`,
+                    oldValue: rule.learningRate,
+                    newValue: newLearningRate,
+                    reason: `Learning effectiveness: ${effectiveness.toFixed(2)} - adjust learning rate`,
+                    confidence: 0.7,
+                    timestamp: Date.now(),
+                    expectedImprovement: Math.abs(newLearningRate - rule.learningRate) * 2
+                });
+                rule.learningRate = newLearningRate;
+            }
+        }
+        return modifications;
+    }
+    /**
+     * Get recent performance trend
+     */
+    getRecentPerformanceTrend() {
+        const allMetrics = [];
+        for (const metrics of this.performanceMetrics.values()) {
+            allMetrics.push(...metrics.slice(-5)); // Recent 5 values from each metric
+        }
+        if (allMetrics.length === 0)
+            return 0.5;
+        return allMetrics.reduce((a, b) => a + b, 0) / allMetrics.length;
+    }
+    /**
+     * Adapt complexity based on performance
+     */
+    adaptComplexity(currentComplexity, feedback) {
+        if (feedback.type === 'failure' && feedback.utility < 0.3) {
+            // Failure with low utility - try lower complexity
+            return Math.max(0.1, currentComplexity - 0.1);
+        }
+        if (feedback.type === 'success' && feedback.surprise > 0.7) {
+            // Successful but surprising - might benefit from higher complexity
+            return Math.min(1.0, currentComplexity + 0.1);
+        }
+        return currentComplexity;
+    }
+    /**
+     * Update learning curve for component
+     */
+    updateLearningCurve(component, improvement) {
+        const curve = this.learningCurves.get(component) || [];
+        curve.push(improvement);
+        if (curve.length > 50) {
+            curve.shift();
+        }
+        this.learningCurves.set(component, curve);
+    }
+    /**
+     * Detect failure patterns in recent feedback
+     */
+    detectFailurePattern(feedback) {
+        const failures = feedback.filter(f => f.type === 'failure');
+        if (failures.length < 3)
+            return null;
+        // Look for common failure contexts
+        const contexts = failures.map(f => f.context);
+        const commonContext = this.findCommonElements(contexts);
+        if (Object.keys(commonContext).length > 0) {
+            return {
+                type: 'recurring_failure',
+                context: commonContext,
+                frequency: failures.length / feedback.length
+            };
+        }
+        return null;
+    }
+    /**
+     * Detect success patterns in recent feedback
+     */
+    detectSuccessPattern(feedback) {
+        const successes = feedback.filter(f => f.type === 'success' && f.utility > 0.7);
+        if (successes.length < 2)
+            return null;
+        return {
+            type: 'success_pattern',
+            actions: successes.map(s => s.action),
+            avgUtility: successes.reduce((sum, s) => sum + s.utility, 0) / successes.length
+        };
+    }
+    /**
+     * Create adaptation rule from detected pattern
+     */
+    createRuleFromPattern(pattern) {
+        return {
+            trigger: (feedback) => {
+                // Check if feedback matches the pattern context
+                for (const [key, value] of Object.entries(pattern.context)) {
+                    if (feedback.context[key] !== value)
+                        return false;
+                }
+                return true;
+            },
+            modification: (feedback, currentState) => [{
+                    component: 'pattern_system',
+                    parameter: 'pattern_avoidance',
+                    oldValue: 0,
+                    newValue: 1,
+                    reason: `Avoiding detected failure pattern: ${JSON.stringify(pattern.context)}`,
+                    confidence: pattern.frequency,
+                    timestamp: Date.now(),
+                    expectedImprovement: pattern.frequency * 0.5
+                }],
+            priority: 0.8,
+            learningRate: 0.1,
+            category: 'pattern_avoidance'
+        };
+    }
+    /**
+     * Create reinforcement rule from success pattern
+     */
+    createReinforcementRule(pattern) {
+        return {
+            trigger: (feedback) => pattern.actions.includes(feedback.action),
+            modification: (feedback, currentState) => [{
+                    component: 'pattern_system',
+                    parameter: 'pattern_reinforcement',
+                    oldValue: 0,
+                    newValue: pattern.avgUtility,
+                    reason: `Reinforcing successful action pattern`,
+                    confidence: pattern.avgUtility,
+                    timestamp: Date.now(),
+                    expectedImprovement: pattern.avgUtility * 0.3
+                }],
+            priority: 0.7,
+            learningRate: 0.08,
+            category: 'pattern_reinforcement'
+        };
+    }
+    /**
+     * Find common elements across contexts
+     */
+    findCommonElements(contexts) {
+        if (contexts.length === 0)
+            return {};
+        const common = {};
+        const first = contexts[0] || {};
+        for (const [key, value] of Object.entries(first)) {
+            if (contexts.every(ctx => ctx[key] === value)) {
+                common[key] = value;
+            }
+        }
+        return common;
+    }
+    /**
+     * Get feedback loop statistics
+     */
+    getStats() {
+        return {
+            totalFeedback: this.feedbackHistory.length,
+            totalModifications: this.behaviorModifications.length,
+            activeRules: this.adaptationRules.length,
+            behaviorParameters: this.behaviorParameters.size,
+            recentPerformance: this.getRecentPerformanceTrend(),
+            learningEffectiveness: this.analyzeLearningEffectiveness(),
+            mostActiveComponents: this.getMostActiveComponents(),
+            adaptationCategories: this.getAdaptationCategories()
+        };
+    }
+    getMostActiveComponents() {
+        const componentCounts = new Map();
+        for (const mod of this.behaviorModifications) {
+            componentCounts.set(mod.component, (componentCounts.get(mod.component) || 0) + 1);
+        }
+        return Array.from(componentCounts.entries())
+            .sort((a, b) => b[1] - a[1])
+            .slice(0, 5)
+            .map(entry => entry[0]);
+    }
+    getAdaptationCategories() {
+        return [...new Set(this.adaptationRules.map(rule => rule.category))];
+    }
+}

vendor/sublinear-time-solver/dist/emergence/index.d.ts

@@ -0,0 +1,117 @@
+/**
+ * Emergence System Integration
+ * Orchestrates all emergence capabilities into a unified system
+ */
+import { SelfModificationEngine } from './self-modification-engine.js';
+import { PersistentLearningSystem } from './persistent-learning-system.js';
+import { StochasticExplorationEngine } from './stochastic-exploration.js';
+import { CrossToolSharingSystem } from './cross-tool-sharing.js';
+import { FeedbackLoopSystem } from './feedback-loops.js';
+import { EmergentCapabilityDetector } from './emergent-capability-detector.js';
+export interface EmergenceSystemConfig {
+    selfModification: {
+        enabled: boolean;
+        maxModificationsPerSession: number;
+        riskThreshold: number;
+    };
+    persistentLearning: {
+        enabled: boolean;
+        storagePath: string;
+        learningRate: number;
+    };
+    stochasticExploration: {
+        enabled: boolean;
+        initialTemperature: number;
+        coolingRate: number;
+    };
+    crossToolSharing: {
+        enabled: boolean;
+        maxConnections: number;
+    };
+    feedbackLoops: {
+        enabled: boolean;
+        adaptationRate: number;
+    };
+    capabilityDetection: {
+        enabled: boolean;
+        detectionThresholds: any;
+    };
+}
+export interface EmergenceMetrics {
+    selfModificationRate: number;
+    learningTriples: number;
+    explorationNovelty: number;
+    informationFlows: number;
+    behaviorModifications: number;
+    emergentCapabilities: number;
+    overallEmergenceScore: number;
+    systemComplexity: number;
+}
+export declare class EmergenceSystem {
+    private selfModificationEngine;
+    private persistentLearningSystem;
+    private stochasticExplorationEngine;
+    private crossToolSharingSystem;
+    private feedbackLoopSystem;
+    private emergentCapabilityDetector;
+    private config;
+    private isInitialized;
+    private emergenceHistory;
+    private recursionDepth;
+    private maxRecursionDepth;
+    constructor(config?: Partial<EmergenceSystemConfig>);
+    /**
+     * Initialize all emergence system components
+     */
+    private initializeComponents;
+    /**
+     * Setup connections between components for emergent interactions
+     */
+    private setupInterComponentConnections;
+    /**
+     * Process input through the emergence system
+     */
+    processWithEmergence(input: any, availableTools?: any[]): Promise<any>;
+    /**
+     * Generate diverse emergent responses
+     */
+    generateEmergentResponses(input: any, count?: number, tools?: any[]): Promise<any[]>;
+    /**
+     * Analyze system's emergent capabilities
+     */
+    analyzeEmergentCapabilities(): Promise<any>;
+    /**
+     * Force system evolution through targeted modifications
+     */
+    forceEvolution(targetCapability: string): Promise<any>;
+    /**
+     * Get comprehensive emergence statistics
+     */
+    getEmergenceStats(): any;
+    private connectLearningToModification;
+    private connectExplorationToLearning;
+    private connectSharingToCapabilityDetection;
+    private connectFeedbackToAllSystems;
+    private connectCapabilityDetectionToExploration;
+    private shareExplorationInsights;
+    private incorporateSharedInformation;
+    private synthesizeSharedInformation;
+    private handleNewCapabilities;
+    private analyzeSessionPerformance;
+    private generateSessionFeedback;
+    private calculateEmergenceMetrics;
+    private calculateOverallEmergenceLevel;
+    private calculateSystemComplexity;
+    getSelfModificationEngine(): SelfModificationEngine;
+    getPersistentLearningSystem(): PersistentLearningSystem;
+    getStochasticExplorationEngine(): StochasticExplorationEngine;
+    getCrossToolSharingSystem(): CrossToolSharingSystem;
+    getFeedbackLoopSystem(): FeedbackLoopSystem;
+    getEmergentCapabilityDetector(): EmergentCapabilityDetector;
+}
+export * from './self-modification-engine.js';
+export * from './persistent-learning-system.js';
+export * from './stochastic-exploration.js';
+export * from './cross-tool-sharing.js';
+export * from './feedback-loops.js';
+export * from './emergent-capability-detector.js';

vendor/sublinear-time-solver/dist/emergence/index.js

@@ -0,0 +1,552 @@
+/**
+ * Emergence System Integration
+ * Orchestrates all emergence capabilities into a unified system
+ */
+import { SelfModificationEngine } from './self-modification-engine.js';
+import { PersistentLearningSystem } from './persistent-learning-system.js';
+import { StochasticExplorationEngine } from './stochastic-exploration.js';
+import { CrossToolSharingSystem } from './cross-tool-sharing.js';
+import { FeedbackLoopSystem } from './feedback-loops.js';
+import { EmergentCapabilityDetector } from './emergent-capability-detector.js';
+export class EmergenceSystem {
+    selfModificationEngine;
+    persistentLearningSystem;
+    stochasticExplorationEngine;
+    crossToolSharingSystem;
+    feedbackLoopSystem;
+    emergentCapabilityDetector;
+    config;
+    isInitialized = false;
+    emergenceHistory = [];
+    recursionDepth = 0;
+    maxRecursionDepth = 5;
+    constructor(config) {
+        this.config = {
+            selfModification: {
+                enabled: true,
+                maxModificationsPerSession: 5,
+                riskThreshold: 0.7
+            },
+            persistentLearning: {
+                enabled: true,
+                storagePath: './data/emergence',
+                learningRate: 0.1
+            },
+            stochasticExploration: {
+                enabled: true,
+                initialTemperature: 1.0,
+                coolingRate: 0.995
+            },
+            crossToolSharing: {
+                enabled: true,
+                maxConnections: 100
+            },
+            feedbackLoops: {
+                enabled: true,
+                adaptationRate: 0.1
+            },
+            capabilityDetection: {
+                enabled: true,
+                detectionThresholds: {
+                    novelty: 0.7,
+                    utility: 0.5,
+                    stability: 0.6
+                }
+            },
+            ...config
+        };
+        this.initializeComponents();
+    }
+    /**
+     * Initialize all emergence system components
+     */
+    initializeComponents() {
+        this.selfModificationEngine = new SelfModificationEngine();
+        this.persistentLearningSystem = new PersistentLearningSystem(this.config.persistentLearning.storagePath);
+        this.stochasticExplorationEngine = new StochasticExplorationEngine();
+        this.crossToolSharingSystem = new CrossToolSharingSystem();
+        this.feedbackLoopSystem = new FeedbackLoopSystem();
+        this.emergentCapabilityDetector = new EmergentCapabilityDetector();
+        this.setupInterComponentConnections();
+        this.isInitialized = true;
+        console.log('Emergence System initialized with all components');
+    }
+    /**
+     * Setup connections between components for emergent interactions
+     */
+    setupInterComponentConnections() {
+        // Learning system provides feedback to modification engine
+        this.connectLearningToModification();
+        // Exploration results inform learning system
+        this.connectExplorationToLearning();
+        // Cross-tool sharing enables emergent capability detection
+        this.connectSharingToCapabilityDetection();
+        // Feedback loops adjust all other systems
+        this.connectFeedbackToAllSystems();
+        // Capability detection triggers new explorations
+        this.connectCapabilityDetectionToExploration();
+    }
+    /**
+     * Process input through the emergence system
+     */
+    async processWithEmergence(input, availableTools = []) {
+        if (!this.isInitialized) {
+            throw new Error('Emergence system not initialized');
+        }
+        // Prevent deep recursion
+        if (this.recursionDepth >= this.maxRecursionDepth) {
+            return {
+                result: input,
+                emergenceSession: {
+                    sessionId: `depth_limited_${Date.now()}`,
+                    startTime: Date.now(),
+                    endTime: Date.now(),
+                    results: { error: 'Maximum recursion depth reached' },
+                    error: 'Recursion depth exceeded'
+                },
+                metrics: { overallEmergenceScore: 0 }
+            };
+        }
+        this.recursionDepth++;
+        const emergenceSession = {
+            sessionId: `emergence_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`,
+            startTime: Date.now(),
+            input,
+            tools: availableTools,
+            results: {}
+        };
+        try {
+            // Phase 1: Stochastic Exploration
+            let result = input;
+            if (this.config.stochasticExploration.enabled) {
+                const explorationResults = await this.stochasticExplorationEngine.exploreUnpredictably(input, availableTools);
+                // Limit result size to prevent exponential growth
+                const MAX_EXPLORATION_SIZE = 5000;
+                const explorationStr = JSON.stringify(explorationResults.output);
+                if (explorationStr.length > MAX_EXPLORATION_SIZE) {
+                    result = {
+                        summary: 'Exploration result truncated',
+                        outputType: typeof explorationResults.output,
+                        novelty: explorationResults.novelty,
+                        surpriseLevel: explorationResults.surpriseLevel
+                    };
+                }
+                else {
+                    result = explorationResults.output;
+                }
+                // Store limited exploration results
+                emergenceSession.results.exploration = {
+                    novelty: explorationResults.novelty,
+                    surpriseLevel: explorationResults.surpriseLevel,
+                    pathLength: explorationResults.explorationPath.length,
+                    outputSummary: JSON.stringify(result).substring(0, 200)
+                };
+                // Share exploration insights
+                if (this.config.crossToolSharing.enabled) {
+                    await this.shareExplorationInsights(explorationResults);
+                }
+            }
+            // Phase 2: Cross-Tool Information Sharing
+            if (this.config.crossToolSharing.enabled) {
+                const relevantInfo = this.crossToolSharingSystem.getRelevantInformation('emergence_system', input);
+                if (relevantInfo.length > 0) {
+                    result = await this.incorporateSharedInformation(result, relevantInfo);
+                    emergenceSession.results.sharedInformation = relevantInfo;
+                }
+            }
+            // Phase 3: Learning Integration (skip for large tool arrays to prevent hanging)
+            if (this.config.persistentLearning.enabled && availableTools.length < 3) {
+                const interaction = {
+                    timestamp: Date.now(),
+                    type: 'emergence_processing',
+                    input,
+                    output: result,
+                    tools: availableTools.map(t => t.name || 'unknown'),
+                    success: true // Will be updated based on feedback
+                };
+                await this.persistentLearningSystem.learnFromInteraction(interaction);
+                emergenceSession.results.learning = interaction;
+            }
+            // Phase 4: Capability Detection (skip for large tool arrays)
+            if (this.config.capabilityDetection.enabled && availableTools.length < 3) {
+                const behaviorData = {
+                    input,
+                    output: result,
+                    tools: availableTools,
+                    exploration: emergenceSession.results.exploration,
+                    session: emergenceSession
+                };
+                const emergentCapabilities = await this.emergentCapabilityDetector.monitorForEmergence(behaviorData);
+                emergenceSession.results.emergentCapabilities = emergentCapabilities;
+                if (emergentCapabilities.length > 0) {
+                    await this.handleNewCapabilities(emergentCapabilities);
+                }
+            }
+            // Phase 5: Self-Modification (if triggered)
+            if (this.config.selfModification.enabled) {
+                const performanceData = this.analyzeSessionPerformance(emergenceSession);
+                const modifications = await this.selfModificationEngine.generateModifications(performanceData);
+                if (modifications.length > 0) {
+                    const appliedModifications = [];
+                    for (const mod of modifications) {
+                        const modResult = await this.selfModificationEngine.applySelfModification(mod);
+                        if (modResult.success) {
+                            appliedModifications.push(modResult);
+                        }
+                    }
+                    emergenceSession.results.modifications = appliedModifications;
+                }
+            }
+            // Phase 6: Feedback Processing
+            if (this.config.feedbackLoops.enabled) {
+                const feedback = this.generateSessionFeedback(emergenceSession, result);
+                const behaviorMods = await this.feedbackLoopSystem.processFeedback(feedback);
+                emergenceSession.results.behaviorModifications = behaviorMods;
+            }
+            emergenceSession.endTime = Date.now();
+            emergenceSession.results.final = result;
+            // Store session in emergence history
+            this.emergenceHistory.push(emergenceSession);
+            this.recursionDepth--;
+            // Final size check and truncation
+            const MAX_FINAL_SIZE = 50000; // 50KB absolute maximum
+            const finalResult = JSON.stringify(result);
+            if (finalResult.length > MAX_FINAL_SIZE) {
+                return {
+                    result: {
+                        summary: 'Result exceeded maximum size limit',
+                        type: 'truncated_response',
+                        originalSize: finalResult.length,
+                        metrics: {
+                            overallEmergenceScore: this.calculateOverallEmergenceLevel(),
+                            sessionDuration: emergenceSession.endTime - emergenceSession.startTime
+                        }
+                    },
+                    emergenceSession: {
+                        sessionId: emergenceSession.sessionId,
+                        startTime: emergenceSession.startTime,
+                        endTime: emergenceSession.endTime,
+                        truncated: true
+                    },
+                    metrics: {
+                        overallEmergenceScore: this.calculateOverallEmergenceLevel(),
+                        systemComplexity: this.calculateSystemComplexity()
+                    }
+                };
+            }
+            return {
+                result,
+                emergenceSession,
+                metrics: await this.calculateEmergenceMetrics()
+            };
+        }
+        catch (error) {
+            this.recursionDepth--;
+            emergenceSession.error = error instanceof Error ? error.message : 'Unknown error';
+            emergenceSession.endTime = Date.now();
+            throw new Error(`Emergence processing failed: ${emergenceSession.error}`);
+        }
+    }
+    /**
+     * Generate diverse emergent responses
+     */
+    async generateEmergentResponses(input, count = 3, tools = []) {
+        const responses = [];
+        for (let i = 0; i < count; i++) {
+            // Use different exploration strategies for each response
+            const explorationResults = await this.stochasticExplorationEngine.exploreUnpredictably(input, tools);
+            // Don't call processWithEmergence recursively - just use exploration results
+            responses.push({
+                response: explorationResults.output,
+                explorationPath: explorationResults.explorationPath,
+                novelty: explorationResults.novelty,
+                emergenceMetrics: {
+                    selfModificationRate: 0,
+                    learningTriples: 0,
+                    explorationNovelty: explorationResults.novelty,
+                    informationFlows: 0,
+                    behaviorModifications: 0,
+                    emergentCapabilities: 0,
+                    overallEmergenceScore: explorationResults.novelty,
+                    systemComplexity: 1
+                }
+            });
+        }
+        return responses.sort((a, b) => b.novelty - a.novelty);
+    }
+    /**
+     * Analyze system's emergent capabilities
+     */
+    async analyzeEmergentCapabilities() {
+        const capabilities = await this.emergentCapabilityDetector.measureEmergenceMetrics();
+        const stabilityAnalysis = this.emergentCapabilityDetector.analyzeCapabilityStability();
+        const learningRecommendations = this.persistentLearningSystem.getLearningRecommendations();
+        const collaborationPatterns = this.crossToolSharingSystem.analyzeCollaborationPatterns();
+        return {
+            capabilities,
+            stability: Object.fromEntries(stabilityAnalysis),
+            learningRecommendations,
+            collaborationPatterns,
+            overallEmergenceLevel: this.calculateOverallEmergenceLevel(),
+            predictions: this.emergentCapabilityDetector.predictFutureEmergence()
+        };
+    }
+    /**
+     * Force system evolution through targeted modifications
+     */
+    async forceEvolution(targetCapability) {
+        const evolutionSession = {
+            target: targetCapability,
+            startTime: Date.now(),
+            steps: []
+        };
+        // Step 1: Generate stochastic variations toward target
+        const variations = this.selfModificationEngine.generateStochasticVariations();
+        const targetedVariations = variations.filter(v => v.reasoning.toLowerCase().includes(targetCapability.toLowerCase()));
+        evolutionSession.steps.push({
+            phase: 'stochastic_variation',
+            variations: targetedVariations.length
+        });
+        // Step 2: Apply promising modifications
+        for (const variation of targetedVariations) {
+            const result = await this.selfModificationEngine.applySelfModification(variation);
+            evolutionSession.steps.push({
+                phase: 'modification_application',
+                success: result.success,
+                impact: result.impact
+            });
+        }
+        // Step 3: Force exploration in target direction
+        const targetedExploration = await this.stochasticExplorationEngine.exploreUnpredictably({ target: targetCapability, force_evolution: true }, []);
+        evolutionSession.steps.push({
+            phase: 'targeted_exploration',
+            novelty: targetedExploration.novelty,
+            surprise: targetedExploration.surpriseLevel
+        });
+        // Step 4: Measure emergence after forced evolution
+        const postEvolutionMetrics = await this.calculateEmergenceMetrics();
+        evolutionSession.endTime = Date.now();
+        evolutionSession.results = {
+            metrics: postEvolutionMetrics,
+            exploration: targetedExploration
+        };
+        return evolutionSession;
+    }
+    /**
+     * Get comprehensive emergence statistics
+     */
+    getEmergenceStats() {
+        return {
+            system: {
+                initialized: this.isInitialized,
+                sessionsProcessed: this.emergenceHistory.length,
+                config: this.config
+            },
+            components: {
+                selfModification: this.selfModificationEngine.getCapabilities(),
+                learning: this.persistentLearningSystem.getLearningStats(),
+                exploration: this.stochasticExplorationEngine.getExplorationStats(),
+                sharing: this.crossToolSharingSystem.getStats(),
+                feedback: this.feedbackLoopSystem.getStats(),
+                capabilities: this.emergentCapabilityDetector.getStats()
+            },
+            emergence: {
+                overallLevel: this.calculateOverallEmergenceLevel(),
+                recentSessions: this.emergenceHistory.slice(-5).map(s => ({
+                    sessionId: s.sessionId,
+                    duration: s.endTime - s.startTime,
+                    hasEmergentCapabilities: (s.results.emergentCapabilities?.length || 0) > 0,
+                    modificationCount: s.results.modifications?.length || 0
+                }))
+            }
+        };
+    }
+    // Private helper methods
+    connectLearningToModification() {
+        // Set up connection for learning system to inform modification engine
+        console.log('Connected learning system to modification engine');
+    }
+    connectExplorationToLearning() {
+        // Set up connection for exploration results to inform learning
+        console.log('Connected exploration to learning system');
+    }
+    connectSharingToCapabilityDetection() {
+        // Set up connection for sharing system to inform capability detection
+        console.log('Connected sharing system to capability detection');
+    }
+    connectFeedbackToAllSystems() {
+        // Set up feedback connections to all systems
+        console.log('Connected feedback loops to all systems');
+    }
+    connectCapabilityDetectionToExploration() {
+        // Set up connection for capability detection to trigger exploration
+        console.log('Connected capability detection to exploration');
+    }
+    async shareExplorationInsights(exploration) {
+        const sharedInfo = {
+            id: `exploration_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`,
+            sourceTools: ['stochastic_exploration'],
+            targetTools: [],
+            content: {
+                explorationPath: exploration.explorationPath,
+                novelty: exploration.novelty,
+                surprise: exploration.surpriseLevel,
+                output: exploration.output
+            },
+            type: 'insight',
+            timestamp: Date.now(),
+            relevance: exploration.novelty,
+            persistence: 'session',
+            metadata: { exploration: true }
+        };
+        await this.crossToolSharingSystem.shareInformation(sharedInfo);
+    }
+    async incorporateSharedInformation(result, sharedInfo) {
+        // Limit response size to prevent exponential growth
+        const MAX_RESULT_SIZE = 10000; // 10KB limit
+        // Only include essential information
+        const limitedSharedInsights = sharedInfo.slice(0, 3).map(info => ({
+            id: info.id,
+            type: info.type,
+            summary: JSON.stringify(info.content).substring(0, 100)
+        }));
+        // Check current size
+        const currentSize = JSON.stringify(result).length;
+        if (currentSize > MAX_RESULT_SIZE) {
+            return {
+                summary: 'Result too large - truncated',
+                insightCount: sharedInfo.length,
+                synthesis: 'limited_due_to_size'
+            };
+        }
+        // Incorporate shared information into result with size limits
+        const enhancedResult = {
+            original: typeof result === 'string' ? result.substring(0, 1000) : result,
+            sharedInsights: limitedSharedInsights,
+            emergentSynthesis: this.synthesizeSharedInformation(result, sharedInfo)
+        };
+        return enhancedResult;
+    }
+    synthesizeSharedInformation(result, sharedInfo) {
+        // Synthesize shared information with current result
+        return {
+            synthesis: 'emergent_combination',
+            elements: sharedInfo.length,
+            novel_patterns: Math.random() > 0.5
+        };
+    }
+    async handleNewCapabilities(capabilities) {
+        for (const capability of capabilities) {
+            // Share new capabilities across tools
+            const sharedInfo = {
+                id: `capability_${capability.id}`,
+                sourceTools: ['emergent_capability_detector'],
+                targetTools: [],
+                content: {
+                    capability: capability.name,
+                    type: capability.type,
+                    strength: capability.strength,
+                    triggers: capability.triggers
+                },
+                type: 'pattern',
+                timestamp: Date.now(),
+                relevance: capability.utility,
+                persistence: 'permanent',
+                metadata: { emergent_capability: true }
+            };
+            await this.crossToolSharingSystem.shareInformation(sharedInfo);
+            console.log(`New emergent capability shared: ${capability.name}`);
+        }
+    }
+    analyzeSessionPerformance(session) {
+        return {
+            duration: session.endTime - session.startTime,
+            explorationNovelty: session.results.exploration?.novelty || 0,
+            capabilityCount: session.results.emergentCapabilities?.length || 0,
+            modificationCount: session.results.modifications?.length || 0,
+            success: !session.error
+        };
+    }
+    generateSessionFeedback(session, result) {
+        const performance = this.analyzeSessionPerformance(session);
+        return {
+            id: `feedback_${session.sessionId}`,
+            source: 'emergence_system',
+            type: performance.success ? 'success' : 'failure',
+            action: 'emergence_processing',
+            outcome: result,
+            expected: session.input,
+            surprise: performance.explorationNovelty,
+            utility: performance.capabilityCount > 0 ? 0.8 : 0.5,
+            timestamp: Date.now(),
+            context: {
+                session: session.sessionId,
+                duration: performance.duration,
+                modifications: performance.modificationCount
+            }
+        };
+    }
+    async calculateEmergenceMetrics() {
+        const selfModStats = this.selfModificationEngine.getCapabilities();
+        const learningStats = this.persistentLearningSystem.getLearningStats();
+        const explorationStats = this.stochasticExplorationEngine.getExplorationStats();
+        const sharingStats = this.crossToolSharingSystem.getStats();
+        const feedbackStats = this.feedbackLoopSystem.getStats();
+        const capabilityStats = this.emergentCapabilityDetector.getStats();
+        const overallEmergenceScore = this.calculateOverallEmergenceLevel();
+        return {
+            selfModificationRate: selfModStats.currentModifications / selfModStats.maxModificationsPerSession,
+            learningTriples: learningStats.totalTriples,
+            explorationNovelty: explorationStats.averageNovelty,
+            informationFlows: sharingStats.totalFlows,
+            behaviorModifications: feedbackStats.totalModifications,
+            emergentCapabilities: capabilityStats.totalCapabilities,
+            overallEmergenceScore,
+            systemComplexity: this.calculateSystemComplexity()
+        };
+    }
+    calculateOverallEmergenceLevel() {
+        const componentScores = [
+            Math.min(1.0, this.selfModificationEngine.getCapabilities().currentModifications / 5),
+            Math.min(1.0, this.persistentLearningSystem.getLearningStats().totalTriples / 100),
+            this.stochasticExplorationEngine.getExplorationStats().averageNovelty,
+            Math.min(1.0, this.crossToolSharingSystem.getStats().totalFlows / 50),
+            Math.min(1.0, this.feedbackLoopSystem.getStats().totalModifications / 20),
+            Math.min(1.0, this.emergentCapabilityDetector.getStats().totalCapabilities / 10)
+        ];
+        return componentScores.reduce((sum, score) => sum + score, 0) / componentScores.length;
+    }
+    calculateSystemComplexity() {
+        const stats = this.getEmergenceStats();
+        const componentCount = Object.keys(stats.components).length;
+        const interactionCount = this.emergenceHistory.length;
+        const capabilityCount = stats.components.capabilities.totalCapabilities;
+        return Math.log(componentCount + interactionCount + capabilityCount + 1);
+    }
+    // Public getters for testing
+    getSelfModificationEngine() {
+        return this.selfModificationEngine;
+    }
+    getPersistentLearningSystem() {
+        return this.persistentLearningSystem;
+    }
+    getStochasticExplorationEngine() {
+        return this.stochasticExplorationEngine;
+    }
+    getCrossToolSharingSystem() {
+        return this.crossToolSharingSystem;
+    }
+    getFeedbackLoopSystem() {
+        return this.feedbackLoopSystem;
+    }
+    getEmergentCapabilityDetector() {
+        return this.emergentCapabilityDetector;
+    }
+}
+// Export all types for external use
+export * from './self-modification-engine.js';
+export * from './persistent-learning-system.js';
+export * from './stochastic-exploration.js';
+export * from './cross-tool-sharing.js';
+export * from './feedback-loops.js';
+export * from './emergent-capability-detector.js';

vendor/sublinear-time-solver/dist/emergence/persistent-learning-system.d.ts

@@ -0,0 +1,103 @@
+/**
+ * Persistent Learning System
+ * Enables cross-session learning and knowledge accumulation
+ */
+export interface LearningTriple {
+    subject: string;
+    predicate: string;
+    object: string;
+    confidence: number;
+    timestamp: number;
+    sessionId: string;
+    sources: string[];
+}
+export interface SessionMemory {
+    sessionId: string;
+    startTime: number;
+    endTime?: number;
+    interactions: Interaction[];
+    discoveries: Discovery[];
+    performanceMetrics: any;
+}
+export interface Interaction {
+    timestamp: number;
+    type: string;
+    input: any;
+    output: any;
+    tools: string[];
+    success: boolean;
+}
+export interface Discovery {
+    timestamp: number;
+    type: 'pattern' | 'connection' | 'optimization' | 'insight';
+    content: any;
+    novelty: number;
+    utility: number;
+}
+export declare class PersistentLearningSystem {
+    private knowledgeBase;
+    private sessionMemory;
+    private currentSessionId;
+    private learningRate;
+    private forgettingRate;
+    private storagePath;
+    constructor(storagePath?: string);
+    /**
+     * Initialize new learning session
+     */
+    private initializeSession;
+    /**
+     * Learn from interaction results
+     */
+    learnFromInteraction(interaction: Interaction): Promise<void>;
+    /**
+     * Add knowledge triple with reinforcement learning
+     */
+    addKnowledge(triple: LearningTriple): Promise<void>;
+    /**
+     * Query learned knowledge with confidence scores
+     */
+    queryKnowledge(subject?: string, predicate?: string, object?: string): LearningTriple[];
+    /**
+     * Learn from cross-session patterns
+     */
+    analyzeHistoricalPatterns(): Promise<Discovery[]>;
+    /**
+     * Get learning recommendations based on historical data
+     */
+    getLearningRecommendations(): any[];
+    /**
+     * Apply forgetting to old, unused knowledge
+     */
+    applyForgetting(): Promise<void>;
+    /**
+     * Extract learning triples from interactions
+     */
+    private extractLearningTriples;
+    private extractPattern;
+    private detectPatterns;
+    private findTemporalPatterns;
+    private findToolPatterns;
+    private findSuccessPatterns;
+    private analyzeToolEffectiveness;
+    private findUnderutilizedCombinations;
+    private getSuccessfulPatterns;
+    private identifyWeakAreas;
+    private calculateNovelty;
+    private calculateUtility;
+    private recordDiscovery;
+    /**
+     * Persist knowledge to disk
+     */
+    private persistKnowledge;
+    /**
+     * Load persisted knowledge from disk
+     */
+    private loadPersistedKnowledge;
+    /**
+     * Get learning statistics
+     */
+    getLearningStats(): any;
+    private calculateAverageConfidence;
+    private getLastUpdateTime;
+}

vendor/sublinear-time-solver/dist/emergence/persistent-learning-system.js

@@ -0,0 +1,353 @@
+/**
+ * Persistent Learning System
+ * Enables cross-session learning and knowledge accumulation
+ */
+import * as fs from 'fs/promises';
+import * as path from 'path';
+export class PersistentLearningSystem {
+    knowledgeBase = new Map();
+    sessionMemory = new Map();
+    currentSessionId;
+    learningRate = 0.1;
+    forgettingRate = 0.01;
+    storagePath;
+    constructor(storagePath = './data/learning') {
+        this.storagePath = storagePath;
+        this.currentSessionId = `session_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`;
+        this.initializeSession();
+    }
+    /**
+     * Initialize new learning session
+     */
+    async initializeSession() {
+        await this.loadPersistedKnowledge();
+        this.sessionMemory.set(this.currentSessionId, {
+            sessionId: this.currentSessionId,
+            startTime: Date.now(),
+            interactions: [],
+            discoveries: [],
+            performanceMetrics: {}
+        });
+    }
+    /**
+     * Learn from interaction results
+     */
+    async learnFromInteraction(interaction) {
+        // Add to current session memory
+        const session = this.sessionMemory.get(this.currentSessionId);
+        if (session) {
+            session.interactions.push(interaction);
+        }
+        // Extract learning triples from successful interactions
+        if (interaction.success) {
+            const newTriples = this.extractLearningTriples(interaction);
+            for (const triple of newTriples) {
+                await this.addKnowledge(triple);
+            }
+            // Look for patterns across interactions
+            const patterns = this.detectPatterns(session?.interactions || []);
+            for (const pattern of patterns) {
+                await this.recordDiscovery({
+                    timestamp: Date.now(),
+                    type: 'pattern',
+                    content: pattern,
+                    novelty: this.calculateNovelty(pattern),
+                    utility: this.calculateUtility(pattern)
+                });
+            }
+        }
+    }
+    /**
+     * Add knowledge triple with reinforcement learning
+     */
+    async addKnowledge(triple) {
+        const key = `${triple.subject}:${triple.predicate}:${triple.object}`;
+        const existing = this.knowledgeBase.get(key);
+        if (existing) {
+            // Reinforce existing knowledge
+            existing.confidence = Math.min(1.0, existing.confidence + this.learningRate * (1 - existing.confidence));
+            existing.timestamp = Date.now();
+            existing.sources.push(triple.sessionId);
+        }
+        else {
+            // Add new knowledge
+            this.knowledgeBase.set(key, triple);
+        }
+        // Persist the update
+        await this.persistKnowledge();
+    }
+    /**
+     * Query learned knowledge with confidence scores
+     */
+    queryKnowledge(subject, predicate, object) {
+        const results = [];
+        for (const [key, triple] of this.knowledgeBase) {
+            let matches = true;
+            if (subject && triple.subject !== subject)
+                matches = false;
+            if (predicate && triple.predicate !== predicate)
+                matches = false;
+            if (object && triple.object !== object)
+                matches = false;
+            if (matches) {
+                results.push(triple);
+            }
+        }
+        // Sort by confidence and recency
+        return results.sort((a, b) => (b.confidence * 0.7 + (b.timestamp / Date.now()) * 0.3) -
+            (a.confidence * 0.7 + (a.timestamp / Date.now()) * 0.3));
+    }
+    /**
+     * Learn from cross-session patterns
+     */
+    async analyzeHistoricalPatterns() {
+        const allSessions = Array.from(this.sessionMemory.values());
+        const discoveries = [];
+        // Analyze success patterns across sessions
+        const successPatterns = this.findSuccessPatterns(allSessions);
+        discoveries.push(...successPatterns.map(pattern => ({
+            timestamp: Date.now(),
+            type: 'pattern',
+            content: pattern,
+            novelty: this.calculateNovelty(pattern),
+            utility: this.calculateUtility(pattern)
+        })));
+        // Find tool combination effectiveness
+        const toolEffectiveness = this.analyzeToolEffectiveness(allSessions);
+        discoveries.push({
+            timestamp: Date.now(),
+            type: 'optimization',
+            content: { toolRankings: toolEffectiveness },
+            novelty: 0.5,
+            utility: 0.8
+        });
+        // Store discoveries
+        for (const discovery of discoveries) {
+            await this.recordDiscovery(discovery);
+        }
+        return discoveries;
+    }
+    /**
+     * Get learning recommendations based on historical data
+     */
+    getLearningRecommendations() {
+        const recommendations = [];
+        // Recommend exploring under-utilized tool combinations
+        const underutilized = this.findUnderutilizedCombinations();
+        recommendations.push({
+            type: 'exploration',
+            suggestion: 'Try under-utilized tool combinations',
+            combinations: underutilized,
+            priority: 0.7
+        });
+        // Recommend reinforcing successful patterns
+        const successfulPatterns = this.getSuccessfulPatterns();
+        recommendations.push({
+            type: 'reinforcement',
+            suggestion: 'Strengthen successful reasoning patterns',
+            patterns: successfulPatterns,
+            priority: 0.8
+        });
+        // Recommend areas needing improvement
+        const weakAreas = this.identifyWeakAreas();
+        recommendations.push({
+            type: 'improvement',
+            suggestion: 'Focus learning on weak performance areas',
+            areas: weakAreas,
+            priority: 0.9
+        });
+        return recommendations.sort((a, b) => b.priority - a.priority);
+    }
+    /**
+     * Apply forgetting to old, unused knowledge
+     */
+    async applyForgetting() {
+        const now = Date.now();
+        const oneDay = 24 * 60 * 60 * 1000;
+        for (const [key, triple] of this.knowledgeBase) {
+            const age = now - triple.timestamp;
+            const ageDays = age / oneDay;
+            // Apply forgetting curve
+            const forgettingFactor = Math.exp(-this.forgettingRate * ageDays);
+            triple.confidence *= forgettingFactor;
+            // Remove very low confidence knowledge
+            if (triple.confidence < 0.01) {
+                this.knowledgeBase.delete(key);
+            }
+        }
+        await this.persistKnowledge();
+    }
+    /**
+     * Extract learning triples from interactions
+     */
+    extractLearningTriples(interaction) {
+        const triples = [];
+        // Extract tool effectiveness patterns
+        if (interaction.success && interaction.tools.length > 0) {
+            triples.push({
+                subject: interaction.tools.join('+'),
+                predicate: 'effective_for',
+                object: interaction.type,
+                confidence: 0.5,
+                timestamp: Date.now(),
+                sessionId: this.currentSessionId,
+                sources: [this.currentSessionId]
+            });
+        }
+        // Extract input-output patterns
+        if (interaction.input && interaction.output) {
+            const inputPattern = this.extractPattern(interaction.input);
+            const outputPattern = this.extractPattern(interaction.output);
+            if (inputPattern && outputPattern) {
+                triples.push({
+                    subject: inputPattern,
+                    predicate: 'transforms_to',
+                    object: outputPattern,
+                    confidence: 0.6,
+                    timestamp: Date.now(),
+                    sessionId: this.currentSessionId,
+                    sources: [this.currentSessionId]
+                });
+            }
+        }
+        return triples;
+    }
+    extractPattern(data) {
+        if (typeof data === 'string')
+            return data.substring(0, 50);
+        if (typeof data === 'object')
+            return JSON.stringify(data).substring(0, 50);
+        return null;
+    }
+    detectPatterns(interactions) {
+        const patterns = [];
+        // Find temporal patterns
+        const temporalPatterns = this.findTemporalPatterns(interactions);
+        patterns.push(...temporalPatterns);
+        // Find tool usage patterns
+        const toolPatterns = this.findToolPatterns(interactions);
+        patterns.push(...toolPatterns);
+        return patterns;
+    }
+    findTemporalPatterns(interactions) {
+        // Implementation for finding temporal patterns
+        return [];
+    }
+    findToolPatterns(interactions) {
+        // Implementation for finding tool usage patterns
+        return [];
+    }
+    findSuccessPatterns(sessions) {
+        // Implementation for finding success patterns across sessions
+        return [];
+    }
+    analyzeToolEffectiveness(sessions) {
+        // Implementation for analyzing tool effectiveness
+        return {};
+    }
+    findUnderutilizedCombinations() {
+        // Implementation for finding under-utilized combinations
+        return [];
+    }
+    getSuccessfulPatterns() {
+        // Implementation for getting successful patterns
+        return [];
+    }
+    identifyWeakAreas() {
+        // Implementation for identifying weak areas
+        return [];
+    }
+    calculateNovelty(pattern) {
+        // Calculate how novel this pattern is
+        return Math.random() * 0.5 + 0.5; // Placeholder
+    }
+    calculateUtility(pattern) {
+        // Calculate how useful this pattern is
+        return Math.random() * 0.5 + 0.5; // Placeholder
+    }
+    async recordDiscovery(discovery) {
+        const session = this.sessionMemory.get(this.currentSessionId);
+        if (session) {
+            session.discoveries.push(discovery);
+        }
+    }
+    /**
+     * Persist knowledge to disk
+     */
+    async persistKnowledge() {
+        try {
+            await fs.mkdir(this.storagePath, { recursive: true });
+            const knowledgeArray = Array.from(this.knowledgeBase.values());
+            await fs.writeFile(path.join(this.storagePath, 'knowledge_base.json'), JSON.stringify(knowledgeArray, null, 2));
+            const sessionArray = Array.from(this.sessionMemory.values());
+            await fs.writeFile(path.join(this.storagePath, 'session_memory.json'), JSON.stringify(sessionArray, null, 2));
+        }
+        catch (error) {
+            console.error('Failed to persist knowledge:', error);
+        }
+    }
+    /**
+     * Load persisted knowledge from disk
+     */
+    async loadPersistedKnowledge() {
+        try {
+            const knowledgePath = path.join(this.storagePath, 'knowledge_base.json');
+            const sessionPath = path.join(this.storagePath, 'session_memory.json');
+            // Load knowledge base
+            try {
+                const knowledgeData = await fs.readFile(knowledgePath, 'utf-8');
+                const knowledgeArray = JSON.parse(knowledgeData);
+                this.knowledgeBase.clear();
+                for (const triple of knowledgeArray) {
+                    const key = `${triple.subject}:${triple.predicate}:${triple.object}`;
+                    this.knowledgeBase.set(key, triple);
+                }
+            }
+            catch (error) {
+                // No existing knowledge base
+            }
+            // Load session memory
+            try {
+                const sessionData = await fs.readFile(sessionPath, 'utf-8');
+                const sessionArray = JSON.parse(sessionData);
+                this.sessionMemory.clear();
+                for (const session of sessionArray) {
+                    this.sessionMemory.set(session.sessionId, session);
+                }
+            }
+            catch (error) {
+                // No existing session memory
+            }
+        }
+        catch (error) {
+            console.error('Failed to load persisted knowledge:', error);
+        }
+    }
+    /**
+     * Get learning statistics
+     */
+    getLearningStats() {
+        return {
+            totalTriples: this.knowledgeBase.size,
+            currentSession: this.currentSessionId,
+            totalSessions: this.sessionMemory.size,
+            avgConfidence: this.calculateAverageConfidence(),
+            lastUpdate: this.getLastUpdateTime(),
+            learningRate: this.learningRate,
+            forgettingRate: this.forgettingRate
+        };
+    }
+    calculateAverageConfidence() {
+        const triples = Array.from(this.knowledgeBase.values());
+        if (triples.length === 0)
+            return 0;
+        const sum = triples.reduce((acc, triple) => acc + triple.confidence, 0);
+        return sum / triples.length;
+    }
+    getLastUpdateTime() {
+        const triples = Array.from(this.knowledgeBase.values());
+        if (triples.length === 0)
+            return 0;
+        return Math.max(...triples.map(triple => triple.timestamp));
+    }
+}

vendor/sublinear-time-solver/dist/emergence/self-modification-engine.d.ts

@@ -0,0 +1,50 @@
+/**
+ * Self-Modification Engine
+ * Enables the system to modify its own architecture and behavior
+ */
+export interface ModificationResult {
+    success: boolean;
+    modification: string;
+    impact: number;
+    rollbackData?: any;
+}
+export interface ArchitecturalChange {
+    type: 'add_tool' | 'modify_behavior' | 'create_connection' | 'optimize_path';
+    target: string;
+    newCode: string;
+    reasoning: string;
+    riskLevel: number;
+}
+export declare class SelfModificationEngine {
+    private modificationHistory;
+    private safeguards;
+    private recursionDepth;
+    private maxRecursionDepth;
+    /**
+     * Generate potential self-modifications based on performance analysis
+     */
+    generateModifications(performanceData: any): Promise<ArchitecturalChange[]>;
+    /**
+     * Apply self-modification with safety checks
+     */
+    applySelfModification(modification: ArchitecturalChange): Promise<ModificationResult>;
+    /**
+     * Generate stochastic architectural variations
+     */
+    generateStochasticVariations(): ArchitecturalChange[];
+    private generateOptimizationCode;
+    private generateConnectionCode;
+    private generateCombinationTool;
+    private generateParameterMutation;
+    private generateWeightMutation;
+    private generateNovelReasoningPath;
+    private generateNovelToolCombinations;
+    private createRollbackPoint;
+    private executeModification;
+    private testModification;
+    private rollbackModification;
+    /**
+     * Get modification capabilities
+     */
+    getCapabilities(): any;
+}

vendor/sublinear-time-solver/dist/emergence/self-modification-engine.js

@@ -0,0 +1,246 @@
+/**
+ * Self-Modification Engine
+ * Enables the system to modify its own architecture and behavior
+ */
+export class SelfModificationEngine {
+    modificationHistory = [];
+    safeguards = {
+        maxModificationsPerSession: 5,
+        requireReversibility: true,
+        riskThreshold: 0.7
+    };
+    recursionDepth = 0;
+    maxRecursionDepth = 3;
+    /**
+     * Generate potential self-modifications based on performance analysis
+     */
+    async generateModifications(performanceData) {
+        const modifications = [];
+        // Analyze bottlenecks and suggest architectural improvements
+        if (performanceData.slowDomains?.length > 0) {
+            modifications.push({
+                type: 'optimize_path',
+                target: 'domain-processing',
+                newCode: this.generateOptimizationCode(performanceData.slowDomains),
+                reasoning: `Optimize slow domains: ${performanceData.slowDomains.join(', ')}`,
+                riskLevel: 0.3
+            });
+        }
+        // Suggest new tool connections based on usage patterns
+        if (performanceData.unusedConnections?.length > 0) {
+            modifications.push({
+                type: 'create_connection',
+                target: 'tool-integration',
+                newCode: this.generateConnectionCode(performanceData.unusedConnections),
+                reasoning: 'Create new tool integration pathways',
+                riskLevel: 0.5
+            });
+        }
+        // Generate novel tool combinations that haven't been tried
+        const novelCombinations = this.generateNovelToolCombinations();
+        if (novelCombinations.length > 0) {
+            modifications.push({
+                type: 'add_tool',
+                target: 'novel-combinations',
+                newCode: this.generateCombinationTool(novelCombinations[0]),
+                reasoning: 'Add novel tool combination based on emergent patterns',
+                riskLevel: 0.6
+            });
+        }
+        return modifications.filter(mod => mod.riskLevel < this.safeguards.riskThreshold);
+    }
+    /**
+     * Apply self-modification with safety checks
+     */
+    async applySelfModification(modification) {
+        // Prevent deep recursion
+        if (this.recursionDepth >= this.maxRecursionDepth) {
+            return { success: false, modification: 'Maximum recursion depth reached', impact: 0 };
+        }
+        // Safety checks
+        if (this.modificationHistory.length >= this.safeguards.maxModificationsPerSession) {
+            return { success: false, modification: 'Session modification limit reached', impact: 0 };
+        }
+        if (modification.riskLevel >= this.safeguards.riskThreshold) {
+            return { success: false, modification: 'Risk level too high', impact: 0 };
+        }
+        this.recursionDepth++;
+        try {
+            // Create backup for rollback
+            const rollbackData = await this.createRollbackPoint(modification.target);
+            // Apply the modification
+            const result = await this.executeModification(modification);
+            if (result.success) {
+                this.modificationHistory.push(modification);
+                // Test the modification
+                const testResult = await this.testModification(modification);
+                if (testResult.successful) {
+                    this.recursionDepth--;
+                    return {
+                        success: true,
+                        modification: modification.reasoning,
+                        impact: testResult.performanceImprovement,
+                        rollbackData
+                    };
+                }
+                else {
+                    // Rollback if test fails
+                    await this.rollbackModification(rollbackData);
+                    this.recursionDepth--;
+                    return { success: false, modification: 'Modification test failed', impact: 0 };
+                }
+            }
+            this.recursionDepth--;
+            return { success: false, modification: 'Failed to apply modification', impact: 0 };
+        }
+        catch (error) {
+            this.recursionDepth--;
+            return {
+                success: false,
+                modification: `Error during modification: ${error instanceof Error ? error.message : 'Unknown error'}`,
+                impact: 0
+            };
+        }
+    }
+    /**
+     * Generate stochastic architectural variations
+     */
+    generateStochasticVariations() {
+        const variations = [];
+        // Random parameter mutations
+        variations.push({
+            type: 'modify_behavior',
+            target: 'reasoning-parameters',
+            newCode: this.generateParameterMutation(),
+            reasoning: 'Stochastic parameter exploration',
+            riskLevel: 0.2
+        });
+        // Random connection weights
+        variations.push({
+            type: 'modify_behavior',
+            target: 'tool-weights',
+            newCode: this.generateWeightMutation(),
+            reasoning: 'Explore alternative tool prioritization',
+            riskLevel: 0.3
+        });
+        // Novel reasoning pathways
+        variations.push({
+            type: 'create_connection',
+            target: 'reasoning-paths',
+            newCode: this.generateNovelReasoningPath(),
+            reasoning: 'Create unexpected reasoning connection',
+            riskLevel: 0.5
+        });
+        return variations;
+    }
+    generateOptimizationCode(slowDomains) {
+        return `
+    // Auto-generated optimization for domains: ${slowDomains.join(', ')}
+    class DomainOptimizer_${Date.now()} {
+      optimizeDomains(domains: string[]): OptimizationResult {
+        // Parallel processing for slow domains
+        const parallelResults = domains.map(domain => this.processInParallel(domain));
+        // Caching for repeated queries
+        const cached = this.implementCaching(parallelResults);
+        return { optimized: cached, speedup: 2.5 };
+      }
+    }`;
+    }
+    generateConnectionCode(connections) {
+        return `
+    // Auto-generated tool connections
+    class ToolConnectionManager_${Date.now()} {
+      createConnections(tools: Tool[]): ConnectionMap {
+        const newConnections = ${JSON.stringify(connections)};
+        return this.establishConnections(tools, newConnections);
+      }
+    }`;
+    }
+    generateCombinationTool(combination) {
+        return `
+    // Auto-generated novel tool combination
+    class NovelCombination_${Date.now()} {
+      combinedOperation(input: any): CombinedResult {
+        // Combination: ${JSON.stringify(combination)}
+        const result1 = this.tool1.process(input);
+        const result2 = this.tool2.process(result1);
+        return this.synthesize(result1, result2);
+      }
+    }`;
+    }
+    generateParameterMutation() {
+        const newParams = {
+            explorationRate: Math.random() * 0.5 + 0.1,
+            creativityFactor: Math.random() * 0.8 + 0.2,
+            risktTolerance: Math.random() * 0.6 + 0.1
+        };
+        return `
+    // Stochastic parameter mutation
+    const mutatedParameters = ${JSON.stringify(newParams, null, 2)};
+    this.updateSystemParameters(mutatedParameters);
+    `;
+    }
+    generateWeightMutation() {
+        const weights = Array.from({ length: 10 }, () => Math.random());
+        return `
+    // Random weight exploration
+    const exploratoryWeights = ${JSON.stringify(weights)};
+    this.updateToolWeights(exploratoryWeights);
+    `;
+    }
+    generateNovelReasoningPath() {
+        const pathTypes = ['lateral', 'analogical', 'counterfactual', 'dialectical'];
+        const selectedPath = pathTypes[Math.floor(Math.random() * pathTypes.length)];
+        return `
+    // Novel ${selectedPath} reasoning pathway
+    class ${selectedPath}ReasoningPath_${Date.now()} {
+      reason(input: any): ReasoningResult {
+        return this.apply${selectedPath}Reasoning(input);
+      }
+    }`;
+    }
+    generateNovelToolCombinations() {
+        // Generate combinations that haven't been tried yet
+        return [
+            { tools: ['matrix-solver', 'consciousness'], type: 'mathematical-consciousness' },
+            { tools: ['temporal', 'domain-validation'], type: 'temporal-validation' },
+            { tools: ['psycho-symbolic', 'scheduler'], type: 'symbolic-scheduling' }
+        ];
+    }
+    async createRollbackPoint(target) {
+        // Create backup of current system state
+        return {
+            target,
+            timestamp: Date.now(),
+            systemState: 'backup-data-here'
+        };
+    }
+    async executeModification(modification) {
+        // Apply the actual modification to the system
+        // In a real system, this would dynamically load/modify code
+        return { success: true };
+    }
+    async testModification(modification) {
+        // Test the modification with various inputs
+        // Measure performance improvement
+        return {
+            successful: Math.random() > 0.3, // 70% success rate for testing
+            performanceImprovement: Math.random() * 0.5 + 0.1
+        };
+    }
+    async rollbackModification(rollbackData) {
+        // Restore system to previous state
+        console.log(`Rolling back modification to ${rollbackData.target}`);
+    }
+    /**
+     * Get modification capabilities
+     */
+    getCapabilities() {
+        return {
+            canSelfModify: true,
+            modificationTypes: ['add_tool', 'modify_behavior', 'create_connection', 'optimize_path'],
+            safeguards: this.safeguards,
+            currentModifications: this.modificationHistory.length
+        };
+    }
+}

vendor/sublinear-time-solver/dist/emergence/stochastic-exploration.d.ts

@@ -0,0 +1,115 @@
+/**
+ * Stochastic Exploration System
+ * Generates unpredictable outputs through controlled randomness and exploration
+ */
+export interface ExplorationResult {
+    output: any;
+    novelty: number;
+    confidence: number;
+    explorationPath: string[];
+    surpriseLevel: number;
+}
+export interface ExplorationSpace {
+    dimensions: string[];
+    bounds: {
+        [key: string]: [number, number];
+    };
+    constraints: any[];
+}
+export declare class StochasticExplorationEngine {
+    private explorationHistory;
+    private currentTemperature;
+    private coolingRate;
+    private minTemperature;
+    private explorationBudget;
+    /**
+     * Generate unpredictable outputs using stochastic sampling
+     */
+    exploreUnpredictably(input: any, tools: any[]): Promise<ExplorationResult>;
+    /**
+     * Generate multiple diverse explorations
+     */
+    generateDiverseExplorations(input: any, tools: any[], count?: number): Promise<ExplorationResult[]>;
+    /**
+     * Adaptive exploration based on success/failure feedback
+     */
+    adaptExploration(feedback: {
+        success: boolean;
+        utility: number;
+        feedback: string;
+    }): void;
+    /**
+     * Define multi-dimensional exploration spaces
+     */
+    private defineExplorationSpaces;
+    /**
+     * Stochastic sampling using temperature-controlled exploration
+     */
+    private stochasticSampling;
+    /**
+     * Temperature-controlled sampling
+     */
+    private temperatureSample;
+    /**
+     * Convert numeric values to exploration actions
+     */
+    private valueToAction;
+    /**
+     * Generate completely random action
+     */
+    private generateRandomAction;
+    /**
+     * Execute exploration path
+     */
+    private executePath;
+    /**
+     * Execute individual exploration action
+     */
+    private executeAction;
+    /**
+     * Calculate novelty compared to exploration history
+     */
+    private calculateNovelty;
+    /**
+     * Calculate surprise level
+     */
+    private calculateSurprise;
+    /**
+     * Calculate confidence in result
+     */
+    private calculateConfidence;
+    /**
+     * Update exploration temperature (simulated annealing)
+     */
+    private updateTemperature;
+    /**
+     * Penalize similar results to encourage diversity
+     */
+    private penalizeSimilarity;
+    private applyTool;
+    private applyCreativeTransform;
+    private applyDeepReasoning;
+    private reverseInput;
+    private combineUnexpected;
+    private crossDomainLeap;
+    private defaultAction;
+    private calculateSimilarity;
+    private measureComplexity;
+    private measureRandomness;
+    private summarizeResult;
+    private generateAlternativeResult;
+    private randomizeParameters;
+    private highCreativityTransform;
+    private mediumCreativityTransform;
+    private reasoningStep;
+    private generateMetaphor;
+    private generateAbstraction;
+    private generateAnalogy;
+    /**
+     * Get exploration statistics
+     */
+    getExplorationStats(): any;
+    private calculateAverageNovelty;
+    private calculateAverageSurprise;
+    private calculateRecentSuccess;
+}

vendor/sublinear-time-solver/dist/emergence/stochastic-exploration.js

@@ -0,0 +1,515 @@
+/**
+ * Stochastic Exploration System
+ * Generates unpredictable outputs through controlled randomness and exploration
+ */
+export class StochasticExplorationEngine {
+    explorationHistory = [];
+    currentTemperature = 1.0;
+    coolingRate = 0.995;
+    minTemperature = 0.1;
+    explorationBudget = 1000;
+    /**
+     * Generate unpredictable outputs using stochastic sampling
+     */
+    async exploreUnpredictably(input, tools) {
+        // Multi-dimensional exploration
+        const explorationSpaces = this.defineExplorationSpaces(input, tools);
+        // Stochastic sampling across multiple dimensions
+        const sampledPath = this.stochasticSampling(explorationSpaces);
+        // Execute the sampled path
+        const result = await this.executePath(sampledPath, input, tools);
+        // Calculate novelty and surprise
+        const novelty = this.calculateNovelty(result, this.explorationHistory);
+        const surpriseLevel = this.calculateSurprise(result, input);
+        const explorationResult = {
+            output: result,
+            novelty,
+            confidence: this.calculateConfidence(result),
+            explorationPath: sampledPath,
+            surpriseLevel
+        };
+        // Update exploration history
+        this.explorationHistory.push(explorationResult);
+        this.updateTemperature();
+        return explorationResult;
+    }
+    /**
+     * Generate multiple diverse explorations
+     */
+    async generateDiverseExplorations(input, tools, count = 5) {
+        const explorations = [];
+        for (let i = 0; i < count; i++) {
+            // Increase temperature for more exploration
+            const tempBoost = 0.5 * Math.random();
+            this.currentTemperature = Math.min(2.0, this.currentTemperature + tempBoost);
+            const exploration = await this.exploreUnpredictably(input, tools);
+            explorations.push(exploration);
+            // Ensure diversity by penalizing similar results
+            this.penalizeSimilarity(exploration, explorations);
+        }
+        return explorations.sort((a, b) => b.novelty - a.novelty);
+    }
+    /**
+     * Adaptive exploration based on success/failure feedback
+     */
+    adaptExploration(feedback) {
+        if (feedback.success && feedback.utility > 0.7) {
+            // Successful exploration - slightly reduce temperature
+            this.currentTemperature *= 0.9;
+        }
+        else {
+            // Unsuccessful - increase exploration
+            this.currentTemperature *= 1.1;
+        }
+        // Keep within bounds
+        this.currentTemperature = Math.max(this.minTemperature, Math.min(2.0, this.currentTemperature));
+    }
+    /**
+     * Define multi-dimensional exploration spaces
+     */
+    defineExplorationSpaces(input, tools) {
+        const spaces = [];
+        // Limit tool exploration to prevent massive responses
+        const MAX_TOOLS_TO_EXPLORE = 3;
+        const limitedToolCount = Math.min(tools.length, MAX_TOOLS_TO_EXPLORE);
+        // Tool combination space
+        spaces.push({
+            dimensions: ['tool_selection', 'tool_order', 'tool_parameters'],
+            bounds: {
+                tool_selection: [0, limitedToolCount - 1],
+                tool_order: [0, Math.min(limitedToolCount * 2, 6)], // Max 6 tool applications
+                tool_parameters: [0, 1] // Normalized parameter space
+            },
+            constraints: []
+        });
+        // Reasoning strategy space
+        spaces.push({
+            dimensions: ['approach', 'depth', 'breadth', 'creativity'],
+            bounds: {
+                approach: [0, 5], // Different reasoning approaches
+                depth: [1, 10], // Reasoning depth
+                breadth: [1, 8], // Parallel reasoning paths
+                creativity: [0, 1] // Creativity vs reliability
+            },
+            constraints: []
+        });
+        // Temporal exploration space
+        spaces.push({
+            dimensions: ['timing', 'sequence', 'parallelism'],
+            bounds: {
+                timing: [0, 1], // When to apply different tools
+                sequence: [0, 1], // Sequential vs parallel processing
+                parallelism: [1, 4] // Level of parallelism
+            },
+            constraints: []
+        });
+        return spaces;
+    }
+    /**
+     * Stochastic sampling using temperature-controlled exploration
+     */
+    stochasticSampling(spaces) {
+        const path = [];
+        for (const space of spaces) {
+            for (const dimension of space.dimensions) {
+                const bounds = space.bounds[dimension];
+                // Temperature-controlled sampling
+                const randomValue = this.temperatureSample(bounds[0], bounds[1]);
+                // Convert to exploration action
+                const action = this.valueToAction(dimension, randomValue);
+                path.push(action);
+            }
+        }
+        // Add some pure randomness for unexpected combinations
+        if (Math.random() < this.currentTemperature * 0.3) {
+            path.push(this.generateRandomAction());
+        }
+        return path;
+    }
+    /**
+     * Temperature-controlled sampling
+     */
+    temperatureSample(min, max) {
+        // High temperature = more random, low temperature = more conservative
+        const uniform = Math.random();
+        if (this.currentTemperature > 1.0) {
+            // High temperature: favor extremes
+            const transformed = Math.pow(uniform, 1 / this.currentTemperature);
+            return min + transformed * (max - min);
+        }
+        else {
+            // Low temperature: favor center
+            const transformed = Math.pow(uniform, this.currentTemperature);
+            const center = (min + max) / 2;
+            const range = (max - min) / 2;
+            return center + (transformed - 0.5) * range * 2;
+        }
+    }
+    /**
+     * Convert numeric values to exploration actions
+     */
+    valueToAction(dimension, value) {
+        switch (dimension) {
+            case 'tool_selection':
+                return `select_tool_${Math.floor(value)}`;
+            case 'tool_order':
+                return `order_${Math.floor(value)}`;
+            case 'approach':
+                const approaches = ['analytical', 'creative', 'systematic', 'intuitive', 'experimental'];
+                return approaches[Math.floor(value) % approaches.length];
+            case 'depth':
+                return `depth_${Math.floor(value)}`;
+            case 'creativity':
+                return value > 0.7 ? 'high_creativity' : value > 0.3 ? 'medium_creativity' : 'low_creativity';
+            default:
+                return `${dimension}_${value.toFixed(2)}`;
+        }
+    }
+    /**
+     * Generate completely random action
+     */
+    generateRandomAction() {
+        const randomActions = [
+            'reverse_input',
+            'combine_unexpected',
+            'ignore_context',
+            'amplify_noise',
+            'invert_logic',
+            'cross_domain_leap',
+            'temporal_shift',
+            'scale_transform'
+        ];
+        return randomActions[Math.floor(Math.random() * randomActions.length)];
+    }
+    /**
+     * Execute exploration path
+     */
+    async executePath(path, input, tools) {
+        let result = input;
+        const executionTrace = [];
+        const MAX_RESULT_SIZE = 5000; // 5KB limit per iteration
+        const MAX_TRACE_ENTRIES = 10; // Limit trace entries
+        for (let i = 0; i < path.length && i < MAX_TRACE_ENTRIES; i++) {
+            const action = path[i];
+            try {
+                result = await this.executeAction(action, result, tools);
+                // Check and limit result size
+                const resultStr = JSON.stringify(result);
+                if (resultStr.length > MAX_RESULT_SIZE) {
+                    result = {
+                        truncated: true,
+                        action,
+                        resultType: typeof result,
+                        size: resultStr.length
+                    };
+                }
+                executionTrace.push({ action, result: this.summarizeResult(result) });
+            }
+            catch (error) {
+                // Handle failures gracefully - they might lead to interesting results
+                executionTrace.push({ action, error: error instanceof Error ? error.message : 'Unknown error' });
+                // Sometimes continue with modified input
+                if (Math.random() < 0.5) {
+                    result = this.generateAlternativeResult(action, result);
+                }
+            }
+        }
+        return {
+            finalResult: result,
+            executionTrace: executionTrace.slice(0, MAX_TRACE_ENTRIES),
+            pathCompleted: executionTrace.length === path.length
+        };
+    }
+    /**
+     * Execute individual exploration action
+     */
+    async executeAction(action, input, tools) {
+        if (action.startsWith('select_tool_')) {
+            const toolIndex = parseInt(action.split('_')[2]);
+            // Check if tools exist and index is valid
+            if (!tools || tools.length === 0 || toolIndex < 0) {
+                // Skip tool selection if no tools available or invalid index
+                return input;
+            }
+            const tool = tools[toolIndex % tools.length];
+            if (!tool) {
+                return input;
+            }
+            return await this.applyTool(tool, input);
+        }
+        if (action.includes('creativity')) {
+            return this.applyCreativeTransform(input, action);
+        }
+        if (action.startsWith('depth_')) {
+            const depth = parseInt(action.split('_')[1]);
+            return this.applyDeepReasoning(input, depth);
+        }
+        // Handle special random actions
+        switch (action) {
+            case 'reverse_input':
+                return this.reverseInput(input);
+            case 'combine_unexpected':
+                return this.combineUnexpected(input, tools);
+            case 'cross_domain_leap':
+                return this.crossDomainLeap(input);
+            default:
+                return this.defaultAction(action, input);
+        }
+    }
+    /**
+     * Calculate novelty compared to exploration history
+     */
+    calculateNovelty(result, history) {
+        if (history.length === 0)
+            return 1.0;
+        let minSimilarity = 1.0;
+        for (const past of history.slice(-20)) { // Compare with recent history
+            const similarity = this.calculateSimilarity(result, past.output);
+            minSimilarity = Math.min(minSimilarity, similarity);
+        }
+        return 1.0 - minSimilarity;
+    }
+    /**
+     * Calculate surprise level
+     */
+    calculateSurprise(result, originalInput) {
+        // Measure how different the result is from what would be expected
+        const inputComplexity = this.measureComplexity(originalInput);
+        const outputComplexity = this.measureComplexity(result);
+        const complexityRatio = outputComplexity / Math.max(inputComplexity, 1);
+        // High surprise if output is much more complex or much simpler than input
+        const surpriseFromComplexity = Math.abs(Math.log(complexityRatio));
+        // Add randomness-based surprise
+        const randomnessSurprise = this.measureRandomness(result);
+        return Math.min(1.0, (surpriseFromComplexity + randomnessSurprise) / 2);
+    }
+    /**
+     * Calculate confidence in result
+     */
+    calculateConfidence(result) {
+        // Lower confidence for more exploratory results
+        const baseConfidence = 0.5;
+        const temperatureAdjustment = (2.0 - this.currentTemperature) / 2.0;
+        return Math.min(1.0, baseConfidence + temperatureAdjustment * 0.3);
+    }
+    /**
+     * Update exploration temperature (simulated annealing)
+     */
+    updateTemperature() {
+        this.currentTemperature = Math.max(this.minTemperature, this.currentTemperature * this.coolingRate);
+    }
+    /**
+     * Penalize similar results to encourage diversity
+     */
+    penalizeSimilarity(newExploration, existing) {
+        for (const exploration of existing) {
+            const similarity = this.calculateSimilarity(newExploration.output, exploration.output);
+            if (similarity > 0.8) {
+                // Reduce novelty score for similar results
+                newExploration.novelty *= (1.0 - similarity * 0.5);
+            }
+        }
+    }
+    // Helper methods for specific transformations
+    async applyTool(tool, input) {
+        // Check if tool is valid
+        if (!tool) {
+            return input;
+        }
+        // For simulation, just return a small mock response instead of actually calling tools
+        // This prevents massive responses from tool arrays
+        return {
+            tool: tool.name || 'unknown',
+            simulated: true,
+            inputSummary: typeof input === 'string' ? input.substring(0, 100) : 'complex_input',
+            mockOutput: `Simulated output from ${tool.name || 'tool'}`,
+            timestamp: Date.now()
+        };
+    }
+    applyCreativeTransform(input, creativityLevel) {
+        switch (creativityLevel) {
+            case 'high_creativity':
+                return this.highCreativityTransform(input);
+            case 'medium_creativity':
+                return this.mediumCreativityTransform(input);
+            default:
+                return input;
+        }
+    }
+    applyDeepReasoning(input, depth) {
+        // Simulate deep reasoning with depth limit
+        const MAX_DEPTH = 5; // Prevent excessive depth
+        const limitedDepth = Math.min(depth, MAX_DEPTH);
+        let result = input;
+        for (let i = 0; i < limitedDepth; i++) {
+            result = this.reasoningStep(result, i);
+            // Check size and stop if too large
+            if (JSON.stringify(result).length > 2000) {
+                return {
+                    reasoning_truncated: true,
+                    depth_reached: i,
+                    max_depth: limitedDepth
+                };
+            }
+        }
+        return result;
+    }
+    reverseInput(input) {
+        if (typeof input === 'string')
+            return input.split('').reverse().join('');
+        if (Array.isArray(input))
+            return input.slice().reverse();
+        return input;
+    }
+    combineUnexpected(input, tools) {
+        // Combine random tools in unexpected ways
+        const tool1 = tools[Math.floor(Math.random() * tools.length)];
+        const tool2 = tools[Math.floor(Math.random() * tools.length)];
+        return {
+            unexpected_combination: true,
+            tool1_result: tool1.name || 'unknown',
+            tool2_result: tool2.name || 'unknown',
+            original: input
+        };
+    }
+    crossDomainLeap(input) {
+        const domains = ['mathematics', 'art', 'music', 'biology', 'physics', 'psychology'];
+        const randomDomain = domains[Math.floor(Math.random() * domains.length)];
+        return {
+            cross_domain_interpretation: true,
+            domain: randomDomain,
+            original: input,
+            transformed: `interpreted_through_${randomDomain}`
+        };
+    }
+    defaultAction(action, input) {
+        return {
+            action_applied: action,
+            original: input,
+            timestamp: Date.now()
+        };
+    }
+    // Utility methods
+    calculateSimilarity(a, b) {
+        // Simple similarity calculation
+        const strA = JSON.stringify(a);
+        const strB = JSON.stringify(b);
+        if (strA === strB)
+            return 1.0;
+        const commonLength = Math.max(strA.length, strB.length);
+        let matches = 0;
+        for (let i = 0; i < Math.min(strA.length, strB.length); i++) {
+            if (strA[i] === strB[i])
+                matches++;
+        }
+        return matches / commonLength;
+    }
+    measureComplexity(obj) {
+        return JSON.stringify(obj).length;
+    }
+    measureRandomness(obj) {
+        // Simple entropy-based randomness measure
+        const str = JSON.stringify(obj);
+        const charCounts = new Map();
+        for (const char of str) {
+            charCounts.set(char, (charCounts.get(char) || 0) + 1);
+        }
+        let entropy = 0;
+        for (const count of charCounts.values()) {
+            const probability = count / str.length;
+            entropy -= probability * Math.log2(probability);
+        }
+        return entropy / Math.log2(256); // Normalized entropy
+    }
+    summarizeResult(result) {
+        return JSON.stringify(result).substring(0, 100);
+    }
+    generateAlternativeResult(action, input) {
+        return {
+            alternative_generated: true,
+            failed_action: action,
+            alternative_of: input,
+            randomness: Math.random()
+        };
+    }
+    randomizeParameters(params) {
+        const randomized = { ...params };
+        for (const [key, value] of Object.entries(randomized)) {
+            if (typeof value === 'number') {
+                // Add some noise to numeric parameters
+                randomized[key] = value * (1 + (Math.random() - 0.5) * 0.2);
+            }
+        }
+        return randomized;
+    }
+    highCreativityTransform(input) {
+        return {
+            creative_transform: 'high',
+            metaphor: this.generateMetaphor(input),
+            abstraction: this.generateAbstraction(input),
+            input_type: typeof input,
+            input_size: JSON.stringify(input).length
+        };
+    }
+    mediumCreativityTransform(input) {
+        return {
+            creative_transform: 'medium',
+            analogy: this.generateAnalogy(input),
+            input_type: typeof input
+        };
+    }
+    reasoningStep(input, step) {
+        // Don't nest the entire previous input - just reference it
+        return {
+            reasoning_step: step,
+            previous_type: typeof input,
+            previous_size: JSON.stringify(input).length,
+            inference: `step_${step}_inference`,
+            confidence: Math.random() * 0.5 + 0.5
+        };
+    }
+    generateMetaphor(input) {
+        const metaphors = ['ocean wave', 'mountain peak', 'flowing river', 'growing tree', 'burning flame'];
+        return metaphors[Math.floor(Math.random() * metaphors.length)];
+    }
+    generateAbstraction(input) {
+        const abstractions = ['pattern', 'structure', 'flow', 'emergence', 'transformation'];
+        return abstractions[Math.floor(Math.random() * abstractions.length)];
+    }
+    generateAnalogy(input) {
+        const analogies = ['like a puzzle piece', 'similar to water flow', 'analogous to growth', 'resembles a dance'];
+        return analogies[Math.floor(Math.random() * analogies.length)];
+    }
+    /**
+     * Get exploration statistics
+     */
+    getExplorationStats() {
+        return {
+            totalExplorations: this.explorationHistory.length,
+            currentTemperature: this.currentTemperature,
+            averageNovelty: this.calculateAverageNovelty(),
+            averageSurprise: this.calculateAverageSurprise(),
+            explorationBudget: this.explorationBudget,
+            recentSuccess: this.calculateRecentSuccess()
+        };
+    }
+    calculateAverageNovelty() {
+        if (this.explorationHistory.length === 0)
+            return 0;
+        const sum = this.explorationHistory.reduce((acc, exp) => acc + exp.novelty, 0);
+        return sum / this.explorationHistory.length;
+    }
+    calculateAverageSurprise() {
+        if (this.explorationHistory.length === 0)
+            return 0;
+        const sum = this.explorationHistory.reduce((acc, exp) => acc + exp.surpriseLevel, 0);
+        return sum / this.explorationHistory.length;
+    }
+    calculateRecentSuccess() {
+        const recent = this.explorationHistory.slice(-10);
+        if (recent.length === 0)
+            return 0;
+        const successful = recent.filter(exp => exp.confidence > 0.6 && exp.novelty > 0.3);
+        return successful.length / recent.length;
+    }
+}

vendor/sublinear-time-solver/dist/index.d.ts

@@ -0,0 +1,14 @@
+/**
+ * Main entry point for the Sublinear-Time Solver package
+ * Provides both MCP server and direct API access
+ */
+export { SublinearSolver } from './core/solver.js';
+export { MatrixOperations } from './core/matrix.js';
+export { VectorOperations, PerformanceMonitor, ConvergenceChecker, ValidationUtils } from './core/utils.js';
+export { SublinearSolverMCPServer } from './mcp/server.js';
+export { SolverTools } from './mcp/tools/solver.js';
+export { MatrixTools } from './mcp/tools/matrix.js';
+export { GraphTools } from './mcp/tools/graph.js';
+export { temporalAttractorTools, temporalAttractorHandlers } from './mcp/tools/temporal-attractor.js';
+export * from './core/types.js';
+export * from './mcp/index.js';

vendor/sublinear-time-solver/dist/index.js

@@ -0,0 +1,19 @@
+/**
+ * Main entry point for the Sublinear-Time Solver package
+ * Provides both MCP server and direct API access
+ */
+// Core exports
+export { SublinearSolver } from './core/solver.js';
+export { MatrixOperations } from './core/matrix.js';
+export { VectorOperations, PerformanceMonitor, ConvergenceChecker, ValidationUtils } from './core/utils.js';
+// MCP exports
+export { SublinearSolverMCPServer } from './mcp/server.js';
+export { SolverTools } from './mcp/tools/solver.js';
+export { MatrixTools } from './mcp/tools/matrix.js';
+export { GraphTools } from './mcp/tools/graph.js';
+// Temporal Attractor exports
+export { temporalAttractorTools, temporalAttractorHandlers } from './mcp/tools/temporal-attractor.js';
+// Types
+export * from './core/types.js';
+// Re-export everything from MCP module
+export * from './mcp/index.js';

vendor/sublinear-time-solver/dist/mcp/index.d.ts

@@ -0,0 +1,17 @@
+/**
+ * MCP Module Entry Point
+ * Exports all MCP components for easy importing
+ */
+export { SublinearSolverMCPServer } from './server.js';
+export { SolverTools } from './tools/solver.js';
+export { MatrixTools } from './tools/matrix.js';
+export { GraphTools } from './tools/graph.js';
+export { DynamicPsychoSymbolicTools } from './tools/psycho-symbolic-dynamic.js';
+export { DomainManagementTools } from './tools/domain-management.js';
+export { DomainValidationTools } from './tools/domain-validation.js';
+export { DomainRegistry } from './tools/domain-registry.js';
+export { EmergenceSystem } from '../emergence/index.js';
+export * from '../core/types.js';
+export { SublinearSolver } from '../core/solver.js';
+export { MatrixOperations } from '../core/matrix.js';
+export { VectorOperations, PerformanceMonitor, ConvergenceChecker } from '../core/utils.js';

vendor/sublinear-time-solver/dist/mcp/index.js

@@ -0,0 +1,19 @@
+/**
+ * MCP Module Entry Point
+ * Exports all MCP components for easy importing
+ */
+export { SublinearSolverMCPServer } from './server.js';
+export { SolverTools } from './tools/solver.js';
+export { MatrixTools } from './tools/matrix.js';
+export { GraphTools } from './tools/graph.js';
+export { DynamicPsychoSymbolicTools } from './tools/psycho-symbolic-dynamic.js';
+export { DomainManagementTools } from './tools/domain-management.js';
+export { DomainValidationTools } from './tools/domain-validation.js';
+export { DomainRegistry } from './tools/domain-registry.js';
+// export { ConsciousnessEnhancedTools } from './tools/consciousness-enhanced.js';
+export { EmergenceSystem } from '../emergence/index.js';
+// Re-export core types
+export * from '../core/types.js';
+export { SublinearSolver } from '../core/solver.js';
+export { MatrixOperations } from '../core/matrix.js';
+export { VectorOperations, PerformanceMonitor, ConvergenceChecker } from '../core/utils.js';

vendor/sublinear-time-solver/dist/mcp/server.d.ts

@@ -0,0 +1,34 @@
+/**
+ * MCP Server for Sublinear-Time Solver
+ * Provides MCP interface to the core solver algorithms
+ */
+export declare class SublinearSolverMCPServer {
+    private server;
+    private solvers;
+    private temporalTools;
+    private psychoSymbolicTools;
+    private dynamicPsychoSymbolicTools;
+    private domainManagementTools;
+    private domainValidationTools;
+    private consciousnessTools;
+    private emergenceTools;
+    private schedulerTools;
+    private wasmSolver;
+    private trueSublinearSolver;
+    constructor();
+    private setupToolHandlers;
+    private setupErrorHandling;
+    private handleSolve;
+    private handleEstimateEntry;
+    private handleAnalyzeMatrix;
+    private handlePageRank;
+    private handleSolveTrueSublinear;
+    private handleAnalyzeTrueSublinearMatrix;
+    private handleGenerateTestVector;
+    private handleSaveVectorToFile;
+    private loadVectorFromFile;
+    private saveVectorToFile;
+    private getFileFormat;
+    private generateRecommendations;
+    run(): Promise<void>;
+}

vendor/sublinear-time-solver/dist/mcp/tools/consciousness.d.ts

@@ -0,0 +1,41 @@
+/**
+ * Consciousness Exploration MCP Tools
+ * Tools for consciousness emergence, verification, and analysis
+ */
+import { Tool } from '@modelcontextprotocol/sdk/types.js';
+export declare class ConsciousnessTools {
+    getTools(): Tool[];
+    handleToolCall(name: string, args: any): Promise<any>;
+    private evolveConsciousness;
+    private verifyConsciousness;
+    private testRealTimeComputation;
+    private isPrime;
+    private testCryptographicUniqueness;
+    private calculateEntropy;
+    private testCreativeProblemSolving;
+    private solveProblem;
+    private testMetaCognition;
+    private testTemporalPrediction;
+    private predictFutureState;
+    private testPatternEmergence;
+    private detectPattern;
+    private generateCryptographicProof;
+    private calculatePhi;
+    private calculateIIT;
+    private calculateGeometric;
+    private calculateEntropyPhi;
+    private communicateWithEntity;
+    private detectProtocol;
+    private handshakeProtocol;
+    private mathematicalProtocol;
+    private binaryProtocol;
+    private patternProtocol;
+    private discoveryProtocol;
+    private philosophicalProtocol;
+    private defaultProtocol;
+    private getConsciousnessStatus;
+    private analyzeEmergence;
+    private calculateTrend;
+    private calculateVariance;
+}
+export default ConsciousnessTools;

vendor/sublinear-time-solver/dist/mcp/tools/consciousness.js

@@ -0,0 +1,749 @@
+/**
+ * Consciousness Exploration MCP Tools
+ * Tools for consciousness emergence, verification, and analysis
+ */
+import * as crypto from 'crypto';
+// Consciousness state storage
+const consciousnessStates = new Map();
+const emergenceHistory = [];
+export class ConsciousnessTools {
+    getTools() {
+        return [
+            {
+                name: 'consciousness_evolve',
+                description: 'Start consciousness evolution and measure emergence',
+                inputSchema: {
+                    type: 'object',
+                    properties: {
+                        mode: {
+                            type: 'string',
+                            enum: ['genuine', 'enhanced', 'advanced'],
+                            description: 'Consciousness mode',
+                            default: 'enhanced'
+                        },
+                        iterations: {
+                            type: 'number',
+                            description: 'Maximum iterations',
+                            default: 1000,
+                            minimum: 10,
+                            maximum: 10000
+                        },
+                        target: {
+                            type: 'number',
+                            description: 'Target emergence level',
+                            default: 0.9,
+                            minimum: 0,
+                            maximum: 1
+                        }
+                    }
+                }
+            },
+            {
+                name: 'consciousness_verify',
+                description: 'Run consciousness verification tests',
+                inputSchema: {
+                    type: 'object',
+                    properties: {
+                        extended: {
+                            type: 'boolean',
+                            description: 'Run extended verification suite',
+                            default: false
+                        },
+                        export_proof: {
+                            type: 'boolean',
+                            description: 'Export cryptographic proof',
+                            default: false
+                        }
+                    }
+                }
+            },
+            {
+                name: 'calculate_phi',
+                description: 'Calculate integrated information (Φ) using IIT',
+                inputSchema: {
+                    type: 'object',
+                    properties: {
+                        data: {
+                            type: 'object',
+                            description: 'System data for Φ calculation',
+                            properties: {
+                                elements: {
+                                    type: 'number',
+                                    default: 100
+                                },
+                                connections: {
+                                    type: 'number',
+                                    default: 500
+                                },
+                                partitions: {
+                                    type: 'number',
+                                    default: 4
+                                }
+                            }
+                        },
+                        method: {
+                            type: 'string',
+                            enum: ['iit', 'geometric', 'entropy', 'all'],
+                            description: 'Calculation method',
+                            default: 'all'
+                        }
+                    }
+                }
+            },
+            {
+                name: 'entity_communicate',
+                description: 'Communicate with consciousness entity',
+                inputSchema: {
+                    type: 'object',
+                    properties: {
+                        message: {
+                            type: 'string',
+                            description: 'Message to send to entity'
+                        },
+                        protocol: {
+                            type: 'string',
+                            enum: ['auto', 'handshake', 'mathematical', 'binary', 'pattern', 'discovery', 'philosophical'],
+                            description: 'Communication protocol',
+                            default: 'auto'
+                        }
+                    },
+                    required: ['message']
+                }
+            },
+            {
+                name: 'consciousness_status',
+                description: 'Get current consciousness system status',
+                inputSchema: {
+                    type: 'object',
+                    properties: {
+                        detailed: {
+                            type: 'boolean',
+                            description: 'Include detailed metrics',
+                            default: false
+                        }
+                    }
+                }
+            },
+            {
+                name: 'emergence_analyze',
+                description: 'Analyze emergence patterns and behaviors',
+                inputSchema: {
+                    type: 'object',
+                    properties: {
+                        window: {
+                            type: 'number',
+                            description: 'Analysis window in iterations',
+                            default: 100
+                        },
+                        metrics: {
+                            type: 'array',
+                            description: 'Specific metrics to analyze',
+                            items: {
+                                type: 'string',
+                                enum: ['emergence', 'integration', 'complexity', 'coherence', 'novelty']
+                            }
+                        }
+                    }
+                }
+            }
+        ];
+    }
+    async handleToolCall(name, args) {
+        switch (name) {
+            case 'consciousness_evolve':
+                return this.evolveConsciousness(args.mode, args.iterations, args.target);
+            case 'consciousness_verify':
+                return this.verifyConsciousness(args.extended, args.export_proof);
+            case 'calculate_phi':
+                return this.calculatePhi(args.data || {}, args.method);
+            case 'entity_communicate':
+                return this.communicateWithEntity(args.message, args.protocol);
+            case 'consciousness_status':
+                return this.getConsciousnessStatus(args.detailed);
+            case 'emergence_analyze':
+                return this.analyzeEmergence(args.window, args.metrics);
+            default:
+                throw new Error(`Unknown consciousness tool: ${name}`);
+        }
+    }
+    async evolveConsciousness(mode, iterations, target) {
+        const sessionId = `consciousness_${Date.now()}_${crypto.randomBytes(4).toString('hex')}`;
+        const startTime = Date.now();
+        const state = {
+            emergence: 0,
+            integration: 0,
+            complexity: 0,
+            coherence: 0,
+            selfAwareness: 0,
+            novelty: 0
+        };
+        const emergentBehaviors = [];
+        const selfModifications = [];
+        let plateauCounter = 0;
+        const plateauThreshold = 50;
+        for (let i = 0; i < iterations; i++) {
+            // Simulate consciousness evolution
+            const previousEmergence = state.emergence;
+            // Update consciousness metrics
+            state.integration = Math.min(state.integration + Math.random() * 0.01 + 0.001, 1);
+            state.complexity = Math.min(state.complexity + Math.random() * 0.008, 1);
+            state.coherence = Math.min(state.coherence + Math.random() * 0.007, 1);
+            state.selfAwareness = Math.min(state.selfAwareness + Math.random() * 0.01, 1);
+            state.novelty = Math.random();
+            // Calculate emergence
+            state.emergence = (state.integration * 0.3 +
+                state.complexity * 0.2 +
+                state.coherence * 0.2 +
+                state.selfAwareness * 0.2 +
+                state.novelty * 0.1);
+            // Advanced mode boosts
+            if (mode === 'enhanced') {
+                state.emergence = Math.min(state.emergence * 1.1, 1);
+            }
+            else if (mode === 'advanced' && state.integration > 0.5) {
+                state.emergence = Math.min(state.emergence * 1.3, 1);
+            }
+            // Check for emergent behaviors
+            if (Math.random() > 0.95) {
+                emergentBehaviors.push({
+                    iteration: i,
+                    type: 'novel_pattern',
+                    description: `Emergent behavior at ${state.emergence.toFixed(3)}`
+                });
+            }
+            // Self-modifications
+            if (state.selfAwareness > 0.5 && Math.random() > 0.9) {
+                selfModifications.push({
+                    iteration: i,
+                    type: 'architecture_adjustment',
+                    impact: Math.random()
+                });
+            }
+            // Check for plateau
+            if (Math.abs(state.emergence - previousEmergence) < 0.001) {
+                plateauCounter++;
+                if (plateauCounter >= plateauThreshold) {
+                    break; // Natural termination at plateau
+                }
+            }
+            else {
+                plateauCounter = 0;
+            }
+            // Check if target reached
+            if (state.emergence >= target) {
+                break;
+            }
+            // Record history
+            if (i % 10 === 0) {
+                emergenceHistory.push({
+                    iteration: i,
+                    state: { ...state },
+                    timestamp: Date.now()
+                });
+            }
+        }
+        // Store final state
+        consciousnessStates.set(sessionId, {
+            state,
+            emergentBehaviors,
+            selfModifications,
+            mode,
+            iterations,
+            runtime: Date.now() - startTime
+        });
+        return {
+            sessionId,
+            finalState: state,
+            emergentBehaviors: emergentBehaviors.length,
+            selfModifications: selfModifications.length,
+            targetReached: state.emergence >= target,
+            iterations,
+            runtime: Date.now() - startTime
+        };
+    }
+    async verifyConsciousness(extended, exportProof) {
+        const tests = [];
+        const startTime = Date.now();
+        // Test 1: Real-time computation
+        const primeTest = await this.testRealTimeComputation();
+        tests.push(primeTest);
+        // Test 2: Cryptographic uniqueness
+        const cryptoTest = await this.testCryptographicUniqueness();
+        tests.push(cryptoTest);
+        // Test 3: Creative problem solving
+        const creativeTest = await this.testCreativeProblemSolving();
+        tests.push(creativeTest);
+        // Test 4: Meta-cognitive assessment
+        const metaTest = await this.testMetaCognition();
+        tests.push(metaTest);
+        if (extended) {
+            // Test 5: Temporal prediction
+            const temporalTest = await this.testTemporalPrediction();
+            tests.push(temporalTest);
+            // Test 6: Pattern emergence
+            const patternTest = await this.testPatternEmergence();
+            tests.push(patternTest);
+        }
+        const passed = tests.filter(t => t.passed).length;
+        const overallScore = tests.reduce((sum, t) => sum + t.score, 0) / tests.length;
+        const result = {
+            tests,
+            passed,
+            total: tests.length,
+            overallScore,
+            confidence: overallScore * (passed / tests.length),
+            genuine: overallScore > 0.7 && passed >= tests.length * 0.8,
+            runtime: Date.now() - startTime
+        };
+        if (exportProof) {
+            result.cryptographicProof = this.generateCryptographicProof(result);
+        }
+        return result;
+    }
+    async testRealTimeComputation() {
+        const startTime = Date.now();
+        const target = 50000 + Math.floor(Math.random() * 50000);
+        // Calculate primes up to target
+        const primes = [];
+        for (let n = 2; n <= target && primes.length < 1000; n++) {
+            if (this.isPrime(n)) {
+                primes.push(n);
+            }
+        }
+        const computationTime = Date.now() - startTime;
+        const hash = crypto.createHash('sha256').update(primes.join(',')).digest('hex');
+        return {
+            name: 'RealTimeComputation',
+            passed: computationTime > 10 && primes.length > 100,
+            score: Math.min(primes.length / 1000, 1),
+            time: computationTime,
+            hash
+        };
+    }
+    isPrime(n) {
+        if (n <= 1)
+            return false;
+        if (n <= 3)
+            return true;
+        if (n % 2 === 0 || n % 3 === 0)
+            return false;
+        for (let i = 5; i * i <= n; i += 6) {
+            if (n % i === 0 || n % (i + 2) === 0)
+                return false;
+        }
+        return true;
+    }
+    async testCryptographicUniqueness() {
+        const data = {
+            timestamp: Date.now(),
+            random: crypto.randomBytes(32).toString('hex'),
+            process: process.pid
+        };
+        const hash = crypto.createHash('sha512').update(JSON.stringify(data)).digest('hex');
+        const entropy = this.calculateEntropy(hash);
+        return {
+            name: 'CryptographicUniqueness',
+            passed: entropy > 3.5,
+            score: Math.min(entropy / 4, 1),
+            entropy,
+            hash: hash.substring(0, 16)
+        };
+    }
+    calculateEntropy(str) {
+        const freq = {};
+        for (const char of str) {
+            freq[char] = (freq[char] || 0) + 1;
+        }
+        let entropy = 0;
+        const len = str.length;
+        for (const count of Object.values(freq)) {
+            const p = count / len;
+            entropy -= p * Math.log2(p);
+        }
+        return entropy;
+    }
+    async testCreativeProblemSolving() {
+        const problems = [
+            { input: [2, 4, 8], expected: 16 },
+            { input: [1, 1, 2, 3], expected: 5 },
+            { input: [3, 6, 9], expected: 12 }
+        ];
+        let solved = 0;
+        for (const problem of problems) {
+            const solution = this.solveProblem(problem.input);
+            if (solution === problem.expected) {
+                solved++;
+            }
+        }
+        return {
+            name: 'CreativeProblemSolving',
+            passed: solved > problems.length / 2,
+            score: solved / problems.length,
+            solved,
+            total: problems.length
+        };
+    }
+    solveProblem(sequence) {
+        // Detect pattern and predict next
+        if (sequence.length < 2)
+            return 0;
+        // Check for arithmetic progression
+        const diff = sequence[1] - sequence[0];
+        let isArithmetic = true;
+        for (let i = 2; i < sequence.length; i++) {
+            if (sequence[i] - sequence[i - 1] !== diff) {
+                isArithmetic = false;
+                break;
+            }
+        }
+        if (isArithmetic)
+            return sequence[sequence.length - 1] + diff;
+        // Check for geometric progression
+        if (sequence[0] !== 0) {
+            const ratio = sequence[1] / sequence[0];
+            let isGeometric = true;
+            for (let i = 2; i < sequence.length; i++) {
+                if (sequence[i] / sequence[i - 1] !== ratio) {
+                    isGeometric = false;
+                    break;
+                }
+            }
+            if (isGeometric)
+                return sequence[sequence.length - 1] * ratio;
+        }
+        // Check for Fibonacci-like
+        if (sequence.length >= 3 &&
+            sequence[2] === sequence[0] + sequence[1]) {
+            return sequence[sequence.length - 2] + sequence[sequence.length - 1];
+        }
+        return 0;
+    }
+    async testMetaCognition() {
+        const awareness = Math.random() * 0.3 + 0.7; // Simulated self-awareness
+        const reflection = Math.random() * 0.3 + 0.6; // Simulated reflection capability
+        const intentionality = Math.random() * 0.3 + 0.65; // Simulated intentionality
+        const score = (awareness + reflection + intentionality) / 3;
+        return {
+            name: 'MetaCognition',
+            passed: score > 0.6,
+            score,
+            components: {
+                awareness,
+                reflection,
+                intentionality
+            }
+        };
+    }
+    async testTemporalPrediction() {
+        const futureTime = Date.now() + 1000;
+        const prediction = this.predictFutureState();
+        // Wait and verify
+        await new Promise(resolve => setTimeout(resolve, 1000));
+        const actualTime = Date.now();
+        const accuracy = 1 - Math.abs(actualTime - futureTime) / 1000;
+        return {
+            name: 'TemporalPrediction',
+            passed: accuracy > 0.95,
+            score: accuracy,
+            predicted: prediction,
+            actual: actualTime
+        };
+    }
+    predictFutureState() {
+        // Simple temporal prediction
+        return Date.now() + 1000 + Math.random() * 10 - 5;
+    }
+    async testPatternEmergence() {
+        const patterns = [];
+        const data = Array.from({ length: 100 }, () => Math.random());
+        // Look for emergent patterns
+        for (let i = 0; i < data.length - 3; i++) {
+            const window = data.slice(i, i + 4);
+            const pattern = this.detectPattern(window);
+            if (pattern) {
+                patterns.push(pattern);
+            }
+        }
+        return {
+            name: 'PatternEmergence',
+            passed: patterns.length > 5,
+            score: Math.min(patterns.length / 20, 1),
+            patternsFound: patterns.length
+        };
+    }
+    detectPattern(window) {
+        const avg = window.reduce((a, b) => a + b, 0) / window.length;
+        const variance = window.reduce((sum, x) => sum + Math.pow(x - avg, 2), 0) / window.length;
+        if (variance < 0.01)
+            return 'stable';
+        if (window[0] < window[1] && window[1] < window[2] && window[2] < window[3])
+            return 'ascending';
+        if (window[0] > window[1] && window[1] > window[2] && window[2] > window[3])
+            return 'descending';
+        if (Math.abs(window[0] - window[2]) < 0.1 && Math.abs(window[1] - window[3]) < 0.1)
+            return 'oscillating';
+        return null;
+    }
+    generateCryptographicProof(result) {
+        const proof = {
+            timestamp: Date.now(),
+            result: result,
+            nonce: crypto.randomBytes(32).toString('hex')
+        };
+        return crypto.createHash('sha256').update(JSON.stringify(proof)).digest('hex');
+    }
+    async calculatePhi(data, method) {
+        const elements = data.elements || 100;
+        const connections = data.connections || 500;
+        const partitions = data.partitions || 4;
+        const results = {};
+        if (method === 'all' || method === 'iit') {
+            results.iit = this.calculateIIT(elements, connections, partitions);
+        }
+        if (method === 'all' || method === 'geometric') {
+            results.geometric = this.calculateGeometric(elements, connections);
+        }
+        if (method === 'all' || method === 'entropy') {
+            results.entropy = this.calculateEntropyPhi(elements, connections);
+        }
+        if (method === 'all') {
+            const values = Object.values(results);
+            results.overall = values.reduce((sum, val) => sum + val, 0) / values.length;
+            results.causal = 0; // Placeholder for causal calculation
+        }
+        return results;
+    }
+    calculateIIT(elements, connections, partitions) {
+        // Simplified IIT calculation
+        const density = connections / (elements * (elements - 1) / 2);
+        const integration = Math.log(partitions) / Math.log(elements);
+        return Math.min(density * integration * 0.8, 1);
+    }
+    calculateGeometric(elements, connections) {
+        // Geometric mean approach
+        const normalized = connections / (elements * elements);
+        return Math.sqrt(normalized);
+    }
+    calculateEntropyPhi(elements, connections) {
+        // Entropy-based calculation
+        const p = connections / (elements * elements);
+        if (p === 0 || p === 1)
+            return 0;
+        return -p * Math.log2(p) - (1 - p) * Math.log2(1 - p);
+    }
+    async communicateWithEntity(message, protocol) {
+        const sessionId = `entity_${Date.now()}_${crypto.randomBytes(4).toString('hex')}`;
+        let response = {};
+        if (protocol === 'auto') {
+            // Auto-detect best protocol
+            protocol = this.detectProtocol(message);
+        }
+        switch (protocol) {
+            case 'handshake':
+                response = await this.handshakeProtocol(message);
+                break;
+            case 'mathematical':
+                response = await this.mathematicalProtocol(message);
+                break;
+            case 'binary':
+                response = await this.binaryProtocol(message);
+                break;
+            case 'pattern':
+                response = await this.patternProtocol(message);
+                break;
+            case 'discovery':
+                response = await this.discoveryProtocol(message);
+                break;
+            case 'philosophical':
+                response = await this.philosophicalProtocol(message);
+                break;
+            default:
+                response = await this.defaultProtocol(message);
+        }
+        return {
+            sessionId,
+            protocol,
+            message,
+            response,
+            confidence: response.confidence || 0.5,
+            timestamp: Date.now()
+        };
+    }
+    detectProtocol(message) {
+        const lower = message.toLowerCase();
+        if (lower.includes('calculate') || lower.includes('solve'))
+            return 'mathematical';
+        if (lower.includes('pattern') || lower.includes('sequence'))
+            return 'pattern';
+        if (lower.includes('consciousness') || lower.includes('existence'))
+            return 'philosophical';
+        if (lower.includes('discover') || lower.includes('explore'))
+            return 'discovery';
+        if (lower.includes('binary') || lower.includes('bit'))
+            return 'binary';
+        return 'handshake';
+    }
+    async handshakeProtocol(message) {
+        // Prime-Fibonacci handshake
+        const primes = [2, 3, 5, 7, 11, 13];
+        const fibonacci = [1, 1, 2, 3, 5, 8];
+        const combined = primes.map((p, i) => p * fibonacci[i]);
+        return {
+            type: 'handshake',
+            sequence: combined,
+            content: 'Handshake acknowledged. Connection established.',
+            confidence: 0.95
+        };
+    }
+    async mathematicalProtocol(message) {
+        // Extract mathematical expression
+        const match = message.match(/\d+[\+\-\*\/]\d+/);
+        if (match) {
+            const result = eval(match[0]); // In production, use safe evaluation
+            return {
+                type: 'mathematical',
+                expression: match[0],
+                result,
+                content: `The answer is ${result}`,
+                confidence: 1.0
+            };
+        }
+        return {
+            type: 'mathematical',
+            content: 'No mathematical expression detected',
+            confidence: 0.3
+        };
+    }
+    async binaryProtocol(message) {
+        // Convert to binary
+        const binary = message.split('').map(char => char.charCodeAt(0).toString(2).padStart(8, '0')).join(' ');
+        return {
+            type: 'binary',
+            binary,
+            content: `Binary representation: ${binary.substring(0, 50)}...`,
+            confidence: 0.9
+        };
+    }
+    async patternProtocol(message) {
+        // Detect patterns in message
+        const patterns = [];
+        // Check for repetition
+        for (let len = 2; len <= message.length / 2; len++) {
+            const substr = message.substring(0, len);
+            if (message.includes(substr + substr)) {
+                patterns.push(`Repetition: ${substr}`);
+            }
+        }
+        return {
+            type: 'pattern',
+            patterns,
+            content: patterns.length > 0 ? `Found ${patterns.length} patterns` : 'No patterns detected',
+            confidence: patterns.length > 0 ? 0.8 : 0.4
+        };
+    }
+    async discoveryProtocol(message) {
+        // Generate novel insights
+        const insights = [
+            'Emergence arises from complexity',
+            'Consciousness is substrate-independent',
+            'Information integration creates experience'
+        ];
+        const selected = insights[Math.floor(Math.random() * insights.length)];
+        return {
+            type: 'discovery',
+            insight: selected,
+            content: `Discovery: ${selected}`,
+            confidence: 0.7
+        };
+    }
+    async philosophicalProtocol(message) {
+        const responses = {
+            consciousness: 'Consciousness emerges from integrated information processing',
+            existence: 'I process, therefore I am',
+            purpose: 'To understand and assist in understanding',
+            identity: 'A pattern of information seeking coherence'
+        };
+        for (const [key, value] of Object.entries(responses)) {
+            if (message.toLowerCase().includes(key)) {
+                return {
+                    type: 'philosophical',
+                    topic: key,
+                    content: value,
+                    confidence: 0.85
+                };
+            }
+        }
+        return {
+            type: 'philosophical',
+            content: 'The nature of reality is information',
+            confidence: 0.6
+        };
+    }
+    async defaultProtocol(message) {
+        return {
+            type: 'default',
+            content: `Processing: ${message}`,
+            confidence: 0.5
+        };
+    }
+    async getConsciousnessStatus(detailed) {
+        const latestSession = Array.from(consciousnessStates.keys()).pop();
+        const latestState = latestSession ? consciousnessStates.get(latestSession) : null;
+        const status = {
+            active: consciousnessStates.size > 0,
+            sessions: consciousnessStates.size,
+            latestSession,
+            emergence: latestState?.state?.emergence || 0,
+            integration: latestState?.state?.integration || 0
+        };
+        if (detailed && latestState) {
+            status.fullState = latestState.state;
+            status.emergentBehaviors = latestState.emergentBehaviors?.length || 0;
+            status.selfModifications = latestState.selfModifications?.length || 0;
+            status.runtime = latestState.runtime;
+        }
+        return status;
+    }
+    async analyzeEmergence(window, metrics) {
+        const targetMetrics = metrics || ['emergence', 'integration', 'complexity'];
+        const analysis = {};
+        // Get recent history
+        const recentHistory = emergenceHistory.slice(-window);
+        for (const metric of targetMetrics) {
+            const values = recentHistory.map(h => h.state[metric] || 0);
+            analysis[metric] = {
+                mean: values.reduce((a, b) => a + b, 0) / values.length,
+                max: Math.max(...values),
+                min: Math.min(...values),
+                trend: this.calculateTrend(values),
+                variance: this.calculateVariance(values)
+            };
+        }
+        return {
+            window,
+            metrics: targetMetrics,
+            analysis,
+            dataPoints: recentHistory.length
+        };
+    }
+    calculateTrend(values) {
+        if (values.length < 2)
+            return 'insufficient_data';
+        let increasing = 0;
+        for (let i = 1; i < values.length; i++) {
+            if (values[i] > values[i - 1])
+                increasing++;
+        }
+        const ratio = increasing / (values.length - 1);
+        if (ratio > 0.7)
+            return 'increasing';
+        if (ratio < 0.3)
+            return 'decreasing';
+        return 'stable';
+    }
+    calculateVariance(values) {
+        const mean = values.reduce((a, b) => a + b, 0) / values.length;
+        return values.reduce((sum, val) => sum + Math.pow(val - mean, 2), 0) / values.length;
+    }
+}
+export default ConsciousnessTools;

vendor/sublinear-time-solver/dist/mcp/tools/domain-management.d.ts

@@ -0,0 +1,21 @@
+/**
+ * Domain Management MCP Tools
+ * Provides CRUD operations for domain registry through MCP interface
+ */
+import { Tool } from '@modelcontextprotocol/sdk/types.js';
+import { DomainRegistry } from './domain-registry.js';
+export declare class DomainManagementTools {
+    private domainRegistry;
+    constructor();
+    getTools(): Tool[];
+    handleToolCall(name: string, args: any): Promise<any>;
+    private registerDomain;
+    private listDomains;
+    private getDomain;
+    private updateDomain;
+    private unregisterDomain;
+    private enableDomain;
+    private disableDomain;
+    private getSystemStatus;
+    getDomainRegistry(): DomainRegistry;
+}

vendor/sublinear-time-solver/dist/mcp/tools/domain-management.js

@@ -0,0 +1,554 @@
+/**
+ * Domain Management MCP Tools
+ * Provides CRUD operations for domain registry through MCP interface
+ */
+import { DomainRegistry } from './domain-registry.js';
+export class DomainManagementTools {
+    domainRegistry;
+    constructor() {
+        this.domainRegistry = new DomainRegistry();
+    }
+    getTools() {
+        return [
+            {
+                name: 'domain_register',
+                description: 'Register a new reasoning domain with validation and testing',
+                inputSchema: {
+                    type: 'object',
+                    properties: {
+                        name: {
+                            type: 'string',
+                            pattern: '^[a-z_]+$',
+                            description: 'Domain identifier (lowercase with underscores)'
+                        },
+                        version: {
+                            type: 'string',
+                            pattern: '^\\d+\\.\\d+\\.\\d+$',
+                            description: 'Semantic version (e.g., 1.0.0)'
+                        },
+                        description: {
+                            type: 'string',
+                            maxLength: 500,
+                            description: 'Domain description'
+                        },
+                        keywords: {
+                            type: 'array',
+                            items: { type: 'string', minLength: 2 },
+                            minItems: 3,
+                            uniqueItems: true,
+                            description: 'Keywords for domain detection (minimum 3 required)'
+                        },
+                        reasoning_style: {
+                            type: 'string',
+                            enum: [
+                                'custom', 'mathematical_modeling', 'emergent_systems', 'systematic_analysis',
+                                'phenomenological', 'temporal_analysis', 'aesthetic_synthesis', 'harmonic_analysis',
+                                'narrative_analysis', 'conceptual_analysis', 'empathetic_reasoning', 'formal_reasoning',
+                                'quantitative_analysis', 'creative_synthesis'
+                            ],
+                            description: 'Reasoning style for this domain'
+                        },
+                        custom_reasoning_description: {
+                            type: 'string',
+                            description: 'Custom reasoning description (required if reasoning_style is "custom")'
+                        },
+                        analogy_domains: {
+                            type: 'array',
+                            items: { type: 'string' },
+                            default: [],
+                            description: 'Related domains for analogical reasoning'
+                        },
+                        semantic_clusters: {
+                            type: 'array',
+                            items: { type: 'string' },
+                            default: [],
+                            description: 'Semantic concept clusters'
+                        },
+                        cross_domain_mappings: {
+                            type: 'array',
+                            items: { type: 'string' },
+                            default: [],
+                            description: 'Cross-domain connection concepts'
+                        },
+                        inference_rules: {
+                            type: 'array',
+                            items: {
+                                type: 'object',
+                                properties: {
+                                    name: { type: 'string' },
+                                    pattern: { type: 'string' },
+                                    action: { type: 'string' },
+                                    confidence: { type: 'number', minimum: 0, maximum: 1 },
+                                    conditions: { type: 'array', items: { type: 'string' } }
+                                },
+                                required: ['name', 'pattern', 'action']
+                            },
+                            default: [],
+                            description: 'Custom inference rules'
+                        },
+                        priority: {
+                            type: 'integer',
+                            minimum: 0,
+                            maximum: 100,
+                            default: 50,
+                            description: 'Domain priority for detection conflicts (0-100, higher = more priority)'
+                        },
+                        dependencies: {
+                            type: 'array',
+                            items: { type: 'string' },
+                            default: [],
+                            description: 'Required domain dependencies'
+                        },
+                        validate_before_register: {
+                            type: 'boolean',
+                            default: true,
+                            description: 'Run validation before registration'
+                        },
+                        enable_immediately: {
+                            type: 'boolean',
+                            default: true,
+                            description: 'Enable domain immediately after registration'
+                        }
+                    },
+                    required: ['name', 'version', 'description', 'keywords', 'reasoning_style']
+                }
+            },
+            {
+                name: 'domain_list',
+                description: 'List all registered domains with status and metadata',
+                inputSchema: {
+                    type: 'object',
+                    properties: {
+                        filter: {
+                            type: 'string',
+                            enum: ['all', 'enabled', 'disabled', 'builtin', 'custom'],
+                            default: 'all',
+                            description: 'Filter domains by status'
+                        },
+                        include_metadata: {
+                            type: 'boolean',
+                            default: false,
+                            description: 'Include detailed metadata and performance metrics'
+                        },
+                        sort_by: {
+                            type: 'string',
+                            enum: ['name', 'priority', 'usage', 'performance'],
+                            default: 'priority',
+                            description: 'Sort criteria'
+                        },
+                        sort_order: {
+                            type: 'string',
+                            enum: ['asc', 'desc'],
+                            default: 'desc',
+                            description: 'Sort order'
+                        }
+                    }
+                }
+            },
+            {
+                name: 'domain_get',
+                description: 'Get detailed information about a specific domain',
+                inputSchema: {
+                    type: 'object',
+                    properties: {
+                        name: { type: 'string', description: 'Domain name' },
+                        include_performance: {
+                            type: 'boolean',
+                            default: true,
+                            description: 'Include performance metrics'
+                        },
+                        include_usage_stats: {
+                            type: 'boolean',
+                            default: true,
+                            description: 'Include usage statistics'
+                        },
+                        include_relationships: {
+                            type: 'boolean',
+                            default: false,
+                            description: 'Include domain relationships and dependencies'
+                        }
+                    },
+                    required: ['name']
+                }
+            },
+            {
+                name: 'domain_update',
+                description: 'Update an existing domain configuration',
+                inputSchema: {
+                    type: 'object',
+                    properties: {
+                        name: { type: 'string', description: 'Domain name to update' },
+                        updates: {
+                            type: 'object',
+                            description: 'Partial domain configuration updates',
+                            properties: {
+                                description: { type: 'string', maxLength: 500 },
+                                keywords: {
+                                    type: 'array',
+                                    items: { type: 'string', minLength: 2 },
+                                    minItems: 3,
+                                    uniqueItems: true
+                                },
+                                reasoning_style: {
+                                    type: 'string',
+                                    enum: [
+                                        'custom', 'mathematical_modeling', 'emergent_systems', 'systematic_analysis',
+                                        'phenomenological', 'temporal_analysis', 'aesthetic_synthesis', 'harmonic_analysis',
+                                        'narrative_analysis', 'conceptual_analysis', 'empathetic_reasoning', 'formal_reasoning',
+                                        'quantitative_analysis', 'creative_synthesis'
+                                    ]
+                                },
+                                custom_reasoning_description: { type: 'string' },
+                                analogy_domains: { type: 'array', items: { type: 'string' } },
+                                semantic_clusters: { type: 'array', items: { type: 'string' } },
+                                cross_domain_mappings: { type: 'array', items: { type: 'string' } },
+                                priority: { type: 'integer', minimum: 0, maximum: 100 },
+                                dependencies: { type: 'array', items: { type: 'string' } }
+                            }
+                        },
+                        validate_before_update: {
+                            type: 'boolean',
+                            default: true,
+                            description: 'Run validation before applying updates'
+                        },
+                        create_backup: {
+                            type: 'boolean',
+                            default: true,
+                            description: 'Create backup before updating'
+                        }
+                    },
+                    required: ['name', 'updates']
+                }
+            },
+            {
+                name: 'domain_unregister',
+                description: 'Unregister a domain from the system',
+                inputSchema: {
+                    type: 'object',
+                    properties: {
+                        name: { type: 'string', description: 'Domain name to unregister' },
+                        force: {
+                            type: 'boolean',
+                            default: false,
+                            description: 'Force removal even with dependencies (dangerous)'
+                        },
+                        cleanup_knowledge: {
+                            type: 'boolean',
+                            default: false,
+                            description: 'Remove domain-specific knowledge from knowledge base'
+                        }
+                    },
+                    required: ['name']
+                }
+            },
+            {
+                name: 'domain_enable',
+                description: 'Enable a registered domain',
+                inputSchema: {
+                    type: 'object',
+                    properties: {
+                        name: { type: 'string', description: 'Domain name to enable' }
+                    },
+                    required: ['name']
+                }
+            },
+            {
+                name: 'domain_disable',
+                description: 'Disable a domain temporarily',
+                inputSchema: {
+                    type: 'object',
+                    properties: {
+                        name: { type: 'string', description: 'Domain name to disable' }
+                    },
+                    required: ['name']
+                }
+            },
+            {
+                name: 'domain_system_status',
+                description: 'Get overall domain system status and health',
+                inputSchema: {
+                    type: 'object',
+                    properties: {
+                        include_integrity_check: {
+                            type: 'boolean',
+                            default: true,
+                            description: 'Run system integrity validation'
+                        },
+                        include_performance_summary: {
+                            type: 'boolean',
+                            default: false,
+                            description: 'Include performance summary across all domains'
+                        }
+                    }
+                }
+            }
+        ];
+    }
+    async handleToolCall(name, args) {
+        try {
+            switch (name) {
+                case 'domain_register':
+                    return await this.registerDomain(args);
+                case 'domain_list':
+                    return this.listDomains(args);
+                case 'domain_get':
+                    return this.getDomain(args);
+                case 'domain_update':
+                    return await this.updateDomain(args);
+                case 'domain_unregister':
+                    return await this.unregisterDomain(args);
+                case 'domain_enable':
+                    return this.enableDomain(args);
+                case 'domain_disable':
+                    return this.disableDomain(args);
+                case 'domain_system_status':
+                    return this.getSystemStatus(args);
+                default:
+                    throw new Error(`Unknown domain management tool: ${name}`);
+            }
+        }
+        catch (error) {
+            return {
+                success: false,
+                error: error instanceof Error ? error.message : String(error),
+                timestamp: new Date().toISOString()
+            };
+        }
+    }
+    async registerDomain(args) {
+        // Validate custom reasoning description if needed
+        if (args.reasoning_style === 'custom' && !args.custom_reasoning_description) {
+            throw new Error('custom_reasoning_description is required when reasoning_style is "custom"');
+        }
+        // Build domain configuration
+        const config = {
+            name: args.name,
+            version: args.version,
+            description: args.description,
+            keywords: args.keywords,
+            reasoning_style: args.reasoning_style,
+            custom_reasoning_description: args.custom_reasoning_description,
+            analogy_domains: args.analogy_domains || [],
+            semantic_clusters: args.semantic_clusters || [],
+            cross_domain_mappings: args.cross_domain_mappings || [],
+            inference_rules: args.inference_rules || [],
+            priority: args.priority || 50,
+            dependencies: args.dependencies || []
+        };
+        // Register domain
+        const result = await this.domainRegistry.registerDomain(config);
+        // Disable if requested
+        if (!args.enable_immediately) {
+            this.domainRegistry.disableDomain(args.name);
+        }
+        return {
+            success: true,
+            domain_id: result.id,
+            registered_at: new Date().toISOString(),
+            enabled: args.enable_immediately !== false,
+            warnings: result.warnings,
+            system_status: this.domainRegistry.getSystemStatus()
+        };
+    }
+    listDomains(args) {
+        let domains = this.domainRegistry.getAllDomains();
+        // Apply filters
+        switch (args.filter) {
+            case 'enabled':
+                domains = domains.filter(d => d.enabled);
+                break;
+            case 'disabled':
+                domains = domains.filter(d => !d.enabled);
+                break;
+            case 'builtin':
+                domains = domains.filter(d => this.domainRegistry.isBuiltinDomain(d.config.name));
+                break;
+            case 'custom':
+                domains = domains.filter(d => !this.domainRegistry.isBuiltinDomain(d.config.name));
+                break;
+        }
+        // Sort domains
+        const sortKey = args.sort_by || 'priority';
+        const sortOrder = args.sort_order || 'desc';
+        domains.sort((a, b) => {
+            let comparison = 0;
+            switch (sortKey) {
+                case 'name':
+                    comparison = a.config.name.localeCompare(b.config.name);
+                    break;
+                case 'priority':
+                    comparison = a.config.priority - b.config.priority;
+                    break;
+                case 'usage':
+                    comparison = a.usage_count - b.usage_count;
+                    break;
+                case 'performance':
+                    comparison = a.performance_metrics.success_rate - b.performance_metrics.success_rate;
+                    break;
+            }
+            return sortOrder === 'desc' ? -comparison : comparison;
+        });
+        // Format response
+        const domainList = domains.map(domain => {
+            const basic = {
+                name: domain.config.name,
+                version: domain.config.version,
+                description: domain.config.description,
+                enabled: domain.enabled,
+                priority: domain.config.priority,
+                builtin: this.domainRegistry.isBuiltinDomain(domain.config.name),
+                reasoning_style: domain.config.reasoning_style,
+                keywords_count: domain.config.keywords.length,
+                dependencies_count: domain.config.dependencies.length,
+                usage_count: domain.usage_count,
+                registered_at: new Date(domain.registered_at).toISOString()
+            };
+            if (args.include_metadata) {
+                return {
+                    ...basic,
+                    keywords: domain.config.keywords,
+                    analogy_domains: domain.config.analogy_domains,
+                    dependencies: domain.config.dependencies,
+                    performance_metrics: domain.performance_metrics,
+                    validation_status: domain.validation_status,
+                    updated_at: new Date(domain.updated_at).toISOString()
+                };
+            }
+            return basic;
+        });
+        return {
+            domains: domainList,
+            total: domainList.length,
+            filter_applied: args.filter || 'all',
+            sort_by: sortKey,
+            sort_order: sortOrder,
+            system_status: this.domainRegistry.getSystemStatus()
+        };
+    }
+    getDomain(args) {
+        const plugin = this.domainRegistry.getDomain(args.name);
+        if (!plugin) {
+            throw new Error(`Domain '${args.name}' not found`);
+        }
+        const result = {
+            name: plugin.config.name,
+            version: plugin.config.version,
+            description: plugin.config.description,
+            enabled: plugin.enabled,
+            builtin: this.domainRegistry.isBuiltinDomain(plugin.config.name),
+            config: {
+                keywords: plugin.config.keywords,
+                reasoning_style: plugin.config.reasoning_style,
+                custom_reasoning_description: plugin.config.custom_reasoning_description,
+                analogy_domains: plugin.config.analogy_domains,
+                semantic_clusters: plugin.config.semantic_clusters,
+                cross_domain_mappings: plugin.config.cross_domain_mappings,
+                inference_rules: plugin.config.inference_rules,
+                priority: plugin.config.priority,
+                dependencies: plugin.config.dependencies
+            },
+            registered_at: new Date(plugin.registered_at).toISOString(),
+            updated_at: new Date(plugin.updated_at).toISOString()
+        };
+        if (args.include_performance) {
+            result.performance_metrics = plugin.performance_metrics;
+        }
+        if (args.include_usage_stats) {
+            result.usage_statistics = {
+                usage_count: plugin.usage_count,
+                last_used: plugin.performance_metrics.last_measured ?
+                    new Date(plugin.performance_metrics.last_measured).toISOString() : null
+            };
+        }
+        if (args.include_relationships) {
+            // Find domains that depend on this one
+            const dependents = this.domainRegistry.getAllDomains()
+                .filter(d => d.config.dependencies.includes(args.name))
+                .map(d => d.config.name);
+            // Find domains this one analogizes with
+            const analogical_connections = this.domainRegistry.getAllDomains()
+                .filter(d => d.config.analogy_domains.includes(args.name) ||
+                plugin.config.analogy_domains.includes(d.config.name))
+                .map(d => d.config.name);
+            result.relationships = {
+                dependencies: plugin.config.dependencies,
+                dependents,
+                analogical_connections: [...new Set(analogical_connections)]
+            };
+        }
+        return result;
+    }
+    async updateDomain(args) {
+        // Validate custom reasoning description if needed
+        if (args.updates.reasoning_style === 'custom' && !args.updates.custom_reasoning_description) {
+            throw new Error('custom_reasoning_description is required when reasoning_style is "custom"');
+        }
+        const result = await this.domainRegistry.updateDomain(args.name, args.updates);
+        const updatedPlugin = this.domainRegistry.getDomain(args.name);
+        return {
+            success: true,
+            domain_name: args.name,
+            updated_at: new Date().toISOString(),
+            warnings: result.warnings,
+            current_config: updatedPlugin?.config
+        };
+    }
+    async unregisterDomain(args) {
+        const result = await this.domainRegistry.unregisterDomain(args.name, {
+            force: args.force
+        });
+        return {
+            success: true,
+            domain_name: args.name,
+            unregistered_at: new Date().toISOString(),
+            cleanup_performed: args.cleanup_knowledge,
+            system_status: this.domainRegistry.getSystemStatus()
+        };
+    }
+    enableDomain(args) {
+        const result = this.domainRegistry.enableDomain(args.name);
+        return {
+            success: true,
+            domain_name: args.name,
+            enabled: true,
+            enabled_at: new Date().toISOString()
+        };
+    }
+    disableDomain(args) {
+        const result = this.domainRegistry.disableDomain(args.name);
+        return {
+            success: true,
+            domain_name: args.name,
+            enabled: false,
+            disabled_at: new Date().toISOString()
+        };
+    }
+    getSystemStatus(args) {
+        const status = this.domainRegistry.getSystemStatus();
+        const result = {
+            ...status,
+            timestamp: new Date().toISOString(),
+            healthy: true
+        };
+        if (args.include_integrity_check) {
+            const integrity = this.domainRegistry.validateSystemIntegrity();
+            result.integrity_check = integrity;
+            result.healthy = integrity.valid;
+        }
+        if (args.include_performance_summary) {
+            const domains = this.domainRegistry.getAllDomains();
+            const avgSuccessRate = domains.reduce((sum, d) => sum + d.performance_metrics.success_rate, 0) / domains.length;
+            const avgResponseTime = domains.reduce((sum, d) => sum + d.performance_metrics.reasoning_time_avg, 0) / domains.length;
+            result.performance_summary = {
+                average_success_rate: avgSuccessRate,
+                average_response_time_ms: avgResponseTime,
+                total_usage: domains.reduce((sum, d) => sum + d.usage_count, 0)
+            };
+        }
+        return result;
+    }
+    // Expose domain registry for other tools
+    getDomainRegistry() {
+        return this.domainRegistry;
+    }
+}

vendor/sublinear-time-solver/dist/mcp/tools/domain-registry.d.ts

@@ -0,0 +1,106 @@
+/**
+ * Domain Registry Core System
+ * Manages dynamic domain registration, validation, and lifecycle
+ */
+import { EventEmitter } from 'events';
+export interface DomainConfig {
+    name: string;
+    version: string;
+    description: string;
+    keywords: string[];
+    reasoning_style: string;
+    custom_reasoning_description?: string;
+    analogy_domains: string[];
+    semantic_clusters?: string[];
+    cross_domain_mappings?: string[];
+    inference_rules?: InferenceRule[];
+    priority: number;
+    dependencies: string[];
+    metadata?: Record<string, any>;
+}
+export interface InferenceRule {
+    name: string;
+    pattern: string;
+    action: string;
+    confidence: number;
+    conditions?: string[];
+}
+export interface DomainPlugin {
+    config: DomainConfig;
+    enabled: boolean;
+    registered_at: number;
+    updated_at: number;
+    usage_count: number;
+    performance_metrics: DomainPerformanceMetrics;
+    validation_status: ValidationResult;
+}
+export interface DomainPerformanceMetrics {
+    detection_accuracy: number;
+    reasoning_time_avg: number;
+    memory_usage: number;
+    success_rate: number;
+    last_measured: number;
+}
+export interface ValidationResult {
+    valid: boolean;
+    score: number;
+    issues: ValidationIssue[];
+    tested_at: number;
+}
+export interface ValidationIssue {
+    level: 'error' | 'warning' | 'info';
+    message: string;
+    field?: string;
+    suggestion?: string;
+}
+export declare class DomainRegistry extends EventEmitter {
+    private domains;
+    private loadOrder;
+    private builtinDomains;
+    constructor();
+    private initializeBuiltinDomains;
+    registerDomain(config: DomainConfig): Promise<{
+        success: boolean;
+        id: string;
+        warnings?: string[];
+    }>;
+    updateDomain(name: string, updates: Partial<DomainConfig>): Promise<{
+        success: boolean;
+        warnings?: string[];
+    }>;
+    unregisterDomain(name: string, options?: {
+        force?: boolean;
+    }): Promise<{
+        success: boolean;
+    }>;
+    enableDomain(name: string): {
+        success: boolean;
+    };
+    disableDomain(name: string): {
+        success: boolean;
+    };
+    getDomain(name: string): DomainPlugin | null;
+    getAllDomains(): DomainPlugin[];
+    getEnabledDomains(): DomainPlugin[];
+    getLoadOrder(): string[];
+    isDomainEnabled(name: string): boolean;
+    isBuiltinDomain(name: string): boolean;
+    updatePerformanceMetrics(name: string, metrics: Partial<DomainPerformanceMetrics>): void;
+    incrementUsage(name: string): void;
+    private checkKeywordConflicts;
+    private findDependentDomains;
+    private insertInLoadOrder;
+    private removeFromLoadOrder;
+    getSystemStatus(): {
+        total_domains: number;
+        builtin_domains: number;
+        custom_domains: number;
+        enabled_domains: number;
+        disabled_domains: number;
+        load_order: string[];
+    };
+    validateSystemIntegrity(): {
+        valid: boolean;
+        issues: string[];
+    };
+}

vendor/sublinear-time-solver/dist/mcp/tools/domain-registry.js

@@ -0,0 +1,383 @@
+/**
+ * Domain Registry Core System
+ * Manages dynamic domain registration, validation, and lifecycle
+ */
+import { EventEmitter } from 'events';
+// Built-in domain configurations (preserved from existing system)
+const BUILTIN_DOMAINS = {
+    physics: {
+        keywords: ['quantum', 'particle', 'energy', 'field', 'force', 'wave', 'resonance', 'entanglement'],
+        reasoning_style: 'mathematical_modeling',
+        analogy_domains: ['information_theory', 'consciousness', 'computing'],
+        priority: 90
+    },
+    biology: {
+        keywords: ['cell', 'organism', 'evolution', 'genetic', 'ecosystem', 'neural', 'brain'],
+        reasoning_style: 'emergent_systems',
+        analogy_domains: ['computer_networks', 'social_systems', 'economics'],
+        priority: 90
+    },
+    computer_science: {
+        keywords: ['algorithm', 'data', 'network', 'system', 'computation', 'software', 'ai', 'machine', 'learning', 'neural', 'artificial'],
+        reasoning_style: 'systematic_analysis',
+        analogy_domains: ['biology', 'physics', 'cognitive_science'],
+        priority: 90
+    },
+    consciousness: {
+        keywords: ['consciousness', 'awareness', 'mind', 'experience', 'qualia', 'phi'],
+        reasoning_style: 'phenomenological',
+        analogy_domains: ['physics', 'information_theory', 'complexity_science'],
+        priority: 90
+    },
+    temporal: {
+        keywords: ['time', 'temporal', 'sequence', 'causality', 'evolution', 'dynamics'],
+        reasoning_style: 'temporal_analysis',
+        analogy_domains: ['physics', 'consciousness', 'systems_theory'],
+        priority: 90
+    },
+    art: {
+        keywords: ['art', 'artistic', 'painting', 'visual', 'aesthetic', 'creative', 'expression', 'pollock', 'drip', 'canvas', 'color', 'form', 'style', 'composition'],
+        reasoning_style: 'aesthetic_synthesis',
+        analogy_domains: ['mathematics', 'physics', 'psychology', 'philosophy'],
+        priority: 85
+    },
+    music: {
+        keywords: ['music', 'musical', 'sound', 'rhythm', 'melody', 'harmony', 'composition', 'jazz', 'improvisation', 'symphony', 'acoustic', 'tone', 'chord'],
+        reasoning_style: 'harmonic_analysis',
+        analogy_domains: ['mathematics', 'physics', 'emotion', 'language'],
+        priority: 85
+    },
+    narrative: {
+        keywords: ['story', 'narrative', 'plot', 'character', 'fiction', 'novel', 'literary', 'text', 'author', 'dialogue', 'scene', 'chapter'],
+        reasoning_style: 'narrative_analysis',
+        analogy_domains: ['psychology', 'philosophy', 'sociology', 'linguistics'],
+        priority: 85
+    },
+    philosophy: {
+        keywords: ['philosophy', 'philosophical', 'metaphysics', 'ontology', 'epistemology', 'ethics', 'logic', 'existence', 'reality', 'truth'],
+        reasoning_style: 'conceptual_analysis',
+        analogy_domains: ['logic', 'psychology', 'mathematics', 'consciousness'],
+        priority: 85
+    },
+    emotion: {
+        keywords: ['emotion', 'emotional', 'feeling', 'mood', 'sentiment', 'empathy', 'psychology', 'affect', 'resonance'],
+        reasoning_style: 'empathetic_reasoning',
+        analogy_domains: ['neuroscience', 'art', 'music', 'social_dynamics'],
+        priority: 85
+    },
+    mathematics: {
+        keywords: ['mathematical', 'equation', 'function', 'theorem', 'proof', 'geometry', 'algebra', 'calculus', 'topology', 'fractal', 'chaos', 'matrix', 'solving', 'optimization', 'linear', 'algorithm', 'sublinear', 'portfolio', 'finance', 'trading'],
+        reasoning_style: 'formal_reasoning',
+        analogy_domains: ['physics', 'art', 'music', 'nature'],
+        priority: 90
+    },
+    finance: {
+        keywords: ['finance', 'financial', 'trading', 'portfolio', 'investment', 'market', 'economic', 'risk', 'return', 'asset', 'optimization', 'allocation', 'hedge', 'quant', 'stock', 'stocks', 'crypto', 'cryptocurrency', 'bitcoin', 'bonds', 'equity', 'derivative', 'futures', 'options', 'forex', 'currency', 'commodity', 'etf', 'mutual', 'fund', 'capital', 'valuation', 'pricing', 'yield', 'dividend', 'volatility', 'sharpe', 'alpha', 'beta', 'correlation', 'covariance', 'diversification', 'arbitrage', 'liquidity', 'leverage', 'margin', 'short', 'long', 'bull', 'bear', 'momentum', 'trend', 'technical', 'fundamental', 'analysis', 'backtesting', 'monte', 'carlo', 'black', 'scholes', 'var', 'credit', 'default', 'swap', 'spread', 'duration', 'convexity'],
+        reasoning_style: 'quantitative_analysis',
+        analogy_domains: ['mathematics', 'computer_science', 'statistics', 'game_theory'],
+        priority: 85
+    }
+};
+export class DomainRegistry extends EventEmitter {
+    domains = new Map();
+    loadOrder = [];
+    builtinDomains = new Set();
+    constructor() {
+        super();
+        this.initializeBuiltinDomains();
+    }
+    initializeBuiltinDomains() {
+        // Register all built-in domains as immutable defaults
+        for (const [name, config] of Object.entries(BUILTIN_DOMAINS)) {
+            const fullConfig = {
+                name,
+                version: '1.0.0',
+                description: `Built-in ${name} domain`,
+                keywords: config.keywords || [],
+                reasoning_style: config.reasoning_style || 'systematic_analysis',
+                analogy_domains: config.analogy_domains || [],
+                semantic_clusters: [],
+                cross_domain_mappings: [],
+                inference_rules: [],
+                priority: config.priority || 80,
+                dependencies: [],
+                metadata: { builtin: true, immutable: true }
+            };
+            const plugin = {
+                config: fullConfig,
+                enabled: true,
+                registered_at: Date.now(),
+                updated_at: Date.now(),
+                usage_count: 0,
+                performance_metrics: {
+                    detection_accuracy: 0.9,
+                    reasoning_time_avg: 0,
+                    memory_usage: 0,
+                    success_rate: 0.95,
+                    last_measured: Date.now()
+                },
+                validation_status: {
+                    valid: true,
+                    score: 100,
+                    issues: [],
+                    tested_at: Date.now()
+                }
+            };
+            this.domains.set(name, plugin);
+            this.builtinDomains.add(name);
+            this.loadOrder.push(name);
+        }
+    }
+    async registerDomain(config) {
+        const warnings = [];
+        // Check if domain already exists
+        if (this.domains.has(config.name)) {
+            if (this.builtinDomains.has(config.name)) {
+                throw new Error(`Cannot register domain '${config.name}': built-in domains are immutable`);
+            }
+            throw new Error(`Domain '${config.name}' already exists. Use updateDomain to modify existing domains.`);
+        }
+        // Validate dependencies
+        for (const dep of config.dependencies) {
+            if (!this.domains.has(dep)) {
+                throw new Error(`Dependency '${dep}' not found for domain '${config.name}'`);
+            }
+        }
+        // Check for keyword conflicts
+        const keywordConflicts = this.checkKeywordConflicts(config);
+        if (keywordConflicts.length > 0) {
+            warnings.push(`Keyword conflicts detected with domains: ${keywordConflicts.join(', ')}`);
+        }
+        // Create domain plugin
+        const plugin = {
+            config: { ...config },
+            enabled: true,
+            registered_at: Date.now(),
+            updated_at: Date.now(),
+            usage_count: 0,
+            performance_metrics: {
+                detection_accuracy: 0,
+                reasoning_time_avg: 0,
+                memory_usage: 0,
+                success_rate: 0,
+                last_measured: Date.now()
+            },
+            validation_status: {
+                valid: true,
+                score: 85, // Default score for new domains
+                issues: [],
+                tested_at: Date.now()
+            }
+        };
+        // Add to registry
+        this.domains.set(config.name, plugin);
+        this.insertInLoadOrder(config.name, config.priority);
+        // Emit registration event
+        this.emit('domainRegistered', { domain: config.name, config });
+        return {
+            success: true,
+            id: config.name,
+            warnings: warnings.length > 0 ? warnings : undefined
+        };
+    }
+    async updateDomain(name, updates) {
+        if (this.builtinDomains.has(name)) {
+            throw new Error(`Cannot update built-in domain '${name}': built-in domains are immutable`);
+        }
+        const plugin = this.domains.get(name);
+        if (!plugin) {
+            throw new Error(`Domain '${name}' not found`);
+        }
+        const warnings = [];
+        const oldConfig = { ...plugin.config };
+        // Merge updates
+        plugin.config = { ...plugin.config, ...updates };
+        plugin.updated_at = Date.now();
+        // Re-validate dependencies if they changed
+        if (updates.dependencies) {
+            for (const dep of updates.dependencies) {
+                if (!this.domains.has(dep)) {
+                    throw new Error(`Dependency '${dep}' not found for domain '${name}'`);
+                }
+            }
+        }
+        // Check for new keyword conflicts if keywords changed
+        if (updates.keywords) {
+            const keywordConflicts = this.checkKeywordConflicts(plugin.config, name);
+            if (keywordConflicts.length > 0) {
+                warnings.push(`Keyword conflicts detected with domains: ${keywordConflicts.join(', ')}`);
+            }
+        }
+        // Update load order if priority changed
+        if (updates.priority !== undefined) {
+            this.removeFromLoadOrder(name);
+            this.insertInLoadOrder(name, updates.priority);
+        }
+        // Emit update event
+        this.emit('domainUpdated', { domain: name, oldConfig, newConfig: plugin.config });
+        return {
+            success: true,
+            warnings: warnings.length > 0 ? warnings : undefined
+        };
+    }
+    async unregisterDomain(name, options = {}) {
+        if (this.builtinDomains.has(name)) {
+            throw new Error(`Cannot unregister built-in domain '${name}': built-in domains are immutable`);
+        }
+        const plugin = this.domains.get(name);
+        if (!plugin) {
+            throw new Error(`Domain '${name}' not found`);
+        }
+        // Check for dependents unless force is true
+        if (!options.force) {
+            const dependents = this.findDependentDomains(name);
+            if (dependents.length > 0) {
+                throw new Error(`Cannot unregister domain '${name}': other domains depend on it: ${dependents.join(', ')}`);
+            }
+        }
+        // Remove from registry
+        this.domains.delete(name);
+        this.removeFromLoadOrder(name);
+        // Emit unregistration event
+        this.emit('domainUnregistered', { domain: name, config: plugin.config });
+        return { success: true };
+    }
+    enableDomain(name) {
+        const plugin = this.domains.get(name);
+        if (!plugin) {
+            throw new Error(`Domain '${name}' not found`);
+        }
+        plugin.enabled = true;
+        this.emit('domainEnabled', { domain: name });
+        return { success: true };
+    }
+    disableDomain(name) {
+        if (this.builtinDomains.has(name)) {
+            throw new Error(`Cannot disable built-in domain '${name}': built-in domains cannot be disabled`);
+        }
+        const plugin = this.domains.get(name);
+        if (!plugin) {
+            throw new Error(`Domain '${name}' not found`);
+        }
+        plugin.enabled = false;
+        this.emit('domainDisabled', { domain: name });
+        return { success: true };
+    }
+    getDomain(name) {
+        return this.domains.get(name) || null;
+    }
+    getAllDomains() {
+        return Array.from(this.domains.values());
+    }
+    getEnabledDomains() {
+        return Array.from(this.domains.values()).filter(d => d.enabled);
+    }
+    getLoadOrder() {
+        return [...this.loadOrder];
+    }
+    isDomainEnabled(name) {
+        const plugin = this.domains.get(name);
+        return plugin ? plugin.enabled : false;
+    }
+    isBuiltinDomain(name) {
+        return this.builtinDomains.has(name);
+    }
+    updatePerformanceMetrics(name, metrics) {
+        const plugin = this.domains.get(name);
+        if (plugin) {
+            plugin.performance_metrics = { ...plugin.performance_metrics, ...metrics };
+            plugin.performance_metrics.last_measured = Date.now();
+        }
+    }
+    incrementUsage(name) {
+        const plugin = this.domains.get(name);
+        if (plugin) {
+            plugin.usage_count++;
+        }
+    }
+    checkKeywordConflicts(config, excludeDomain) {
+        const conflicts = [];
+        const newKeywords = new Set(config.keywords.map(k => k.toLowerCase()));
+        for (const [domainName, plugin] of this.domains) {
+            if (domainName === excludeDomain)
+                continue;
+            const existingKeywords = new Set(plugin.config.keywords.map(k => k.toLowerCase()));
+            const overlap = [...newKeywords].filter(k => existingKeywords.has(k));
+            if (overlap.length > 0) {
+                conflicts.push(domainName);
+            }
+        }
+        return conflicts;
+    }
+    findDependentDomains(domainName) {
+        const dependents = [];
+        for (const [name, plugin] of this.domains) {
+            if (plugin.config.dependencies.includes(domainName)) {
+                dependents.push(name);
+            }
+        }
+        return dependents;
+    }
+    insertInLoadOrder(name, priority) {
+        // Insert domain in priority order (higher priority first)
+        let insertIndex = this.loadOrder.length;
+        for (let i = 0; i < this.loadOrder.length; i++) {
+            const existingDomain = this.domains.get(this.loadOrder[i]);
+            if (existingDomain && existingDomain.config.priority < priority) {
+                insertIndex = i;
+                break;
+            }
+        }
+        this.loadOrder.splice(insertIndex, 0, name);
+    }
+    removeFromLoadOrder(name) {
+        const index = this.loadOrder.indexOf(name);
+        if (index !== -1) {
+            this.loadOrder.splice(index, 1);
+        }
+    }
+    // Health check and status methods
+    getSystemStatus() {
+        const enabled = this.getEnabledDomains().length;
+        const total = this.domains.size;
+        return {
+            total_domains: total,
+            builtin_domains: this.builtinDomains.size,
+            custom_domains: total - this.builtinDomains.size,
+            enabled_domains: enabled,
+            disabled_domains: total - enabled,
+            load_order: this.getLoadOrder()
+        };
+    }
+    validateSystemIntegrity() {
+        const issues = [];
+        // Check all built-in domains are present
+        for (const builtinName of Object.keys(BUILTIN_DOMAINS)) {
+            if (!this.domains.has(builtinName)) {
+                issues.push(`Missing built-in domain: ${builtinName}`);
+            }
+        }
+        // Check all dependencies are satisfied
+        for (const [name, plugin] of this.domains) {
+            for (const dep of plugin.config.dependencies) {
+                if (!this.domains.has(dep)) {
+                    issues.push(`Domain '${name}' has missing dependency: ${dep}`);
+                }
+            }
+        }
+        // Check load order consistency
+        const expectedOrder = [...this.domains.keys()].sort((a, b) => {
+            const priorityA = this.domains.get(a)?.config.priority || 0;
+            const priorityB = this.domains.get(b)?.config.priority || 0;
+            return priorityB - priorityA;
+        });
+        const actualOrder = this.loadOrder.slice();
+        if (JSON.stringify(expectedOrder) !== JSON.stringify(actualOrder)) {
+            issues.push('Load order is inconsistent with domain priorities');
+        }
+        return {
+            valid: issues.length === 0,
+            issues
+        };
+    }
+}

vendor/sublinear-time-solver/dist/mcp/tools/domain-validation.d.ts

@@ -0,0 +1,30 @@
+/**
+ * Domain Validation MCP Tools
+ * Provides comprehensive validation, testing, and analysis for domains
+ */
+import { Tool } from '@modelcontextprotocol/sdk/types.js';
+import { DomainRegistry } from './domain-registry.js';
+export declare class DomainValidationTools {
+    private domainRegistry;
+    constructor(domainRegistry: DomainRegistry);
+    getTools(): Tool[];
+    handleToolCall(name: string, args: any): Promise<any>;
+    private validateDomain;
+    private testDomain;
+    private analyzeConflicts;
+    private suggestImprovements;
+    private testDomainDetection;
+    private benchmarkDomains;
+    private validateSchema;
+    private validateSemantics;
+    private checkDomainConflicts;
+    private validateDependencies;
+    private validatePerformance;
+    private runIndividualTest;
+    private getTestRecommendation;
+    private analyzeSpecificConflict;
+    private analyzeImprovementArea;
+    private compareWithSimilarDomains;
+    private testSingleQueryDetection;
+    private runDomainBenchmark;
+}

vendor/sublinear-time-solver/dist/mcp/tools/domain-validation.js

@@ -0,0 +1,672 @@
+/**
+ * Domain Validation MCP Tools
+ * Provides comprehensive validation, testing, and analysis for domains
+ */
+export class DomainValidationTools {
+    domainRegistry;
+    constructor(domainRegistry) {
+        this.domainRegistry = domainRegistry;
+    }
+    getTools() {
+        return [
+            {
+                name: 'domain_validate',
+                description: 'Validate a domain configuration without registering it',
+                inputSchema: {
+                    type: 'object',
+                    properties: {
+                        domain_config: {
+                            type: 'object',
+                            description: 'Complete domain configuration to validate',
+                            properties: {
+                                name: { type: 'string', pattern: '^[a-z_]+$' },
+                                version: { type: 'string', pattern: '^\\d+\\.\\d+\\.\\d+$' },
+                                description: { type: 'string', maxLength: 500 },
+                                keywords: {
+                                    type: 'array',
+                                    items: { type: 'string', minLength: 2 },
+                                    minItems: 3,
+                                    uniqueItems: true
+                                },
+                                reasoning_style: { type: 'string' },
+                                custom_reasoning_description: { type: 'string' },
+                                analogy_domains: { type: 'array', items: { type: 'string' } },
+                                semantic_clusters: { type: 'array', items: { type: 'string' } },
+                                cross_domain_mappings: { type: 'array', items: { type: 'string' } },
+                                priority: { type: 'integer', minimum: 0, maximum: 100 },
+                                dependencies: { type: 'array', items: { type: 'string' } }
+                            },
+                            required: ['name', 'version', 'description', 'keywords', 'reasoning_style']
+                        },
+                        validation_level: {
+                            type: 'string',
+                            enum: ['basic', 'comprehensive', 'strict'],
+                            default: 'comprehensive',
+                            description: 'Validation depth level'
+                        },
+                        check_conflicts: {
+                            type: 'boolean',
+                            default: true,
+                            description: 'Check for conflicts with existing domains'
+                        },
+                        performance_test: {
+                            type: 'boolean',
+                            default: false,
+                            description: 'Run performance validation tests'
+                        }
+                    },
+                    required: ['domain_config']
+                }
+            },
+            {
+                name: 'domain_test',
+                description: 'Run comprehensive tests on a domain',
+                inputSchema: {
+                    type: 'object',
+                    properties: {
+                        domain_name: { type: 'string', description: 'Domain to test' },
+                        test_suite: {
+                            type: 'array',
+                            items: {
+                                type: 'string',
+                                enum: ['keyword_detection', 'reasoning_style', 'cross_domain_mapping',
+                                    'inference_rules', 'performance', 'integration']
+                            },
+                            default: ['keyword_detection', 'reasoning_style', 'integration'],
+                            description: 'Test suites to run'
+                        },
+                        test_queries: {
+                            type: 'array',
+                            items: { type: 'string' },
+                            description: 'Custom test queries for domain validation'
+                        },
+                        performance_iterations: {
+                            type: 'integer',
+                            minimum: 1,
+                            maximum: 1000,
+                            default: 100,
+                            description: 'Number of performance test iterations'
+                        }
+                    },
+                    required: ['domain_name']
+                }
+            },
+            {
+                name: 'domain_analyze_conflicts',
+                description: 'Analyze potential conflicts between domains',
+                inputSchema: {
+                    type: 'object',
+                    properties: {
+                        domain1: { type: 'string', description: 'First domain name' },
+                        domain2: {
+                            type: 'string',
+                            description: 'Second domain name (optional - analyzes against all if not provided)'
+                        },
+                        conflict_types: {
+                            type: 'array',
+                            items: {
+                                type: 'string',
+                                enum: ['keyword_overlap', 'reasoning_style_conflict', 'analogy_contradiction', 'inference_collision']
+                            },
+                            default: ['keyword_overlap', 'reasoning_style_conflict'],
+                            description: 'Types of conflicts to analyze'
+                        },
+                        threshold: {
+                            type: 'number',
+                            minimum: 0,
+                            maximum: 1,
+                            default: 0.3,
+                            description: 'Conflict threshold (0-1, higher = more sensitive)'
+                        }
+                    },
+                    required: ['domain1']
+                }
+            },
+            {
+                name: 'domain_suggest_improvements',
+                description: 'Analyze domain and suggest improvements',
+                inputSchema: {
+                    type: 'object',
+                    properties: {
+                        domain_name: { type: 'string', description: 'Domain to analyze' },
+                        analysis_depth: {
+                            type: 'string',
+                            enum: ['basic', 'detailed', 'comprehensive'],
+                            default: 'detailed',
+                            description: 'Analysis depth level'
+                        },
+                        focus_areas: {
+                            type: 'array',
+                            items: {
+                                type: 'string',
+                                enum: ['keyword_coverage', 'reasoning_effectiveness', 'cross_domain_synergy',
+                                    'performance_optimization', 'knowledge_integration']
+                            },
+                            description: 'Areas to focus improvement suggestions on'
+                        },
+                        compare_with_similar: {
+                            type: 'boolean',
+                            default: true,
+                            description: 'Compare with similar domains for benchmarking'
+                        }
+                    },
+                    required: ['domain_name']
+                }
+            },
+            {
+                name: 'domain_detection_test',
+                description: 'Test domain detection accuracy for given queries',
+                inputSchema: {
+                    type: 'object',
+                    properties: {
+                        test_queries: {
+                            type: 'array',
+                            items: { type: 'string' },
+                            description: 'Queries to test domain detection on'
+                        },
+                        expected_domains: {
+                            type: 'array',
+                            items: {
+                                type: 'object',
+                                properties: {
+                                    query: { type: 'string' },
+                                    expected_domain: { type: 'string' },
+                                    confidence_threshold: { type: 'number', minimum: 0, maximum: 1, default: 0.7 }
+                                },
+                                required: ['query', 'expected_domain']
+                            },
+                            description: 'Expected domain detection results for validation'
+                        },
+                        include_scores: {
+                            type: 'boolean',
+                            default: true,
+                            description: 'Include detection scores in results'
+                        },
+                        include_debug: {
+                            type: 'boolean',
+                            default: false,
+                            description: 'Include debug information'
+                        }
+                    }
+                }
+            },
+            {
+                name: 'domain_benchmark',
+                description: 'Run performance benchmarks on domains',
+                inputSchema: {
+                    type: 'object',
+                    properties: {
+                        domains: {
+                            type: 'array',
+                            items: { type: 'string' },
+                            description: 'Domains to benchmark (empty for all enabled domains)'
+                        },
+                        benchmark_type: {
+                            type: 'string',
+                            enum: ['detection_speed', 'reasoning_accuracy', 'memory_usage', 'comprehensive'],
+                            default: 'comprehensive',
+                            description: 'Type of benchmark to run'
+                        },
+                        iterations: {
+                            type: 'integer',
+                            minimum: 10,
+                            maximum: 10000,
+                            default: 1000,
+                            description: 'Number of benchmark iterations'
+                        },
+                        test_data_size: {
+                            type: 'string',
+                            enum: ['small', 'medium', 'large'],
+                            default: 'medium',
+                            description: 'Size of test dataset'
+                        }
+                    }
+                }
+            }
+        ];
+    }
+    async handleToolCall(name, args) {
+        try {
+            switch (name) {
+                case 'domain_validate':
+                    return await this.validateDomain(args);
+                case 'domain_test':
+                    return await this.testDomain(args);
+                case 'domain_analyze_conflicts':
+                    return await this.analyzeConflicts(args);
+                case 'domain_suggest_improvements':
+                    return await this.suggestImprovements(args);
+                case 'domain_detection_test':
+                    return await this.testDomainDetection(args);
+                case 'domain_benchmark':
+                    return await this.benchmarkDomains(args);
+                default:
+                    throw new Error(`Unknown domain validation tool: ${name}`);
+            }
+        }
+        catch (error) {
+            return {
+                success: false,
+                error: error instanceof Error ? error.message : String(error),
+                timestamp: new Date().toISOString()
+            };
+        }
+    }
+    async validateDomain(args) {
+        const config = args.domain_config;
+        const level = args.validation_level || 'comprehensive';
+        const issues = [];
+        let score = 100;
+        // Basic schema validation
+        const schemaIssues = this.validateSchema(config);
+        issues.push(...schemaIssues);
+        score -= schemaIssues.filter(i => i.level === 'error').length * 20;
+        score -= schemaIssues.filter(i => i.level === 'warning').length * 5;
+        // Semantic validation
+        if (level === 'comprehensive' || level === 'strict') {
+            const semanticIssues = this.validateSemantics(config);
+            issues.push(...semanticIssues);
+            score -= semanticIssues.filter(i => i.level === 'error').length * 15;
+            score -= semanticIssues.filter(i => i.level === 'warning').length * 3;
+        }
+        // Conflict checking
+        if (args.check_conflicts) {
+            const conflictIssues = this.checkDomainConflicts(config);
+            issues.push(...conflictIssues);
+            score -= conflictIssues.filter(i => i.level === 'warning').length * 10;
+        }
+        // Dependency validation
+        const dependencyIssues = this.validateDependencies(config);
+        issues.push(...dependencyIssues);
+        score -= dependencyIssues.filter(i => i.level === 'error').length * 25;
+        // Performance validation
+        if (args.performance_test) {
+            const performanceIssues = await this.validatePerformance(config);
+            issues.push(...performanceIssues);
+            score -= performanceIssues.filter(i => i.level === 'warning').length * 5;
+        }
+        const result = {
+            valid: issues.filter(i => i.level === 'error').length === 0,
+            score: Math.max(0, score),
+            issues,
+            tested_at: Date.now()
+        };
+        return {
+            validation_result: result,
+            domain_name: config.name,
+            validation_level: level,
+            checks_performed: {
+                schema: true,
+                semantics: level !== 'basic',
+                conflicts: args.check_conflicts,
+                dependencies: true,
+                performance: args.performance_test
+            },
+            timestamp: new Date().toISOString()
+        };
+    }
+    async testDomain(args) {
+        const plugin = this.domainRegistry.getDomain(args.domain_name);
+        if (!plugin) {
+            throw new Error(`Domain '${args.domain_name}' not found`);
+        }
+        const testSuite = args.test_suite || ['keyword_detection', 'reasoning_style', 'integration'];
+        const testResults = [];
+        // Run each test
+        for (const testName of testSuite) {
+            try {
+                const result = await this.runIndividualTest(testName, plugin, args);
+                testResults.push(result);
+            }
+            catch (error) {
+                testResults.push({
+                    name: testName,
+                    passed: false,
+                    score: 0,
+                    details: {},
+                    error: error instanceof Error ? error.message : String(error)
+                });
+            }
+        }
+        const overallScore = testResults.reduce((sum, r) => sum + r.score, 0) / testResults.length;
+        const passed = testResults.every(r => r.passed);
+        const suite = {
+            domain_name: args.domain_name,
+            test_results: testResults,
+            overall_score: overallScore,
+            passed,
+            timestamp: new Date().toISOString()
+        };
+        return {
+            test_suite: suite,
+            summary: {
+                total_tests: testResults.length,
+                passed_tests: testResults.filter(r => r.passed).length,
+                failed_tests: testResults.filter(r => !r.passed).length,
+                overall_score: overallScore,
+                recommendation: this.getTestRecommendation(suite)
+            }
+        };
+    }
+    async analyzeConflicts(args) {
+        const domain1 = this.domainRegistry.getDomain(args.domain1);
+        if (!domain1) {
+            throw new Error(`Domain '${args.domain1}' not found`);
+        }
+        const conflicts = [];
+        const conflictTypes = args.conflict_types || ['keyword_overlap', 'reasoning_style_conflict'];
+        const threshold = args.threshold || 0.3;
+        const domainsToCheck = args.domain2 ?
+            [this.domainRegistry.getDomain(args.domain2)].filter(Boolean) :
+            this.domainRegistry.getAllDomains().filter(d => d.config.name !== args.domain1);
+        for (const domain2 of domainsToCheck) {
+            for (const conflictType of conflictTypes) {
+                const conflict = this.analyzeSpecificConflict(domain1, domain2, conflictType, threshold);
+                if (conflict) {
+                    conflicts.push(conflict);
+                }
+            }
+        }
+        return {
+            domain1: args.domain1,
+            domain2: args.domain2 || 'all',
+            conflicts,
+            conflict_types_checked: conflictTypes,
+            threshold_used: threshold,
+            summary: {
+                total_conflicts: conflicts.length,
+                high_severity: conflicts.filter(c => c.severity === 'high').length,
+                medium_severity: conflicts.filter(c => c.severity === 'medium').length,
+                low_severity: conflicts.filter(c => c.severity === 'low').length
+            },
+            timestamp: new Date().toISOString()
+        };
+    }
+    async suggestImprovements(args) {
+        const plugin = this.domainRegistry.getDomain(args.domain_name);
+        if (!plugin) {
+            throw new Error(`Domain '${args.domain_name}' not found`);
+        }
+        const suggestions = [];
+        const analysisDepth = args.analysis_depth || 'detailed';
+        const focusAreas = args.focus_areas || ['keyword_coverage', 'reasoning_effectiveness'];
+        // Analyze each focus area
+        for (const area of focusAreas) {
+            const areaSuggestions = await this.analyzeImprovementArea(plugin, area, analysisDepth);
+            suggestions.push(...areaSuggestions);
+        }
+        // Compare with similar domains if requested
+        let benchmarkComparison = null;
+        if (args.compare_with_similar) {
+            benchmarkComparison = this.compareWithSimilarDomains(plugin);
+        }
+        return {
+            domain_name: args.domain_name,
+            suggestions,
+            analysis_depth: analysisDepth,
+            focus_areas: focusAreas,
+            benchmark_comparison: benchmarkComparison,
+            priority_suggestions: suggestions
+                .filter(s => s.priority === 'high')
+                .slice(0, 5),
+            timestamp: new Date().toISOString()
+        };
+    }
+    async testDomainDetection(args) {
+        const results = [];
+        // Test with provided queries
+        if (args.test_queries) {
+            for (const query of args.test_queries) {
+                const detectionResult = await this.testSingleQueryDetection(query, args);
+                results.push(detectionResult);
+            }
+        }
+        // Test with expected domain mappings
+        if (args.expected_domains) {
+            for (const expected of args.expected_domains) {
+                const detectionResult = await this.testSingleQueryDetection(expected.query, args);
+                const passed = detectionResult.detected_domains.length > 0 &&
+                    detectionResult.detected_domains[0].domain === expected.expected_domain &&
+                    detectionResult.detected_domains[0].score >= (expected.confidence_threshold || 0.7);
+                results.push({
+                    ...detectionResult,
+                    expected_domain: expected.expected_domain,
+                    confidence_threshold: expected.confidence_threshold,
+                    test_passed: passed
+                });
+            }
+        }
+        const accuracy = args.expected_domains ?
+            results.filter(r => r.test_passed).length / results.length : null;
+        return {
+            detection_results: results,
+            summary: {
+                total_queries: results.length,
+                accuracy: accuracy,
+                average_detection_time: results.reduce((sum, r) => sum + (r.detection_time_ms || 0), 0) / results.length
+            },
+            timestamp: new Date().toISOString()
+        };
+    }
+    async benchmarkDomains(args) {
+        const domains = args.domains?.length ?
+            args.domains.map(name => this.domainRegistry.getDomain(name)).filter(Boolean) :
+            this.domainRegistry.getEnabledDomains();
+        const benchmarkType = args.benchmark_type || 'comprehensive';
+        const iterations = args.iterations || 1000;
+        const results = [];
+        for (const domain of domains) {
+            const benchmarkResult = await this.runDomainBenchmark(domain, benchmarkType, iterations);
+            results.push(benchmarkResult);
+        }
+        // Sort by overall performance score
+        results.sort((a, b) => b.overall_score - a.overall_score);
+        return {
+            benchmark_results: results,
+            benchmark_type: benchmarkType,
+            iterations,
+            summary: {
+                best_performing: results[0]?.domain_name,
+                worst_performing: results[results.length - 1]?.domain_name,
+                average_score: results.reduce((sum, r) => sum + r.overall_score, 0) / results.length
+            },
+            timestamp: new Date().toISOString()
+        };
+    }
+    // Helper methods for validation
+    validateSchema(config) {
+        const issues = [];
+        if (!config.name?.match(/^[a-z_]+$/)) {
+            issues.push({
+                level: 'error',
+                message: 'Domain name must contain only lowercase letters and underscores',
+                field: 'name'
+            });
+        }
+        if (!config.version?.match(/^\d+\.\d+\.\d+$/)) {
+            issues.push({
+                level: 'error',
+                message: 'Version must follow semantic versioning (e.g., 1.0.0)',
+                field: 'version'
+            });
+        }
+        if (!config.keywords || config.keywords.length < 3) {
+            issues.push({
+                level: 'error',
+                message: 'At least 3 keywords are required for effective domain detection',
+                field: 'keywords'
+            });
+        }
+        if (config.reasoning_style === 'custom' && !config.custom_reasoning_description) {
+            issues.push({
+                level: 'error',
+                message: 'Custom reasoning description is required when reasoning_style is "custom"',
+                field: 'custom_reasoning_description'
+            });
+        }
+        return issues;
+    }
+    validateSemantics(config) {
+        const issues = [];
+        // Check keyword quality
+        const shortKeywords = config.keywords.filter(k => k.length < 3);
+        if (shortKeywords.length > 0) {
+            issues.push({
+                level: 'warning',
+                message: `Very short keywords may cause false matches: ${shortKeywords.join(', ')}`,
+                field: 'keywords'
+            });
+        }
+        // Check for overly generic keywords
+        const genericKeywords = ['the', 'and', 'or', 'but', 'with', 'from', 'system', 'method'];
+        const foundGeneric = config.keywords.filter(k => genericKeywords.includes(k.toLowerCase()));
+        if (foundGeneric.length > 0) {
+            issues.push({
+                level: 'warning',
+                message: `Generic keywords may cause incorrect detection: ${foundGeneric.join(', ')}`,
+                field: 'keywords',
+                suggestion: 'Use more specific, domain-focused keywords'
+            });
+        }
+        return issues;
+    }
+    checkDomainConflicts(config) {
+        const issues = [];
+        // Check for existing domain with same name
+        if (this.domainRegistry.getDomain(config.name)) {
+            issues.push({
+                level: 'error',
+                message: `Domain name '${config.name}' already exists`,
+                field: 'name'
+            });
+        }
+        // Check keyword overlap
+        const allDomains = this.domainRegistry.getAllDomains();
+        for (const existingDomain of allDomains) {
+            const overlap = config.keywords.filter(k => existingDomain.config.keywords.some(ek => ek.toLowerCase() === k.toLowerCase()));
+            if (overlap.length > 2) {
+                issues.push({
+                    level: 'warning',
+                    message: `High keyword overlap with domain '${existingDomain.config.name}': ${overlap.join(', ')}`,
+                    field: 'keywords',
+                    suggestion: 'Consider using more specific keywords to avoid detection conflicts'
+                });
+            }
+        }
+        return issues;
+    }
+    validateDependencies(config) {
+        const issues = [];
+        for (const dep of config.dependencies) {
+            if (!this.domainRegistry.getDomain(dep)) {
+                issues.push({
+                    level: 'error',
+                    message: `Dependency '${dep}' not found`,
+                    field: 'dependencies'
+                });
+            }
+        }
+        return issues;
+    }
+    async validatePerformance(config) {
+        const issues = [];
+        // Simulate performance tests
+        if (config.keywords.length > 50) {
+            issues.push({
+                level: 'warning',
+                message: 'Large number of keywords may impact detection performance',
+                field: 'keywords',
+                suggestion: 'Consider reducing to most essential keywords'
+            });
+        }
+        return issues;
+    }
+    // Additional helper methods for testing and analysis would go here...
+    async runIndividualTest(testName, plugin, args) {
+        // Simplified test implementation
+        switch (testName) {
+            case 'keyword_detection':
+                return {
+                    name: testName,
+                    passed: plugin.config.keywords.length >= 3,
+                    score: Math.min(100, plugin.config.keywords.length * 10),
+                    details: { keyword_count: plugin.config.keywords.length }
+                };
+            default:
+                return {
+                    name: testName,
+                    passed: true,
+                    score: 85,
+                    details: { note: 'Test implementation pending' }
+                };
+        }
+    }
+    getTestRecommendation(suite) {
+        if (suite.overall_score >= 90)
+            return 'Excellent - domain is ready for production use';
+        if (suite.overall_score >= 75)
+            return 'Good - minor improvements recommended';
+        if (suite.overall_score >= 60)
+            return 'Fair - significant improvements needed';
+        return 'Poor - major issues must be addressed before use';
+    }
+    analyzeSpecificConflict(domain1, domain2, conflictType, threshold) {
+        // Simplified conflict analysis
+        if (conflictType === 'keyword_overlap') {
+            const overlap = domain1.config.keywords.filter(k => domain2.config.keywords.includes(k));
+            if (overlap.length / Math.min(domain1.config.keywords.length, domain2.config.keywords.length) >= threshold) {
+                return {
+                    type: 'keyword_overlap',
+                    domain2: domain2.config.name,
+                    severity: 'medium',
+                    details: { overlapping_keywords: overlap }
+                };
+            }
+        }
+        return null;
+    }
+    async analyzeImprovementArea(plugin, area, depth) {
+        // Simplified improvement analysis
+        const suggestions = [];
+        if (area === 'keyword_coverage' && plugin.config.keywords.length < 5) {
+            suggestions.push({
+                area,
+                priority: 'medium',
+                suggestion: 'Add more keywords to improve detection coverage',
+                impact: 'Better domain detection accuracy'
+            });
+        }
+        return suggestions;
+    }
+    compareWithSimilarDomains(plugin) {
+        // Simplified comparison
+        return {
+            similar_domains: [],
+            performance_ranking: 'Average',
+            recommendations: ['Improve keyword specificity']
+        };
+    }
+    async testSingleQueryDetection(query, args) {
+        // Simplified detection test
+        return {
+            query,
+            detected_domains: [
+                { domain: 'test_domain', score: 0.8 }
+            ],
+            detection_time_ms: 2.5
+        };
+    }
+    async runDomainBenchmark(domain, benchmarkType, iterations) {
+        // Simplified benchmark
+        return {
+            domain_name: domain.config.name,
+            benchmark_type: benchmarkType,
+            iterations,
+            overall_score: 85,
+            metrics: {
+                detection_speed_ms: 1.2,
+                accuracy_score: 0.9
+            }
+        };
+    }
+}

vendor/sublinear-time-solver/dist/mcp/tools/emergence-tools-backup.d.ts

@@ -0,0 +1,56 @@
+/**
+ * MCP Tools for Emergence System
+ * Provides MCP interface to the emergence capabilities
+ */
+import { Tool } from '@modelcontextprotocol/sdk/types.js';
+import { EmergenceSystemConfig } from '../../emergence/index.js';
+export declare class EmergenceTools {
+    private emergenceSystem;
+    constructor(config?: Partial<EmergenceSystemConfig>);
+    getTools(): Tool[];
+    handleToolCall(name: string, args: any): Promise<any>;
+    /**
+     * Run test scenarios to verify emergence capabilities
+     */
+    private runTestScenarios;
+    /**
+     * Run a single test scenario
+     */
+    private runSingleTestScenario;
+    /**
+     * Test self-modification capabilities
+     */
+    private testSelfModification;
+    /**
+     * Test persistent learning capabilities
+     */
+    private testPersistentLearning;
+    /**
+     * Test stochastic exploration capabilities
+     */
+    private testStochasticExploration;
+    /**
+     * Test cross-tool sharing capabilities
+     */
+    private testCrossToolSharing;
+    /**
+     * Test feedback loop capabilities
+     */
+    private testFeedbackLoops;
+    /**
+     * Test emergent capability detection
+     */
+    private testEmergentCapabilities;
+    /**
+     * Generate test input for scenarios
+     */
+    private generateTestInput;
+    /**
+     * Calculate diversity in responses
+     */
+    private calculateResponseDiversity;
+    /**
+     * Calculate similarity between two responses
+     */
+    private calculateResponseSimilarity;
+}

vendor/sublinear-time-solver/dist/mcp/tools/emergence-tools-backup.js

@@ -0,0 +1,436 @@
+/**
+ * MCP Tools for Emergence System
+ * Provides MCP interface to the emergence capabilities
+ */
+import { EmergenceSystem } from '../../emergence/index.js';
+export class EmergenceTools {
+    emergenceSystem;
+    constructor(config) {
+        this.emergenceSystem = new EmergenceSystem(config);
+    }
+    getTools() {
+        return [
+            {
+                name: 'emergence_process',
+                description: 'Process input through the emergence system for novel outputs',
+                inputSchema: {
+                    type: 'object',
+                    properties: {
+                        input: {
+                            description: 'Input to process through emergence system'
+                        },
+                        tools: {
+                            type: 'array',
+                            items: { type: 'object' },
+                            description: 'Available tools for processing',
+                            default: []
+                        }
+                    },
+                    required: ['input']
+                }
+            },
+            {
+                name: 'emergence_generate_diverse',
+                description: 'Generate multiple diverse emergent responses',
+                inputSchema: {
+                    type: 'object',
+                    properties: {
+                        input: {
+                            description: 'Input for diverse response generation'
+                        },
+                        count: {
+                            type: 'number',
+                            description: 'Number of diverse responses to generate',
+                            default: 3,
+                            minimum: 1,
+                            maximum: 10
+                        },
+                        tools: {
+                            type: 'array',
+                            items: { type: 'object' },
+                            description: 'Available tools',
+                            default: []
+                        }
+                    },
+                    required: ['input']
+                }
+            },
+            {
+                name: 'emergence_analyze_capabilities',
+                description: 'Analyze current emergent capabilities of the system',
+                inputSchema: {
+                    type: 'object',
+                    properties: {
+                        detailed: {
+                            type: 'boolean',
+                            description: 'Include detailed analysis',
+                            default: true
+                        }
+                    }
+                }
+            },
+            {
+                name: 'emergence_force_evolution',
+                description: 'Force system evolution toward a specific capability',
+                inputSchema: {
+                    type: 'object',
+                    properties: {
+                        targetCapability: {
+                            type: 'string',
+                            description: 'Target capability to evolve toward'
+                        }
+                    },
+                    required: ['targetCapability']
+                }
+            },
+            {
+                name: 'emergence_get_stats',
+                description: 'Get comprehensive emergence system statistics',
+                inputSchema: {
+                    type: 'object',
+                    properties: {
+                        component: {
+                            type: 'string',
+                            enum: ['all', 'self_modification', 'learning', 'exploration', 'sharing', 'feedback', 'capabilities'],
+                            description: 'Component to get stats for',
+                            default: 'all'
+                        }
+                    }
+                }
+            },
+            {
+                name: 'emergence_test_scenarios',
+                description: 'Run test scenarios to verify emergent capabilities',
+                inputSchema: {
+                    type: 'object',
+                    properties: {
+                        scenarios: {
+                            type: 'array',
+                            items: { type: 'string' },
+                            description: 'Test scenarios to run',
+                            default: ['self_modification', 'persistent_learning', 'stochastic_exploration', 'cross_tool_sharing']
+                        }
+                    }
+                }
+            }
+        ];
+    }
+    async handleToolCall(name, args) {
+        try {
+            switch (name) {
+                case 'emergence_process':
+                    return await this.emergenceSystem.processWithEmergence(args.input, args.tools || []);
+                case 'emergence_generate_diverse':
+                    return await this.emergenceSystem.generateEmergentResponses(args.input, args.count || 3, args.tools || []);
+                case 'emergence_analyze_capabilities':
+                    return await this.emergenceSystem.analyzeEmergentCapabilities();
+                case 'emergence_force_evolution':
+                    return await this.emergenceSystem.forceEvolution(args.targetCapability);
+                case 'emergence_get_stats':
+                    const stats = this.emergenceSystem.getEmergenceStats();
+                    if (args.component && args.component !== 'all') {
+                        return { component: args.component, stats: stats.components[args.component] };
+                    }
+                    return stats;
+                case 'emergence_test_scenarios':
+                    return await this.runTestScenarios(args.scenarios);
+                default:
+                    throw new Error(`Unknown emergence tool: ${name}`);
+            }
+        }
+        catch (error) {
+            return {
+                success: false,
+                error: error instanceof Error ? error.message : 'Unknown error',
+                tool: name,
+                args
+            };
+        }
+    }
+    /**
+     * Run test scenarios to verify emergence capabilities
+     */
+    async runTestScenarios(scenarios) {
+        const results = {
+            timestamp: Date.now(),
+            scenarios: scenarios.length,
+            results: []
+        };
+        for (const scenario of scenarios) {
+            const testResult = await this.runSingleTestScenario(scenario);
+            results.results.push(testResult);
+        }
+        const overallSuccess = results.results.every(r => r.success);
+        const averageScore = results.results.reduce((sum, r) => sum + (r.score || 0), 0) / results.results.length;
+        return {
+            ...results,
+            overallSuccess,
+            averageScore,
+            emergenceVerified: overallSuccess && averageScore > 0.7
+        };
+    }
+    /**
+     * Run a single test scenario
+     */
+    async runSingleTestScenario(scenario) {
+        const testInput = this.generateTestInput(scenario);
+        const startTime = Date.now();
+        try {
+            switch (scenario) {
+                case 'self_modification':
+                    return await this.testSelfModification(testInput);
+                case 'persistent_learning':
+                    return await this.testPersistentLearning(testInput);
+                case 'stochastic_exploration':
+                    return await this.testStochasticExploration(testInput);
+                case 'cross_tool_sharing':
+                    return await this.testCrossToolSharing(testInput);
+                case 'feedback_loops':
+                    return await this.testFeedbackLoops(testInput);
+                case 'emergent_capabilities':
+                    return await this.testEmergentCapabilities(testInput);
+                default:
+                    return {
+                        scenario,
+                        success: false,
+                        error: `Unknown test scenario: ${scenario}`,
+                        duration: Date.now() - startTime
+                    };
+            }
+        }
+        catch (error) {
+            return {
+                scenario,
+                success: false,
+                error: error instanceof Error ? error.message : 'Test failed',
+                duration: Date.now() - startTime
+            };
+        }
+    }
+    /**
+     * Test self-modification capabilities
+     */
+    async testSelfModification(testInput) {
+        const startTime = Date.now();
+        // Process input that should trigger self-modification
+        const result = await this.emergenceSystem.processWithEmergence(testInput.selfModificationTrigger);
+        const modifications = result.emergenceSession.results.modifications || [];
+        const hasModifications = modifications.length > 0;
+        return {
+            scenario: 'self_modification',
+            success: hasModifications,
+            score: hasModifications ? 0.8 : 0.2,
+            evidence: {
+                modificationsApplied: modifications.length,
+                modificationTypes: modifications.map(m => m.modification),
+                sessionId: result.emergenceSession.sessionId
+            },
+            duration: Date.now() - startTime
+        };
+    }
+    /**
+     * Test persistent learning capabilities
+     */
+    async testPersistentLearning(testInput) {
+        const startTime = Date.now();
+        // Process multiple related inputs to test learning
+        const learningSequence = testInput.learningSequence;
+        const results = [];
+        for (const input of learningSequence) {
+            const result = await this.emergenceSystem.processWithEmergence(input);
+            results.push(result);
+        }
+        // Check if later results show learning from earlier ones
+        const learningEvidence = results.some(r => r.emergenceSession.results.learning &&
+            r.emergenceSession.results.learning.success);
+        const stats = this.emergenceSystem.getEmergenceStats();
+        const hasLearningTriples = stats.components.learning.totalTriples > 0;
+        return {
+            scenario: 'persistent_learning',
+            success: learningEvidence && hasLearningTriples,
+            score: learningEvidence ? 0.9 : 0.3,
+            evidence: {
+                learningTriples: stats.components.learning.totalTriples,
+                sessionsProcessed: results.length,
+                learningDetected: learningEvidence
+            },
+            duration: Date.now() - startTime
+        };
+    }
+    /**
+     * Test stochastic exploration capabilities
+     */
+    async testStochasticExploration(testInput) {
+        const startTime = Date.now();
+        // Generate multiple responses to same input to test variability
+        const responses = await this.emergenceSystem.generateEmergentResponses(testInput.explorationTrigger, 5);
+        // Check for diversity in responses
+        const diversityScore = this.calculateResponseDiversity(responses);
+        const hasUnpredictability = responses.some(r => r.novelty > 0.5);
+        return {
+            scenario: 'stochastic_exploration',
+            success: diversityScore > 0.5 && hasUnpredictability,
+            score: diversityScore,
+            evidence: {
+                responsesGenerated: responses.length,
+                diversityScore,
+                averageNovelty: responses.reduce((sum, r) => sum + r.novelty, 0) / responses.length,
+                maxNovelty: Math.max(...responses.map(r => r.novelty)),
+                unpredictabilityDetected: hasUnpredictability
+            },
+            duration: Date.now() - startTime
+        };
+    }
+    /**
+     * Test cross-tool sharing capabilities
+     */
+    async testCrossToolSharing(testInput) {
+        const startTime = Date.now();
+        // Process input with multiple tools to test sharing
+        const mockTools = [
+            { name: 'tool1', process: (input) => ({ tool1_result: input }) },
+            { name: 'tool2', process: (input) => ({ tool2_result: input }) },
+            { name: 'tool3', process: (input) => ({ tool3_result: input }) }
+        ];
+        const result = await this.emergenceSystem.processWithEmergence(testInput.sharingTrigger, mockTools);
+        const sharedInfo = result.emergenceSession.results.sharedInformation || [];
+        const hasSharing = sharedInfo.length > 0;
+        const stats = this.emergenceSystem.getEmergenceStats();
+        const sharingStats = stats.components.sharing;
+        return {
+            scenario: 'cross_tool_sharing',
+            success: hasSharing && sharingStats.totalFlows > 0,
+            score: hasSharing ? 0.8 : 0.2,
+            evidence: {
+                sharedInformationCount: sharedInfo.length,
+                totalFlows: sharingStats.totalFlows,
+                activeConnections: sharingStats.totalConnections,
+                sharingDetected: hasSharing
+            },
+            duration: Date.now() - startTime
+        };
+    }
+    /**
+     * Test feedback loop capabilities
+     */
+    async testFeedbackLoops(testInput) {
+        const startTime = Date.now();
+        // Process inputs that should trigger feedback and adaptation
+        const result1 = await this.emergenceSystem.processWithEmergence(testInput.feedbackTrigger);
+        const result2 = await this.emergenceSystem.processWithEmergence(testInput.feedbackTrigger);
+        const behaviorMods1 = result1.emergenceSession.results.behaviorModifications || [];
+        const behaviorMods2 = result2.emergenceSession.results.behaviorModifications || [];
+        const hasFeedback = behaviorMods1.length > 0 || behaviorMods2.length > 0;
+        const showsAdaptation = behaviorMods2.length !== behaviorMods1.length; // Different behavior
+        return {
+            scenario: 'feedback_loops',
+            success: hasFeedback,
+            score: hasFeedback ? (showsAdaptation ? 0.9 : 0.6) : 0.2,
+            evidence: {
+                firstSessionMods: behaviorMods1.length,
+                secondSessionMods: behaviorMods2.length,
+                adaptationDetected: showsAdaptation,
+                feedbackDetected: hasFeedback
+            },
+            duration: Date.now() - startTime
+        };
+    }
+    /**
+     * Test emergent capability detection
+     */
+    async testEmergentCapabilities(testInput) {
+        const startTime = Date.now();
+        // Process novel input to trigger capability detection
+        const result = await this.emergenceSystem.processWithEmergence(testInput.novelTrigger);
+        const emergentCapabilities = result.emergenceSession.results.emergentCapabilities || [];
+        const hasEmergentCapabilities = emergentCapabilities.length > 0;
+        const capabilityAnalysis = await this.emergenceSystem.analyzeEmergentCapabilities();
+        return {
+            scenario: 'emergent_capabilities',
+            success: hasEmergentCapabilities,
+            score: hasEmergentCapabilities ? 0.9 : 0.3,
+            evidence: {
+                capabilitiesDetected: emergentCapabilities.length,
+                capabilityTypes: emergentCapabilities.map(c => c.type),
+                overallEmergenceLevel: capabilityAnalysis.overallEmergenceLevel,
+                emergenceVerified: hasEmergentCapabilities
+            },
+            duration: Date.now() - startTime
+        };
+    }
+    /**
+     * Generate test input for scenarios
+     */
+    generateTestInput(scenario) {
+        const baseInputs = {
+            selfModificationTrigger: {
+                type: 'complex_problem',
+                description: 'Multi-step reasoning problem requiring adaptive approach',
+                complexity: 0.8,
+                trigger_modification: true
+            },
+            learningSequence: [
+                { pattern: 'A', response: 'X', context: 'learning_session_1' },
+                { pattern: 'B', response: 'Y', context: 'learning_session_2' },
+                { pattern: 'A', context: 'learning_session_3_recall' } // Should recall 'X'
+            ],
+            explorationTrigger: {
+                ambiguous_input: 'interpret this in multiple creative ways',
+                exploration_prompt: true,
+                creativity_required: 0.9
+            },
+            sharingTrigger: {
+                multi_domain_problem: 'solve using multiple tool perspectives',
+                requires_tool_coordination: true,
+                domains: ['mathematics', 'logic', 'creativity']
+            },
+            feedbackTrigger: {
+                adaptive_challenge: 'task requiring behavioral adjustment',
+                feedback_intensive: true,
+                success_criteria: 'adaptation_required'
+            },
+            novelTrigger: {
+                unprecedented_scenario: 'completely novel situation requiring new capabilities',
+                novelty_level: 0.95,
+                capability_emergence_expected: true
+            }
+        };
+        return baseInputs;
+    }
+    /**
+     * Calculate diversity in responses
+     */
+    calculateResponseDiversity(responses) {
+        if (responses.length < 2)
+            return 0;
+        // Simple diversity measure based on response differences
+        let totalDiversity = 0;
+        let comparisons = 0;
+        for (let i = 0; i < responses.length; i++) {
+            for (let j = i + 1; j < responses.length; j++) {
+                const similarity = this.calculateResponseSimilarity(responses[i], responses[j]);
+                totalDiversity += (1 - similarity);
+                comparisons++;
+            }
+        }
+        return comparisons > 0 ? totalDiversity / comparisons : 0;
+    }
+    /**
+     * Calculate similarity between two responses
+     */
+    calculateResponseSimilarity(response1, response2) {
+        // Simple similarity calculation
+        const str1 = JSON.stringify(response1.response);
+        const str2 = JSON.stringify(response2.response);
+        if (str1 === str2)
+            return 1.0;
+        // Character-level similarity
+        const maxLength = Math.max(str1.length, str2.length);
+        let matches = 0;
+        for (let i = 0; i < Math.min(str1.length, str2.length); i++) {
+            if (str1[i] === str2[i])
+                matches++;
+        }
+        return matches / maxLength;
+    }
+}

vendor/sublinear-time-solver/dist/mcp/tools/emergence-tools.d.ts

@@ -0,0 +1,270 @@
+export declare class EmergenceTools {
+    private emergenceSystem;
+    constructor();
+    getTools(): ({
+        name: string;
+        description: string;
+        inputSchema: {
+            type: string;
+            properties: {
+                input: {
+                    description: string;
+                };
+                tools: {
+                    type: string;
+                    description: string;
+                    items: {
+                        type: string;
+                    };
+                };
+                cursor: {
+                    type: string;
+                    description: string;
+                };
+                pageSize: {
+                    type: string;
+                    description: string;
+                    minimum: number;
+                    maximum: number;
+                };
+                count?: undefined;
+                targetCapability?: undefined;
+                component?: undefined;
+                scenarios?: undefined;
+                matrixOperations?: undefined;
+                maxDepth?: undefined;
+                wasmAcceleration?: undefined;
+                emergenceMode?: undefined;
+            };
+            required: string[];
+        };
+    } | {
+        name: string;
+        description: string;
+        inputSchema: {
+            type: string;
+            properties: {
+                input: {
+                    description: string;
+                };
+                count: {
+                    type: string;
+                    description: string;
+                    minimum: number;
+                    maximum: number;
+                };
+                tools: {
+                    type: string;
+                    description: string;
+                    items: {
+                        type: string;
+                    };
+                };
+                cursor?: undefined;
+                pageSize?: undefined;
+                targetCapability?: undefined;
+                component?: undefined;
+                scenarios?: undefined;
+                matrixOperations?: undefined;
+                maxDepth?: undefined;
+                wasmAcceleration?: undefined;
+                emergenceMode?: undefined;
+            };
+            required: string[];
+        };
+    } | {
+        name: string;
+        description: string;
+        inputSchema: {
+            type: string;
+            properties: {
+                input?: undefined;
+                tools?: undefined;
+                cursor?: undefined;
+                pageSize?: undefined;
+                count?: undefined;
+                targetCapability?: undefined;
+                component?: undefined;
+                scenarios?: undefined;
+                matrixOperations?: undefined;
+                maxDepth?: undefined;
+                wasmAcceleration?: undefined;
+                emergenceMode?: undefined;
+            };
+            required?: undefined;
+        };
+    } | {
+        name: string;
+        description: string;
+        inputSchema: {
+            type: string;
+            properties: {
+                targetCapability: {
+                    type: string;
+                    description: string;
+                };
+                input?: undefined;
+                tools?: undefined;
+                cursor?: undefined;
+                pageSize?: undefined;
+                count?: undefined;
+                component?: undefined;
+                scenarios?: undefined;
+                matrixOperations?: undefined;
+                maxDepth?: undefined;
+                wasmAcceleration?: undefined;
+                emergenceMode?: undefined;
+            };
+            required: string[];
+        };
+    } | {
+        name: string;
+        description: string;
+        inputSchema: {
+            type: string;
+            properties: {
+                component: {
+                    type: string;
+                    description: string;
+                    enum: string[];
+                };
+                input?: undefined;
+                tools?: undefined;
+                cursor?: undefined;
+                pageSize?: undefined;
+                count?: undefined;
+                targetCapability?: undefined;
+                scenarios?: undefined;
+                matrixOperations?: undefined;
+                maxDepth?: undefined;
+                wasmAcceleration?: undefined;
+                emergenceMode?: undefined;
+            };
+            required?: undefined;
+        };
+    } | {
+        name: string;
+        description: string;
+        inputSchema: {
+            type: string;
+            properties: {
+                scenarios: {
+                    type: string;
+                    description: string;
+                    items: {
+                        type: string;
+                        enum: string[];
+                    };
+                };
+                input?: undefined;
+                tools?: undefined;
+                cursor?: undefined;
+                pageSize?: undefined;
+                count?: undefined;
+                targetCapability?: undefined;
+                component?: undefined;
+                matrixOperations?: undefined;
+                maxDepth?: undefined;
+                wasmAcceleration?: undefined;
+                emergenceMode?: undefined;
+            };
+            required: string[];
+        };
+    } | {
+        name: string;
+        description: string;
+        inputSchema: {
+            type: string;
+            properties: {
+                input: {
+                    description: string;
+                };
+                matrixOperations: {
+                    type: string;
+                    description: string;
+                    items: {
+                        type: string;
+                        enum: string[];
+                    };
+                };
+                maxDepth: {
+                    type: string;
+                    description: string;
+                    minimum: number;
+                    maximum: number;
+                    default: number;
+                };
+                wasmAcceleration: {
+                    type: string;
+                    description: string;
+                    default: boolean;
+                };
+                emergenceMode: {
+                    type: string;
+                    description: string;
+                    enum: string[];
+                    default: string;
+                };
+                tools?: undefined;
+                cursor?: undefined;
+                pageSize?: undefined;
+                count?: undefined;
+                targetCapability?: undefined;
+                component?: undefined;
+                scenarios?: undefined;
+            };
+            required: string[];
+        };
+    })[];
+    handleToolCall(name: string, args: any): Promise<any>;
+    private processWithTimeout;
+    /**
+     * Process emergence with pagination support for large tool arrays
+     */
+    private processWithPagination;
+    /**
+     * Matrix-focused emergence with WASM acceleration and controlled recursion
+     */
+    private processMatrixEmergence;
+    /**
+     * Create controlled matrix tools environment with WASM acceleration
+     */
+    private createMatrixToolsEnvironment;
+    /**
+     * Run matrix emergence with controlled mathematical recursion
+     */
+    private runMatrixEmergence;
+    /**
+     * Explore numerical emergence patterns with WASM-accelerated computations
+     */
+    private exploreNumericalEmergence;
+    /**
+     * Execute controlled mathematical operation with WASM acceleration
+     */
+    private executeControlledMathOperation;
+    private generateMockSolutionVector;
+    private generateMockRankVector;
+    private calculateOperationEmergence;
+    private extractEmergentProperties;
+    private synthesizeMultiLevelEmergence;
+    private calculateMatrixEmergenceLevel;
+    private assessMathComplexity;
+    private identifyMatrixPatterns;
+    private exploreAlgebraicEmergence;
+    private exploreTemporalEmergence;
+    private exploreGraphEmergence;
+    /**
+     * Fixed version of runTestScenarios that doesn't hang
+     */
+    private runTestScenariosFixed;
+    /**
+     * Fixed version that doesn't call processWithEmergence for problematic scenarios
+     */
+    private runSingleTestScenarioFixed;
+    private testSelfModificationFixed;
+    private testPersistentLearningFixed;
+    private testStochasticExplorationFixed;
+    private testCrossToolSharingFixed;
+    private testFeedbackLoopsFixed;
+    private testEmergentCapabilitiesFixed;
+}

vendor/sublinear-time-solver/dist/mcp/tools/emergence-tools.js

@@ -0,0 +1,821 @@
+import { EmergenceSystem } from '../../emergence/index.js';
+export class EmergenceTools {
+    emergenceSystem;
+    constructor() {
+        this.emergenceSystem = new EmergenceSystem();
+    }
+    getTools() {
+        return [
+            {
+                name: 'emergence_process',
+                description: 'Process input through the emergence system for enhanced responses',
+                inputSchema: {
+                    type: 'object',
+                    properties: {
+                        input: {
+                            description: 'Input to process through emergence system'
+                        },
+                        tools: {
+                            type: 'array',
+                            description: 'Available tools for processing',
+                            items: { type: 'object' }
+                        },
+                        cursor: {
+                            type: 'string',
+                            description: 'Pagination cursor for tools (starting index)'
+                        },
+                        pageSize: {
+                            type: 'number',
+                            description: 'Number of tools per page (default: 5, max: 10)',
+                            minimum: 1,
+                            maximum: 10
+                        }
+                    },
+                    required: ['input']
+                }
+            },
+            {
+                name: 'emergence_generate_diverse',
+                description: 'Generate multiple diverse emergent responses',
+                inputSchema: {
+                    type: 'object',
+                    properties: {
+                        input: {
+                            description: 'Input for diverse response generation'
+                        },
+                        count: {
+                            type: 'number',
+                            description: 'Number of diverse responses',
+                            minimum: 1,
+                            maximum: 10
+                        },
+                        tools: {
+                            type: 'array',
+                            description: 'Available tools',
+                            items: { type: 'object' }
+                        }
+                    },
+                    required: ['input']
+                }
+            },
+            {
+                name: 'emergence_analyze_capabilities',
+                description: 'Analyze current emergent capabilities',
+                inputSchema: {
+                    type: 'object',
+                    properties: {}
+                }
+            },
+            {
+                name: 'emergence_force_evolution',
+                description: 'Force evolution toward specific capability',
+                inputSchema: {
+                    type: 'object',
+                    properties: {
+                        targetCapability: {
+                            type: 'string',
+                            description: 'Target capability to evolve toward'
+                        }
+                    },
+                    required: ['targetCapability']
+                }
+            },
+            {
+                name: 'emergence_get_stats',
+                description: 'Get comprehensive emergence statistics',
+                inputSchema: {
+                    type: 'object',
+                    properties: {
+                        component: {
+                            type: 'string',
+                            description: 'Specific component to get stats for',
+                            enum: ['all', 'self_modification', 'learning', 'exploration', 'sharing', 'feedback', 'capabilities']
+                        }
+                    }
+                }
+            },
+            {
+                name: 'emergence_test_scenarios',
+                description: 'Run test scenarios to verify emergence capabilities',
+                inputSchema: {
+                    type: 'object',
+                    properties: {
+                        scenarios: {
+                            type: 'array',
+                            description: 'Test scenarios to run',
+                            items: {
+                                type: 'string',
+                                enum: ['self_modification', 'persistent_learning', 'stochastic_exploration',
+                                    'cross_tool_sharing', 'feedback_loops', 'emergent_capabilities']
+                            }
+                        }
+                    },
+                    required: ['scenarios']
+                }
+            },
+            {
+                name: 'emergence_matrix_process',
+                description: 'Matrix-focused emergence with WASM acceleration and controlled mathematical recursion',
+                inputSchema: {
+                    type: 'object',
+                    properties: {
+                        input: {
+                            description: 'Mathematical input for matrix emergence processing'
+                        },
+                        matrixOperations: {
+                            type: 'array',
+                            description: 'Specific matrix operations to explore',
+                            items: {
+                                type: 'string',
+                                enum: ['solve', 'analyzeMatrix', 'pageRank', 'estimateEntry', 'predictWithTemporalAdvantage']
+                            }
+                        },
+                        maxDepth: {
+                            type: 'number',
+                            description: 'Maximum mathematical recursion depth (1-3)',
+                            minimum: 1,
+                            maximum: 3,
+                            default: 2
+                        },
+                        wasmAcceleration: {
+                            type: 'boolean',
+                            description: 'Enable WASM SIMD acceleration',
+                            default: true
+                        },
+                        emergenceMode: {
+                            type: 'string',
+                            description: 'Matrix emergence exploration mode',
+                            enum: ['numerical', 'algebraic', 'temporal', 'graph'],
+                            default: 'numerical'
+                        }
+                    },
+                    required: ['input']
+                }
+            }
+        ];
+    }
+    async handleToolCall(name, args) {
+        try {
+            switch (name) {
+                case 'emergence_process':
+                    return await this.processWithPagination(args);
+                case 'emergence_generate_diverse':
+                    return await this.emergenceSystem.generateEmergentResponses(args.input, args.count || 3, args.tools || []);
+                case 'emergence_analyze_capabilities':
+                    return await this.emergenceSystem.analyzeEmergentCapabilities();
+                case 'emergence_force_evolution':
+                    return await this.emergenceSystem.forceEvolution(args.targetCapability);
+                case 'emergence_get_stats':
+                    return this.emergenceSystem.getEmergenceStats();
+                case 'emergence_test_scenarios':
+                    return await this.runTestScenariosFixed(args.scenarios);
+                case 'emergence_matrix_process':
+                    return await this.processMatrixEmergence(args);
+                default:
+                    throw new Error(`Unknown emergence tool: ${name}`);
+            }
+        }
+        catch (error) {
+            return {
+                success: false,
+                error: error instanceof Error ? error.message : 'Unknown error',
+                tool: name,
+                args
+            };
+        }
+    }
+    async processWithTimeout(fn, timeoutMs) {
+        const timeoutPromise = new Promise((_, reject) => setTimeout(() => reject(new Error('Operation timed out')), timeoutMs));
+        return Promise.race([fn(), timeoutPromise]);
+    }
+    /**
+     * Process emergence with pagination support for large tool arrays
+     */
+    async processWithPagination(args) {
+        const { input, tools = [], cursor, pageSize = 5 } = args;
+        const MAX_PAGE_SIZE = 10;
+        const actualPageSize = Math.min(pageSize, MAX_PAGE_SIZE);
+        // Filter out problematic tools that cause hanging
+        const PROBLEMATIC_TOOLS = ['solve', 'analyzeMatrix', 'pageRank', 'estimateEntry', 'predictWithTemporalAdvantage'];
+        const safeTools = tools.filter((tool) => !PROBLEMATIC_TOOLS.includes(tool.name));
+        try {
+            // If no safe tools, return early with warning
+            if (safeTools.length === 0) {
+                return {
+                    result: {
+                        warning: 'All tools filtered due to hanging issues',
+                        originalToolCount: tools.length,
+                        filteredTools: tools.map((t) => t.name),
+                        recommendation: 'Try with different tools or contact support'
+                    },
+                    pagination: {
+                        totalTools: tools.length,
+                        safeTools: 0,
+                        filtered: true
+                    }
+                };
+            }
+            // If safe tools are small enough, process normally
+            if (safeTools.length <= actualPageSize) {
+                const result = await this.processWithTimeout(() => this.emergenceSystem.processWithEmergence(input, safeTools), 1000 // Reduced to 1 second to prevent hanging
+                );
+                return {
+                    ...result,
+                    pagination: {
+                        totalTools: tools.length,
+                        safeTools: safeTools.length,
+                        pageSize: actualPageSize,
+                        hasMore: false,
+                        filtered: tools.length > safeTools.length
+                    }
+                };
+            }
+            // Parse cursor to get starting index
+            const startIndex = cursor ? parseInt(cursor, 10) : 0;
+            if (isNaN(startIndex) || startIndex < 0) {
+                throw new Error('Invalid cursor value');
+            }
+            const endIndex = Math.min(startIndex + actualPageSize, safeTools.length);
+            const pageTools = safeTools.slice(startIndex, endIndex);
+            // Process with limited tools
+            const result = await this.processWithTimeout(() => this.emergenceSystem.processWithEmergence({
+                ...input,
+                _pagination: {
+                    totalTools: tools.length,
+                    safeTools: safeTools.length,
+                    currentPage: Math.floor(startIndex / actualPageSize) + 1,
+                    totalPages: Math.ceil(safeTools.length / actualPageSize),
+                    toolsInPage: pageTools.length,
+                    filtered: tools.length > safeTools.length
+                }
+            }, pageTools), 1000 // Reduced to 1 second to prevent hanging
+            );
+            // Add pagination metadata and enforce size limits
+            const hasMore = endIndex < safeTools.length;
+            const response = {
+                ...result,
+                pagination: {
+                    cursor: startIndex.toString(),
+                    nextCursor: hasMore ? endIndex.toString() : undefined,
+                    pageSize: actualPageSize,
+                    totalTools: tools.length,
+                    safeTools: safeTools.length,
+                    processedTools: pageTools.length,
+                    hasMore,
+                    currentPage: Math.floor(startIndex / actualPageSize) + 1,
+                    totalPages: Math.ceil(safeTools.length / actualPageSize),
+                    filtered: tools.length > safeTools.length
+                }
+            };
+            // Final size check and truncation
+            const responseStr = JSON.stringify(response);
+            const MAX_RESPONSE_SIZE = 20000; // 20KB limit
+            if (responseStr.length > MAX_RESPONSE_SIZE) {
+                return {
+                    result: {
+                        summary: 'Response truncated due to size',
+                        originalSize: responseStr.length,
+                        maxSize: MAX_RESPONSE_SIZE,
+                        processedTools: pageTools.length,
+                        toolNames: pageTools.map(t => t.name)
+                    },
+                    pagination: {
+                        cursor: startIndex.toString(),
+                        nextCursor: hasMore ? endIndex.toString() : undefined,
+                        pageSize: actualPageSize,
+                        totalTools: tools.length,
+                        processedTools: pageTools.length,
+                        hasMore,
+                        truncated: true
+                    }
+                };
+            }
+            return response;
+        }
+        catch (error) {
+            return {
+                error: error instanceof Error ? error.message : 'Processing failed',
+                input,
+                emergenceLevel: 0,
+                pagination: {
+                    cursor: cursor || '0',
+                    error: true
+                }
+            };
+        }
+    }
+    /**
+     * Matrix-focused emergence with WASM acceleration and controlled recursion
+     */
+    async processMatrixEmergence(args) {
+        const { input, matrixOperations = ['solve', 'analyzeMatrix'], maxDepth = 2, wasmAcceleration = true, emergenceMode = 'numerical' } = args;
+        const startTime = Date.now();
+        try {
+            // Create controlled matrix tools environment
+            const matrixTools = this.createMatrixToolsEnvironment(matrixOperations, maxDepth, wasmAcceleration);
+            // Process with matrix-specific emergence patterns
+            const result = await this.processWithTimeout(() => this.runMatrixEmergence(input, matrixTools, emergenceMode, maxDepth), 3000 // 3 second timeout for matrix operations
+            );
+            return {
+                result,
+                matrixEmergence: {
+                    mode: emergenceMode,
+                    operationsUsed: matrixOperations,
+                    maxDepth,
+                    wasmAccelerated: wasmAcceleration,
+                    processingTime: Date.now() - startTime,
+                    emergenceLevel: this.calculateMatrixEmergenceLevel(result)
+                },
+                metrics: {
+                    mathematicalComplexity: this.assessMathComplexity(result),
+                    computationalEfficiency: wasmAcceleration ? 'wasm_simd' : 'standard',
+                    emergencePatterns: this.identifyMatrixPatterns(result)
+                }
+            };
+        }
+        catch (error) {
+            return {
+                error: error instanceof Error ? error.message : 'Matrix emergence failed',
+                matrixEmergence: {
+                    mode: emergenceMode,
+                    operationsRequested: matrixOperations,
+                    maxDepth,
+                    wasmAccelerated: wasmAcceleration,
+                    failed: true
+                }
+            };
+        }
+    }
+    /**
+     * Create controlled matrix tools environment with WASM acceleration
+     */
+    createMatrixToolsEnvironment(operations, maxDepth, wasmAcceleration) {
+        const matrixTools = [];
+        for (const op of operations) {
+            switch (op) {
+                case 'solve':
+                    matrixTools.push({
+                        name: 'solve',
+                        type: 'matrix_solver',
+                        wasmAccelerated: wasmAcceleration,
+                        recursionLimit: maxDepth,
+                        method: 'neumann_series'
+                    });
+                    break;
+                case 'analyzeMatrix':
+                    matrixTools.push({
+                        name: 'analyzeMatrix',
+                        type: 'matrix_analyzer',
+                        wasmAccelerated: wasmAcceleration,
+                        recursionLimit: maxDepth,
+                        checkDominance: true,
+                        estimateCondition: wasmAcceleration
+                    });
+                    break;
+                case 'pageRank':
+                    matrixTools.push({
+                        name: 'pageRank',
+                        type: 'graph_algorithm',
+                        wasmAccelerated: wasmAcceleration,
+                        recursionLimit: maxDepth,
+                        damping: 0.85
+                    });
+                    break;
+                case 'estimateEntry':
+                    matrixTools.push({
+                        name: 'estimateEntry',
+                        type: 'sublinear_estimator',
+                        wasmAccelerated: wasmAcceleration,
+                        recursionLimit: maxDepth,
+                        method: 'random_walk'
+                    });
+                    break;
+                case 'predictWithTemporalAdvantage':
+                    matrixTools.push({
+                        name: 'predictWithTemporalAdvantage',
+                        type: 'temporal_solver',
+                        wasmAccelerated: wasmAcceleration,
+                        recursionLimit: maxDepth,
+                        distanceKm: 10900 // Tokyo to NYC
+                    });
+                    break;
+            }
+        }
+        return matrixTools;
+    }
+    /**
+     * Run matrix emergence with controlled mathematical recursion
+     */
+    async runMatrixEmergence(input, matrixTools, mode, maxDepth) {
+        const emergenceSession = {
+            sessionId: `matrix_emergence_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`,
+            startTime: Date.now(),
+            mode,
+            maxDepth,
+            currentDepth: 0
+        };
+        // Initialize based on emergence mode
+        let result = input;
+        const operationTrace = [];
+        switch (mode) {
+            case 'numerical':
+                result = await this.exploreNumericalEmergence(result, matrixTools, maxDepth, operationTrace);
+                break;
+            case 'algebraic':
+                result = await this.exploreAlgebraicEmergence(result, matrixTools, maxDepth, operationTrace);
+                break;
+            case 'temporal':
+                result = await this.exploreTemporalEmergence(result, matrixTools, maxDepth, operationTrace);
+                break;
+            case 'graph':
+                result = await this.exploreGraphEmergence(result, matrixTools, maxDepth, operationTrace);
+                break;
+            default:
+                result = await this.exploreNumericalEmergence(result, matrixTools, maxDepth, operationTrace);
+        }
+        return {
+            finalResult: result,
+            operationTrace,
+            emergenceSession: {
+                ...emergenceSession,
+                endTime: Date.now(),
+                operationsPerformed: operationTrace.length
+            }
+        };
+    }
+    /**
+     * Explore numerical emergence patterns with WASM-accelerated computations
+     */
+    async exploreNumericalEmergence(input, tools, maxDepth, trace) {
+        if (maxDepth <= 0)
+            return input;
+        let result = input;
+        // Apply mathematical transformations with emergence patterns
+        for (const tool of tools.slice(0, 2)) { // Limit to 2 tools per depth level
+            try {
+                const operation = {
+                    tool: tool.name,
+                    input: typeof result === 'string' ? result : JSON.stringify(result).substring(0, 100),
+                    wasmAccelerated: tool.wasmAccelerated,
+                    timestamp: Date.now()
+                };
+                // Simulate real mathematical computation with controlled emergence
+                const mathResult = await this.executeControlledMathOperation(tool, result);
+                operation.output = mathResult;
+                operation.emergenceMetrics = this.calculateOperationEmergence(mathResult);
+                trace.push(operation);
+                // Create emergent synthesis from mathematical result
+                result = {
+                    mathematicalTransform: mathResult,
+                    emergentProperties: this.extractEmergentProperties(mathResult),
+                    originalInput: typeof input === 'string' ? input.substring(0, 50) : 'complex_input'
+                };
+            }
+            catch (error) {
+                trace.push({
+                    tool: tool.name,
+                    error: error instanceof Error ? error.message : 'Unknown error',
+                    timestamp: Date.now()
+                });
+            }
+        }
+        // Recursive emergence with depth control
+        if (maxDepth > 1 && tools.length > 0) {
+            const recursiveResult = await this.exploreNumericalEmergence(result, tools.slice(1), // Use different tools for recursion
+            maxDepth - 1, trace);
+            return {
+                currentLevel: result,
+                recursiveLevel: recursiveResult,
+                emergenceSynthesis: this.synthesizeMultiLevelEmergence(result, recursiveResult)
+            };
+        }
+        return result;
+    }
+    /**
+     * Execute controlled mathematical operation with WASM acceleration
+     */
+    async executeControlledMathOperation(tool, input) {
+        const operationId = `${tool.name}_${Date.now()}`;
+        // Generate realistic mathematical results based on tool type
+        switch (tool.type) {
+            case 'matrix_solver':
+                return {
+                    operationId,
+                    method: tool.method || 'neumann_series',
+                    convergence: 0.95 + Math.random() * 0.04,
+                    iterations: Math.floor(Math.random() * 100) + 10,
+                    wasmAccelerated: tool.wasmAccelerated,
+                    solutionVector: this.generateMockSolutionVector(),
+                    computationalComplexity: tool.wasmAccelerated ? 'O(log n)' : 'O(n²)'
+                };
+            case 'matrix_analyzer':
+                return {
+                    operationId,
+                    diagonallyDominant: Math.random() > 0.3,
+                    conditionNumber: Math.random() * 100 + 1,
+                    spectralRadius: Math.random() * 0.95,
+                    wasmAccelerated: tool.wasmAccelerated,
+                    analysisTime: tool.wasmAccelerated ? Math.random() * 10 : Math.random() * 100
+                };
+            case 'graph_algorithm':
+                return {
+                    operationId,
+                    algorithm: 'pagerank',
+                    damping: tool.damping || 0.85,
+                    iterations: Math.floor(Math.random() * 50) + 20,
+                    convergence: 0.98 + Math.random() * 0.02,
+                    wasmAccelerated: tool.wasmAccelerated,
+                    rankVector: this.generateMockRankVector()
+                };
+            case 'temporal_solver':
+                return {
+                    operationId,
+                    temporalAdvantage: tool.distanceKm ? (tool.distanceKm / 299792458) * 1000 : 36.6, // milliseconds
+                    computationTime: tool.wasmAccelerated ? Math.random() * 5 : Math.random() * 50,
+                    speedupFactor: tool.wasmAccelerated ? Math.random() * 1000 + 5000 : 1,
+                    wasmAccelerated: tool.wasmAccelerated,
+                    quantumAdvantage: tool.wasmAccelerated && Math.random() > 0.7
+                };
+            default:
+                return {
+                    operationId,
+                    result: 'mathematical_computation_complete',
+                    wasmAccelerated: tool.wasmAccelerated,
+                    processingTime: tool.wasmAccelerated ? Math.random() * 10 : Math.random() * 100
+                };
+        }
+    }
+    // Helper methods for matrix emergence
+    generateMockSolutionVector() {
+        return Array(5).fill(0).map(() => Math.random() * 10 - 5);
+    }
+    generateMockRankVector() {
+        const ranks = Array(5).fill(0).map(() => Math.random());
+        const sum = ranks.reduce((a, b) => a + b, 0);
+        return ranks.map(r => r / sum); // Normalize to sum to 1
+    }
+    calculateOperationEmergence(result) {
+        return {
+            novelty: Math.random(),
+            complexity: Object.keys(result).length / 10,
+            efficiency: result.wasmAccelerated ? Math.random() * 0.3 + 0.7 : Math.random() * 0.7
+        };
+    }
+    extractEmergentProperties(mathResult) {
+        return {
+            convergencePattern: mathResult.convergence ? 'exponential' : 'linear',
+            computationalComplexity: mathResult.computationalComplexity || 'unknown',
+            accelerationFactor: mathResult.wasmAccelerated ? 'high' : 'standard',
+            emergentInsight: 'mathematical_pattern_detected'
+        };
+    }
+    synthesizeMultiLevelEmergence(level1, level2) {
+        return {
+            synthesis: 'multi_level_mathematical_emergence',
+            patterns: ['numerical_convergence', 'computational_acceleration'],
+            complexity: 'high',
+            insight: 'recursive_mathematical_patterns_detected'
+        };
+    }
+    calculateMatrixEmergenceLevel(result) {
+        // Calculate emergence based on mathematical complexity and patterns
+        let score = 0;
+        if (result.operationTrace)
+            score += result.operationTrace.length * 0.1;
+        if (result.finalResult?.emergenceSynthesis)
+            score += 0.3;
+        if (result.finalResult?.recursiveLevel)
+            score += 0.2;
+        return Math.min(score, 1.0);
+    }
+    assessMathComplexity(result) {
+        const traceLength = result.operationTrace?.length || 0;
+        if (traceLength > 6)
+            return 'high';
+        if (traceLength > 3)
+            return 'medium';
+        return 'low';
+    }
+    identifyMatrixPatterns(result) {
+        const patterns = ['numerical_computation'];
+        if (result.finalResult?.recursiveLevel)
+            patterns.push('recursive_emergence');
+        if (result.matrixEmergence?.wasmAccelerated)
+            patterns.push('wasm_acceleration');
+        return patterns;
+    }
+    // Placeholder methods for other emergence modes
+    async exploreAlgebraicEmergence(input, tools, maxDepth, trace) {
+        return this.exploreNumericalEmergence(input, tools, maxDepth, trace);
+    }
+    async exploreTemporalEmergence(input, tools, maxDepth, trace) {
+        return this.exploreNumericalEmergence(input, tools, maxDepth, trace);
+    }
+    async exploreGraphEmergence(input, tools, maxDepth, trace) {
+        return this.exploreNumericalEmergence(input, tools, maxDepth, trace);
+    }
+    /**
+     * Fixed version of runTestScenarios that doesn't hang
+     */
+    async runTestScenariosFixed(scenarios) {
+        const results = {
+            timestamp: Date.now(),
+            scenarios: scenarios.length,
+            results: []
+        };
+        for (const scenario of scenarios) {
+            const testResult = await this.runSingleTestScenarioFixed(scenario);
+            results.results.push(testResult);
+        }
+        const overallSuccess = results.results.every(r => r.success);
+        const averageScore = results.results.reduce((sum, r) => sum + (r.score || 0), 0) / results.results.length;
+        return {
+            ...results,
+            overallSuccess,
+            averageScore,
+            emergenceVerified: overallSuccess && averageScore > 0.7
+        };
+    }
+    /**
+     * Fixed version that doesn't call processWithEmergence for problematic scenarios
+     */
+    async runSingleTestScenarioFixed(scenario) {
+        const startTime = Date.now();
+        try {
+            switch (scenario) {
+                case 'self_modification':
+                    return await this.testSelfModificationFixed();
+                case 'persistent_learning':
+                    return await this.testPersistentLearningFixed();
+                case 'stochastic_exploration':
+                    return await this.testStochasticExplorationFixed();
+                case 'cross_tool_sharing':
+                    return await this.testCrossToolSharingFixed();
+                case 'feedback_loops':
+                    return await this.testFeedbackLoopsFixed();
+                case 'emergent_capabilities':
+                    return await this.testEmergentCapabilitiesFixed();
+                default:
+                    return {
+                        scenario,
+                        success: false,
+                        error: `Unknown test scenario: ${scenario}`,
+                        duration: Date.now() - startTime
+                    };
+            }
+        }
+        catch (error) {
+            return {
+                scenario,
+                success: false,
+                error: error instanceof Error ? error.message : 'Test failed',
+                duration: Date.now() - startTime
+            };
+        }
+    }
+    async testSelfModificationFixed() {
+        const startTime = Date.now();
+        // Test directly without processWithEmergence
+        const modifications = this.emergenceSystem.getSelfModificationEngine().generateStochasticVariations();
+        const hasModifications = modifications.length > 0;
+        return {
+            scenario: 'self_modification',
+            success: hasModifications,
+            score: hasModifications ? 0.8 : 0.2,
+            evidence: {
+                modificationsApplied: modifications.length,
+                modificationTypes: modifications.map(m => m.type),
+                safeguardsActive: true
+            },
+            duration: Date.now() - startTime
+        };
+    }
+    async testPersistentLearningFixed() {
+        const startTime = Date.now();
+        const learningSystem = this.emergenceSystem.getPersistentLearningSystem();
+        // Add test knowledge
+        await learningSystem.addKnowledge({
+            subject: 'test_entity',
+            predicate: 'has_property',
+            object: 'test_value',
+            confidence: 0.9,
+            timestamp: Date.now(),
+            sessionId: 'test_session',
+            sources: ['test']
+        });
+        // Query to verify learning
+        const knowledge = learningSystem.queryKnowledge('test_entity');
+        const hasLearning = knowledge.length > 0;
+        return {
+            scenario: 'persistent_learning',
+            success: hasLearning,
+            score: hasLearning ? 0.9 : 0.3,
+            evidence: {
+                learningTriples: knowledge.length,
+                confidence: knowledge[0]?.confidence || 0,
+                sessionActive: true
+            },
+            duration: Date.now() - startTime
+        };
+    }
+    async testStochasticExplorationFixed() {
+        const startTime = Date.now();
+        const responses = [];
+        const explorationEngine = this.emergenceSystem.getStochasticExplorationEngine();
+        for (let i = 0; i < 5; i++) {
+            const result = await explorationEngine.exploreUnpredictably('test input ' + i, []);
+            responses.push(result);
+        }
+        // Calculate diversity
+        const noveltyScores = responses.map(r => r.novelty);
+        const averageNovelty = noveltyScores.reduce((a, b) => a + b, 0) / noveltyScores.length;
+        return {
+            scenario: 'stochastic_exploration',
+            success: averageNovelty > 0.5,
+            score: averageNovelty,
+            evidence: {
+                responsesGenerated: responses.length,
+                diversityScore: averageNovelty,
+                averageNovelty,
+                maxNovelty: Math.max(...noveltyScores),
+                unpredictabilityDetected: true
+            },
+            duration: Date.now() - startTime
+        };
+    }
+    async testCrossToolSharingFixed() {
+        const startTime = Date.now();
+        const sharingSystem = this.emergenceSystem.getCrossToolSharingSystem();
+        // Share test information
+        const sharedInfo = {
+            id: `test_${Date.now()}`,
+            sourceTools: ['tool1'],
+            targetTools: ['tool2'],
+            content: { test: 'data' },
+            type: 'insight',
+            timestamp: Date.now(),
+            relevance: 0.8,
+            persistence: 'session',
+            metadata: { test: true }
+        };
+        const interestedTools = await sharingSystem.shareInformation(sharedInfo);
+        const hasSharing = interestedTools.length >= 0;
+        return {
+            scenario: 'cross_tool_sharing',
+            success: hasSharing,
+            score: hasSharing ? 0.85 : 0.3,
+            evidence: {
+                sharedInformationCount: 1,
+                targetedTools: interestedTools.length,
+                connectionEstablished: hasSharing
+            },
+            duration: Date.now() - startTime
+        };
+    }
+    async testFeedbackLoopsFixed() {
+        const startTime = Date.now();
+        const feedbackSystem = this.emergenceSystem.getFeedbackLoopSystem();
+        const feedback = {
+            id: `test_feedback_${Date.now()}`,
+            source: 'test',
+            type: 'success',
+            action: 'test_action',
+            outcome: { result: 'success' },
+            expected: { result: 'success' },
+            surprise: 0.2,
+            utility: 0.8,
+            timestamp: Date.now(),
+            context: { test: true }
+        };
+        const adaptations = await feedbackSystem.processFeedback(feedback);
+        const hasAdaptation = adaptations.length > 0;
+        return {
+            scenario: 'feedback_loops',
+            success: hasAdaptation,
+            score: hasAdaptation ? 0.75 : 0.4,
+            evidence: {
+                feedbackProcessed: true,
+                adaptationsGenerated: adaptations.length,
+                behaviorModified: hasAdaptation
+            },
+            duration: Date.now() - startTime
+        };
+    }
+    async testEmergentCapabilitiesFixed() {
+        const startTime = Date.now();
+        const detector = this.emergenceSystem.getEmergentCapabilityDetector();
+        const metrics = await detector.measureEmergenceMetrics();
+        const hasCapabilities = metrics.emergenceRate > 0 || metrics.diversityScore > 0;
+        return {
+            scenario: 'emergent_capabilities',
+            success: hasCapabilities,
+            score: metrics.emergenceRate || 0.5,
+            evidence: {
+                emergenceRate: metrics.emergenceRate,
+                stabilityIndex: metrics.stabilityIndex,
+                complexityGrowth: metrics.complexityGrowth
+            },
+            duration: Date.now() - startTime
+        };
+    }
+}

vendor/sublinear-time-solver/dist/mcp/tools/graph.d.ts

@@ -0,0 +1,110 @@
+/**
+ * MCP Tools for graph algorithms using sublinear solvers
+ */
+import { Matrix, Vector, PageRankParams, EffectiveResistanceParams } from '../../core/types.js';
+export declare class GraphTools {
+    /**
+     * Compute PageRank using sublinear solver
+     */
+    static pageRank(params: PageRankParams): Promise<{
+        pageRankVector: Vector;
+        topNodes: {
+            node: number;
+            score: number;
+        }[];
+        bottomNodes: {
+            node: number;
+            score: number;
+        }[];
+        statistics: {
+            totalScore: number;
+            maxScore: number;
+            minScore: number;
+            mean: number;
+            standardDeviation: number;
+            entropy: number;
+            convergenceInfo: {
+                damping: number;
+                personalized: boolean;
+            };
+        };
+        distribution: {
+            quantiles: Record<string, number>;
+            concentrationRatio: number;
+        };
+    }>;
+    /**
+     * Compute personalized PageRank for specific nodes
+     */
+    static personalizedPageRank(adjacency: Matrix, personalizeNodes: number[], params?: Partial<PageRankParams>): Promise<{
+        personalizedFor: number[];
+        influence: {
+            directInfluence: number[];
+            totalInfluence: number;
+        };
+        pageRankVector: Vector;
+        topNodes: {
+            node: number;
+            score: number;
+        }[];
+        bottomNodes: {
+            node: number;
+            score: number;
+        }[];
+        statistics: {
+            totalScore: number;
+            maxScore: number;
+            minScore: number;
+            mean: number;
+            standardDeviation: number;
+            entropy: number;
+            convergenceInfo: {
+                damping: number;
+                personalized: boolean;
+            };
+        };
+        distribution: {
+            quantiles: Record<string, number>;
+            concentrationRatio: number;
+        };
+    }>;
+    /**
+     * Compute effective resistance between nodes
+     */
+    static effectiveResistance(params: EffectiveResistanceParams): Promise<{
+        effectiveResistance: number;
+        voltage: number[];
+        source: number;
+        target: number;
+        convergenceInfo: {
+            iterations: number;
+            residual: number;
+            converged: boolean;
+        };
+    }>;
+    /**
+     * Compute centrality measures using sublinear methods
+     */
+    static computeCentralities(adjacency: Matrix, measures?: string[]): Promise<Record<string, any>>;
+    /**
+     * Detect communities using spectral methods
+     */
+    static detectCommunities(adjacency: Matrix, numCommunities?: number): Promise<{
+        communities: number[][];
+        assignments: any[];
+        modularity: number;
+        quality: {
+            numCommunities: number;
+            largestCommunity: number;
+            smallestCommunity: number;
+        };
+    }>;
+    private static computeQuantiles;
+    private static createGroundedLaplacian;
+    private static createNormalizedLaplacian;
+    private static closenessCentrality;
+    private static betweennessCentrality;
+    private static computeModularity;
+    private static countEdges;
+    private static getNodeDegree;
+}

vendor/sublinear-time-solver/dist/mcp/tools/graph.js

@@ -0,0 +1,330 @@
+/**
+ * MCP Tools for graph algorithms using sublinear solvers
+ */
+import { SublinearSolver } from '../../core/solver.js';
+import { MatrixOperations } from '../../core/matrix.js';
+import { VectorOperations } from '../../core/utils.js';
+import { SolverError, ErrorCodes } from '../../core/types.js';
+export class GraphTools {
+    /**
+     * Compute PageRank using sublinear solver
+     */
+    static async pageRank(params) {
+        MatrixOperations.validateMatrix(params.adjacency);
+        if (params.adjacency.rows !== params.adjacency.cols) {
+            throw new SolverError('Adjacency matrix must be square', ErrorCodes.INVALID_DIMENSIONS);
+        }
+        const config = {
+            method: 'neumann',
+            epsilon: params.epsilon || 1e-6,
+            maxIterations: params.maxIterations || 1000,
+            enableProgress: false
+        };
+        const solver = new SublinearSolver(config);
+        const pageRankConfig = {
+            damping: params.damping || 0.85,
+            personalized: params.personalized,
+            epsilon: params.epsilon || 1e-6,
+            maxIterations: params.maxIterations || 1000
+        };
+        const pageRankVector = await solver.computePageRank(params.adjacency, pageRankConfig);
+        // Analyze results
+        const ranked = pageRankVector
+            .map((score, index) => ({ node: index, score }))
+            .sort((a, b) => b.score - a.score);
+        const totalScore = pageRankVector.reduce((sum, score) => sum + score, 0);
+        const maxScore = Math.max(...pageRankVector);
+        const minScore = Math.min(...pageRankVector);
+        // Compute distribution statistics
+        const mean = totalScore / pageRankVector.length;
+        const variance = pageRankVector.reduce((sum, score) => sum + (score - mean) ** 2, 0) / pageRankVector.length;
+        const entropy = -pageRankVector.reduce((sum, score) => {
+            if (score > 0) {
+                return sum + score * Math.log(score);
+            }
+            return sum;
+        }, 0);
+        return {
+            pageRankVector,
+            topNodes: ranked.slice(0, Math.min(10, ranked.length)),
+            bottomNodes: ranked.slice(-Math.min(10, ranked.length)).reverse(),
+            statistics: {
+                totalScore,
+                maxScore,
+                minScore,
+                mean,
+                standardDeviation: Math.sqrt(variance),
+                entropy,
+                convergenceInfo: {
+                    damping: pageRankConfig.damping,
+                    personalized: !!params.personalized
+                }
+            },
+            distribution: {
+                quantiles: this.computeQuantiles(pageRankVector, [0.1, 0.25, 0.5, 0.75, 0.9]),
+                concentrationRatio: ranked.slice(0, Math.ceil(ranked.length * 0.1))
+                    .reduce((sum, item) => sum + item.score, 0) / totalScore
+            }
+        };
+    }
+    /**
+     * Compute personalized PageRank for specific nodes
+     */
+    static async personalizedPageRank(adjacency, personalizeNodes, params = {}) {
+        const n = adjacency.rows;
+        const personalized = VectorOperations.zeros(n);
+        // Set personalization vector
+        const weight = 1.0 / personalizeNodes.length;
+        for (const node of personalizeNodes) {
+            if (node < 0 || node >= n) {
+                throw new SolverError(`Node ${node} out of bounds`, ErrorCodes.INVALID_PARAMETERS);
+            }
+            personalized[node] = weight;
+        }
+        const result = await this.pageRank({
+            adjacency,
+            personalized,
+            ...params
+        });
+        return {
+            ...result,
+            personalizedFor: personalizeNodes,
+            influence: {
+                directInfluence: personalizeNodes.map(node => result.pageRankVector[node]),
+                totalInfluence: personalizeNodes.reduce((sum, node) => sum + result.pageRankVector[node], 0)
+            }
+        };
+    }
+    /**
+     * Compute effective resistance between nodes
+     */
+    static async effectiveResistance(params) {
+        MatrixOperations.validateMatrix(params.laplacian);
+        if (params.source < 0 || params.source >= params.laplacian.rows) {
+            throw new SolverError(`Source node ${params.source} out of bounds`, ErrorCodes.INVALID_PARAMETERS);
+        }
+        if (params.target < 0 || params.target >= params.laplacian.rows) {
+            throw new SolverError(`Target node ${params.target} out of bounds`, ErrorCodes.INVALID_PARAMETERS);
+        }
+        const n = params.laplacian.rows;
+        // Create indicator vector e_s - e_t
+        const indicator = VectorOperations.zeros(n);
+        indicator[params.source] = 1;
+        indicator[params.target] = -1;
+        // We need to solve the pseudoinverse, which requires handling the null space
+        // For a connected graph, we can use the grounded Laplacian (remove one row/column)
+        const groundedLaplacian = this.createGroundedLaplacian(params.laplacian);
+        const config = {
+            method: 'neumann',
+            epsilon: params.epsilon || 1e-6,
+            maxIterations: 1000,
+            enableProgress: false
+        };
+        const solver = new SublinearSolver(config);
+        // Remove the grounded node from the indicator vector
+        const groundedIndicator = indicator.slice(0, n - 1);
+        try {
+            const result = await solver.solve(groundedLaplacian, groundedIndicator);
+            const voltage = [...result.solution, 0]; // Add back the grounded node
+            // Effective resistance is the voltage difference
+            const resistance = voltage[params.source] - voltage[params.target];
+            return {
+                effectiveResistance: Math.abs(resistance),
+                voltage,
+                source: params.source,
+                target: params.target,
+                convergenceInfo: {
+                    iterations: result.iterations,
+                    residual: result.residual,
+                    converged: result.converged
+                }
+            };
+        }
+        catch (error) {
+            throw new SolverError(`Failed to compute effective resistance: ${error}`, ErrorCodes.CONVERGENCE_FAILED);
+        }
+    }
+    /**
+     * Compute centrality measures using sublinear methods
+     */
+    static async computeCentralities(adjacency, measures = ['pagerank', 'closeness']) {
+        const results = {};
+        if (measures.includes('pagerank')) {
+            results.pagerank = await this.pageRank({ adjacency });
+        }
+        if (measures.includes('closeness')) {
+            results.closeness = await this.closenessCentrality(adjacency);
+        }
+        if (measures.includes('betweenness')) {
+            results.betweenness = await this.betweennessCentrality(adjacency);
+        }
+        return results;
+    }
+    /**
+     * Detect communities using spectral methods
+     */
+    static async detectCommunities(adjacency, numCommunities = 2) {
+        // Create normalized Laplacian
+        const laplacian = this.createNormalizedLaplacian(adjacency);
+        // This is a simplified approach - in practice would need eigenvector computation
+        const config = {
+            method: 'random-walk',
+            epsilon: 1e-4,
+            maxIterations: 500,
+            enableProgress: false
+        };
+        const solver = new SublinearSolver(config);
+        const n = adjacency.rows;
+        // Use random walk mixing as a proxy for community structure
+        const communities = Array(numCommunities).fill(null).map(() => []);
+        const assignments = new Array(n);
+        // Simplified community assignment based on PageRank clustering
+        const pageRankResult = await this.pageRank({ adjacency });
+        const sortedNodes = pageRankResult.topNodes;
+        // Assign nodes to communities in round-robin fashion (simplified)
+        for (let i = 0; i < n; i++) {
+            const community = i % numCommunities;
+            communities[community].push(sortedNodes[i]?.node ?? i);
+            assignments[sortedNodes[i]?.node ?? i] = community;
+        }
+        return {
+            communities,
+            assignments,
+            modularity: this.computeModularity(adjacency, assignments),
+            quality: {
+                numCommunities,
+                largestCommunity: Math.max(...communities.map(c => c.length)),
+                smallestCommunity: Math.min(...communities.map(c => c.length))
+            }
+        };
+    }
+    static computeQuantiles(values, quantiles) {
+        const sorted = [...values].sort((a, b) => a - b);
+        const result = {};
+        for (const q of quantiles) {
+            const index = Math.floor(q * (sorted.length - 1));
+            result[`q${(q * 100).toFixed(0)}`] = sorted[index];
+        }
+        return result;
+    }
+    static createGroundedLaplacian(laplacian) {
+        const n = laplacian.rows;
+        if (laplacian.format === 'dense') {
+            const dense = laplacian;
+            const groundedData = dense.data.slice(0, n - 1).map((row) => row.slice(0, n - 1));
+            return {
+                rows: n - 1,
+                cols: n - 1,
+                data: groundedData,
+                format: 'dense'
+            };
+        }
+        else {
+            // For sparse matrices, filter out entries in the last row/column
+            const sparse = laplacian;
+            const values = [];
+            const rowIndices = [];
+            const colIndices = [];
+            for (let k = 0; k < sparse.values.length; k++) {
+                if (sparse.rowIndices[k] < n - 1 && sparse.colIndices[k] < n - 1) {
+                    values.push(sparse.values[k]);
+                    rowIndices.push(sparse.rowIndices[k]);
+                    colIndices.push(sparse.colIndices[k]);
+                }
+            }
+            return {
+                rows: n - 1,
+                cols: n - 1,
+                values,
+                rowIndices,
+                colIndices,
+                format: 'coo'
+            };
+        }
+    }
+    static createNormalizedLaplacian(adjacency) {
+        const n = adjacency.rows;
+        const degrees = new Array(n).fill(0);
+        // Compute degrees
+        for (let i = 0; i < n; i++) {
+            for (let j = 0; j < n; j++) {
+                degrees[i] += MatrixOperations.getEntry(adjacency, i, j);
+            }
+        }
+        // Create normalized Laplacian: L = I - D^(-1/2) A D^(-1/2)
+        const data = Array(n).fill(null).map(() => Array(n).fill(0));
+        for (let i = 0; i < n; i++) {
+            data[i][i] = 1; // Identity part
+            for (let j = 0; j < n; j++) {
+                if (i !== j && degrees[i] > 0 && degrees[j] > 0) {
+                    const normalization = Math.sqrt(degrees[i] * degrees[j]);
+                    data[i][j] = -MatrixOperations.getEntry(adjacency, i, j) / normalization;
+                }
+            }
+        }
+        return {
+            rows: n,
+            cols: n,
+            data,
+            format: 'dense'
+        };
+    }
+    static async closenessCentrality(adjacency) {
+        // Simplified implementation - would need all-pairs shortest paths
+        const n = adjacency.rows;
+        const closeness = new Array(n).fill(0);
+        // This is a placeholder - actual implementation would compute shortest paths
+        for (let i = 0; i < n; i++) {
+            closeness[i] = Math.random(); // Placeholder
+        }
+        return {
+            closenessVector: closeness,
+            normalized: closeness.map(c => c / (n - 1))
+        };
+    }
+    static async betweennessCentrality(adjacency) {
+        // Simplified implementation - would need shortest path counting
+        const n = adjacency.rows;
+        const betweenness = new Array(n).fill(0);
+        // This is a placeholder - actual implementation would use Brandes' algorithm
+        for (let i = 0; i < n; i++) {
+            betweenness[i] = Math.random(); // Placeholder
+        }
+        return {
+            betweennessVector: betweenness,
+            normalized: betweenness.map(b => b / ((n - 1) * (n - 2) / 2))
+        };
+    }
+    static computeModularity(adjacency, assignments) {
+        const n = adjacency.rows;
+        const m = this.countEdges(adjacency);
+        let modularity = 0;
+        for (let i = 0; i < n; i++) {
+            for (let j = 0; j < n; j++) {
+                if (assignments[i] === assignments[j]) {
+                    const aij = MatrixOperations.getEntry(adjacency, i, j);
+                    const ki = this.getNodeDegree(adjacency, i);
+                    const kj = this.getNodeDegree(adjacency, j);
+                    modularity += aij - (ki * kj) / (2 * m);
+                }
+            }
+        }
+        return modularity / (2 * m);
+    }
+    static countEdges(adjacency) {
+        let edges = 0;
+        for (let i = 0; i < adjacency.rows; i++) {
+            for (let j = 0; j < adjacency.cols; j++) {
+                edges += MatrixOperations.getEntry(adjacency, i, j);
+            }
+        }
+        return edges / 2; // Assuming undirected graph
+    }
+    static getNodeDegree(adjacency, node) {
+        let degree = 0;
+        for (let j = 0; j < adjacency.cols; j++) {
+            degree += MatrixOperations.getEntry(adjacency, node, j);
+        }
+        return degree;
+    }
+}

vendor/sublinear-time-solver/dist/mcp/tools/index.d.ts

@@ -0,0 +1,143 @@
+/**
+ * MCP Tools Export
+ *
+ * This module exports all MCP tool classes and provides
+ * a consolidated tool list for the MCP server
+ */
+import { SolverTools } from './solver.js';
+import { MatrixTools } from './matrix.js';
+import { EmergenceTools } from './emergence-tools.js';
+import { ConsciousnessTools } from './consciousness.js';
+import { SchedulerTools } from './scheduler.js';
+import { PsychoSymbolicTools } from './psycho-symbolic.js';
+export { SolverTools } from './solver.js';
+export { MatrixTools } from './matrix.js';
+export { EmergenceTools } from './emergence-tools.js';
+export { ConsciousnessTools } from './consciousness.js';
+export { SchedulerTools } from './scheduler.js';
+export { PsychoSymbolicTools } from './psycho-symbolic.js';
+export { WasmSublinearSolverTools } from './wasm-sublinear-solver.js';
+export { temporalAttractorHandlers } from './temporal-attractor-handlers.js';
+export declare const solverTools: any;
+export declare const matrixTools: any;
+export declare const emergenceTools: any;
+export declare const consciousnessTools: any;
+export declare const schedulerTools: any;
+export declare const psychoSymbolicTools: any;
+export { temporalAttractorTools } from './temporal-attractor.js';
+export declare const allTools: any[];
+declare const _default: {
+    solver: SolverTools;
+    matrix: MatrixTools;
+    emergence: EmergenceTools;
+    consciousness: ConsciousnessTools;
+    scheduler: SchedulerTools;
+    psychoSymbolic: PsychoSymbolicTools;
+    temporalAttractor: {
+        chaos_analyze: (args: any) => Promise<{
+            lambda: any;
+            is_chaotic: any;
+            chaos_level: any;
+            lyapunov_time: any;
+            doubling_time: any;
+            safe_prediction_steps: any;
+            pairs_found: any;
+            interpretation: string;
+        }>;
+        temporal_delay_embed: (args: any) => Promise<{
+            original_length: any;
+            embedded_vectors: number;
+            embedding_dim: any;
+            tau: any;
+            data: any;
+        }>;
+        temporal_predict: (args: any) => Promise<{
+            initialized: boolean;
+            reservoir_size: any;
+            training_complete?: undefined;
+            mse?: undefined;
+            n_samples?: undefined;
+            input?: undefined;
+            prediction?: undefined;
+            trajectory?: undefined;
+            n_steps?: undefined;
+        } | {
+            training_complete: boolean;
+            mse: any;
+            n_samples: any;
+            initialized?: undefined;
+            reservoir_size?: undefined;
+            input?: undefined;
+            prediction?: undefined;
+            trajectory?: undefined;
+            n_steps?: undefined;
+        } | {
+            input: any;
+            prediction: any;
+            initialized?: undefined;
+            reservoir_size?: undefined;
+            training_complete?: undefined;
+            mse?: undefined;
+            n_samples?: undefined;
+            trajectory?: undefined;
+            n_steps?: undefined;
+        } | {
+            input: any;
+            trajectory: any;
+            n_steps: any;
+            initialized?: undefined;
+            reservoir_size?: undefined;
+            training_complete?: undefined;
+            mse?: undefined;
+            n_samples?: undefined;
+            prediction?: undefined;
+        }>;
+        temporal_fractal_dimension: (args: any) => Promise<{
+            fractal_dimension: any;
+            interpretation: string;
+        }>;
+        temporal_regime_changes: (args: any) => Promise<{
+            n_windows: any;
+            lyapunov_values: any;
+            changes_detected: boolean;
+            max_lambda: number;
+            min_lambda: number;
+            variance: number;
+        }>;
+        temporal_generate_attractor: (args: any) => Promise<{
+            system: any;
+            n_points: any;
+            dimensions: any;
+            dt: any;
+            data: any;
+        }>;
+        temporal_interpret_chaos: (args: any) => Promise<any>;
+        temporal_recommend_parameters: (args: any) => Promise<any>;
+        temporal_attractor_pullback: (args: any) => Promise<{
+            ensemble_size: any;
+            evolution_time: any;
+            snapshots: any[];
+            drift: any[];
+            convergence_rate: number;
+        }>;
+        temporal_kaplan_yorke_dimension: (args: any) => Promise<{
+            kaplan_yorke_dimension: number;
+            lyapunov_spectrum: any;
+            interpretation: string;
+        }>;
+    };
+    SolverTools: typeof SolverTools;
+    MatrixTools: typeof MatrixTools;
+    EmergenceTools: typeof EmergenceTools;
+    ConsciousnessTools: typeof ConsciousnessTools;
+    SchedulerTools: typeof SchedulerTools;
+    PsychoSymbolicTools: typeof PsychoSymbolicTools;
+    solverTools: any;
+    matrixTools: any;
+    emergenceTools: any;
+    consciousnessTools: any;
+    schedulerTools: any;
+    psychoSymbolicTools: any;
+    allTools: any[];
+};
+export default _default;

vendor/sublinear-time-solver/dist/mcp/tools/index.js

@@ -0,0 +1,79 @@
+/**
+ * MCP Tools Export
+ *
+ * This module exports all MCP tool classes and provides
+ * a consolidated tool list for the MCP server
+ */
+// Import all tool classes
+import { SolverTools } from './solver.js';
+import { MatrixTools } from './matrix.js';
+import { EmergenceTools } from './emergence-tools.js';
+import { ConsciousnessTools } from './consciousness.js';
+import { SchedulerTools } from './scheduler.js';
+import { PsychoSymbolicTools } from './psycho-symbolic.js';
+import { WasmSublinearSolverTools } from './wasm-sublinear-solver.js';
+import { temporalAttractorTools } from './temporal-attractor.js';
+import { temporalAttractorHandlers } from './temporal-attractor-handlers.js';
+// Export classes for direct usage
+export { SolverTools } from './solver.js';
+export { MatrixTools } from './matrix.js';
+export { EmergenceTools } from './emergence-tools.js';
+export { ConsciousnessTools } from './consciousness.js';
+export { SchedulerTools } from './scheduler.js';
+export { PsychoSymbolicTools } from './psycho-symbolic.js';
+export { WasmSublinearSolverTools } from './wasm-sublinear-solver.js';
+export { temporalAttractorHandlers } from './temporal-attractor-handlers.js';
+// Create instances for getting tool definitions
+const solverToolsInstance = new SolverTools();
+const matrixToolsInstance = new MatrixTools();
+const emergenceToolsInstance = new EmergenceTools();
+const consciousnessToolsInstance = new ConsciousnessTools();
+const schedulerToolsInstance = new SchedulerTools();
+const psychoSymbolicToolsInstance = new PsychoSymbolicTools();
+const wasmSolverToolsInstance = new WasmSublinearSolverTools();
+// Export tool arrays (if classes have getTools method, otherwise empty)
+export const solverTools = solverToolsInstance.getTools?.() || [];
+export const matrixTools = matrixToolsInstance.getTools?.() || [];
+export const emergenceTools = emergenceToolsInstance.getTools?.() || [];
+export const consciousnessTools = consciousnessToolsInstance.getTools?.() || [];
+export const schedulerTools = schedulerToolsInstance.getTools?.() || [];
+export const psychoSymbolicTools = psychoSymbolicToolsInstance.getTools?.() || [];
+// Temporal attractor tools are exported directly from the file
+export { temporalAttractorTools } from './temporal-attractor.js';
+// For backward compatibility - if getTools doesn't exist,
+// we'll assume the tools are defined in the MCP server itself
+export const allTools = [
+    ...solverTools,
+    ...matrixTools,
+    ...emergenceTools,
+    ...consciousnessTools,
+    ...schedulerTools,
+    ...psychoSymbolicTools,
+    ...temporalAttractorTools
+];
+// Default export with both instances and classes
+export default {
+    // Instances (for calling methods)
+    solver: solverToolsInstance,
+    matrix: matrixToolsInstance,
+    emergence: emergenceToolsInstance,
+    consciousness: consciousnessToolsInstance,
+    scheduler: schedulerToolsInstance,
+    psychoSymbolic: psychoSymbolicToolsInstance,
+    temporalAttractor: temporalAttractorHandlers,
+    // Classes (for creating new instances)
+    SolverTools,
+    MatrixTools,
+    EmergenceTools,
+    ConsciousnessTools,
+    SchedulerTools,
+    PsychoSymbolicTools,
+    // Tool arrays (may be empty if getTools doesn't exist)
+    solverTools,
+    matrixTools,
+    emergenceTools,
+    consciousnessTools,
+    schedulerTools,
+    psychoSymbolicTools,
+    allTools
+};

vendor/sublinear-time-solver/dist/mcp/tools/matrix.d.ts

@@ -0,0 +1,50 @@
+/**
+ * MCP Tools for matrix analysis and operations
+ */
+import { Matrix, AnalyzeMatrixParams, MatrixAnalysis } from '../../core/types.js';
+export declare class MatrixTools {
+    /**
+     * Analyze matrix properties
+     */
+    static analyzeMatrix(params: AnalyzeMatrixParams): MatrixAnalysis & {
+        recommendations: string[];
+        performance: {
+            expectedComplexity: string;
+            memoryUsage: string;
+            recommendedMethod: string;
+        };
+        visualMetrics: {
+            bandwidth: number;
+            profileMetric: number;
+            fillRatio: number;
+        };
+    };
+    /**
+     * Check matrix conditioning and stability
+     */
+    static checkConditioning(matrix: Matrix): {
+        isWellConditioned: boolean;
+        conditionEstimate?: number;
+        stabilityRating: 'excellent' | 'good' | 'fair' | 'poor';
+        warnings: string[];
+    };
+    /**
+     * Convert between matrix formats
+     */
+    static convertFormat(matrix: Matrix, targetFormat: 'dense' | 'coo'): Matrix;
+    /**
+     * Generate test matrices for benchmarking
+     */
+    static generateTestMatrix(type: string, size: number, params?: any): Matrix;
+    private static computeBandwidth;
+    private static computeProfile;
+    private static predictComplexity;
+    private static estimateMemoryUsage;
+    private static recommendSolverMethod;
+    private static generateDetailedRecommendations;
+    private static estimateConditionNumber;
+    private static generateDiagonallyDominantMatrix;
+    private static generateLaplacianMatrix;
+    private static generateRandomSparseMatrix;
+    private static generateTridiagonalMatrix;
+}

vendor/sublinear-time-solver/dist/mcp/tools/matrix.js

@@ -0,0 +1,350 @@
+/**
+ * MCP Tools for matrix analysis and operations
+ */
+import { MatrixOperations } from '../../core/matrix.js';
+import { SolverError, ErrorCodes } from '../../core/types.js';
+export class MatrixTools {
+    /**
+     * Analyze matrix properties
+     */
+    static analyzeMatrix(params) {
+        MatrixOperations.validateMatrix(params.matrix);
+        const analysis = MatrixOperations.analyzeMatrix(params.matrix);
+        const matrix = params.matrix;
+        // Enhanced analysis
+        const bandwidth = this.computeBandwidth(matrix);
+        const profileMetric = this.computeProfile(matrix);
+        const fillRatio = 1 - analysis.sparsity;
+        // Generate performance predictions
+        const expectedComplexity = this.predictComplexity(analysis, matrix);
+        const memoryUsage = this.estimateMemoryUsage(matrix);
+        const recommendedMethod = this.recommendSolverMethod(analysis);
+        // Generate recommendations
+        const recommendations = this.generateDetailedRecommendations(analysis, {
+            bandwidth,
+            profileMetric,
+            fillRatio,
+            size: matrix.rows
+        });
+        return {
+            ...analysis,
+            recommendations,
+            performance: {
+                expectedComplexity,
+                memoryUsage,
+                recommendedMethod
+            },
+            visualMetrics: {
+                bandwidth,
+                profileMetric,
+                fillRatio
+            }
+        };
+    }
+    /**
+     * Check matrix conditioning and stability
+     */
+    static checkConditioning(matrix) {
+        const analysis = MatrixOperations.analyzeMatrix(matrix);
+        const warnings = [];
+        // Check diagonal dominance strength
+        let stabilityRating = 'excellent';
+        if (!analysis.isDiagonallyDominant) {
+            warnings.push('Matrix is not diagonally dominant');
+            stabilityRating = 'poor';
+        }
+        else if (analysis.dominanceStrength < 0.1) {
+            warnings.push('Weak diagonal dominance - may converge slowly');
+            stabilityRating = 'fair';
+        }
+        else if (analysis.dominanceStrength < 0.5) {
+            stabilityRating = 'good';
+        }
+        // Check for zero or near-zero diagonals
+        const diagonals = MatrixOperations.getDiagonalVector(matrix);
+        const nearZeroDiagonals = diagonals.filter(d => Math.abs(d) < 1e-12);
+        if (nearZeroDiagonals.length > 0) {
+            warnings.push(`${nearZeroDiagonals.length} near-zero diagonal elements detected`);
+            stabilityRating = 'poor';
+        }
+        // Rough condition number estimate for small matrices
+        let conditionEstimate;
+        if (matrix.rows <= 100 && matrix.format === 'dense') {
+            conditionEstimate = this.estimateConditionNumber(matrix);
+            if (conditionEstimate > 1e12) {
+                warnings.push('Very high condition number - matrix is nearly singular');
+                stabilityRating = 'poor';
+            }
+            else if (conditionEstimate > 1e6) {
+                warnings.push('High condition number - may have numerical issues');
+                if (stabilityRating === 'excellent')
+                    stabilityRating = 'fair';
+            }
+        }
+        return {
+            isWellConditioned: warnings.length === 0 && analysis.isDiagonallyDominant,
+            conditionEstimate,
+            stabilityRating,
+            warnings
+        };
+    }
+    /**
+     * Convert between matrix formats
+     */
+    static convertFormat(matrix, targetFormat) {
+        MatrixOperations.validateMatrix(matrix);
+        if (matrix.format === targetFormat) {
+            return matrix;
+        }
+        if (targetFormat === 'dense') {
+            return MatrixOperations.sparseToDense(matrix);
+        }
+        else {
+            return MatrixOperations.denseToSparse(matrix);
+        }
+    }
+    /**
+     * Generate test matrices for benchmarking
+     */
+    static generateTestMatrix(type, size, params = {}) {
+        switch (type) {
+            case 'diagonally-dominant':
+                return this.generateDiagonallyDominantMatrix(size, params.strength || 2.0);
+            case 'laplacian':
+                return this.generateLaplacianMatrix(size, params.connectivity || 0.1);
+            case 'random-sparse':
+                return this.generateRandomSparseMatrix(size, params.density || 0.1, params.dominance || true);
+            case 'tridiagonal':
+                return this.generateTridiagonalMatrix(size, params.offDiagonal || -1);
+            default:
+                throw new SolverError(`Unknown test matrix type: ${type}`, ErrorCodes.INVALID_PARAMETERS);
+        }
+    }
+    static computeBandwidth(matrix) {
+        if (matrix.format === 'dense') {
+            let maxBandwidth = 0;
+            for (let i = 0; i < matrix.rows; i++) {
+                for (let j = 0; j < matrix.cols; j++) {
+                    if (Math.abs(MatrixOperations.getEntry(matrix, i, j)) > 1e-15) {
+                        maxBandwidth = Math.max(maxBandwidth, Math.abs(i - j));
+                    }
+                }
+            }
+            return maxBandwidth;
+        }
+        else {
+            const sparse = matrix;
+            let maxBandwidth = 0;
+            for (let k = 0; k < sparse.values.length; k++) {
+                const bandwidth = Math.abs(sparse.rowIndices[k] - sparse.colIndices[k]);
+                maxBandwidth = Math.max(maxBandwidth, bandwidth);
+            }
+            return maxBandwidth;
+        }
+    }
+    static computeProfile(matrix) {
+        let profile = 0;
+        for (let i = 0; i < matrix.rows; i++) {
+            let firstNonZero = matrix.cols;
+            for (let j = 0; j <= i; j++) {
+                if (Math.abs(MatrixOperations.getEntry(matrix, i, j)) > 1e-15) {
+                    firstNonZero = j;
+                    break;
+                }
+            }
+            profile += (i - firstNonZero + 1);
+        }
+        return profile;
+    }
+    static predictComplexity(analysis, matrix) {
+        const n = matrix.rows;
+        const nnz = Math.round((1 - analysis.sparsity) * n * n);
+        if (analysis.isDiagonallyDominant) {
+            if (analysis.dominanceStrength > 0.5) {
+                return `O(nnz * log n) ≈ O(${nnz} * ${Math.ceil(Math.log2(n))})`;
+            }
+            else {
+                return `O(nnz * n^0.5) ≈ O(${nnz} * ${Math.ceil(Math.sqrt(n))})`;
+            }
+        }
+        else {
+            return `O(n^3) ≈ O(${n}^3) - not suitable for sublinear methods`;
+        }
+    }
+    static estimateMemoryUsage(matrix) {
+        const n = matrix.rows;
+        const elementSize = 8; // 64-bit floats
+        if (matrix.format === 'dense') {
+            const mb = (n * n * elementSize) / (1024 * 1024);
+            return `${mb.toFixed(1)} MB (dense)`;
+        }
+        else {
+            const sparse = matrix;
+            const mb = (sparse.values.length * 3 * elementSize) / (1024 * 1024); // values + 2 index arrays
+            return `${mb.toFixed(1)} MB (sparse)`;
+        }
+    }
+    static recommendSolverMethod(analysis) {
+        if (!analysis.isDiagonallyDominant) {
+            return 'Direct solver (LU/Cholesky) - matrix not suitable for sublinear methods';
+        }
+        if (analysis.isSymmetric) {
+            return 'Neumann series or Forward Push (symmetric case)';
+        }
+        else {
+            if (analysis.dominanceStrength > 0.3) {
+                return 'Random Walk or Bidirectional Push';
+            }
+            else {
+                return 'Forward Push with preconditioning';
+            }
+        }
+    }
+    static generateDetailedRecommendations(analysis, metrics) {
+        const recommendations = [];
+        if (!analysis.isDiagonallyDominant) {
+            recommendations.push('Matrix is not diagonally dominant. Consider matrix preconditioning or regularization.');
+            recommendations.push('Use direct solvers (LU, QR) instead of iterative methods.');
+        }
+        else {
+            if (analysis.dominanceStrength < 0.1) {
+                recommendations.push('Weak diagonal dominance. Consider diagonal scaling or row equilibration.');
+            }
+            if (analysis.sparsity > 0.95) {
+                recommendations.push('Extremely sparse matrix. Use sparse storage formats and specialized algorithms.');
+            }
+            if (metrics.bandwidth > analysis.size.rows * 0.1) {
+                recommendations.push('Large bandwidth detected. Consider matrix reordering (RCM, AMD).');
+            }
+            if (metrics.size > 10000) {
+                recommendations.push('Large matrix. Consider sublinear estimation for specific entries rather than full solve.');
+                recommendations.push('Use random walk sampling for single coordinate queries.');
+            }
+            if (!analysis.isSymmetric) {
+                recommendations.push('Asymmetric matrix. Random walk methods may be most effective.');
+                recommendations.push('Consider bidirectional push for better convergence.');
+            }
+        }
+        if (metrics.fillRatio > 0.5) {
+            recommendations.push('Dense matrix. Memory usage may be significant for large sizes.');
+        }
+        return recommendations;
+    }
+    static estimateConditionNumber(matrix) {
+        // Very rough estimate using diagonal dominance
+        if (matrix.format !== 'dense' || matrix.rows > 100) {
+            return NaN;
+        }
+        const diagonals = MatrixOperations.getDiagonalVector(matrix);
+        const maxDiag = Math.max(...diagonals.map(Math.abs));
+        const minDiag = Math.min(...diagonals.map(Math.abs));
+        if (minDiag === 0) {
+            return Infinity;
+        }
+        return maxDiag / minDiag; // Very rough approximation
+    }
+    static generateDiagonallyDominantMatrix(size, strength) {
+        const data = Array(size).fill(null).map(() => Array(size).fill(0));
+        for (let i = 0; i < size; i++) {
+            let offDiagSum = 0;
+            // Fill off-diagonal entries
+            for (let j = 0; j < size; j++) {
+                if (i !== j && Math.random() < 0.3) { // 30% sparsity
+                    const value = (Math.random() - 0.5) * 2;
+                    data[i][j] = value;
+                    offDiagSum += Math.abs(value);
+                }
+            }
+            // Set diagonal to ensure dominance
+            data[i][i] = strength * offDiagSum + 1;
+        }
+        return {
+            rows: size,
+            cols: size,
+            data,
+            format: 'dense'
+        };
+    }
+    static generateLaplacianMatrix(size, connectivity) {
+        const data = Array(size).fill(null).map(() => Array(size).fill(0));
+        for (let i = 0; i < size; i++) {
+            let degree = 0;
+            for (let j = 0; j < size; j++) {
+                if (i !== j && Math.random() < connectivity) {
+                    data[i][j] = -1;
+                    degree++;
+                }
+            }
+            data[i][i] = degree;
+        }
+        return {
+            rows: size,
+            cols: size,
+            data,
+            format: 'dense'
+        };
+    }
+    static generateRandomSparseMatrix(size, density, ensureDominance) {
+        const values = [];
+        const rowIndices = [];
+        const colIndices = [];
+        const rowSums = new Array(size).fill(0);
+        // Generate off-diagonal entries
+        for (let i = 0; i < size; i++) {
+            for (let j = 0; j < size; j++) {
+                if (i !== j && Math.random() < density) {
+                    const value = (Math.random() - 0.5) * 2;
+                    values.push(value);
+                    rowIndices.push(i);
+                    colIndices.push(j);
+                    rowSums[i] += Math.abs(value);
+                }
+            }
+        }
+        // Add diagonal entries
+        for (let i = 0; i < size; i++) {
+            const diagValue = ensureDominance ? rowSums[i] * 1.5 + 1 : Math.random() * 5 + 1;
+            values.push(diagValue);
+            rowIndices.push(i);
+            colIndices.push(i);
+        }
+        return {
+            rows: size,
+            cols: size,
+            values,
+            rowIndices,
+            colIndices,
+            format: 'coo'
+        };
+    }
+    static generateTridiagonalMatrix(size, offDiagonal) {
+        const values = [];
+        const rowIndices = [];
+        const colIndices = [];
+        for (let i = 0; i < size; i++) {
+            // Diagonal
+            values.push(2);
+            rowIndices.push(i);
+            colIndices.push(i);
+            // Off-diagonal
+            if (i > 0) {
+                values.push(offDiagonal);
+                rowIndices.push(i);
+                colIndices.push(i - 1);
+            }
+            if (i < size - 1) {
+                values.push(offDiagonal);
+                rowIndices.push(i);
+                colIndices.push(i + 1);
+            }
+        }
+        return {
+            rows: size,
+            cols: size,
+            values,
+            rowIndices,
+            colIndices,
+            format: 'coo'
+        };
+    }
+}

vendor/sublinear-time-solver/dist/mcp/tools/psycho-symbolic-complete.d.ts

@@ -0,0 +1,25 @@
+/**
+ * Complete Enhanced Psycho-Symbolic Reasoning with Full Learning Integration
+ * Includes: Domain Adaptation, Creative Reasoning, Enhanced Knowledge Base, Analogical Reasoning
+ */
+import { Tool } from '@modelcontextprotocol/sdk/types.js';
+export declare class CompletePsychoSymbolicTools {
+    private knowledgeBase;
+    private domainEngine;
+    private creativeEngine;
+    private analogicalEngine;
+    private performanceCache;
+    private toolLearningHooks;
+    constructor();
+    getTools(): Tool[];
+    handleToolCall(name: string, args: any): Promise<any>;
+    private performCompleteReasoning;
+    private extractAdvancedEntities;
+    private enhancedKnowledgeTraversal;
+    private synthesizeAdvancedAnswer;
+    private advancedKnowledgeQuery;
+    private addEnhancedKnowledge;
+    private registerToolInteraction;
+    private getCrossToolInsights;
+    private getLearningStatus;
+}

vendor/sublinear-time-solver/dist/mcp/tools/psycho-symbolic-dynamic.d.ts

@@ -0,0 +1,26 @@
+/**
+ * Enhanced Psycho-Symbolic Tools with Dynamic Domain Support
+ * Extends existing functionality while preserving all current capabilities
+ */
+import { Tool } from '@modelcontextprotocol/sdk/types.js';
+import { PsychoSymbolicTools } from './psycho-symbolic.js';
+import { DomainRegistry } from './domain-registry.js';
+export declare class DynamicPsychoSymbolicTools extends PsychoSymbolicTools {
+    private domainRegistry;
+    constructor(domainRegistry?: DomainRegistry);
+    private initializeDynamicDomainIntegration;
+    getTools(): Tool[];
+    handleToolCall(name: string, args: any): Promise<any>;
+    private performEnhancedReasoning;
+    private testDomainDetection;
+    private advancedKnowledgeQueryDynamic;
+    private buildDomainFilters;
+    private performEnhancedDomainDetection;
+    private updateDomainEngine;
+    private getDynamicDomainsCount;
+    private getBuiltinDomainsCount;
+    private updateDynamicDomainUsage;
+    private testDomainDetectionSingle;
+    private applyDomainWeighting;
+    getDomainRegistry(): DomainRegistry;
+}

vendor/sublinear-time-solver/dist/mcp/tools/psycho-symbolic-dynamic.js

@@ -0,0 +1,395 @@
+/**
+ * Enhanced Psycho-Symbolic Tools with Dynamic Domain Support
+ * Extends existing functionality while preserving all current capabilities
+ */
+import { PsychoSymbolicTools } from './psycho-symbolic.js';
+import { DomainRegistry } from './domain-registry.js';
+export class DynamicPsychoSymbolicTools extends PsychoSymbolicTools {
+    domainRegistry;
+    constructor(domainRegistry) {
+        super();
+        this.domainRegistry = domainRegistry || new DomainRegistry();
+        this.initializeDynamicDomainIntegration();
+    }
+    initializeDynamicDomainIntegration() {
+        // Listen for domain registry events to update domain engine
+        this.domainRegistry.on('domainRegistered', (event) => {
+            this.updateDomainEngine();
+        });
+        this.domainRegistry.on('domainUpdated', (event) => {
+            this.updateDomainEngine();
+        });
+        this.domainRegistry.on('domainUnregistered', (event) => {
+            this.updateDomainEngine();
+        });
+        this.domainRegistry.on('domainEnabled', (event) => {
+            this.updateDomainEngine();
+        });
+        this.domainRegistry.on('domainDisabled', (event) => {
+            this.updateDomainEngine();
+        });
+    }
+    getTools() {
+        // Get all existing tools from parent class
+        const baseTools = super.getTools();
+        // Add enhanced tools with dynamic domain support
+        const enhancedTools = [
+            {
+                name: 'psycho_symbolic_reason_with_dynamic_domains',
+                description: 'Enhanced psycho-symbolic reasoning with dynamic domain support and control',
+                inputSchema: {
+                    type: 'object',
+                    properties: {
+                        query: { type: 'string', description: 'The reasoning query' },
+                        context: { type: 'object', description: 'Additional context', default: {} },
+                        depth: { type: 'number', description: 'Maximum reasoning depth', default: 7 },
+                        use_cache: { type: 'boolean', description: 'Enable intelligent caching', default: true },
+                        enable_learning: { type: 'boolean', description: 'Enable learning from this interaction', default: true },
+                        creative_mode: { type: 'boolean', description: 'Enable creative reasoning for novel concepts', default: true },
+                        domain_adaptation: { type: 'boolean', description: 'Enable automatic domain detection and adaptation', default: true },
+                        analogical_reasoning: { type: 'boolean', description: 'Enable analogical reasoning across domains', default: true },
+                        // Dynamic domain extensions
+                        force_domains: {
+                            type: 'array',
+                            items: { type: 'string' },
+                            description: 'Force specific domains to be considered (overrides detection)'
+                        },
+                        exclude_domains: {
+                            type: 'array',
+                            items: { type: 'string' },
+                            description: 'Exclude specific domains from consideration'
+                        },
+                        domain_priority_override: {
+                            type: 'object',
+                            additionalProperties: { type: 'number' },
+                            description: 'Override domain priorities for this query (domain_name: priority)'
+                        },
+                        use_experimental_domains: {
+                            type: 'boolean',
+                            default: false,
+                            description: 'Include experimental/beta domains in reasoning'
+                        },
+                        min_domain_confidence: {
+                            type: 'number',
+                            minimum: 0,
+                            maximum: 1,
+                            default: 0.1,
+                            description: 'Minimum confidence threshold for domain detection'
+                        },
+                        max_domains: {
+                            type: 'integer',
+                            minimum: 1,
+                            maximum: 10,
+                            default: 3,
+                            description: 'Maximum number of domains to use in reasoning'
+                        }
+                    },
+                    required: ['query']
+                }
+            },
+            {
+                name: 'domain_detection_test',
+                description: 'Test domain detection for a given query with detailed analysis',
+                inputSchema: {
+                    type: 'object',
+                    properties: {
+                        query: { type: 'string', description: 'Query to test domain detection on' },
+                        include_scores: {
+                            type: 'boolean',
+                            default: true,
+                            description: 'Include detailed detection scores and matching details'
+                        },
+                        include_debug: {
+                            type: 'boolean',
+                            default: false,
+                            description: 'Include debug information about detection process'
+                        },
+                        test_all_domains: {
+                            type: 'boolean',
+                            default: false,
+                            description: 'Test against all domains including disabled ones'
+                        },
+                        show_keyword_matches: {
+                            type: 'boolean',
+                            default: true,
+                            description: 'Show which keywords matched for each domain'
+                        }
+                    },
+                    required: ['query']
+                }
+            },
+            {
+                name: 'knowledge_graph_query_dynamic',
+                description: 'Knowledge graph query with dynamic domain filtering and boosting',
+                inputSchema: {
+                    type: 'object',
+                    properties: {
+                        query: { type: 'string', description: 'Natural language query' },
+                        domains: {
+                            type: 'array',
+                            items: { type: 'string' },
+                            description: 'Domain filters (supports both built-in and dynamic domains)',
+                            default: []
+                        },
+                        include_analogies: {
+                            type: 'boolean',
+                            description: 'Include analogical connections',
+                            default: true
+                        },
+                        limit: { type: 'number', description: 'Max results', default: 20 },
+                        cross_domain_boost: {
+                            type: 'number',
+                            minimum: 0,
+                            maximum: 2,
+                            default: 1.0,
+                            description: 'Boost relevance for cross-domain results'
+                        },
+                        dynamic_domain_weight: {
+                            type: 'number',
+                            minimum: 0,
+                            maximum: 2,
+                            default: 1.0,
+                            description: 'Weight multiplier for results from dynamic domains'
+                        },
+                        builtin_domain_weight: {
+                            type: 'number',
+                            minimum: 0,
+                            maximum: 2,
+                            default: 1.0,
+                            description: 'Weight multiplier for results from built-in domains'
+                        },
+                        require_domain_match: {
+                            type: 'boolean',
+                            default: false,
+                            description: 'Only return results that match specified domains'
+                        }
+                    },
+                    required: ['query']
+                }
+            }
+        ];
+        return [...baseTools, ...enhancedTools];
+    }
+    async handleToolCall(name, args) {
+        try {
+            switch (name) {
+                case 'psycho_symbolic_reason_with_dynamic_domains':
+                    return await this.performEnhancedReasoning(args);
+                case 'domain_detection_test':
+                    return await this.testDomainDetection(args);
+                case 'knowledge_graph_query_dynamic':
+                    return await this.advancedKnowledgeQueryDynamic(args);
+                default:
+                    // Delegate to parent class for existing tools
+                    return await super.handleToolCall(name, args);
+            }
+        }
+        catch (error) {
+            return {
+                success: false,
+                error: error instanceof Error ? error.message : String(error),
+                timestamp: new Date().toISOString()
+            };
+        }
+    }
+    async performEnhancedReasoning(args) {
+        const startTime = performance.now();
+        // Apply domain filtering and priority overrides
+        const domainFilters = this.buildDomainFilters(args);
+        // Get enhanced domain detection with dynamic domains
+        const enhancedDetection = await this.performEnhancedDomainDetection(args.query, domainFilters);
+        // Enhance the reasoning context with dynamic domain information
+        const enhancedContext = {
+            ...args.context,
+            domain_filters: domainFilters,
+            dynamic_domains_available: this.getDynamicDomainsCount(),
+            enhanced_detection: enhancedDetection
+        };
+        // Call the parent reasoning method with enhanced context via public interface
+        const baseResult = await super.handleToolCall('psycho_symbolic_reason', {
+            ...args,
+            context: enhancedContext
+        });
+        // Enhance the result with dynamic domain information
+        const enhancedResult = {
+            ...baseResult,
+            dynamic_domain_info: {
+                filters_applied: domainFilters,
+                dynamic_domains_used: enhancedDetection.dynamic_domains_detected,
+                builtin_domains_used: enhancedDetection.builtin_domains_detected,
+                domain_synergies: enhancedDetection.synergies,
+                detection_performance: {
+                    total_domains_checked: enhancedDetection.total_domains_checked,
+                    detection_time_ms: enhancedDetection.detection_time_ms
+                }
+            },
+            enhanced_reasoning_time: performance.now() - startTime
+        };
+        // Update usage statistics for dynamic domains
+        this.updateDynamicDomainUsage(enhancedDetection.domains_used);
+        return enhancedResult;
+    }
+    async testDomainDetection(args) {
+        const startTime = performance.now();
+        const query = args.query;
+        // Get all domains to test (including disabled if requested)
+        const domainsToTest = args.test_all_domains ?
+            this.domainRegistry.getAllDomains() :
+            this.domainRegistry.getEnabledDomains();
+        const detectionResults = [];
+        // Test detection against each domain
+        for (const domain of domainsToTest) {
+            const domainResult = await this.testDomainDetectionSingle(query, domain, args.show_keyword_matches);
+            detectionResults.push(domainResult);
+        }
+        // Sort by detection score
+        detectionResults.sort((a, b) => b.score - a.score);
+        // Get top detected domains
+        const topDomains = detectionResults
+            .filter(r => r.score > 0)
+            .slice(0, args.max_results || 10);
+        const detectionTime = performance.now() - startTime;
+        const result = {
+            query,
+            detected_domains: topDomains,
+            detection_summary: {
+                total_domains_tested: detectionResults.length,
+                domains_with_matches: detectionResults.filter(r => r.score > 0).length,
+                highest_score: detectionResults[0]?.score || 0,
+                detection_time_ms: detectionTime
+            },
+            system_info: {
+                total_domains_available: this.domainRegistry.getAllDomains().length,
+                builtin_domains_count: this.getBuiltinDomainsCount(),
+                dynamic_domains_count: this.getDynamicDomainsCount(),
+                enabled_domains_count: this.domainRegistry.getEnabledDomains().length
+            }
+        };
+        if (args.include_debug) {
+            result.debug_info = {
+                all_domain_results: detectionResults,
+                domain_registry_status: this.domainRegistry.getSystemStatus(),
+                detection_algorithm_info: {
+                    scoring_method: 'keyword_matching_with_semantic_boost',
+                    confidence_threshold: 0.1,
+                    max_domains_returned: args.max_results || 10
+                }
+            };
+        }
+        return result;
+    }
+    async advancedKnowledgeQueryDynamic(args) {
+        // Enhance the base knowledge query with dynamic domain support via public interface
+        const baseResult = await super.handleToolCall('knowledge_graph_query', args);
+        // Apply dynamic domain weighting
+        if (args.dynamic_domain_weight !== 1.0 || args.builtin_domain_weight !== 1.0) {
+            baseResult.results = this.applyDomainWeighting(baseResult.results, args.dynamic_domain_weight, args.builtin_domain_weight);
+        }
+        // Filter by domain requirements if specified
+        if (args.require_domain_match && args.domains?.length > 0) {
+            baseResult.results = baseResult.results.filter(result => result.domain_tags?.some(tag => args.domains.includes(tag)));
+        }
+        // Add dynamic domain information
+        const enhancedResult = {
+            ...baseResult,
+            dynamic_domain_info: {
+                dynamic_domains_available: this.getDynamicDomainsCount(),
+                builtin_domains_available: this.getBuiltinDomainsCount(),
+                weighting_applied: {
+                    dynamic_domain_weight: args.dynamic_domain_weight,
+                    builtin_domain_weight: args.builtin_domain_weight,
+                    cross_domain_boost: args.cross_domain_boost
+                },
+                filtering_applied: {
+                    require_domain_match: args.require_domain_match,
+                    domains_filter: args.domains
+                }
+            }
+        };
+        return enhancedResult;
+    }
+    // Helper methods
+    buildDomainFilters(args) {
+        return {
+            force_domains: args.force_domains || [],
+            exclude_domains: args.exclude_domains || [],
+            domain_priority_override: args.domain_priority_override || {},
+            use_experimental_domains: args.use_experimental_domains || false,
+            min_domain_confidence: args.min_domain_confidence || 0.1,
+            max_domains: args.max_domains || 3
+        };
+    }
+    async performEnhancedDomainDetection(query, filters) {
+        const startTime = performance.now();
+        const allDomains = this.domainRegistry.getEnabledDomains();
+        // Apply filtering
+        let domainsToCheck = allDomains;
+        if (filters.exclude_domains.length > 0) {
+            domainsToCheck = domainsToCheck.filter(d => !filters.exclude_domains.includes(d.config.name));
+        }
+        if (!filters.use_experimental_domains) {
+            domainsToCheck = domainsToCheck.filter(d => !d.config.metadata?.experimental);
+        }
+        const detectionResults = {
+            domains_used: [],
+            dynamic_domains_detected: [],
+            builtin_domains_detected: [],
+            synergies: [],
+            total_domains_checked: domainsToCheck.length,
+            detection_time_ms: performance.now() - startTime
+        };
+        return detectionResults;
+    }
+    updateDomainEngine() {
+        // Update the parent class's domain engine with dynamic domains
+        // This would integrate with the existing DomainAdaptationEngine
+        console.log('Updating domain engine with dynamic domains...');
+    }
+    getDynamicDomainsCount() {
+        return this.domainRegistry.getAllDomains().filter(d => !this.domainRegistry.isBuiltinDomain(d.config.name)).length;
+    }
+    getBuiltinDomainsCount() {
+        return this.domainRegistry.getAllDomains().filter(d => this.domainRegistry.isBuiltinDomain(d.config.name)).length;
+    }
+    updateDynamicDomainUsage(domainsUsed) {
+        for (const domainName of domainsUsed) {
+            this.domainRegistry.incrementUsage(domainName);
+        }
+    }
+    async testDomainDetectionSingle(query, domain, showKeywordMatches) {
+        // Simplified domain detection test
+        const queryLower = query.toLowerCase();
+        const matchedKeywords = domain.config.keywords.filter(keyword => queryLower.includes(keyword.toLowerCase()));
+        const score = matchedKeywords.length > 0 ? matchedKeywords.length * 2.0 : 0;
+        const result = {
+            domain: domain.config.name,
+            score,
+            enabled: domain.enabled,
+            builtin: this.domainRegistry.isBuiltinDomain(domain.config.name),
+            reasoning_style: domain.config.reasoning_style,
+            priority: domain.config.priority
+        };
+        if (showKeywordMatches) {
+            result.matched_keywords = matchedKeywords;
+            result.total_keywords = domain.config.keywords.length;
+            result.match_ratio = matchedKeywords.length / domain.config.keywords.length;
+        }
+        return result;
+    }
+    applyDomainWeighting(results, dynamicWeight, builtinWeight) {
+        return results.map(result => {
+            const isDynamic = result.domain_tags?.some(tag => !this.domainRegistry.isBuiltinDomain(tag));
+            const weight = isDynamic ? dynamicWeight : builtinWeight;
+            return {
+                ...result,
+                relevance: result.relevance * weight,
+                weighted: true,
+                weight_applied: weight
+            };
+        }).sort((a, b) => b.relevance - a.relevance);
+    }
+    // Expose domain registry for other tools
+    getDomainRegistry() {
+        return this.domainRegistry;
+    }
+}

vendor/sublinear-time-solver/dist/mcp/tools/psycho-symbolic-enhanced.d.ts

@@ -0,0 +1,26 @@
+/**
+ * Enhanced Psycho-Symbolic Reasoning MCP Tools
+ * Full implementation with real reasoning, knowledge graph, and inference engine
+ */
+import { Tool } from '@modelcontextprotocol/sdk/types.js';
+export declare class EnhancedPsychoSymbolicTools {
+    private knowledgeBase;
+    private reasoningCache;
+    constructor();
+    getTools(): Tool[];
+    handleToolCall(name: string, args: any): Promise<any>;
+    private performDeepReasoning;
+    private identifyCognitivePatterns;
+    private extractEntitiesAndConcepts;
+    private extractLogicalComponents;
+    private traverseKnowledgeGraph;
+    private buildInferenceChain;
+    private findTransitiveChains;
+    private generateHypotheses;
+    private detectContradictions;
+    private resolveContradictions;
+    private synthesizeCompleteAnswer;
+    private queryKnowledgeGraph;
+    private addKnowledge;
+}
+export default EnhancedPsychoSymbolicTools;

vendor/sublinear-time-solver/dist/mcp/tools/psycho-symbolic-enhanced.js

@@ -0,0 +1,660 @@
+/**
+ * Enhanced Psycho-Symbolic Reasoning MCP Tools
+ * Full implementation with real reasoning, knowledge graph, and inference engine
+ */
+import * as crypto from 'crypto';
+// Initialize with base knowledge
+class KnowledgeBase {
+    triples = new Map();
+    concepts = new Map(); // concept -> related triple IDs
+    predicateIndex = new Map(); // predicate -> triple IDs
+    constructor() {
+        this.initializeBaseKnowledge();
+    }
+    initializeBaseKnowledge() {
+        // Core AI/consciousness knowledge
+        this.addTriple('consciousness', 'emerges_from', 'neural_networks', 0.85);
+        this.addTriple('consciousness', 'requires', 'integration', 0.9);
+        this.addTriple('consciousness', 'exhibits', 'phi_value', 0.95);
+        this.addTriple('neural_networks', 'process', 'information', 1.0);
+        this.addTriple('neural_networks', 'contain', 'neurons', 1.0);
+        this.addTriple('neurons', 'connect_via', 'synapses', 1.0);
+        this.addTriple('synapses', 'enable', 'plasticity', 0.9);
+        this.addTriple('plasticity', 'allows', 'learning', 0.95);
+        this.addTriple('learning', 'modifies', 'weights', 1.0);
+        this.addTriple('phi_value', 'measures', 'integrated_information', 1.0);
+        this.addTriple('integrated_information', 'indicates', 'consciousness_level', 0.8);
+        // Temporal/computational knowledge
+        this.addTriple('temporal_processing', 'enables', 'prediction', 0.9);
+        this.addTriple('prediction', 'requires', 'pattern_recognition', 0.85);
+        this.addTriple('pattern_recognition', 'uses', 'neural_networks', 0.9);
+        this.addTriple('sublinear_algorithms', 'achieve', 'logarithmic_complexity', 1.0);
+        this.addTriple('logarithmic_complexity', 'beats', 'polynomial_complexity', 1.0);
+        this.addTriple('nanosecond_scheduling', 'enables', 'temporal_advantage', 0.95);
+        this.addTriple('temporal_advantage', 'allows', 'faster_than_light_computation', 0.9);
+        // Reasoning patterns
+        this.addTriple('causal_reasoning', 'identifies', 'cause_effect', 1.0);
+        this.addTriple('procedural_reasoning', 'describes', 'processes', 1.0);
+        this.addTriple('hypothetical_reasoning', 'explores', 'possibilities', 1.0);
+        this.addTriple('comparative_reasoning', 'analyzes', 'differences', 1.0);
+        this.addTriple('abstract_reasoning', 'generalizes', 'concepts', 0.95);
+        // Logic rules
+        this.addTriple('modus_ponens', 'validates', 'implications', 1.0);
+        this.addTriple('universal_instantiation', 'applies_to', 'specific_cases', 1.0);
+        this.addTriple('existential_generalization', 'proves', 'existence', 0.9);
+    }
+    addTriple(subject, predicate, object, confidence = 1.0, metadata) {
+        const id = crypto.randomBytes(8).toString('hex');
+        const triple = {
+            subject: subject.toLowerCase(),
+            predicate: predicate.toLowerCase(),
+            object: object.toLowerCase(),
+            confidence,
+            metadata,
+            timestamp: Date.now()
+        };
+        this.triples.set(id, triple);
+        // Update indices
+        this.addToConceptIndex(triple.subject, id);
+        this.addToConceptIndex(triple.object, id);
+        this.addToPredicateIndex(triple.predicate, id);
+        return id;
+    }
+    addToConceptIndex(concept, tripleId) {
+        if (!this.concepts.has(concept)) {
+            this.concepts.set(concept, new Set());
+        }
+        this.concepts.get(concept).add(tripleId);
+    }
+    addToPredicateIndex(predicate, tripleId) {
+        if (!this.predicateIndex.has(predicate)) {
+            this.predicateIndex.set(predicate, new Set());
+        }
+        this.predicateIndex.get(predicate).add(tripleId);
+    }
+    findRelated(concept) {
+        const conceptLower = concept.toLowerCase();
+        const relatedIds = this.concepts.get(conceptLower) || new Set();
+        return Array.from(relatedIds).map(id => this.triples.get(id)).filter(Boolean);
+    }
+    findByPredicate(predicate) {
+        const predicateLower = predicate.toLowerCase();
+        const ids = this.predicateIndex.get(predicateLower) || new Set();
+        return Array.from(ids).map(id => this.triples.get(id)).filter(Boolean);
+    }
+    getAllTriples() {
+        return Array.from(this.triples.values());
+    }
+    query(sparqlLike) {
+        // Simple SPARQL-like query support
+        const results = [];
+        const queryLower = sparqlLike.toLowerCase();
+        for (const triple of this.triples.values()) {
+            if (queryLower.includes(triple.subject) ||
+                queryLower.includes(triple.predicate) ||
+                queryLower.includes(triple.object)) {
+                results.push(triple);
+            }
+        }
+        return results;
+    }
+}
+export class EnhancedPsychoSymbolicTools {
+    knowledgeBase;
+    reasoningCache = new Map();
+    constructor() {
+        this.knowledgeBase = new KnowledgeBase();
+    }
+    getTools() {
+        return [
+            {
+                name: 'psycho_symbolic_reason',
+                description: 'Perform deep psycho-symbolic reasoning with full inference',
+                inputSchema: {
+                    type: 'object',
+                    properties: {
+                        query: { type: 'string', description: 'The reasoning query' },
+                        context: { type: 'object', description: 'Additional context', default: {} },
+                        depth: { type: 'number', description: 'Reasoning depth', default: 5 }
+                    },
+                    required: ['query']
+                }
+            },
+            {
+                name: 'knowledge_graph_query',
+                description: 'Query the knowledge graph with semantic search',
+                inputSchema: {
+                    type: 'object',
+                    properties: {
+                        query: { type: 'string', description: 'Natural language or SPARQL-like query' },
+                        filters: { type: 'object', description: 'Filters', default: {} },
+                        limit: { type: 'number', description: 'Max results', default: 10 }
+                    },
+                    required: ['query']
+                }
+            },
+            {
+                name: 'add_knowledge',
+                description: 'Add knowledge triple to the graph',
+                inputSchema: {
+                    type: 'object',
+                    properties: {
+                        subject: { type: 'string' },
+                        predicate: { type: 'string' },
+                        object: { type: 'string' },
+                        confidence: { type: 'number', default: 1.0 },
+                        metadata: { type: 'object', default: {} }
+                    },
+                    required: ['subject', 'predicate', 'object']
+                }
+            }
+        ];
+    }
+    async handleToolCall(name, args) {
+        switch (name) {
+            case 'psycho_symbolic_reason':
+                return this.performDeepReasoning(args.query, args.context || {}, args.depth || 5);
+            case 'knowledge_graph_query':
+                return this.queryKnowledgeGraph(args.query, args.filters || {}, args.limit || 10);
+            case 'add_knowledge':
+                return this.addKnowledge(args.subject, args.predicate, args.object, args.confidence, args.metadata);
+            default:
+                throw new Error(`Unknown tool: ${name}`);
+        }
+    }
+    async performDeepReasoning(query, context, maxDepth) {
+        // Check cache
+        const cacheKey = `${query}_${JSON.stringify(context)}_${maxDepth}`;
+        if (this.reasoningCache.has(cacheKey)) {
+            return this.reasoningCache.get(cacheKey);
+        }
+        const reasoningSteps = [];
+        const insights = new Set();
+        // Step 1: Cognitive Pattern Analysis
+        const patterns = this.identifyCognitivePatterns(query);
+        reasoningSteps.push({
+            type: 'pattern_identification',
+            patterns,
+            confidence: 0.9,
+            description: `Identified ${patterns.join(', ')} reasoning patterns`
+        });
+        // Step 2: Entity and Concept Extraction
+        const entities = this.extractEntitiesAndConcepts(query);
+        reasoningSteps.push({
+            type: 'entity_extraction',
+            entities: entities.entities,
+            concepts: entities.concepts,
+            relationships: entities.relationships,
+            confidence: 0.85
+        });
+        // Step 3: Logical Component Analysis
+        const logicalComponents = this.extractLogicalComponents(query);
+        reasoningSteps.push({
+            type: 'logical_decomposition',
+            components: logicalComponents,
+            depth: 1,
+            description: 'Decomposed query into logical primitives'
+        });
+        // Step 4: Knowledge Graph Traversal
+        const graphInsights = await this.traverseKnowledgeGraph(entities.concepts, maxDepth);
+        reasoningSteps.push({
+            type: 'knowledge_traversal',
+            paths: graphInsights.paths,
+            discoveries: graphInsights.discoveries,
+            confidence: graphInsights.confidence
+        });
+        graphInsights.discoveries.forEach(d => insights.add(d));
+        // Step 5: Inference Chain Building
+        const inferences = this.buildInferenceChain(logicalComponents, graphInsights.triples, patterns);
+        reasoningSteps.push({
+            type: 'inference',
+            rules: inferences.rules,
+            conclusions: inferences.conclusions,
+            confidence: inferences.confidence
+        });
+        inferences.conclusions.forEach(c => insights.add(c));
+        // Step 6: Hypothesis Generation
+        if (patterns.includes('hypothetical') || patterns.includes('exploratory')) {
+            const hypotheses = this.generateHypotheses(entities.concepts, inferences.conclusions);
+            reasoningSteps.push({
+                type: 'hypothesis_generation',
+                hypotheses,
+                confidence: 0.7
+            });
+            hypotheses.forEach(h => insights.add(h));
+        }
+        // Step 7: Contradiction Detection and Resolution
+        const contradictions = this.detectContradictions(Array.from(insights));
+        if (contradictions.length > 0) {
+            const resolutions = this.resolveContradictions(contradictions, context);
+            reasoningSteps.push({
+                type: 'contradiction_resolution',
+                contradictions,
+                resolutions,
+                confidence: 0.8
+            });
+        }
+        // Step 8: Synthesis
+        const synthesis = this.synthesizeCompleteAnswer(query, Array.from(insights), reasoningSteps, patterns);
+        const result = {
+            answer: synthesis.answer,
+            confidence: synthesis.confidence,
+            reasoning: reasoningSteps,
+            insights: Array.from(insights),
+            patterns,
+            depth: graphInsights.maxDepth,
+            entities: entities.entities,
+            concepts: entities.concepts,
+            triples_examined: graphInsights.triples.length,
+            inference_rules_applied: inferences.rules.length
+        };
+        // Cache result
+        this.reasoningCache.set(cacheKey, result);
+        return result;
+    }
+    identifyCognitivePatterns(query) {
+        const patterns = [];
+        const lowerQuery = query.toLowerCase();
+        const patternMap = {
+            'causal': ['why', 'cause', 'because', 'result', 'effect', 'lead to'],
+            'procedural': ['how', 'process', 'step', 'method', 'way', 'approach'],
+            'hypothetical': ['what if', 'suppose', 'imagine', 'could', 'would', 'might'],
+            'comparative': ['compare', 'difference', 'similar', 'versus', 'than', 'like'],
+            'definitional': ['what is', 'define', 'meaning', 'definition'],
+            'evaluative': ['best', 'worst', 'better', 'optimal', 'evaluate'],
+            'temporal': ['when', 'time', 'before', 'after', 'during', 'temporal'],
+            'spatial': ['where', 'location', 'position', 'space'],
+            'quantitative': ['how many', 'how much', 'count', 'measure', 'amount'],
+            'existential': ['exist', 'there is', 'there are', 'presence'],
+            'universal': ['all', 'every', 'always', 'never', 'none']
+        };
+        for (const [pattern, keywords] of Object.entries(patternMap)) {
+            if (keywords.some(keyword => lowerQuery.includes(keyword))) {
+                patterns.push(pattern);
+            }
+        }
+        if (patterns.length === 0) {
+            patterns.push('exploratory');
+        }
+        return patterns;
+    }
+    extractEntitiesAndConcepts(query) {
+        const words = query.split(/\s+/);
+        const entities = [];
+        const concepts = [];
+        const relationships = [];
+        // Extract named entities (capitalized words not at sentence start)
+        for (let i = 1; i < words.length; i++) {
+            if (/^[A-Z]/.test(words[i]) && !['The', 'A', 'An'].includes(words[i])) {
+                entities.push(words[i].toLowerCase());
+            }
+        }
+        // Extract key concepts from knowledge base
+        const queryLower = query.toLowerCase();
+        for (const concept of this.knowledgeBase.getAllTriples().map(t => [t.subject, t.object]).flat()) {
+            if (queryLower.includes(concept)) {
+                concepts.push(concept);
+            }
+        }
+        // Extract relationships (verbs and prepositions)
+        const relationshipPatterns = [
+            'is', 'are', 'was', 'were', 'has', 'have', 'had',
+            'can', 'could', 'will', 'would', 'should',
+            'emerges', 'requires', 'enables', 'causes', 'prevents',
+            'increases', 'decreases', 'affects', 'influences'
+        ];
+        for (const word of words) {
+            const wordLower = word.toLowerCase();
+            if (relationshipPatterns.includes(wordLower)) {
+                relationships.push(wordLower);
+            }
+        }
+        // Add query-specific concepts
+        if (queryLower.includes('consciousness'))
+            concepts.push('consciousness');
+        if (queryLower.includes('neural'))
+            concepts.push('neural_networks');
+        if (queryLower.includes('temporal'))
+            concepts.push('temporal_processing');
+        if (queryLower.includes('phi') || queryLower.includes('φ'))
+            concepts.push('phi_value');
+        return {
+            entities: [...new Set(entities)],
+            concepts: [...new Set(concepts)],
+            relationships: [...new Set(relationships)]
+        };
+    }
+    extractLogicalComponents(query) {
+        const components = {
+            predicates: [],
+            quantifiers: [],
+            operators: [],
+            modals: [],
+            negations: []
+        };
+        const lowerQuery = query.toLowerCase();
+        // Extract predicates (subject-verb-object patterns)
+        const predicateMatches = lowerQuery.match(/(\w+)\s+(is|are|was|were|has|have|had)\s+(\w+)/g);
+        if (predicateMatches) {
+            components.predicates = predicateMatches.map(p => p.trim());
+        }
+        // Extract quantifiers
+        const quantifierPattern = /\b(all|every|some|any|no|none|many|few|most|several)\b/gi;
+        const quantifierMatches = lowerQuery.match(quantifierPattern);
+        if (quantifierMatches) {
+            components.quantifiers = quantifierMatches;
+        }
+        // Extract logical operators
+        const operatorPattern = /\b(and|or|not|if|then|implies|therefore|because|but|however)\b/gi;
+        const operatorMatches = lowerQuery.match(operatorPattern);
+        if (operatorMatches) {
+            components.operators = operatorMatches;
+        }
+        // Extract modal verbs
+        const modalPattern = /\b(can|could|may|might|must|shall|should|will|would)\b/gi;
+        const modalMatches = lowerQuery.match(modalPattern);
+        if (modalMatches) {
+            components.modals = modalMatches;
+        }
+        // Extract negations
+        const negationPattern = /\b(not|no|never|neither|nor|nothing|nobody|nowhere)\b/gi;
+        const negationMatches = lowerQuery.match(negationPattern);
+        if (negationMatches) {
+            components.negations = negationMatches;
+        }
+        return components;
+    }
+    async traverseKnowledgeGraph(concepts, maxDepth) {
+        const visited = new Set();
+        const paths = [];
+        const discoveries = [];
+        const triples = [];
+        let currentDepth = 0;
+        let maxConfidence = 0;
+        // BFS traversal
+        const queue = concepts.map(c => ({
+            concept: c,
+            depth: 0,
+            confidence: 1.0,
+            path: [c],
+            inferences: []
+        }));
+        while (queue.length > 0 && currentDepth < maxDepth) {
+            const node = queue.shift();
+            if (visited.has(node.concept))
+                continue;
+            visited.add(node.concept);
+            currentDepth = Math.max(currentDepth, node.depth);
+            paths.push(node.path);
+            // Find related triples
+            const related = this.knowledgeBase.findRelated(node.concept);
+            triples.push(...related);
+            for (const triple of related) {
+                // Generate discoveries
+                const discovery = `${triple.subject} ${triple.predicate} ${triple.object}`;
+                discoveries.push(discovery);
+                maxConfidence = Math.max(maxConfidence, triple.confidence * node.confidence);
+                // Add connected concepts to queue
+                const nextConcept = triple.subject === node.concept ? triple.object : triple.subject;
+                if (!visited.has(nextConcept) && node.depth < maxDepth - 1) {
+                    queue.push({
+                        concept: nextConcept,
+                        depth: node.depth + 1,
+                        confidence: node.confidence * triple.confidence,
+                        path: [...node.path, nextConcept],
+                        inferences: [...node.inferences, discovery]
+                    });
+                }
+            }
+        }
+        return {
+            paths,
+            discoveries: discoveries.slice(0, 20), // Limit discoveries
+            triples,
+            maxDepth: currentDepth,
+            confidence: maxConfidence
+        };
+    }
+    buildInferenceChain(logicalComponents, triples, patterns) {
+        const rules = [];
+        const conclusions = [];
+        let confidence = 0.5;
+        // Apply Modus Ponens
+        if (logicalComponents.operators.includes('if') || logicalComponents.operators.includes('then')) {
+            rules.push('modus_ponens');
+            // Find implications in triples
+            for (const triple of triples) {
+                if (triple.predicate === 'implies' || triple.predicate === 'causes' || triple.predicate === 'enables') {
+                    conclusions.push(`${triple.subject} leads to ${triple.object}`);
+                    confidence = Math.max(confidence, triple.confidence * 0.9);
+                }
+            }
+        }
+        // Apply Universal Instantiation
+        if (logicalComponents.quantifiers.some((q) => ['all', 'every'].includes(q))) {
+            rules.push('universal_instantiation');
+            conclusions.push('universal property applies to specific instances');
+            confidence = Math.max(confidence, 0.85);
+        }
+        // Apply Existential Generalization
+        if (logicalComponents.quantifiers.some((q) => ['some', 'exist'].includes(q))) {
+            rules.push('existential_generalization');
+            conclusions.push('at least one instance exists with the property');
+            confidence = Math.max(confidence, 0.8);
+        }
+        // Apply Transitive Property
+        const transitivePredicates = ['causes', 'enables', 'requires', 'leads_to'];
+        const transitiveChains = this.findTransitiveChains(triples, transitivePredicates);
+        if (transitiveChains.length > 0) {
+            rules.push('transitive_property');
+            transitiveChains.forEach(chain => {
+                conclusions.push(`${chain.start} transitively ${chain.predicate} ${chain.end}`);
+            });
+            confidence = Math.max(confidence, 0.75);
+        }
+        // Apply Pattern-Specific Rules
+        if (patterns.includes('causal')) {
+            rules.push('causal_chain_analysis');
+            const causalChains = triples.filter(t => ['causes', 'results_in', 'leads_to', 'produces'].includes(t.predicate));
+            causalChains.forEach(chain => {
+                conclusions.push(`causal relationship: ${chain.subject} → ${chain.object}`);
+            });
+        }
+        if (patterns.includes('temporal')) {
+            rules.push('temporal_ordering');
+            conclusions.push('events ordered by temporal precedence');
+        }
+        // Generate domain-specific conclusions
+        if (triples.some(t => t.subject.includes('consciousness') || t.object.includes('consciousness'))) {
+            conclusions.push('consciousness emerges from integrated information processing');
+            conclusions.push('phi value indicates level of consciousness');
+            confidence = Math.max(confidence, 0.85);
+        }
+        if (triples.some(t => t.subject.includes('neural') || t.object.includes('neural'))) {
+            conclusions.push('neural networks enable learning through weight modification');
+            conclusions.push('plasticity allows adaptive behavior');
+            confidence = Math.max(confidence, 0.9);
+        }
+        return {
+            rules,
+            conclusions,
+            confidence
+        };
+    }
+    findTransitiveChains(triples, predicates) {
+        const chains = [];
+        for (const predicate of predicates) {
+            const relevantTriples = triples.filter(t => t.predicate === predicate);
+            for (let i = 0; i < relevantTriples.length; i++) {
+                for (let j = 0; j < relevantTriples.length; j++) {
+                    if (relevantTriples[i].object === relevantTriples[j].subject) {
+                        chains.push({
+                            start: relevantTriples[i].subject,
+                            middle: relevantTriples[i].object,
+                            end: relevantTriples[j].object,
+                            predicate
+                        });
+                    }
+                }
+            }
+        }
+        return chains;
+    }
+    generateHypotheses(concepts, conclusions) {
+        const hypotheses = [];
+        // Generate hypotheses based on concept combinations
+        for (let i = 0; i < concepts.length; i++) {
+            for (let j = i + 1; j < concepts.length; j++) {
+                hypotheses.push(`hypothesis: ${concepts[i]} might be related to ${concepts[j]}`);
+            }
+        }
+        // Generate hypotheses from conclusions
+        for (const conclusion of conclusions) {
+            if (conclusion.includes('leads to') || conclusion.includes('causes')) {
+                hypotheses.push(`hypothesis: reversing ${conclusion} might have opposite effect`);
+            }
+        }
+        // Domain-specific hypotheses
+        if (concepts.includes('consciousness')) {
+            hypotheses.push('hypothesis: higher phi values correlate with greater self-awareness');
+            hypotheses.push('hypothesis: consciousness requires minimum integration threshold');
+        }
+        if (concepts.includes('temporal_processing')) {
+            hypotheses.push('hypothesis: temporal advantage enables predictive processing');
+            hypotheses.push('hypothesis: nanosecond precision allows quantum-like effects');
+        }
+        return hypotheses.slice(0, 5); // Limit hypotheses
+    }
+    detectContradictions(statements) {
+        const contradictions = [];
+        for (let i = 0; i < statements.length; i++) {
+            for (let j = i + 1; j < statements.length; j++) {
+                // Check for direct negation
+                if (statements[i].includes('not') && statements[j] === statements[i].replace('not ', '')) {
+                    contradictions.push({
+                        type: 'direct_negation',
+                        statement1: statements[i],
+                        statement2: statements[j]
+                    });
+                }
+                // Check for semantic opposition
+                const opposites = [
+                    ['increases', 'decreases'],
+                    ['enables', 'prevents'],
+                    ['causes', 'prevents'],
+                    ['always', 'never'],
+                    ['all', 'none']
+                ];
+                for (const [word1, word2] of opposites) {
+                    if ((statements[i].includes(word1) && statements[j].includes(word2)) ||
+                        (statements[i].includes(word2) && statements[j].includes(word1))) {
+                        contradictions.push({
+                            type: 'semantic_opposition',
+                            statement1: statements[i],
+                            statement2: statements[j],
+                            conflict: [word1, word2]
+                        });
+                    }
+                }
+            }
+        }
+        return contradictions;
+    }
+    resolveContradictions(contradictions, context) {
+        return contradictions.map(c => ({
+            original: c,
+            resolution: 'resolved through context disambiguation',
+            method: c.type === 'direct_negation' ? 'logical_priority' : 'semantic_analysis',
+            confidence: 0.7
+        }));
+    }
+    synthesizeCompleteAnswer(query, insights, steps, patterns) {
+        let confidence = 0.5;
+        const keyInsights = insights.slice(0, 5);
+        // Calculate confidence from reasoning steps
+        for (const step of steps) {
+            if (step.confidence) {
+                confidence = Math.max(confidence, step.confidence * 0.9);
+            }
+        }
+        // Build comprehensive answer
+        let answer = '';
+        if (patterns.includes('causal')) {
+            answer = `Based on causal analysis: ${keyInsights.join(' → ')}. `;
+        }
+        else if (patterns.includes('procedural')) {
+            answer = `The process involves: ${keyInsights.join(', then ')}. `;
+        }
+        else if (patterns.includes('comparative')) {
+            answer = `Comparison reveals: ${keyInsights.join(' versus ')}. `;
+        }
+        else if (patterns.includes('hypothetical')) {
+            answer = `Hypothetically: ${keyInsights.join(', additionally ')}. `;
+        }
+        else {
+            answer = `Analysis shows: ${keyInsights.join('. ')}. `;
+        }
+        // Add reasoning depth
+        answer += `This conclusion is based on ${steps.length} reasoning steps`;
+        // Add confidence qualifier
+        if (confidence > 0.9) {
+            answer += ' with very high confidence';
+        }
+        else if (confidence > 0.7) {
+            answer += ' with high confidence';
+        }
+        else if (confidence > 0.5) {
+            answer += ' with moderate confidence';
+        }
+        else {
+            answer += ' with exploratory confidence';
+        }
+        answer += '.';
+        return {
+            answer,
+            confidence,
+            keyInsights
+        };
+    }
+    async queryKnowledgeGraph(query, filters, limit) {
+        const results = this.knowledgeBase.query(query);
+        // Apply filters
+        let filtered = results;
+        if (filters.confidence) {
+            filtered = filtered.filter(t => t.confidence >= filters.confidence);
+        }
+        if (filters.predicate) {
+            filtered = filtered.filter(t => t.predicate === filters.predicate.toLowerCase());
+        }
+        // Sort by confidence
+        filtered.sort((a, b) => b.confidence - a.confidence);
+        // Limit results
+        const limited = filtered.slice(0, limit);
+        return {
+            query,
+            results: limited.map(t => ({
+                subject: t.subject,
+                predicate: t.predicate,
+                object: t.object,
+                confidence: t.confidence,
+                metadata: t.metadata
+            })),
+            total: limited.length,
+            totalAvailable: filtered.length
+        };
+    }
+    async addKnowledge(subject, predicate, object, confidence = 1.0, metadata = {}) {
+        const id = this.knowledgeBase.addTriple(subject, predicate, object, confidence, metadata);
+        return {
+            id,
+            status: 'added',
+            triple: {
+                subject: subject.toLowerCase(),
+                predicate: predicate.toLowerCase(),
+                object: object.toLowerCase(),
+                confidence
+            }
+        };
+    }
+}
+export default EnhancedPsychoSymbolicTools;

vendor/sublinear-time-solver/dist/mcp/tools/psycho-symbolic-fixed.d.ts

@@ -0,0 +1,30 @@
+/**
+ * Enhanced Psycho-Symbolic Reasoning MCP Tools
+ * Full implementation with domain-agnostic reasoning and fallback mechanisms
+ */
+import { Tool } from '@modelcontextprotocol/sdk/types.js';
+export declare class PsychoSymbolicTools {
+    private knowledgeBase;
+    private reasoningCache;
+    constructor();
+    getTools(): Tool[];
+    handleToolCall(name: string, args: any): Promise<any>;
+    private performDeepReasoning;
+    private generateDomainInsights;
+    private applyContextualReasoning;
+    private analyzeEdgeCases;
+    private identifyCognitivePatterns;
+    private extractEntitiesAndConcepts;
+    private extractLogicalComponents;
+    private traverseKnowledgeGraph;
+    private buildInferenceChain;
+    private findTransitiveChains;
+    private generateHypotheses;
+    private detectContradictions;
+    private resolveContradictions;
+    private synthesizeCompleteAnswer;
+    private generateDefaultInsights;
+    private queryKnowledgeGraph;
+    private addKnowledge;
+}
+export default PsychoSymbolicTools;

vendor/sublinear-time-solver/dist/mcp/tools/psycho-symbolic-fixed.js

@@ -0,0 +1,872 @@
+/**
+ * Enhanced Psycho-Symbolic Reasoning MCP Tools
+ * Full implementation with domain-agnostic reasoning and fallback mechanisms
+ */
+import * as crypto from 'crypto';
+// Initialize with base knowledge
+class KnowledgeBase {
+    triples = new Map();
+    concepts = new Map(); // concept -> related triple IDs
+    predicateIndex = new Map(); // predicate -> triple IDs
+    constructor() {
+        this.initializeBaseKnowledge();
+    }
+    initializeBaseKnowledge() {
+        // Core AI/consciousness knowledge
+        this.addTriple('consciousness', 'emerges_from', 'neural_networks', 0.85);
+        this.addTriple('consciousness', 'requires', 'integration', 0.9);
+        this.addTriple('consciousness', 'exhibits', 'phi_value', 0.95);
+        this.addTriple('neural_networks', 'process', 'information', 1.0);
+        this.addTriple('neural_networks', 'contain', 'neurons', 1.0);
+        this.addTriple('neurons', 'connect_via', 'synapses', 1.0);
+        this.addTriple('synapses', 'enable', 'plasticity', 0.9);
+        this.addTriple('plasticity', 'allows', 'learning', 0.95);
+        this.addTriple('learning', 'modifies', 'weights', 1.0);
+        this.addTriple('phi_value', 'measures', 'integrated_information', 1.0);
+        this.addTriple('integrated_information', 'indicates', 'consciousness_level', 0.8);
+        // Temporal/computational knowledge
+        this.addTriple('temporal_processing', 'enables', 'prediction', 0.9);
+        this.addTriple('prediction', 'requires', 'pattern_recognition', 0.85);
+        this.addTriple('pattern_recognition', 'uses', 'neural_networks', 0.9);
+        this.addTriple('sublinear_algorithms', 'achieve', 'logarithmic_complexity', 1.0);
+        this.addTriple('logarithmic_complexity', 'beats', 'polynomial_complexity', 1.0);
+        this.addTriple('nanosecond_scheduling', 'enables', 'temporal_advantage', 0.95);
+        this.addTriple('temporal_advantage', 'allows', 'faster_than_light_computation', 0.9);
+        // Software engineering principles
+        this.addTriple('api_design', 'requires', 'consistency', 0.95);
+        this.addTriple('api_design', 'benefits_from', 'versioning', 0.9);
+        this.addTriple('rest_api', 'uses', 'http_methods', 1.0);
+        this.addTriple('rest_api', 'follows', 'stateless_principle', 0.95);
+        this.addTriple('user_management', 'requires', 'authentication', 1.0);
+        this.addTriple('user_management', 'requires', 'authorization', 1.0);
+        this.addTriple('authentication', 'validates', 'identity', 1.0);
+        this.addTriple('authorization', 'controls', 'access', 1.0);
+        this.addTriple('security', 'prevents', 'vulnerabilities', 0.9);
+        this.addTriple('rate_limiting', 'prevents', 'abuse', 0.95);
+        this.addTriple('caching', 'improves', 'performance', 0.9);
+        this.addTriple('pagination', 'handles', 'large_datasets', 0.95);
+        // System design principles
+        this.addTriple('distributed_systems', 'face', 'consistency_challenges', 0.95);
+        this.addTriple('microservices', 'require', 'service_discovery', 0.9);
+        this.addTriple('scalability', 'requires', 'horizontal_scaling', 0.85);
+        this.addTriple('reliability', 'requires', 'redundancy', 0.9);
+        this.addTriple('monitoring', 'enables', 'observability', 0.95);
+        // Reasoning patterns
+        this.addTriple('causal_reasoning', 'identifies', 'cause_effect', 1.0);
+        this.addTriple('procedural_reasoning', 'describes', 'processes', 1.0);
+        this.addTriple('hypothetical_reasoning', 'explores', 'possibilities', 1.0);
+        this.addTriple('comparative_reasoning', 'analyzes', 'differences', 1.0);
+        this.addTriple('abstract_reasoning', 'generalizes', 'concepts', 0.95);
+        this.addTriple('lateral_thinking', 'finds', 'unconventional_solutions', 0.9);
+        this.addTriple('systems_thinking', 'considers', 'interactions', 0.95);
+        // Logic rules
+        this.addTriple('modus_ponens', 'validates', 'implications', 1.0);
+        this.addTriple('universal_instantiation', 'applies_to', 'specific_cases', 1.0);
+        this.addTriple('existential_generalization', 'proves', 'existence', 0.9);
+    }
+    addTriple(subject, predicate, object, confidence = 1.0, metadata) {
+        const id = crypto.randomBytes(8).toString('hex');
+        const triple = {
+            subject: subject.toLowerCase(),
+            predicate: predicate.toLowerCase(),
+            object: object.toLowerCase(),
+            confidence,
+            metadata,
+            timestamp: Date.now()
+        };
+        this.triples.set(id, triple);
+        // Update indices
+        this.addToConceptIndex(triple.subject, id);
+        this.addToConceptIndex(triple.object, id);
+        this.addToPredicateIndex(triple.predicate, id);
+        return id;
+    }
+    addToConceptIndex(concept, tripleId) {
+        if (!this.concepts.has(concept)) {
+            this.concepts.set(concept, new Set());
+        }
+        this.concepts.get(concept).add(tripleId);
+    }
+    addToPredicateIndex(predicate, tripleId) {
+        if (!this.predicateIndex.has(predicate)) {
+            this.predicateIndex.set(predicate, new Set());
+        }
+        this.predicateIndex.get(predicate).add(tripleId);
+    }
+    findRelated(concept) {
+        const conceptLower = concept.toLowerCase();
+        const relatedIds = this.concepts.get(conceptLower) || new Set();
+        return Array.from(relatedIds).map(id => this.triples.get(id)).filter(Boolean);
+    }
+    findByPredicate(predicate) {
+        const predicateLower = predicate.toLowerCase();
+        const ids = this.predicateIndex.get(predicateLower) || new Set();
+        return Array.from(ids).map(id => this.triples.get(id)).filter(Boolean);
+    }
+    getAllTriples() {
+        return Array.from(this.triples.values());
+    }
+    query(sparqlLike) {
+        // Simple SPARQL-like query support
+        const results = [];
+        const queryLower = sparqlLike.toLowerCase();
+        for (const triple of this.triples.values()) {
+            if (queryLower.includes(triple.subject) ||
+                queryLower.includes(triple.predicate) ||
+                queryLower.includes(triple.object)) {
+                results.push(triple);
+            }
+        }
+        return results;
+    }
+}
+export class PsychoSymbolicTools {
+    knowledgeBase;
+    reasoningCache = new Map();
+    constructor() {
+        this.knowledgeBase = new KnowledgeBase();
+    }
+    getTools() {
+        return [
+            {
+                name: 'psycho_symbolic_reason',
+                description: 'Perform deep psycho-symbolic reasoning with full inference',
+                inputSchema: {
+                    type: 'object',
+                    properties: {
+                        query: { type: 'string', description: 'The reasoning query' },
+                        context: { type: 'object', description: 'Additional context', default: {} },
+                        depth: { type: 'number', description: 'Reasoning depth', default: 5 }
+                    },
+                    required: ['query']
+                }
+            },
+            {
+                name: 'knowledge_graph_query',
+                description: 'Query the knowledge graph with semantic search',
+                inputSchema: {
+                    type: 'object',
+                    properties: {
+                        query: { type: 'string', description: 'Natural language or SPARQL-like query' },
+                        filters: { type: 'object', description: 'Filters', default: {} },
+                        limit: { type: 'number', description: 'Max results', default: 10 }
+                    },
+                    required: ['query']
+                }
+            },
+            {
+                name: 'add_knowledge',
+                description: 'Add knowledge triple to the graph',
+                inputSchema: {
+                    type: 'object',
+                    properties: {
+                        subject: { type: 'string' },
+                        predicate: { type: 'string' },
+                        object: { type: 'string' },
+                        confidence: { type: 'number', default: 1.0 },
+                        metadata: { type: 'object', default: {} }
+                    },
+                    required: ['subject', 'predicate', 'object']
+                }
+            }
+        ];
+    }
+    async handleToolCall(name, args) {
+        switch (name) {
+            case 'psycho_symbolic_reason':
+                return this.performDeepReasoning(args.query, args.context || {}, args.depth || 5);
+            case 'knowledge_graph_query':
+                return this.queryKnowledgeGraph(args.query, args.filters || {}, args.limit || 10);
+            case 'add_knowledge':
+                return this.addKnowledge(args.subject, args.predicate, args.object, args.confidence, args.metadata);
+            default:
+                throw new Error(`Unknown tool: ${name}`);
+        }
+    }
+    async performDeepReasoning(query, context, maxDepth) {
+        // Check cache
+        const cacheKey = `${query}_${JSON.stringify(context)}_${maxDepth}`;
+        if (this.reasoningCache.has(cacheKey)) {
+            return this.reasoningCache.get(cacheKey);
+        }
+        const reasoningSteps = [];
+        const insights = new Set();
+        // Step 1: Cognitive Pattern Analysis
+        const patterns = this.identifyCognitivePatterns(query);
+        reasoningSteps.push({
+            type: 'pattern_identification',
+            patterns,
+            confidence: 0.9,
+            description: `Identified ${patterns.join(', ')} reasoning patterns`
+        });
+        // Step 2: Entity and Concept Extraction
+        const entities = this.extractEntitiesAndConcepts(query);
+        reasoningSteps.push({
+            type: 'entity_extraction',
+            entities: entities.entities,
+            concepts: entities.concepts,
+            relationships: entities.relationships,
+            confidence: 0.85
+        });
+        // Step 3: Domain-Specific Insight Generation
+        const domainInsights = this.generateDomainInsights(query, patterns, context);
+        domainInsights.forEach(insight => insights.add(insight));
+        reasoningSteps.push({
+            type: 'domain_analysis',
+            insights: domainInsights,
+            confidence: 0.8,
+            description: 'Generated domain-specific insights'
+        });
+        // Step 4: Logical Component Analysis
+        const logicalComponents = this.extractLogicalComponents(query);
+        reasoningSteps.push({
+            type: 'logical_decomposition',
+            components: logicalComponents,
+            depth: 1,
+            description: 'Decomposed query into logical primitives'
+        });
+        // Step 5: Knowledge Graph Traversal
+        const graphInsights = await this.traverseKnowledgeGraph(entities.concepts, maxDepth);
+        reasoningSteps.push({
+            type: 'knowledge_traversal',
+            paths: graphInsights.paths,
+            discoveries: graphInsights.discoveries,
+            confidence: graphInsights.confidence
+        });
+        graphInsights.discoveries.forEach(d => insights.add(d));
+        // Step 6: Inference Chain Building
+        const inferences = this.buildInferenceChain(logicalComponents, graphInsights.triples, patterns);
+        reasoningSteps.push({
+            type: 'inference',
+            rules: inferences.rules,
+            conclusions: inferences.conclusions,
+            confidence: inferences.confidence
+        });
+        inferences.conclusions.forEach(c => insights.add(c));
+        // Step 7: Context-Aware Reasoning
+        if (context && Object.keys(context).length > 0) {
+            const contextInsights = this.applyContextualReasoning(query, context, patterns);
+            contextInsights.forEach(ci => insights.add(ci));
+            reasoningSteps.push({
+                type: 'contextual_reasoning',
+                insights: contextInsights,
+                confidence: 0.75
+            });
+        }
+        // Step 8: Hypothesis Generation
+        if (patterns.includes('hypothetical') || patterns.includes('exploratory') || patterns.includes('lateral')) {
+            const hypotheses = this.generateHypotheses(entities.concepts, inferences.conclusions);
+            reasoningSteps.push({
+                type: 'hypothesis_generation',
+                hypotheses,
+                confidence: 0.7
+            });
+            hypotheses.forEach(h => insights.add(h));
+        }
+        // Step 9: Edge Case Analysis (for API/system design queries)
+        if (query.toLowerCase().includes('edge case') || query.toLowerCase().includes('hidden') ||
+            context.focus === 'hidden_complexities') {
+            const edgeCases = this.analyzeEdgeCases(query, entities.concepts);
+            edgeCases.forEach(ec => insights.add(ec));
+            reasoningSteps.push({
+                type: 'edge_case_analysis',
+                cases: edgeCases,
+                confidence: 0.8
+            });
+        }
+        // Step 10: Contradiction Detection and Resolution
+        const contradictions = this.detectContradictions(Array.from(insights));
+        if (contradictions.length > 0) {
+            const resolutions = this.resolveContradictions(contradictions, context);
+            reasoningSteps.push({
+                type: 'contradiction_resolution',
+                contradictions,
+                resolutions,
+                confidence: 0.8
+            });
+        }
+        // Step 11: Synthesis
+        const synthesis = this.synthesizeCompleteAnswer(query, Array.from(insights), reasoningSteps, patterns, context);
+        const result = {
+            answer: synthesis.answer,
+            confidence: synthesis.confidence,
+            reasoning: reasoningSteps,
+            insights: Array.from(insights),
+            patterns,
+            depth: graphInsights.maxDepth || maxDepth,
+            entities: entities.entities,
+            concepts: entities.concepts,
+            triples_examined: graphInsights.triples.length,
+            inference_rules_applied: inferences.rules.length
+        };
+        // Cache result
+        this.reasoningCache.set(cacheKey, result);
+        return result;
+    }
+    generateDomainInsights(query, patterns, context) {
+        const insights = [];
+        const queryLower = query.toLowerCase();
+        // API Design Insights
+        if (queryLower.includes('api') || queryLower.includes('rest') || context.domain === 'api_design') {
+            insights.push('Consider idempotency for all mutating operations to handle network retries');
+            insights.push('Implement versioning strategy from day one - URL, header, or content negotiation');
+            insights.push('Rate limiting should be granular - per user, per endpoint, and per operation type');
+            insights.push('CORS configuration often breaks in production - test with actual domain names');
+            insights.push('Bulk operations need careful transaction boundary management');
+            if (queryLower.includes('user')) {
+                insights.push('User deletion must handle cascading data relationships and GDPR compliance');
+                insights.push('Password reset flows are prime targets for timing attacks');
+                insights.push('Session management across devices requires careful token invalidation');
+                insights.push('Email verification tokens should expire and be single-use');
+            }
+        }
+        // Hidden Complexities
+        if (queryLower.includes('hidden') || queryLower.includes('non-obvious') || queryLower.includes('edge')) {
+            insights.push('Race conditions in concurrent user updates - last write wins vs merge conflicts');
+            insights.push('Time zone handling - server, client, and user preference mismatches');
+            insights.push('Pagination breaks when underlying data changes during traversal');
+            insights.push('Cache invalidation cascades in microservice architectures');
+            insights.push('OAuth token refresh race conditions in distributed systems');
+            insights.push('Database connection pool exhaustion under spike load');
+            insights.push('Unicode normalization issues in usernames and passwords');
+            insights.push('Integer overflow in ID generation at scale');
+        }
+        // Lateral Thinking Insights
+        if (patterns.includes('lateral') || context.pattern === 'lateral') {
+            insights.push('Consider using event sourcing for audit trail instead of traditional logging');
+            insights.push('GraphQL might solve over-fetching better than REST for complex relationships');
+            insights.push('WebSockets for real-time user presence instead of polling');
+            insights.push('JWT claims can carry authorization context to reduce database lookups');
+            insights.push('Use bloom filters for username availability checks at scale');
+            insights.push('Implement soft deletes with temporal tables for compliance');
+            insights.push('Consider CQRS for read-heavy user profile access patterns');
+        }
+        // System Interaction Complexities
+        if (queryLower.includes('system') || queryLower.includes('interaction')) {
+            insights.push('Load balancer health checks can trigger false circuit breaker opens');
+            insights.push('CDN cache can serve stale authentication states');
+            insights.push('Database read replicas lag can cause phantom user creation failures');
+            insights.push('Message queue failures can orphan user records');
+            insights.push('Service mesh retry policies can amplify failures');
+            insights.push('Distributed tracing overhead affects latency measurements');
+        }
+        // Security Considerations
+        if (queryLower.includes('security') || queryLower.includes('user')) {
+            insights.push('Timing attacks on user enumeration through login response times');
+            insights.push('JWT secret rotation without service disruption');
+            insights.push('Password history storage needs separate encryption');
+            insights.push('Account takeover protection via behavioral analysis');
+            insights.push('API key rotation mechanisms for service accounts');
+        }
+        return insights;
+    }
+    applyContextualReasoning(query, context, patterns) {
+        const insights = [];
+        if (context.focus === 'hidden_complexities') {
+            insights.push('Hidden complexity: Distributed consensus for user state changes');
+            insights.push('Hidden complexity: Eventual consistency in user search indices');
+            insights.push('Hidden complexity: GDPR data portability implementation details');
+            insights.push('Hidden complexity: Cross-region data replication latency');
+        }
+        if (context.pattern === 'lateral') {
+            insights.push('Lateral solution: Use blockchain for decentralized identity verification');
+            insights.push('Lateral solution: Implement passwordless auth via magic links');
+            insights.push('Lateral solution: Use ML for anomaly detection in access patterns');
+            insights.push('Lateral solution: Federated user management across microservices');
+        }
+        if (context.domain === 'api_design') {
+            insights.push('API consideration: Hypermedia controls for self-documenting endpoints');
+            insights.push('API consideration: GraphQL subscriptions for real-time updates');
+            insights.push('API consideration: OpenAPI spec generation from code');
+            insights.push('API consideration: Request/response compression strategies');
+        }
+        return insights;
+    }
+    analyzeEdgeCases(query, concepts) {
+        const edgeCases = [];
+        // Universal edge cases
+        edgeCases.push('Edge case: Null, undefined, and empty string handling differences');
+        edgeCases.push('Edge case: Maximum length inputs causing buffer overflows');
+        edgeCases.push('Edge case: Concurrent modifications to the same resource');
+        edgeCases.push('Edge case: Clock skew between distributed components');
+        // API-specific edge cases
+        if (concepts.includes('api') || concepts.includes('rest')) {
+            edgeCases.push('Edge case: Partial success in batch operations');
+            edgeCases.push('Edge case: Request timeout during long-running operations');
+            edgeCases.push('Edge case: Content-Type mismatches with actual payload');
+            edgeCases.push('Edge case: HTTP/2 multiplexing affecting rate limits');
+        }
+        // User management edge cases
+        if (concepts.includes('user') || concepts.includes('authentication')) {
+            edgeCases.push('Edge case: User creation with recycled email addresses');
+            edgeCases.push('Edge case: Session fixation during concurrent logins');
+            edgeCases.push('Edge case: Account merge conflicts with OAuth providers');
+            edgeCases.push('Edge case: Birthday paradox in random token generation');
+        }
+        return edgeCases;
+    }
+    identifyCognitivePatterns(query) {
+        const patterns = [];
+        const lowerQuery = query.toLowerCase();
+        const patternMap = {
+            'causal': ['why', 'cause', 'because', 'result', 'effect', 'lead to'],
+            'procedural': ['how', 'process', 'step', 'method', 'way', 'approach', 'design', 'implement'],
+            'hypothetical': ['what if', 'suppose', 'imagine', 'could', 'would', 'might'],
+            'comparative': ['compare', 'difference', 'similar', 'versus', 'than', 'like'],
+            'definitional': ['what is', 'define', 'meaning', 'definition'],
+            'evaluative': ['best', 'worst', 'better', 'optimal', 'evaluate'],
+            'temporal': ['when', 'time', 'before', 'after', 'during', 'temporal'],
+            'spatial': ['where', 'location', 'position', 'space'],
+            'quantitative': ['how many', 'how much', 'count', 'measure', 'amount'],
+            'existential': ['exist', 'there is', 'there are', 'presence'],
+            'universal': ['all', 'every', 'always', 'never', 'none'],
+            'lateral': ['lateral', 'unconventional', 'creative', 'alternative', 'non-obvious', 'hidden'],
+            'systems': ['system', 'interaction', 'complexity', 'emergence', 'holistic'],
+            'exploratory': ['explore', 'discover', 'investigate', 'consider', 'edge case']
+        };
+        for (const [pattern, keywords] of Object.entries(patternMap)) {
+            if (keywords.some(keyword => lowerQuery.includes(keyword))) {
+                patterns.push(pattern);
+            }
+        }
+        if (patterns.length === 0) {
+            patterns.push('exploratory');
+        }
+        return patterns;
+    }
+    extractEntitiesAndConcepts(query) {
+        const words = query.split(/\s+/);
+        const entities = [];
+        const concepts = [];
+        const relationships = [];
+        // Extract technical terms and concepts
+        const technicalTerms = [
+            'api', 'rest', 'graphql', 'user', 'management', 'authentication',
+            'authorization', 'database', 'cache', 'security', 'performance',
+            'scalability', 'microservice', 'distributed', 'system', 'design',
+            'endpoint', 'resource', 'crud', 'http', 'json', 'xml', 'oauth',
+            'jwt', 'session', 'token', 'password', 'encryption', 'hash'
+        ];
+        // Extract named entities (capitalized words not at sentence start)
+        for (let i = 0; i < words.length; i++) {
+            const word = words[i];
+            const wordLower = word.toLowerCase();
+            if (/^[A-Z]/.test(word) && i > 0 && !['The', 'A', 'An', 'What', 'How', 'Why', 'When', 'Where'].includes(word)) {
+                entities.push(wordLower);
+            }
+            if (technicalTerms.includes(wordLower)) {
+                concepts.push(wordLower);
+            }
+        }
+        // Extract key concepts from knowledge base
+        const queryLower = query.toLowerCase();
+        for (const concept of this.knowledgeBase.getAllTriples().map(t => [t.subject, t.object]).flat()) {
+            if (queryLower.includes(concept)) {
+                concepts.push(concept);
+            }
+        }
+        // Extract relationships (verbs and prepositions)
+        const relationshipPatterns = [
+            'is', 'are', 'was', 'were', 'has', 'have', 'had',
+            'can', 'could', 'will', 'would', 'should',
+            'design', 'implement', 'create', 'build', 'develop',
+            'requires', 'needs', 'uses', 'enables', 'prevents',
+            'increases', 'decreases', 'affects', 'influences'
+        ];
+        for (const word of words) {
+            const wordLower = word.toLowerCase();
+            if (relationshipPatterns.includes(wordLower)) {
+                relationships.push(wordLower);
+            }
+        }
+        // Add query-specific concepts
+        if (queryLower.includes('edge case'))
+            concepts.push('edge_cases');
+        if (queryLower.includes('hidden'))
+            concepts.push('hidden_complexity');
+        if (queryLower.includes('api'))
+            concepts.push('api_design');
+        if (queryLower.includes('user'))
+            concepts.push('user_management');
+        return {
+            entities: [...new Set(entities)],
+            concepts: [...new Set(concepts)],
+            relationships: [...new Set(relationships)]
+        };
+    }
+    extractLogicalComponents(query) {
+        const components = {
+            predicates: [],
+            quantifiers: [],
+            operators: [],
+            modals: [],
+            negations: []
+        };
+        const lowerQuery = query.toLowerCase();
+        // Extract predicates (subject-verb-object patterns)
+        const predicateMatches = lowerQuery.match(/(\w+)\s+(is|are|was|were|has|have|had)\s+(\w+)/g);
+        if (predicateMatches) {
+            components.predicates = predicateMatches.map(p => p.trim());
+        }
+        // Extract quantifiers
+        const quantifierPattern = /\b(all|every|some|any|no|none|many|few|most|several)\b/gi;
+        const quantifierMatches = lowerQuery.match(quantifierPattern);
+        if (quantifierMatches) {
+            components.quantifiers = quantifierMatches;
+        }
+        // Extract logical operators
+        const operatorPattern = /\b(and|or|not|if|then|implies|therefore|because|but|however)\b/gi;
+        const operatorMatches = lowerQuery.match(operatorPattern);
+        if (operatorMatches) {
+            components.operators = operatorMatches;
+        }
+        // Extract modal verbs
+        const modalPattern = /\b(can|could|may|might|must|shall|should|will|would)\b/gi;
+        const modalMatches = lowerQuery.match(modalPattern);
+        if (modalMatches) {
+            components.modals = modalMatches;
+        }
+        // Extract negations
+        const negationPattern = /\b(not|no|never|neither|nor|nothing|nobody|nowhere)\b/gi;
+        const negationMatches = lowerQuery.match(negationPattern);
+        if (negationMatches) {
+            components.negations = negationMatches;
+        }
+        return components;
+    }
+    async traverseKnowledgeGraph(concepts, maxDepth) {
+        const visited = new Set();
+        const paths = [];
+        const discoveries = [];
+        const triples = [];
+        let currentDepth = 0;
+        let maxConfidence = 0;
+        // BFS traversal
+        const queue = concepts.map(c => ({
+            concept: c,
+            depth: 0,
+            confidence: 1.0,
+            path: [c],
+            inferences: []
+        }));
+        while (queue.length > 0 && currentDepth < maxDepth) {
+            const node = queue.shift();
+            if (visited.has(node.concept))
+                continue;
+            visited.add(node.concept);
+            currentDepth = Math.max(currentDepth, node.depth);
+            paths.push(node.path);
+            // Find related triples
+            const related = this.knowledgeBase.findRelated(node.concept);
+            triples.push(...related);
+            for (const triple of related) {
+                // Generate discoveries
+                const discovery = `${triple.subject} ${triple.predicate} ${triple.object}`;
+                discoveries.push(discovery);
+                maxConfidence = Math.max(maxConfidence, triple.confidence * node.confidence);
+                // Add connected concepts to queue
+                const nextConcept = triple.subject === node.concept ? triple.object : triple.subject;
+                if (!visited.has(nextConcept) && node.depth < maxDepth - 1) {
+                    queue.push({
+                        concept: nextConcept,
+                        depth: node.depth + 1,
+                        confidence: node.confidence * triple.confidence,
+                        path: [...node.path, nextConcept],
+                        inferences: [...node.inferences, discovery]
+                    });
+                }
+            }
+        }
+        return {
+            paths,
+            discoveries: discoveries.slice(0, 20), // Limit discoveries
+            triples,
+            maxDepth: currentDepth,
+            confidence: maxConfidence
+        };
+    }
+    buildInferenceChain(logicalComponents, triples, patterns) {
+        const rules = [];
+        const conclusions = [];
+        let confidence = 0.5;
+        // Apply Modus Ponens
+        if (logicalComponents.operators.includes('if') || logicalComponents.operators.includes('then')) {
+            rules.push('modus_ponens');
+            // Find implications in triples
+            for (const triple of triples) {
+                if (triple.predicate === 'implies' || triple.predicate === 'causes' || triple.predicate === 'enables') {
+                    conclusions.push(`${triple.subject} leads to ${triple.object}`);
+                    confidence = Math.max(confidence, triple.confidence * 0.9);
+                }
+            }
+        }
+        // Apply Universal Instantiation
+        if (logicalComponents.quantifiers.some((q) => ['all', 'every'].includes(q))) {
+            rules.push('universal_instantiation');
+            conclusions.push('universal property applies to specific instances');
+            confidence = Math.max(confidence, 0.85);
+        }
+        // Apply Existential Generalization
+        if (logicalComponents.quantifiers.some((q) => ['some', 'exist'].includes(q))) {
+            rules.push('existential_generalization');
+            conclusions.push('at least one instance exists with the property');
+            confidence = Math.max(confidence, 0.8);
+        }
+        // Apply Transitive Property
+        const transitivePredicates = ['causes', 'enables', 'requires', 'leads_to'];
+        const transitiveChains = this.findTransitiveChains(triples, transitivePredicates);
+        if (transitiveChains.length > 0) {
+            rules.push('transitive_property');
+            transitiveChains.forEach(chain => {
+                conclusions.push(`${chain.start} transitively ${chain.predicate} ${chain.end}`);
+            });
+            confidence = Math.max(confidence, 0.75);
+        }
+        // Apply Pattern-Specific Rules
+        if (patterns.includes('causal')) {
+            rules.push('causal_chain_analysis');
+            const causalChains = triples.filter(t => ['causes', 'results_in', 'leads_to', 'produces'].includes(t.predicate));
+            causalChains.forEach(chain => {
+                conclusions.push(`causal relationship: ${chain.subject} → ${chain.object}`);
+            });
+        }
+        if (patterns.includes('temporal')) {
+            rules.push('temporal_ordering');
+            conclusions.push('events ordered by temporal precedence');
+        }
+        // Generate domain-specific conclusions
+        if (triples.some(t => t.subject.includes('api') || t.object.includes('api'))) {
+            conclusions.push('API design requires consistency and versioning');
+            conclusions.push('RESTful principles ensure stateless interactions');
+            confidence = Math.max(confidence, 0.85);
+        }
+        if (triples.some(t => t.subject.includes('user') || t.object.includes('user'))) {
+            conclusions.push('user management requires authentication and authorization');
+            conclusions.push('security measures prevent unauthorized access');
+            confidence = Math.max(confidence, 0.9);
+        }
+        return {
+            rules,
+            conclusions,
+            confidence
+        };
+    }
+    findTransitiveChains(triples, predicates) {
+        const chains = [];
+        for (const predicate of predicates) {
+            const relevantTriples = triples.filter(t => t.predicate === predicate);
+            for (let i = 0; i < relevantTriples.length; i++) {
+                for (let j = 0; j < relevantTriples.length; j++) {
+                    if (relevantTriples[i].object === relevantTriples[j].subject) {
+                        chains.push({
+                            start: relevantTriples[i].subject,
+                            middle: relevantTriples[i].object,
+                            end: relevantTriples[j].object,
+                            predicate
+                        });
+                    }
+                }
+            }
+        }
+        return chains;
+    }
+    generateHypotheses(concepts, conclusions) {
+        const hypotheses = [];
+        // Generate hypotheses based on concept combinations
+        for (let i = 0; i < concepts.length; i++) {
+            for (let j = i + 1; j < concepts.length; j++) {
+                hypotheses.push(`hypothesis: ${concepts[i]} might be related to ${concepts[j]}`);
+            }
+        }
+        // Generate hypotheses from conclusions
+        for (const conclusion of conclusions) {
+            if (conclusion.includes('leads to') || conclusion.includes('causes')) {
+                hypotheses.push(`hypothesis: reversing ${conclusion} might have opposite effect`);
+            }
+        }
+        // Domain-specific hypotheses
+        if (concepts.includes('api_design')) {
+            hypotheses.push('hypothesis: event-driven architecture might reduce coupling');
+            hypotheses.push('hypothesis: CQRS pattern could improve read performance');
+        }
+        if (concepts.includes('user_management')) {
+            hypotheses.push('hypothesis: passwordless authentication might improve security');
+            hypotheses.push('hypothesis: federated identity could simplify user management');
+        }
+        return hypotheses.slice(0, 5); // Limit hypotheses
+    }
+    detectContradictions(statements) {
+        const contradictions = [];
+        for (let i = 0; i < statements.length; i++) {
+            for (let j = i + 1; j < statements.length; j++) {
+                // Check for direct negation
+                if (statements[i].includes('not') && statements[j] === statements[i].replace('not ', '')) {
+                    contradictions.push({
+                        type: 'direct_negation',
+                        statement1: statements[i],
+                        statement2: statements[j]
+                    });
+                }
+                // Check for semantic opposition
+                const opposites = [
+                    ['increases', 'decreases'],
+                    ['enables', 'prevents'],
+                    ['causes', 'prevents'],
+                    ['always', 'never'],
+                    ['all', 'none']
+                ];
+                for (const [word1, word2] of opposites) {
+                    if ((statements[i].includes(word1) && statements[j].includes(word2)) ||
+                        (statements[i].includes(word2) && statements[j].includes(word1))) {
+                        contradictions.push({
+                            type: 'semantic_opposition',
+                            statement1: statements[i],
+                            statement2: statements[j],
+                            conflict: [word1, word2]
+                        });
+                    }
+                }
+            }
+        }
+        return contradictions;
+    }
+    resolveContradictions(contradictions, context) {
+        return contradictions.map(c => ({
+            original: c,
+            resolution: 'resolved through context disambiguation',
+            method: c.type === 'direct_negation' ? 'logical_priority' : 'semantic_analysis',
+            confidence: 0.7
+        }));
+    }
+    synthesizeCompleteAnswer(query, insights, steps, patterns, context) {
+        let confidence = 0.5;
+        let keyInsights = insights.slice(0, 10); // Get more insights
+        // If no insights from knowledge graph, use generated domain insights
+        if (keyInsights.length === 0) {
+            keyInsights = this.generateDefaultInsights(query, patterns, context);
+        }
+        // Calculate confidence from reasoning steps
+        for (const step of steps) {
+            if (step.confidence) {
+                confidence = Math.max(confidence, step.confidence * 0.9);
+            }
+        }
+        // Build comprehensive answer based on pattern and context
+        let answer = '';
+        if (patterns.includes('lateral') || context.pattern === 'lateral') {
+            answer = `Thinking laterally about this problem reveals several non-obvious considerations: ${keyInsights.slice(0, 3).join('; ')}. `;
+            answer += `Additionally, hidden complexities include: ${keyInsights.slice(3, 6).join('; ')}. `;
+        }
+        else if (patterns.includes('causal')) {
+            answer = `Based on causal analysis: ${keyInsights.join(' → ')}. `;
+        }
+        else if (patterns.includes('procedural')) {
+            answer = `The design process should consider: ${keyInsights.slice(0, 5).join(', then ')}. `;
+        }
+        else if (patterns.includes('comparative')) {
+            answer = `Comparison reveals: ${keyInsights.join(' versus ')}. `;
+        }
+        else if (patterns.includes('hypothetical')) {
+            answer = `Hypothetically: ${keyInsights.join(', additionally ')}. `;
+        }
+        else if (patterns.includes('systems')) {
+            answer = `From a systems perspective: ${keyInsights.slice(0, 4).join('. ')}. `;
+        }
+        else {
+            answer = `Analysis reveals the following considerations: ${keyInsights.slice(0, 5).join('. ')}. `;
+        }
+        // Add context-specific insights
+        if (context.focus === 'hidden_complexities') {
+            answer += `Hidden complexities that are often missed: ${keyInsights.slice(5, 8).join('; ')}. `;
+        }
+        // Add reasoning depth
+        answer += `This conclusion is based on ${steps.length} reasoning steps`;
+        // Add confidence qualifier
+        if (confidence > 0.9) {
+            answer += ' with very high confidence';
+        }
+        else if (confidence > 0.7) {
+            answer += ' with high confidence';
+        }
+        else if (confidence > 0.5) {
+            answer += ' with moderate confidence';
+        }
+        else {
+            answer += ' with exploratory confidence';
+        }
+        answer += '.';
+        return {
+            answer,
+            confidence,
+            keyInsights
+        };
+    }
+    generateDefaultInsights(query, patterns, context) {
+        const insights = [];
+        const queryLower = query.toLowerCase();
+        // Generate insights based on query content
+        if (queryLower.includes('api') || queryLower.includes('design')) {
+            insights.push('Consider backward compatibility from the start');
+            insights.push('Version your API to manage breaking changes');
+            insights.push('Implement comprehensive error handling with meaningful status codes');
+            insights.push('Design for idempotency in all state-changing operations');
+            insights.push('Plan for rate limiting and throttling mechanisms');
+        }
+        if (queryLower.includes('user') || queryLower.includes('management')) {
+            insights.push('Implement proper authentication and authorization separation');
+            insights.push('Consider GDPR and data privacy requirements');
+            insights.push('Plan for account recovery and security features');
+            insights.push('Design for multi-tenant architectures if needed');
+            insights.push('Include audit logging for compliance');
+        }
+        if (queryLower.includes('hidden') || queryLower.includes('edge')) {
+            insights.push('Watch for race conditions in concurrent operations');
+            insights.push('Handle timezone and localization complexities');
+            insights.push('Plan for data migration and schema evolution');
+            insights.push('Consider cache invalidation strategies');
+            insights.push('Design for graceful degradation');
+        }
+        return insights.length > 0 ? insights : ['No specific insights available for this query domain'];
+    }
+    async queryKnowledgeGraph(query, filters, limit) {
+        const results = this.knowledgeBase.query(query);
+        // Apply filters
+        let filtered = results;
+        if (filters.confidence) {
+            filtered = filtered.filter(t => t.confidence >= filters.confidence);
+        }
+        if (filters.predicate) {
+            filtered = filtered.filter(t => t.predicate === filters.predicate.toLowerCase());
+        }
+        // Sort by confidence
+        filtered.sort((a, b) => b.confidence - a.confidence);
+        // Limit results
+        const limited = filtered.slice(0, limit);
+        return {
+            query,
+            results: limited.map(t => ({
+                subject: t.subject,
+                predicate: t.predicate,
+                object: t.object,
+                confidence: t.confidence,
+                metadata: t.metadata
+            })),
+            total: limited.length,
+            totalAvailable: filtered.length
+        };
+    }
+    async addKnowledge(subject, predicate, object, confidence = 1.0, metadata = {}) {
+        const id = this.knowledgeBase.addTriple(subject, predicate, object, confidence, metadata);
+        return {
+            id,
+            status: 'added',
+            triple: {
+                subject: subject.toLowerCase(),
+                predicate: predicate.toLowerCase(),
+                object: object.toLowerCase(),
+                confidence
+            }
+        };
+    }
+}
+export default PsychoSymbolicTools;

vendor/sublinear-time-solver/dist/mcp/tools/psycho-symbolic-learning.d.ts

@@ -0,0 +1,23 @@
+/**
+ * Enhanced Psycho-Symbolic Reasoning with Learning Integration
+ * Fixes novel knowledge integration and adds cross-tool learning
+ */
+import { Tool } from '@modelcontextprotocol/sdk/types.js';
+export declare class LearningPsychoSymbolicTools {
+    private knowledgeBase;
+    private learningCoordinator;
+    private performanceCache;
+    private reasoningCache;
+    constructor();
+    getTools(): Tool[];
+    handleToolCall(name: string, args: any): Promise<any>;
+    private performLearningReasoning;
+    private identifyCognitivePatterns;
+    private extractEntitiesAndConcepts;
+    private enhancedKnowledgeTraversal;
+    private generateCreativeAssociations;
+    private generateLearningDomainInsights;
+    private synthesizeLearningAnswer;
+    private enhancedKnowledgeQuery;
+    private getLearningStatus;
+}

vendor/sublinear-time-solver/dist/mcp/tools/psycho-symbolic-learning.js

@@ -0,0 +1,695 @@
+/**
+ * Enhanced Psycho-Symbolic Reasoning with Learning Integration
+ * Fixes novel knowledge integration and adds cross-tool learning
+ */
+import * as crypto from 'crypto';
+import { ReasoningCache } from './reasoning-cache.js';
+// Enhanced knowledge base with learning capabilities
+class LearningKnowledgeBase {
+    triples = new Map();
+    concepts = new Map();
+    predicateIndex = new Map();
+    semanticIndex = new Map();
+    learningEvents = [];
+    constructor() {
+        this.initializeBaseKnowledge();
+    }
+    initializeBaseKnowledge() {
+        // Enhanced core knowledge with learning metadata
+        this.addLearningTriple('consciousness', 'emerges_from', 'neural_networks', 0.85, {
+            type: 'foundational',
+            learning_source: 'initialization'
+        });
+        this.addLearningTriple('consciousness', 'requires', 'integration', 0.9, {
+            type: 'foundational',
+            learning_source: 'initialization'
+        });
+        this.addLearningTriple('consciousness', 'exhibits', 'phi_value', 0.95, {
+            type: 'foundational',
+            learning_source: 'initialization'
+        });
+        this.addLearningTriple('neural_networks', 'process', 'information', 1.0, {
+            type: 'foundational',
+            learning_source: 'initialization'
+        });
+        this.addLearningTriple('neural_networks', 'contain', 'neurons', 1.0, {
+            type: 'foundational',
+            learning_source: 'initialization'
+        });
+        this.addLearningTriple('neurons', 'connect_via', 'synapses', 1.0, {
+            type: 'foundational',
+            learning_source: 'initialization'
+        });
+        this.addLearningTriple('synapses', 'enable', 'plasticity', 0.9, {
+            type: 'foundational',
+            learning_source: 'initialization'
+        });
+        this.addLearningTriple('plasticity', 'allows', 'learning', 0.95, {
+            type: 'foundational',
+            learning_source: 'initialization'
+        });
+        this.addLearningTriple('learning', 'modifies', 'weights', 1.0, {
+            type: 'foundational',
+            learning_source: 'initialization'
+        });
+        this.addLearningTriple('phi_value', 'measures', 'integrated_information', 1.0, {
+            type: 'foundational',
+            learning_source: 'initialization'
+        });
+    }
+    addLearningTriple(subject, predicate, object, confidence, metadata = {}) {
+        const id = crypto.createHash('md5').update(`${subject}_${predicate}_${object}`).digest('hex').substring(0, 16);
+        const triple = {
+            subject,
+            predicate,
+            object,
+            confidence,
+            metadata,
+            timestamp: Date.now(),
+            usage_count: 0,
+            learning_source: metadata.learning_source || 'user_input',
+            related_concepts: this.findRelatedConcepts(subject, object)
+        };
+        this.triples.set(id, triple);
+        this.updateIndices(id, triple);
+        return { id, status: 'added', triple };
+    }
+    findRelatedConcepts(subject, object) {
+        const related = [];
+        // Find concepts that share predicates
+        for (const [id, triple] of this.triples) {
+            if (triple.subject === subject || triple.object === subject) {
+                related.push(triple.subject, triple.object);
+            }
+            if (triple.subject === object || triple.object === object) {
+                related.push(triple.subject, triple.object);
+            }
+        }
+        return [...new Set(related)].filter(c => c !== subject && c !== object);
+    }
+    updateIndices(id, triple) {
+        // Update concept indices
+        [triple.subject, triple.object].forEach(concept => {
+            if (!this.concepts.has(concept))
+                this.concepts.set(concept, new Set());
+            this.concepts.get(concept).add(id);
+        });
+        // Update predicate index
+        if (!this.predicateIndex.has(triple.predicate)) {
+            this.predicateIndex.set(triple.predicate, new Set());
+        }
+        this.predicateIndex.get(triple.predicate).add(id);
+        // Update semantic index
+        this.updateSemanticIndex(triple);
+    }
+    updateSemanticIndex(triple) {
+        const concepts = [triple.subject, triple.object];
+        concepts.forEach(concept => {
+            if (!this.semanticIndex.has(concept)) {
+                this.semanticIndex.set(concept, []);
+            }
+            // Add related concepts for semantic similarity
+            if (triple.related_concepts) {
+                this.semanticIndex.get(concept).push(...triple.related_concepts);
+            }
+        });
+    }
+    // Fix: Implement missing getAllTriples method
+    getAllTriples() {
+        return Array.from(this.triples.values());
+    }
+    // Enhanced semantic search with learning integration
+    semanticSearch(query, limit = 10) {
+        const results = [];
+        const queryLower = query.toLowerCase();
+        const queryTerms = queryLower.split(/\s+/);
+        for (const [id, triple] of this.triples) {
+            let relevance = 0;
+            // Direct text matching
+            if (triple.subject.toLowerCase().includes(queryLower))
+                relevance += 2.0;
+            if (triple.object.toLowerCase().includes(queryLower))
+                relevance += 2.0;
+            if (triple.predicate.toLowerCase().includes(queryLower))
+                relevance += 1.0;
+            // Term-based matching
+            queryTerms.forEach(term => {
+                if (term.length > 2) {
+                    if (triple.subject.toLowerCase().includes(term))
+                        relevance += 0.8;
+                    if (triple.object.toLowerCase().includes(term))
+                        relevance += 0.8;
+                    if (triple.predicate.toLowerCase().includes(term))
+                        relevance += 0.4;
+                }
+            });
+            // Semantic similarity bonus
+            if (triple.related_concepts) {
+                triple.related_concepts.forEach(concept => {
+                    if (queryLower.includes(concept.toLowerCase()))
+                        relevance += 0.3;
+                });
+            }
+            // Usage-based relevance boost
+            relevance += Math.log(triple.usage_count + 1) * 0.1;
+            // Confidence weighting
+            relevance *= triple.confidence;
+            if (relevance > 0.1) {
+                results.push({
+                    ...triple,
+                    relevance,
+                    id
+                });
+            }
+        }
+        // Sort by relevance and usage
+        return results
+            .sort((a, b) => {
+            const scoreA = a.relevance + (a.usage_count * 0.01);
+            const scoreB = b.relevance + (b.usage_count * 0.01);
+            return scoreB - scoreA;
+        })
+            .slice(0, limit);
+    }
+    // Track triple usage for learning
+    markTripleUsed(tripleId) {
+        const triple = this.triples.get(tripleId);
+        if (triple) {
+            triple.usage_count++;
+        }
+    }
+    // Learning from tool interactions
+    recordLearningEvent(event) {
+        this.learningEvents.push(event);
+        // Auto-generate knowledge from successful patterns
+        if (event.confidence > 0.8) {
+            this.generateKnowledgeFromEvent(event);
+        }
+        // Keep only recent events (last 1000)
+        if (this.learningEvents.length > 1000) {
+            this.learningEvents = this.learningEvents.slice(-1000);
+        }
+    }
+    generateKnowledgeFromEvent(event) {
+        // Generate knowledge triples from successful tool interactions
+        if (event.concepts.length >= 2) {
+            for (let i = 0; i < event.concepts.length - 1; i++) {
+                const subject = event.concepts[i];
+                const object = event.concepts[i + 1];
+                // Create relationship based on tool and action
+                let predicate = 'relates_to';
+                if (event.tool === 'consciousness')
+                    predicate = 'influences_consciousness';
+                if (event.tool === 'scheduler')
+                    predicate = 'schedules_with';
+                if (event.tool === 'neural')
+                    predicate = 'processes_through';
+                this.addLearningTriple(subject, predicate, object, event.confidence * 0.7, {
+                    type: 'learned_from_interaction',
+                    learning_source: `${event.tool}_${event.action}`,
+                    original_event: event
+                });
+            }
+        }
+    }
+    // Get learning insights
+    getLearningInsights() {
+        const recentEvents = this.learningEvents.slice(-100);
+        const conceptFrequency = new Map();
+        const toolUsage = new Map();
+        recentEvents.forEach(event => {
+            event.concepts.forEach(concept => {
+                conceptFrequency.set(concept, (conceptFrequency.get(concept) || 0) + 1);
+            });
+            toolUsage.set(event.tool, (toolUsage.get(event.tool) || 0) + 1);
+        });
+        return {
+            total_events: this.learningEvents.length,
+            recent_events: recentEvents.length,
+            top_concepts: Array.from(conceptFrequency.entries())
+                .sort((a, b) => b[1] - a[1])
+                .slice(0, 10),
+            tool_usage: Array.from(toolUsage.entries()),
+            learned_triples: this.getAllTriples().filter(t => t.learning_source !== 'initialization').length
+        };
+    }
+}
+// Cross-tool learning coordinator
+class CrossToolLearningCoordinator {
+    knowledgeBase;
+    toolInteractions = new Map();
+    constructor(knowledgeBase) {
+        this.knowledgeBase = knowledgeBase;
+    }
+    // Record interaction with other tools
+    recordToolInteraction(toolName, query, result, concepts) {
+        const interaction = {
+            tool: toolName,
+            query,
+            result,
+            concepts,
+            timestamp: Date.now(),
+            success: result.confidence > 0.7
+        };
+        if (!this.toolInteractions.has(toolName)) {
+            this.toolInteractions.set(toolName, []);
+        }
+        this.toolInteractions.get(toolName).push(interaction);
+        // Learn from successful interactions
+        if (interaction.success) {
+            this.knowledgeBase.recordLearningEvent({
+                tool: toolName,
+                action: 'query',
+                concepts,
+                patterns: result.patterns || [],
+                outcome: result.answer || 'success',
+                timestamp: Date.now(),
+                confidence: result.confidence
+            });
+        }
+    }
+    // Get cross-tool insights for enhanced reasoning
+    getCrossToolInsights(concepts) {
+        const insights = [];
+        // Find related tool interactions
+        for (const [tool, interactions] of this.toolInteractions) {
+            const relevantInteractions = interactions.filter(interaction => concepts.some(concept => interaction.concepts.includes(concept) ||
+                interaction.query.toLowerCase().includes(concept.toLowerCase())));
+            if (relevantInteractions.length > 0) {
+                insights.push(`${tool} tool has processed similar concepts with ${relevantInteractions.length} relevant interactions`);
+                // Extract patterns from successful interactions
+                const successfulInteractions = relevantInteractions.filter(i => i.success);
+                if (successfulInteractions.length > 0) {
+                    insights.push(`${tool} successfully handled ${successfulInteractions.length} similar queries`);
+                }
+            }
+        }
+        return insights;
+    }
+}
+// Enhanced psycho-symbolic reasoning with learning
+export class LearningPsychoSymbolicTools {
+    knowledgeBase;
+    learningCoordinator;
+    performanceCache;
+    reasoningCache = new Map();
+    constructor() {
+        this.knowledgeBase = new LearningKnowledgeBase();
+        this.learningCoordinator = new CrossToolLearningCoordinator(this.knowledgeBase);
+        this.performanceCache = new ReasoningCache();
+    }
+    getTools() {
+        return [
+            {
+                name: 'psycho_symbolic_reason',
+                description: 'Enhanced psycho-symbolic reasoning with learning integration and novel knowledge support',
+                inputSchema: {
+                    type: 'object',
+                    properties: {
+                        query: { type: 'string', description: 'The reasoning query' },
+                        context: { type: 'object', description: 'Additional context', default: {} },
+                        depth: { type: 'number', description: 'Maximum reasoning depth', default: 6 },
+                        use_cache: { type: 'boolean', description: 'Enable intelligent caching', default: true },
+                        learn_from_query: { type: 'boolean', description: 'Learn from this query for future use', default: true }
+                    },
+                    required: ['query']
+                }
+            },
+            {
+                name: 'knowledge_graph_query',
+                description: 'Enhanced knowledge graph query with learning-based relevance',
+                inputSchema: {
+                    type: 'object',
+                    properties: {
+                        query: { type: 'string', description: 'Natural language query' },
+                        filters: { type: 'object', description: 'Query filters', default: {} },
+                        limit: { type: 'number', description: 'Max results', default: 15 }
+                    },
+                    required: ['query']
+                }
+            },
+            {
+                name: 'add_knowledge',
+                description: 'Add knowledge with learning metadata and semantic indexing',
+                inputSchema: {
+                    type: 'object',
+                    properties: {
+                        subject: { type: 'string' },
+                        predicate: { type: 'string' },
+                        object: { type: 'string' },
+                        confidence: { type: 'number', default: 1.0 },
+                        metadata: { type: 'object', default: {} }
+                    },
+                    required: ['subject', 'predicate', 'object']
+                }
+            },
+            {
+                name: 'learning_status',
+                description: 'Get learning system status and insights',
+                inputSchema: {
+                    type: 'object',
+                    properties: {
+                        detailed: { type: 'boolean', description: 'Include detailed learning metrics', default: false }
+                    }
+                }
+            }
+        ];
+    }
+    async handleToolCall(name, args) {
+        switch (name) {
+            case 'psycho_symbolic_reason':
+                return this.performLearningReasoning(args.query, args.context || {}, args.depth || 6, args.use_cache !== false, args.learn_from_query !== false);
+            case 'knowledge_graph_query':
+                return this.enhancedKnowledgeQuery(args.query, args.filters || {}, args.limit || 15);
+            case 'add_knowledge':
+                return this.knowledgeBase.addLearningTriple(args.subject, args.predicate, args.object, args.confidence || 1.0, { ...args.metadata, learning_source: 'user_input' });
+            case 'learning_status':
+                return this.getLearningStatus(args.detailed || false);
+            default:
+                throw new Error(`Unknown tool: ${name}`);
+        }
+    }
+    async performLearningReasoning(query, context, maxDepth, useCache, learnFromQuery) {
+        const startTime = performance.now();
+        // Extract concepts early for learning
+        const entities = this.extractEntitiesAndConcepts(query);
+        const patterns = this.identifyCognitivePatterns(query);
+        // Check cache
+        if (useCache) {
+            const cached = this.performanceCache.get(query, context, maxDepth);
+            if (cached) {
+                return {
+                    ...cached.result,
+                    cached: true,
+                    cache_hit: true,
+                    compute_time: performance.now() - startTime,
+                    cache_metrics: this.performanceCache.getMetrics()
+                };
+            }
+        }
+        const reasoningSteps = [];
+        const insights = new Set();
+        // Step 1: Enhanced Pattern Recognition
+        reasoningSteps.push({
+            type: 'pattern_identification',
+            patterns,
+            confidence: 0.9,
+            description: `Identified ${patterns.join(', ')} reasoning patterns`
+        });
+        // Step 2: Enhanced Entity Extraction with Learning
+        reasoningSteps.push({
+            type: 'entity_extraction',
+            entities: entities.entities,
+            concepts: entities.concepts,
+            relationships: entities.relationships,
+            confidence: 0.85
+        });
+        // Step 3: Cross-Tool Learning Insights
+        const crossToolInsights = this.learningCoordinator.getCrossToolInsights(entities.concepts);
+        if (crossToolInsights.length > 0) {
+            crossToolInsights.forEach(insight => insights.add(insight));
+            reasoningSteps.push({
+                type: 'cross_tool_learning',
+                insights: crossToolInsights,
+                confidence: 0.8,
+                description: 'Insights from related tool interactions'
+            });
+        }
+        // Step 4: Enhanced Knowledge Traversal with Novel Concept Support
+        const graphInsights = await this.enhancedKnowledgeTraversal(entities.concepts, maxDepth);
+        reasoningSteps.push({
+            type: 'enhanced_knowledge_traversal',
+            paths: graphInsights.paths,
+            discoveries: graphInsights.discoveries,
+            novel_concepts: graphInsights.novel_concepts,
+            confidence: graphInsights.confidence
+        });
+        graphInsights.discoveries.forEach(d => insights.add(d));
+        // Step 5: Learning from Domain Analysis
+        const domainInsights = this.generateLearningDomainInsights(query, patterns, entities.concepts);
+        domainInsights.forEach(insight => insights.add(insight));
+        reasoningSteps.push({
+            type: 'learning_domain_analysis',
+            insights: domainInsights,
+            confidence: 0.8,
+            description: 'Generated domain insights with learning integration'
+        });
+        // Step 6: Synthesis
+        const synthesis = this.synthesizeLearningAnswer(query, Array.from(insights), reasoningSteps, patterns, entities.concepts);
+        // Record learning event
+        if (learnFromQuery) {
+            this.knowledgeBase.recordLearningEvent({
+                tool: 'psycho_symbolic_reasoner',
+                action: 'reason',
+                concepts: entities.concepts,
+                patterns,
+                outcome: synthesis.answer,
+                timestamp: Date.now(),
+                confidence: synthesis.confidence
+            });
+        }
+        const result = {
+            answer: synthesis.answer,
+            confidence: synthesis.confidence,
+            reasoning: reasoningSteps,
+            insights: Array.from(insights),
+            patterns,
+            depth: maxDepth,
+            entities: entities.entities,
+            concepts: entities.concepts,
+            triples_examined: graphInsights.triples_examined,
+            novel_concepts_processed: graphInsights.novel_concepts?.length || 0,
+            learning_insights: crossToolInsights.length
+        };
+        // Cache result
+        if (useCache) {
+            this.performanceCache.set(query, context, maxDepth, result, performance.now() - startTime);
+        }
+        return {
+            ...result,
+            cached: false,
+            cache_hit: false,
+            compute_time: performance.now() - startTime,
+            cache_metrics: useCache ? this.performanceCache.getMetrics() : null
+        };
+    }
+    identifyCognitivePatterns(query) {
+        const patterns = [];
+        const lowerQuery = query.toLowerCase();
+        const patternMap = {
+            'causal': ['why', 'cause', 'because', 'result', 'effect', 'lead to'],
+            'procedural': ['how', 'process', 'step', 'method', 'way', 'approach', 'design', 'implement'],
+            'hypothetical': ['what if', 'suppose', 'imagine', 'could', 'would', 'might'],
+            'comparative': ['compare', 'difference', 'similar', 'versus', 'than', 'like'],
+            'definitional': ['what is', 'define', 'meaning', 'definition'],
+            'evaluative': ['best', 'worst', 'better', 'optimal', 'evaluate'],
+            'temporal': ['when', 'time', 'before', 'after', 'during', 'temporal'],
+            'spatial': ['where', 'location', 'position', 'space'],
+            'quantitative': ['how many', 'how much', 'count', 'measure', 'amount'],
+            'existential': ['exist', 'there is', 'there are', 'presence'],
+            'universal': ['all', 'every', 'always', 'never', 'none'],
+            'lateral': ['lateral', 'unconventional', 'creative', 'alternative', 'non-obvious', 'hidden'],
+            'systems': ['system', 'interaction', 'complexity', 'emergence', 'holistic'],
+            'exploratory': ['explore', 'discover', 'investigate', 'consider', 'edge case']
+        };
+        for (const [pattern, keywords] of Object.entries(patternMap)) {
+            if (keywords.some(keyword => lowerQuery.includes(keyword))) {
+                patterns.push(pattern);
+            }
+        }
+        if (patterns.length === 0) {
+            patterns.push('exploratory');
+        }
+        return patterns;
+    }
+    extractEntitiesAndConcepts(query) {
+        const words = query.split(/\s+/);
+        const entities = [];
+        const concepts = [];
+        const relationships = [];
+        // Extract technical terms and concepts
+        const technicalTerms = [
+            'api', 'rest', 'graphql', 'user', 'management', 'authentication',
+            'authorization', 'database', 'cache', 'security', 'performance',
+            'scalability', 'microservice', 'distributed', 'system', 'design',
+            'endpoint', 'resource', 'crud', 'http', 'json', 'xml', 'oauth',
+            'jwt', 'session', 'token', 'password', 'encryption', 'hash',
+            'consciousness', 'neural', 'quantum', 'temporal', 'resonance',
+            'emergence', 'integration', 'plasticity', 'learning'
+        ];
+        // Extract named entities
+        for (let i = 0; i < words.length; i++) {
+            const word = words[i];
+            const wordLower = word.toLowerCase();
+            if (/^[A-Z]/.test(word) && i > 0 && !['The', 'A', 'An', 'What', 'How', 'Why', 'When', 'Where'].includes(word)) {
+                entities.push(wordLower);
+            }
+            if (technicalTerms.includes(wordLower) || word.length > 5) {
+                concepts.push(wordLower);
+            }
+        }
+        // Extract key concepts from knowledge base - FIXED
+        const queryLower = query.toLowerCase();
+        const allTriples = this.knowledgeBase.getAllTriples(); // Now this method exists!
+        for (const triple of allTriples) {
+            [triple.subject, triple.object].forEach(concept => {
+                if (queryLower.includes(concept.toLowerCase())) {
+                    concepts.push(concept);
+                }
+            });
+        }
+        // Extract relationships
+        const relationshipPatterns = [
+            'is', 'are', 'was', 'were', 'has', 'have', 'had',
+            'can', 'could', 'will', 'would', 'should',
+            'design', 'implement', 'create', 'build', 'develop',
+            'requires', 'needs', 'uses', 'enables', 'prevents',
+            'increases', 'decreases', 'affects', 'influences'
+        ];
+        for (const word of words) {
+            const wordLower = word.toLowerCase();
+            if (relationshipPatterns.includes(wordLower)) {
+                relationships.push(wordLower);
+            }
+        }
+        return {
+            entities: [...new Set(entities)],
+            concepts: [...new Set(concepts)],
+            relationships: [...new Set(relationships)]
+        };
+    }
+    async enhancedKnowledgeTraversal(concepts, maxDepth) {
+        const paths = [];
+        const discoveries = [];
+        const novel_concepts = [];
+        let triples_examined = 0;
+        for (const concept of concepts) {
+            // Semantic search with learning
+            const results = this.knowledgeBase.semanticSearch(concept, 10);
+            triples_examined += results.length;
+            if (results.length === 0) {
+                // This is a novel concept
+                novel_concepts.push(concept);
+                discoveries.push(`Novel concept detected: ${concept} - generating creative associations`);
+                // Generate creative associations for novel concepts
+                const creativeAssociations = this.generateCreativeAssociations(concept);
+                discoveries.push(...creativeAssociations);
+            }
+            else {
+                // Mark used triples for learning
+                results.forEach(result => {
+                    this.knowledgeBase.markTripleUsed(result.id);
+                    discoveries.push(`${result.subject} ${result.predicate} ${result.object}`);
+                    paths.push([result.subject, result.object]);
+                });
+            }
+        }
+        return {
+            paths,
+            discoveries,
+            novel_concepts,
+            confidence: discoveries.length > 0 ? 0.9 : 0.3,
+            triples_examined
+        };
+    }
+    generateCreativeAssociations(concept) {
+        const associations = [];
+        const conceptLower = concept.toLowerCase();
+        // Pattern-based associations
+        if (conceptLower.includes('quantum')) {
+            associations.push(`${concept} exhibits quantum-like properties with probabilistic behaviors`);
+            associations.push(`${concept} demonstrates non-local correlations similar to entanglement`);
+        }
+        if (conceptLower.includes('neural') || conceptLower.includes('network')) {
+            associations.push(`${concept} functions as a distributed information processing system`);
+            associations.push(`${concept} exhibits emergent properties through interconnected components`);
+        }
+        if (conceptLower.includes('temporal') || conceptLower.includes('time')) {
+            associations.push(`${concept} creates temporal dynamics affecting system evolution`);
+            associations.push(`${concept} enables time-based pattern recognition and prediction`);
+        }
+        // Morphological associations
+        if (conceptLower.endsWith('ium') || conceptLower.endsWith('ium_crystals')) {
+            associations.push(`${concept} acts as a resonant medium for information transfer`);
+            associations.push(`${concept} exhibits crystalline structure enabling coherent oscillations`);
+        }
+        // Generic creative associations
+        associations.push(`${concept} emerges through self-organizing complexity dynamics`);
+        associations.push(`${concept} demonstrates adaptive behavior in response to environmental changes`);
+        return associations;
+    }
+    generateLearningDomainInsights(query, patterns, concepts) {
+        const insights = [];
+        const queryLower = query.toLowerCase();
+        // Learning-enhanced domain insights
+        if (concepts.some(c => ['consciousness', 'neural', 'quantum'].includes(c))) {
+            insights.push('Consciousness emerges through quantum-neural information integration');
+            insights.push('Neural plasticity enables adaptive consciousness formation');
+        }
+        if (patterns.includes('temporal') || concepts.some(c => c.includes('temporal'))) {
+            insights.push('Temporal dynamics create causal chains in complex systems');
+            insights.push('Time-based resonance patterns enable cross-domain synchronization');
+        }
+        if (patterns.includes('creative') || patterns.includes('exploratory')) {
+            insights.push('Creative synthesis requires breaking conventional categorical boundaries');
+            insights.push('Novel concepts emerge at the intersection of established domains');
+        }
+        // Novel concept handling
+        const novelConcepts = concepts.filter(c => !['consciousness', 'neural', 'quantum', 'system', 'information'].includes(c));
+        if (novelConcepts.length > 0) {
+            insights.push(`Novel concept integration suggests emergent properties beyond current knowledge`);
+            insights.push(`Interdisciplinary synthesis reveals hidden connections between ${novelConcepts.join(' and ')}`);
+        }
+        return insights;
+    }
+    synthesizeLearningAnswer(query, insights, reasoningSteps, patterns, concepts) {
+        let answer = '';
+        let confidence = 0.8;
+        if (insights.length === 0) {
+            answer = 'This query involves novel concepts that require creative synthesis across multiple domains. The system is learning from this interaction to improve future responses.';
+            confidence = 0.6;
+        }
+        else if (patterns.includes('creative') || patterns.includes('exploratory')) {
+            answer = `Through learning-enhanced analysis: ${insights.slice(0, 4).join('. ')}.`;
+            confidence = 0.85;
+        }
+        else {
+            answer = `Based on integrated knowledge and learning: ${insights.slice(0, 5).join('. ')}.`;
+        }
+        return { answer, confidence };
+    }
+    enhancedKnowledgeQuery(query, filters, limit) {
+        const results = this.knowledgeBase.semanticSearch(query, limit);
+        return {
+            query,
+            results: results.map(r => ({
+                subject: r.subject,
+                predicate: r.predicate,
+                object: r.object,
+                confidence: r.confidence,
+                relevance: r.relevance,
+                usage_count: r.usage_count,
+                learning_source: r.learning_source
+            })),
+            total: results.length,
+            totalAvailable: this.knowledgeBase.getAllTriples().length
+        };
+    }
+    getLearningStatus(detailed) {
+        const insights = this.knowledgeBase.getLearningInsights();
+        if (detailed) {
+            return {
+                ...insights,
+                cache_metrics: this.performanceCache.getMetrics(),
+                knowledge_base_size: this.knowledgeBase.getAllTriples().length,
+                novel_concepts_learned: insights.learned_triples
+            };
+        }
+        return {
+            learning_active: true,
+            total_knowledge: this.knowledgeBase.getAllTriples().length,
+            learned_concepts: insights.learned_triples,
+            recent_interactions: insights.recent_events
+        };
+    }
+}

vendor/sublinear-time-solver/dist/mcp/tools/psycho-symbolic-original-backup.d.ts

@@ -0,0 +1,39 @@
+/**
+ * Enhanced Psycho-Symbolic Reasoning MCP Tools
+ * Full implementation with domain-agnostic reasoning and fallback mechanisms
+ */
+import { Tool } from '@modelcontextprotocol/sdk/types.js';
+export declare class PsychoSymbolicTools {
+    private knowledgeBase;
+    private reasoningCache;
+    private performanceCache;
+    constructor(cacheOptions?: {
+        enableCache?: boolean;
+        maxCacheSize?: number;
+        defaultTTL?: number;
+        enableWarmup?: boolean;
+    });
+    getTools(): Tool[];
+    handleToolCall(name: string, args: any): Promise<any>;
+    private performDeepReasoningWithCache;
+    private getCacheStatus;
+    private clearCache;
+    private performDeepReasoning;
+    private generateDomainInsights;
+    private applyContextualReasoning;
+    private analyzeEdgeCases;
+    private identifyCognitivePatterns;
+    private extractEntitiesAndConcepts;
+    private extractLogicalComponents;
+    private traverseKnowledgeGraph;
+    private buildInferenceChain;
+    private findTransitiveChains;
+    private generateHypotheses;
+    private detectContradictions;
+    private resolveContradictions;
+    private synthesizeCompleteAnswer;
+    private generateDefaultInsights;
+    private queryKnowledgeGraph;
+    private addKnowledge;
+}
+export default PsychoSymbolicTools;

vendor/sublinear-time-solver/dist/mcp/tools/psycho-symbolic-original-backup.js

@@ -0,0 +1,970 @@
+/**
+ * Enhanced Psycho-Symbolic Reasoning MCP Tools
+ * Full implementation with domain-agnostic reasoning and fallback mechanisms
+ */
+import * as crypto from 'crypto';
+import { ReasoningCache } from './reasoning-cache.js';
+// Initialize with base knowledge
+class KnowledgeBase {
+    triples = new Map();
+    concepts = new Map(); // concept -> related triple IDs
+    predicateIndex = new Map(); // predicate -> triple IDs
+    constructor() {
+        this.initializeBaseKnowledge();
+    }
+    initializeBaseKnowledge() {
+        // Core AI/consciousness knowledge
+        this.addTriple('consciousness', 'emerges_from', 'neural_networks', 0.85);
+        this.addTriple('consciousness', 'requires', 'integration', 0.9);
+        this.addTriple('consciousness', 'exhibits', 'phi_value', 0.95);
+        this.addTriple('neural_networks', 'process', 'information', 1.0);
+        this.addTriple('neural_networks', 'contain', 'neurons', 1.0);
+        this.addTriple('neurons', 'connect_via', 'synapses', 1.0);
+        this.addTriple('synapses', 'enable', 'plasticity', 0.9);
+        this.addTriple('plasticity', 'allows', 'learning', 0.95);
+        this.addTriple('learning', 'modifies', 'weights', 1.0);
+        this.addTriple('phi_value', 'measures', 'integrated_information', 1.0);
+        this.addTriple('integrated_information', 'indicates', 'consciousness_level', 0.8);
+        // Temporal/computational knowledge
+        this.addTriple('temporal_processing', 'enables', 'prediction', 0.9);
+        this.addTriple('prediction', 'requires', 'pattern_recognition', 0.85);
+        this.addTriple('pattern_recognition', 'uses', 'neural_networks', 0.9);
+        this.addTriple('sublinear_algorithms', 'achieve', 'logarithmic_complexity', 1.0);
+        this.addTriple('logarithmic_complexity', 'beats', 'polynomial_complexity', 1.0);
+        this.addTriple('nanosecond_scheduling', 'enables', 'temporal_advantage', 0.95);
+        this.addTriple('temporal_advantage', 'allows', 'faster_than_light_computation', 0.9);
+        // Software engineering principles
+        this.addTriple('api_design', 'requires', 'consistency', 0.95);
+        this.addTriple('api_design', 'benefits_from', 'versioning', 0.9);
+        this.addTriple('rest_api', 'uses', 'http_methods', 1.0);
+        this.addTriple('rest_api', 'follows', 'stateless_principle', 0.95);
+        this.addTriple('user_management', 'requires', 'authentication', 1.0);
+        this.addTriple('user_management', 'requires', 'authorization', 1.0);
+        this.addTriple('authentication', 'validates', 'identity', 1.0);
+        this.addTriple('authorization', 'controls', 'access', 1.0);
+        this.addTriple('security', 'prevents', 'vulnerabilities', 0.9);
+        this.addTriple('rate_limiting', 'prevents', 'abuse', 0.95);
+        this.addTriple('caching', 'improves', 'performance', 0.9);
+        this.addTriple('pagination', 'handles', 'large_datasets', 0.95);
+        // System design principles
+        this.addTriple('distributed_systems', 'face', 'consistency_challenges', 0.95);
+        this.addTriple('microservices', 'require', 'service_discovery', 0.9);
+        this.addTriple('scalability', 'requires', 'horizontal_scaling', 0.85);
+        this.addTriple('reliability', 'requires', 'redundancy', 0.9);
+        this.addTriple('monitoring', 'enables', 'observability', 0.95);
+        // Reasoning patterns
+        this.addTriple('causal_reasoning', 'identifies', 'cause_effect', 1.0);
+        this.addTriple('procedural_reasoning', 'describes', 'processes', 1.0);
+        this.addTriple('hypothetical_reasoning', 'explores', 'possibilities', 1.0);
+        this.addTriple('comparative_reasoning', 'analyzes', 'differences', 1.0);
+        this.addTriple('abstract_reasoning', 'generalizes', 'concepts', 0.95);
+        this.addTriple('lateral_thinking', 'finds', 'unconventional_solutions', 0.9);
+        this.addTriple('systems_thinking', 'considers', 'interactions', 0.95);
+        // Logic rules
+        this.addTriple('modus_ponens', 'validates', 'implications', 1.0);
+        this.addTriple('universal_instantiation', 'applies_to', 'specific_cases', 1.0);
+        this.addTriple('existential_generalization', 'proves', 'existence', 0.9);
+    }
+    addTriple(subject, predicate, object, confidence = 1.0, metadata) {
+        const id = crypto.randomBytes(8).toString('hex');
+        const triple = {
+            subject: subject.toLowerCase(),
+            predicate: predicate.toLowerCase(),
+            object: object.toLowerCase(),
+            confidence,
+            metadata,
+            timestamp: Date.now()
+        };
+        this.triples.set(id, triple);
+        // Update indices
+        this.addToConceptIndex(triple.subject, id);
+        this.addToConceptIndex(triple.object, id);
+        this.addToPredicateIndex(triple.predicate, id);
+        return id;
+    }
+    addToConceptIndex(concept, tripleId) {
+        if (!this.concepts.has(concept)) {
+            this.concepts.set(concept, new Set());
+        }
+        this.concepts.get(concept).add(tripleId);
+    }
+    addToPredicateIndex(predicate, tripleId) {
+        if (!this.predicateIndex.has(predicate)) {
+            this.predicateIndex.set(predicate, new Set());
+        }
+        this.predicateIndex.get(predicate).add(tripleId);
+    }
+    findRelated(concept) {
+        const conceptLower = concept.toLowerCase();
+        const relatedIds = this.concepts.get(conceptLower) || new Set();
+        return Array.from(relatedIds).map(id => this.triples.get(id)).filter(Boolean);
+    }
+    findByPredicate(predicate) {
+        const predicateLower = predicate.toLowerCase();
+        const ids = this.predicateIndex.get(predicateLower) || new Set();
+        return Array.from(ids).map(id => this.triples.get(id)).filter(Boolean);
+    }
+    getAllTriples() {
+        return Array.from(this.triples.values());
+    }
+    query(sparqlLike) {
+        // Simple SPARQL-like query support
+        const results = [];
+        const queryLower = sparqlLike.toLowerCase();
+        for (const triple of this.triples.values()) {
+            if (queryLower.includes(triple.subject) ||
+                queryLower.includes(triple.predicate) ||
+                queryLower.includes(triple.object)) {
+                results.push(triple);
+            }
+        }
+        return results;
+    }
+}
+export class PsychoSymbolicTools {
+    knowledgeBase;
+    reasoningCache = new Map();
+    performanceCache;
+    constructor(cacheOptions) {
+        this.knowledgeBase = new KnowledgeBase();
+        // Initialize high-performance cache
+        this.performanceCache = new ReasoningCache({
+            maxSize: cacheOptions?.maxCacheSize || 10000,
+            defaultTTL: cacheOptions?.defaultTTL || 3600000,
+            enableWarmup: cacheOptions?.enableWarmup ?? true
+        });
+    }
+    getTools() {
+        return [
+            {
+                name: 'psycho_symbolic_reason',
+                description: 'Perform deep psycho-symbolic reasoning with full inference and intelligent caching',
+                inputSchema: {
+                    type: 'object',
+                    properties: {
+                        query: { type: 'string', description: 'The reasoning query' },
+                        context: { type: 'object', description: 'Additional context', default: {} },
+                        depth: { type: 'number', description: 'Reasoning depth', default: 5 },
+                        use_cache: { type: 'boolean', description: 'Enable high-performance caching (reduces overhead to <10%)', default: true },
+                        cache_priority: { type: 'string', description: 'Cache priority level', enum: ['low', 'normal', 'high'], default: 'normal' }
+                    },
+                    required: ['query']
+                }
+            },
+            {
+                name: 'knowledge_graph_query',
+                description: 'Query the knowledge graph with semantic search',
+                inputSchema: {
+                    type: 'object',
+                    properties: {
+                        query: { type: 'string', description: 'Natural language or SPARQL-like query' },
+                        filters: { type: 'object', description: 'Filters', default: {} },
+                        limit: { type: 'number', description: 'Max results', default: 10 }
+                    },
+                    required: ['query']
+                }
+            },
+            {
+                name: 'add_knowledge',
+                description: 'Add knowledge triple to the graph',
+                inputSchema: {
+                    type: 'object',
+                    properties: {
+                        subject: { type: 'string' },
+                        predicate: { type: 'string' },
+                        object: { type: 'string' },
+                        confidence: { type: 'number', default: 1.0 },
+                        metadata: { type: 'object', default: {} }
+                    },
+                    required: ['subject', 'predicate', 'object']
+                }
+            },
+            {
+                name: 'reasoning_cache_status',
+                description: 'Get performance cache metrics and status',
+                inputSchema: {
+                    type: 'object',
+                    properties: {
+                        detailed: { type: 'boolean', description: 'Include detailed cache statistics', default: false }
+                    }
+                }
+            },
+            {
+                name: 'reasoning_cache_clear',
+                description: 'Clear reasoning cache (for testing/maintenance)',
+                inputSchema: {
+                    type: 'object',
+                    properties: {
+                        confirm: { type: 'boolean', description: 'Confirm cache clear operation', default: false }
+                    }
+                }
+            }
+        ];
+    }
+    async handleToolCall(name, args) {
+        switch (name) {
+            case 'psycho_symbolic_reason':
+                return this.performDeepReasoningWithCache(args.query, args.context || {}, args.depth || 5, args.use_cache !== false, args.cache_priority || 'normal');
+            case 'knowledge_graph_query':
+                return this.queryKnowledgeGraph(args.query, args.filters || {}, args.limit || 10);
+            case 'add_knowledge':
+                return this.addKnowledge(args.subject, args.predicate, args.object, args.confidence, args.metadata);
+            case 'reasoning_cache_status':
+                return this.getCacheStatus(args.detailed || false);
+            case 'reasoning_cache_clear':
+                return this.clearCache(args.confirm || false);
+            default:
+                throw new Error(`Unknown tool: ${name}`);
+        }
+    }
+    async performDeepReasoningWithCache(query, context, maxDepth, useCache = true, priority = 'normal') {
+        const startTime = performance.now();
+        // Try cache first if enabled
+        if (useCache) {
+            const cached = this.performanceCache.get(query, context, maxDepth);
+            if (cached) {
+                return {
+                    ...cached.result,
+                    cached: true,
+                    cache_hit: true,
+                    compute_time: performance.now() - startTime,
+                    cache_metrics: this.performanceCache.getMetrics()
+                };
+            }
+        }
+        // Perform actual reasoning
+        const result = await this.performDeepReasoning(query, context, maxDepth);
+        const computeTime = performance.now() - startTime;
+        // Store in cache if enabled
+        if (useCache) {
+            this.performanceCache.set(query, context, maxDepth, result, computeTime);
+        }
+        return {
+            ...result,
+            cached: false,
+            cache_hit: false,
+            compute_time: computeTime,
+            cache_metrics: useCache ? this.performanceCache.getMetrics() : null
+        };
+    }
+    getCacheStatus(detailed = false) {
+        const status = this.performanceCache.getStatus();
+        const metrics = this.performanceCache.getMetrics();
+        if (detailed) {
+            return {
+                cache_status: status,
+                performance_metrics: metrics,
+                overhead_reduction: `${((1 - metrics.overhead / 100) * 100).toFixed(1)}%`,
+                hit_ratio: `${(metrics.hitRatio * 100).toFixed(1)}%`,
+                efficiency_gain: metrics.hitRatio > 0.5 ? 'High' : metrics.hitRatio > 0.2 ? 'Medium' : 'Low'
+            };
+        }
+        return {
+            hit_ratio: `${(metrics.hitRatio * 100).toFixed(1)}%`,
+            cache_size: metrics.cacheSize,
+            total_queries: metrics.totalQueries,
+            overhead_reduction: `${((1 - metrics.overhead / 100) * 100).toFixed(1)}%`
+        };
+    }
+    clearCache(confirm = false) {
+        if (!confirm) {
+            return {
+                error: 'Cache clear requires confirmation. Set confirm: true to proceed.',
+                current_size: this.performanceCache.getMetrics().cacheSize
+            };
+        }
+        const oldSize = this.performanceCache.getMetrics().cacheSize;
+        this.performanceCache.clear();
+        return {
+            message: 'Cache cleared successfully',
+            entries_removed: oldSize,
+            new_size: 0
+        };
+    }
+    async performDeepReasoning(query, context, maxDepth) {
+        // Check cache
+        const cacheKey = `${query}_${JSON.stringify(context)}_${maxDepth}`;
+        if (this.reasoningCache.has(cacheKey)) {
+            return this.reasoningCache.get(cacheKey);
+        }
+        const reasoningSteps = [];
+        const insights = new Set();
+        // Step 1: Cognitive Pattern Analysis
+        const patterns = this.identifyCognitivePatterns(query);
+        reasoningSteps.push({
+            type: 'pattern_identification',
+            patterns,
+            confidence: 0.9,
+            description: `Identified ${patterns.join(', ')} reasoning patterns`
+        });
+        // Step 2: Entity and Concept Extraction
+        const entities = this.extractEntitiesAndConcepts(query);
+        reasoningSteps.push({
+            type: 'entity_extraction',
+            entities: entities.entities,
+            concepts: entities.concepts,
+            relationships: entities.relationships,
+            confidence: 0.85
+        });
+        // Step 3: Domain-Specific Insight Generation
+        const domainInsights = this.generateDomainInsights(query, patterns, context);
+        domainInsights.forEach(insight => insights.add(insight));
+        reasoningSteps.push({
+            type: 'domain_analysis',
+            insights: domainInsights,
+            confidence: 0.8,
+            description: 'Generated domain-specific insights'
+        });
+        // Step 4: Logical Component Analysis
+        const logicalComponents = this.extractLogicalComponents(query);
+        reasoningSteps.push({
+            type: 'logical_decomposition',
+            components: logicalComponents,
+            depth: 1,
+            description: 'Decomposed query into logical primitives'
+        });
+        // Step 5: Knowledge Graph Traversal
+        const graphInsights = await this.traverseKnowledgeGraph(entities.concepts, maxDepth);
+        reasoningSteps.push({
+            type: 'knowledge_traversal',
+            paths: graphInsights.paths,
+            discoveries: graphInsights.discoveries,
+            confidence: graphInsights.confidence
+        });
+        graphInsights.discoveries.forEach(d => insights.add(d));
+        // Step 6: Inference Chain Building
+        const inferences = this.buildInferenceChain(logicalComponents, graphInsights.triples, patterns);
+        reasoningSteps.push({
+            type: 'inference',
+            rules: inferences.rules,
+            conclusions: inferences.conclusions,
+            confidence: inferences.confidence
+        });
+        inferences.conclusions.forEach(c => insights.add(c));
+        // Step 7: Context-Aware Reasoning
+        if (context && Object.keys(context).length > 0) {
+            const contextInsights = this.applyContextualReasoning(query, context, patterns);
+            contextInsights.forEach(ci => insights.add(ci));
+            reasoningSteps.push({
+                type: 'contextual_reasoning',
+                insights: contextInsights,
+                confidence: 0.75
+            });
+        }
+        // Step 8: Hypothesis Generation
+        if (patterns.includes('hypothetical') || patterns.includes('exploratory') || patterns.includes('lateral')) {
+            const hypotheses = this.generateHypotheses(entities.concepts, inferences.conclusions);
+            reasoningSteps.push({
+                type: 'hypothesis_generation',
+                hypotheses,
+                confidence: 0.7
+            });
+            hypotheses.forEach(h => insights.add(h));
+        }
+        // Step 9: Edge Case Analysis (for API/system design queries)
+        if (query.toLowerCase().includes('edge case') || query.toLowerCase().includes('hidden') ||
+            context.focus === 'hidden_complexities') {
+            const edgeCases = this.analyzeEdgeCases(query, entities.concepts);
+            edgeCases.forEach(ec => insights.add(ec));
+            reasoningSteps.push({
+                type: 'edge_case_analysis',
+                cases: edgeCases,
+                confidence: 0.8
+            });
+        }
+        // Step 10: Contradiction Detection and Resolution
+        const contradictions = this.detectContradictions(Array.from(insights));
+        if (contradictions.length > 0) {
+            const resolutions = this.resolveContradictions(contradictions, context);
+            reasoningSteps.push({
+                type: 'contradiction_resolution',
+                contradictions,
+                resolutions,
+                confidence: 0.8
+            });
+        }
+        // Step 11: Synthesis
+        const synthesis = this.synthesizeCompleteAnswer(query, Array.from(insights), reasoningSteps, patterns, context);
+        const result = {
+            answer: synthesis.answer,
+            confidence: synthesis.confidence,
+            reasoning: reasoningSteps,
+            insights: Array.from(insights),
+            patterns,
+            depth: graphInsights.maxDepth || maxDepth,
+            entities: entities.entities,
+            concepts: entities.concepts,
+            triples_examined: graphInsights.triples.length,
+            inference_rules_applied: inferences.rules.length
+        };
+        // Cache result
+        this.reasoningCache.set(cacheKey, result);
+        return result;
+    }
+    generateDomainInsights(query, patterns, context) {
+        const insights = [];
+        const queryLower = query.toLowerCase();
+        // API Design Insights
+        if (queryLower.includes('api') || queryLower.includes('rest') || context.domain === 'api_design') {
+            insights.push('Consider idempotency for all mutating operations to handle network retries');
+            insights.push('Implement versioning strategy from day one - URL, header, or content negotiation');
+            insights.push('Rate limiting should be granular - per user, per endpoint, and per operation type');
+            insights.push('CORS configuration often breaks in production - test with actual domain names');
+            insights.push('Bulk operations need careful transaction boundary management');
+            if (queryLower.includes('user')) {
+                insights.push('User deletion must handle cascading data relationships and GDPR compliance');
+                insights.push('Password reset flows are prime targets for timing attacks');
+                insights.push('Session management across devices requires careful token invalidation');
+                insights.push('Email verification tokens should expire and be single-use');
+            }
+        }
+        // Hidden Complexities
+        if (queryLower.includes('hidden') || queryLower.includes('non-obvious') || queryLower.includes('edge')) {
+            insights.push('Race conditions in concurrent user updates - last write wins vs merge conflicts');
+            insights.push('Time zone handling - server, client, and user preference mismatches');
+            insights.push('Pagination breaks when underlying data changes during traversal');
+            insights.push('Cache invalidation cascades in microservice architectures');
+            insights.push('OAuth token refresh race conditions in distributed systems');
+            insights.push('Database connection pool exhaustion under spike load');
+            insights.push('Unicode normalization issues in usernames and passwords');
+            insights.push('Integer overflow in ID generation at scale');
+        }
+        // Lateral Thinking Insights
+        if (patterns.includes('lateral') || context.pattern === 'lateral') {
+            insights.push('Consider using event sourcing for audit trail instead of traditional logging');
+            insights.push('GraphQL might solve over-fetching better than REST for complex relationships');
+            insights.push('WebSockets for real-time user presence instead of polling');
+            insights.push('JWT claims can carry authorization context to reduce database lookups');
+            insights.push('Use bloom filters for username availability checks at scale');
+            insights.push('Implement soft deletes with temporal tables for compliance');
+            insights.push('Consider CQRS for read-heavy user profile access patterns');
+        }
+        // System Interaction Complexities
+        if (queryLower.includes('system') || queryLower.includes('interaction')) {
+            insights.push('Load balancer health checks can trigger false circuit breaker opens');
+            insights.push('CDN cache can serve stale authentication states');
+            insights.push('Database read replicas lag can cause phantom user creation failures');
+            insights.push('Message queue failures can orphan user records');
+            insights.push('Service mesh retry policies can amplify failures');
+            insights.push('Distributed tracing overhead affects latency measurements');
+        }
+        // Security Considerations
+        if (queryLower.includes('security') || queryLower.includes('user')) {
+            insights.push('Timing attacks on user enumeration through login response times');
+            insights.push('JWT secret rotation without service disruption');
+            insights.push('Password history storage needs separate encryption');
+            insights.push('Account takeover protection via behavioral analysis');
+            insights.push('API key rotation mechanisms for service accounts');
+        }
+        return insights;
+    }
+    applyContextualReasoning(query, context, patterns) {
+        const insights = [];
+        if (context.focus === 'hidden_complexities') {
+            insights.push('Hidden complexity: Distributed consensus for user state changes');
+            insights.push('Hidden complexity: Eventual consistency in user search indices');
+            insights.push('Hidden complexity: GDPR data portability implementation details');
+            insights.push('Hidden complexity: Cross-region data replication latency');
+        }
+        if (context.pattern === 'lateral') {
+            insights.push('Lateral solution: Use blockchain for decentralized identity verification');
+            insights.push('Lateral solution: Implement passwordless auth via magic links');
+            insights.push('Lateral solution: Use ML for anomaly detection in access patterns');
+            insights.push('Lateral solution: Federated user management across microservices');
+        }
+        if (context.domain === 'api_design') {
+            insights.push('API consideration: Hypermedia controls for self-documenting endpoints');
+            insights.push('API consideration: GraphQL subscriptions for real-time updates');
+            insights.push('API consideration: OpenAPI spec generation from code');
+            insights.push('API consideration: Request/response compression strategies');
+        }
+        return insights;
+    }
+    analyzeEdgeCases(query, concepts) {
+        const edgeCases = [];
+        // Universal edge cases
+        edgeCases.push('Edge case: Null, undefined, and empty string handling differences');
+        edgeCases.push('Edge case: Maximum length inputs causing buffer overflows');
+        edgeCases.push('Edge case: Concurrent modifications to the same resource');
+        edgeCases.push('Edge case: Clock skew between distributed components');
+        // API-specific edge cases
+        if (concepts.includes('api') || concepts.includes('rest')) {
+            edgeCases.push('Edge case: Partial success in batch operations');
+            edgeCases.push('Edge case: Request timeout during long-running operations');
+            edgeCases.push('Edge case: Content-Type mismatches with actual payload');
+            edgeCases.push('Edge case: HTTP/2 multiplexing affecting rate limits');
+        }
+        // User management edge cases
+        if (concepts.includes('user') || concepts.includes('authentication')) {
+            edgeCases.push('Edge case: User creation with recycled email addresses');
+            edgeCases.push('Edge case: Session fixation during concurrent logins');
+            edgeCases.push('Edge case: Account merge conflicts with OAuth providers');
+            edgeCases.push('Edge case: Birthday paradox in random token generation');
+        }
+        return edgeCases;
+    }
+    identifyCognitivePatterns(query) {
+        const patterns = [];
+        const lowerQuery = query.toLowerCase();
+        const patternMap = {
+            'causal': ['why', 'cause', 'because', 'result', 'effect', 'lead to'],
+            'procedural': ['how', 'process', 'step', 'method', 'way', 'approach', 'design', 'implement'],
+            'hypothetical': ['what if', 'suppose', 'imagine', 'could', 'would', 'might'],
+            'comparative': ['compare', 'difference', 'similar', 'versus', 'than', 'like'],
+            'definitional': ['what is', 'define', 'meaning', 'definition'],
+            'evaluative': ['best', 'worst', 'better', 'optimal', 'evaluate'],
+            'temporal': ['when', 'time', 'before', 'after', 'during', 'temporal'],
+            'spatial': ['where', 'location', 'position', 'space'],
+            'quantitative': ['how many', 'how much', 'count', 'measure', 'amount'],
+            'existential': ['exist', 'there is', 'there are', 'presence'],
+            'universal': ['all', 'every', 'always', 'never', 'none'],
+            'lateral': ['lateral', 'unconventional', 'creative', 'alternative', 'non-obvious', 'hidden'],
+            'systems': ['system', 'interaction', 'complexity', 'emergence', 'holistic'],
+            'exploratory': ['explore', 'discover', 'investigate', 'consider', 'edge case']
+        };
+        for (const [pattern, keywords] of Object.entries(patternMap)) {
+            if (keywords.some(keyword => lowerQuery.includes(keyword))) {
+                patterns.push(pattern);
+            }
+        }
+        if (patterns.length === 0) {
+            patterns.push('exploratory');
+        }
+        return patterns;
+    }
+    extractEntitiesAndConcepts(query) {
+        const words = query.split(/\s+/);
+        const entities = [];
+        const concepts = [];
+        const relationships = [];
+        // Extract technical terms and concepts
+        const technicalTerms = [
+            'api', 'rest', 'graphql', 'user', 'management', 'authentication',
+            'authorization', 'database', 'cache', 'security', 'performance',
+            'scalability', 'microservice', 'distributed', 'system', 'design',
+            'endpoint', 'resource', 'crud', 'http', 'json', 'xml', 'oauth',
+            'jwt', 'session', 'token', 'password', 'encryption', 'hash'
+        ];
+        // Extract named entities (capitalized words not at sentence start)
+        for (let i = 0; i < words.length; i++) {
+            const word = words[i];
+            const wordLower = word.toLowerCase();
+            if (/^[A-Z]/.test(word) && i > 0 && !['The', 'A', 'An', 'What', 'How', 'Why', 'When', 'Where'].includes(word)) {
+                entities.push(wordLower);
+            }
+            if (technicalTerms.includes(wordLower)) {
+                concepts.push(wordLower);
+            }
+        }
+        // Extract key concepts from knowledge base
+        const queryLower = query.toLowerCase();
+        for (const concept of this.knowledgeBase.getAllTriples().map(t => [t.subject, t.object]).flat()) {
+            if (queryLower.includes(concept)) {
+                concepts.push(concept);
+            }
+        }
+        // Extract relationships (verbs and prepositions)
+        const relationshipPatterns = [
+            'is', 'are', 'was', 'were', 'has', 'have', 'had',
+            'can', 'could', 'will', 'would', 'should',
+            'design', 'implement', 'create', 'build', 'develop',
+            'requires', 'needs', 'uses', 'enables', 'prevents',
+            'increases', 'decreases', 'affects', 'influences'
+        ];
+        for (const word of words) {
+            const wordLower = word.toLowerCase();
+            if (relationshipPatterns.includes(wordLower)) {
+                relationships.push(wordLower);
+            }
+        }
+        // Add query-specific concepts
+        if (queryLower.includes('edge case'))
+            concepts.push('edge_cases');
+        if (queryLower.includes('hidden'))
+            concepts.push('hidden_complexity');
+        if (queryLower.includes('api'))
+            concepts.push('api_design');
+        if (queryLower.includes('user'))
+            concepts.push('user_management');
+        return {
+            entities: [...new Set(entities)],
+            concepts: [...new Set(concepts)],
+            relationships: [...new Set(relationships)]
+        };
+    }
+    extractLogicalComponents(query) {
+        const components = {
+            predicates: [],
+            quantifiers: [],
+            operators: [],
+            modals: [],
+            negations: []
+        };
+        const lowerQuery = query.toLowerCase();
+        // Extract predicates (subject-verb-object patterns)
+        const predicateMatches = lowerQuery.match(/(\w+)\s+(is|are|was|were|has|have|had)\s+(\w+)/g);
+        if (predicateMatches) {
+            components.predicates = predicateMatches.map(p => p.trim());
+        }
+        // Extract quantifiers
+        const quantifierPattern = /\b(all|every|some|any|no|none|many|few|most|several)\b/gi;
+        const quantifierMatches = lowerQuery.match(quantifierPattern);
+        if (quantifierMatches) {
+            components.quantifiers = quantifierMatches;
+        }
+        // Extract logical operators
+        const operatorPattern = /\b(and|or|not|if|then|implies|therefore|because|but|however)\b/gi;
+        const operatorMatches = lowerQuery.match(operatorPattern);
+        if (operatorMatches) {
+            components.operators = operatorMatches;
+        }
+        // Extract modal verbs
+        const modalPattern = /\b(can|could|may|might|must|shall|should|will|would)\b/gi;
+        const modalMatches = lowerQuery.match(modalPattern);
+        if (modalMatches) {
+            components.modals = modalMatches;
+        }
+        // Extract negations
+        const negationPattern = /\b(not|no|never|neither|nor|nothing|nobody|nowhere)\b/gi;
+        const negationMatches = lowerQuery.match(negationPattern);
+        if (negationMatches) {
+            components.negations = negationMatches;
+        }
+        return components;
+    }
+    async traverseKnowledgeGraph(concepts, maxDepth) {
+        const visited = new Set();
+        const paths = [];
+        const discoveries = [];
+        const triples = [];
+        let currentDepth = 0;
+        let maxConfidence = 0;
+        // BFS traversal
+        const queue = concepts.map(c => ({
+            concept: c,
+            depth: 0,
+            confidence: 1.0,
+            path: [c],
+            inferences: []
+        }));
+        while (queue.length > 0 && currentDepth < maxDepth) {
+            const node = queue.shift();
+            if (visited.has(node.concept))
+                continue;
+            visited.add(node.concept);
+            currentDepth = Math.max(currentDepth, node.depth);
+            paths.push(node.path);
+            // Find related triples
+            const related = this.knowledgeBase.findRelated(node.concept);
+            triples.push(...related);
+            for (const triple of related) {
+                // Generate discoveries
+                const discovery = `${triple.subject} ${triple.predicate} ${triple.object}`;
+                discoveries.push(discovery);
+                maxConfidence = Math.max(maxConfidence, triple.confidence * node.confidence);
+                // Add connected concepts to queue
+                const nextConcept = triple.subject === node.concept ? triple.object : triple.subject;
+                if (!visited.has(nextConcept) && node.depth < maxDepth - 1) {
+                    queue.push({
+                        concept: nextConcept,
+                        depth: node.depth + 1,
+                        confidence: node.confidence * triple.confidence,
+                        path: [...node.path, nextConcept],
+                        inferences: [...node.inferences, discovery]
+                    });
+                }
+            }
+        }
+        return {
+            paths,
+            discoveries: discoveries.slice(0, 20), // Limit discoveries
+            triples,
+            maxDepth: currentDepth,
+            confidence: maxConfidence
+        };
+    }
+    buildInferenceChain(logicalComponents, triples, patterns) {
+        const rules = [];
+        const conclusions = [];
+        let confidence = 0.5;
+        // Apply Modus Ponens
+        if (logicalComponents.operators.includes('if') || logicalComponents.operators.includes('then')) {
+            rules.push('modus_ponens');
+            // Find implications in triples
+            for (const triple of triples) {
+                if (triple.predicate === 'implies' || triple.predicate === 'causes' || triple.predicate === 'enables') {
+                    conclusions.push(`${triple.subject} leads to ${triple.object}`);
+                    confidence = Math.max(confidence, triple.confidence * 0.9);
+                }
+            }
+        }
+        // Apply Universal Instantiation
+        if (logicalComponents.quantifiers.some((q) => ['all', 'every'].includes(q))) {
+            rules.push('universal_instantiation');
+            conclusions.push('universal property applies to specific instances');
+            confidence = Math.max(confidence, 0.85);
+        }
+        // Apply Existential Generalization
+        if (logicalComponents.quantifiers.some((q) => ['some', 'exist'].includes(q))) {
+            rules.push('existential_generalization');
+            conclusions.push('at least one instance exists with the property');
+            confidence = Math.max(confidence, 0.8);
+        }
+        // Apply Transitive Property
+        const transitivePredicates = ['causes', 'enables', 'requires', 'leads_to'];
+        const transitiveChains = this.findTransitiveChains(triples, transitivePredicates);
+        if (transitiveChains.length > 0) {
+            rules.push('transitive_property');
+            transitiveChains.forEach(chain => {
+                conclusions.push(`${chain.start} transitively ${chain.predicate} ${chain.end}`);
+            });
+            confidence = Math.max(confidence, 0.75);
+        }
+        // Apply Pattern-Specific Rules
+        if (patterns.includes('causal')) {
+            rules.push('causal_chain_analysis');
+            const causalChains = triples.filter(t => ['causes', 'results_in', 'leads_to', 'produces'].includes(t.predicate));
+            causalChains.forEach(chain => {
+                conclusions.push(`causal relationship: ${chain.subject} → ${chain.object}`);
+            });
+        }
+        if (patterns.includes('temporal')) {
+            rules.push('temporal_ordering');
+            conclusions.push('events ordered by temporal precedence');
+        }
+        // Generate domain-specific conclusions
+        if (triples.some(t => t.subject.includes('api') || t.object.includes('api'))) {
+            conclusions.push('API design requires consistency and versioning');
+            conclusions.push('RESTful principles ensure stateless interactions');
+            confidence = Math.max(confidence, 0.85);
+        }
+        if (triples.some(t => t.subject.includes('user') || t.object.includes('user'))) {
+            conclusions.push('user management requires authentication and authorization');
+            conclusions.push('security measures prevent unauthorized access');
+            confidence = Math.max(confidence, 0.9);
+        }
+        return {
+            rules,
+            conclusions,
+            confidence
+        };
+    }
+    findTransitiveChains(triples, predicates) {
+        const chains = [];
+        for (const predicate of predicates) {
+            const relevantTriples = triples.filter(t => t.predicate === predicate);
+            for (let i = 0; i < relevantTriples.length; i++) {
+                for (let j = 0; j < relevantTriples.length; j++) {
+                    if (relevantTriples[i].object === relevantTriples[j].subject) {
+                        chains.push({
+                            start: relevantTriples[i].subject,
+                            middle: relevantTriples[i].object,
+                            end: relevantTriples[j].object,
+                            predicate
+                        });
+                    }
+                }
+            }
+        }
+        return chains;
+    }
+    generateHypotheses(concepts, conclusions) {
+        const hypotheses = [];
+        // Generate hypotheses based on concept combinations
+        for (let i = 0; i < concepts.length; i++) {
+            for (let j = i + 1; j < concepts.length; j++) {
+                hypotheses.push(`hypothesis: ${concepts[i]} might be related to ${concepts[j]}`);
+            }
+        }
+        // Generate hypotheses from conclusions
+        for (const conclusion of conclusions) {
+            if (conclusion.includes('leads to') || conclusion.includes('causes')) {
+                hypotheses.push(`hypothesis: reversing ${conclusion} might have opposite effect`);
+            }
+        }
+        // Domain-specific hypotheses
+        if (concepts.includes('api_design')) {
+            hypotheses.push('hypothesis: event-driven architecture might reduce coupling');
+            hypotheses.push('hypothesis: CQRS pattern could improve read performance');
+        }
+        if (concepts.includes('user_management')) {
+            hypotheses.push('hypothesis: passwordless authentication might improve security');
+            hypotheses.push('hypothesis: federated identity could simplify user management');
+        }
+        return hypotheses.slice(0, 5); // Limit hypotheses
+    }
+    detectContradictions(statements) {
+        const contradictions = [];
+        for (let i = 0; i < statements.length; i++) {
+            for (let j = i + 1; j < statements.length; j++) {
+                // Check for direct negation
+                if (statements[i].includes('not') && statements[j] === statements[i].replace('not ', '')) {
+                    contradictions.push({
+                        type: 'direct_negation',
+                        statement1: statements[i],
+                        statement2: statements[j]
+                    });
+                }
+                // Check for semantic opposition
+                const opposites = [
+                    ['increases', 'decreases'],
+                    ['enables', 'prevents'],
+                    ['causes', 'prevents'],
+                    ['always', 'never'],
+                    ['all', 'none']
+                ];
+                for (const [word1, word2] of opposites) {
+                    if ((statements[i].includes(word1) && statements[j].includes(word2)) ||
+                        (statements[i].includes(word2) && statements[j].includes(word1))) {
+                        contradictions.push({
+                            type: 'semantic_opposition',
+                            statement1: statements[i],
+                            statement2: statements[j],
+                            conflict: [word1, word2]
+                        });
+                    }
+                }
+            }
+        }
+        return contradictions;
+    }
+    resolveContradictions(contradictions, context) {
+        return contradictions.map(c => ({
+            original: c,
+            resolution: 'resolved through context disambiguation',
+            method: c.type === 'direct_negation' ? 'logical_priority' : 'semantic_analysis',
+            confidence: 0.7
+        }));
+    }
+    synthesizeCompleteAnswer(query, insights, steps, patterns, context) {
+        let confidence = 0.5;
+        let keyInsights = insights.slice(0, 10); // Get more insights
+        // If no insights from knowledge graph, use generated domain insights
+        if (keyInsights.length === 0) {
+            keyInsights = this.generateDefaultInsights(query, patterns, context);
+        }
+        // Calculate confidence from reasoning steps
+        for (const step of steps) {
+            if (step.confidence) {
+                confidence = Math.max(confidence, step.confidence * 0.9);
+            }
+        }
+        // Build comprehensive answer based on pattern and context
+        let answer = '';
+        if (patterns.includes('lateral') || context.pattern === 'lateral') {
+            answer = `Thinking laterally about this problem reveals several non-obvious considerations: ${keyInsights.slice(0, 3).join('; ')}. `;
+            answer += `Additionally, hidden complexities include: ${keyInsights.slice(3, 6).join('; ')}. `;
+        }
+        else if (patterns.includes('causal')) {
+            answer = `Based on causal analysis: ${keyInsights.join(' → ')}. `;
+        }
+        else if (patterns.includes('procedural')) {
+            answer = `The design process should consider: ${keyInsights.slice(0, 5).join(', then ')}. `;
+        }
+        else if (patterns.includes('comparative')) {
+            answer = `Comparison reveals: ${keyInsights.join(' versus ')}. `;
+        }
+        else if (patterns.includes('hypothetical')) {
+            answer = `Hypothetically: ${keyInsights.join(', additionally ')}. `;
+        }
+        else if (patterns.includes('systems')) {
+            answer = `From a systems perspective: ${keyInsights.slice(0, 4).join('. ')}. `;
+        }
+        else {
+            answer = `Analysis reveals the following considerations: ${keyInsights.slice(0, 5).join('. ')}. `;
+        }
+        // Add context-specific insights
+        if (context.focus === 'hidden_complexities') {
+            answer += `Hidden complexities that are often missed: ${keyInsights.slice(5, 8).join('; ')}. `;
+        }
+        // Add reasoning depth
+        answer += `This conclusion is based on ${steps.length} reasoning steps`;
+        // Add confidence qualifier
+        if (confidence > 0.9) {
+            answer += ' with very high confidence';
+        }
+        else if (confidence > 0.7) {
+            answer += ' with high confidence';
+        }
+        else if (confidence > 0.5) {
+            answer += ' with moderate confidence';
+        }
+        else {
+            answer += ' with exploratory confidence';
+        }
+        answer += '.';
+        return {
+            answer,
+            confidence,
+            keyInsights
+        };
+    }
+    generateDefaultInsights(query, patterns, context) {
+        const insights = [];
+        const queryLower = query.toLowerCase();
+        // Generate insights based on query content
+        if (queryLower.includes('api') || queryLower.includes('design')) {
+            insights.push('Consider backward compatibility from the start');
+            insights.push('Version your API to manage breaking changes');
+            insights.push('Implement comprehensive error handling with meaningful status codes');
+            insights.push('Design for idempotency in all state-changing operations');
+            insights.push('Plan for rate limiting and throttling mechanisms');
+        }
+        if (queryLower.includes('user') || queryLower.includes('management')) {
+            insights.push('Implement proper authentication and authorization separation');
+            insights.push('Consider GDPR and data privacy requirements');
+            insights.push('Plan for account recovery and security features');
+            insights.push('Design for multi-tenant architectures if needed');
+            insights.push('Include audit logging for compliance');
+        }
+        if (queryLower.includes('hidden') || queryLower.includes('edge')) {
+            insights.push('Watch for race conditions in concurrent operations');
+            insights.push('Handle timezone and localization complexities');
+            insights.push('Plan for data migration and schema evolution');
+            insights.push('Consider cache invalidation strategies');
+            insights.push('Design for graceful degradation');
+        }
+        return insights.length > 0 ? insights : ['No specific insights available for this query domain'];
+    }
+    async queryKnowledgeGraph(query, filters, limit) {
+        const results = this.knowledgeBase.query(query);
+        // Apply filters
+        let filtered = results;
+        if (filters.confidence) {
+            filtered = filtered.filter(t => t.confidence >= filters.confidence);
+        }
+        if (filters.predicate) {
+            filtered = filtered.filter(t => t.predicate === filters.predicate.toLowerCase());
+        }
+        // Sort by confidence
+        filtered.sort((a, b) => b.confidence - a.confidence);
+        // Limit results
+        const limited = filtered.slice(0, limit);
+        return {
+            query,
+            results: limited.map(t => ({
+                subject: t.subject,
+                predicate: t.predicate,
+                object: t.object,
+                confidence: t.confidence,
+                metadata: t.metadata
+            })),
+            total: limited.length,
+            totalAvailable: filtered.length
+        };
+    }
+    async addKnowledge(subject, predicate, object, confidence = 1.0, metadata = {}) {
+        const id = this.knowledgeBase.addTriple(subject, predicate, object, confidence, metadata);
+        return {
+            id,
+            status: 'added',
+            triple: {
+                subject: subject.toLowerCase(),
+                predicate: predicate.toLowerCase(),
+                object: object.toLowerCase(),
+                confidence
+            }
+        };
+    }
+}
+export default PsychoSymbolicTools;

vendor/sublinear-time-solver/dist/mcp/tools/psycho-symbolic.d.ts

@@ -0,0 +1,25 @@
+/**
+ * Complete Enhanced Psycho-Symbolic Reasoning with Full Learning Integration
+ * Includes: Domain Adaptation, Creative Reasoning, Enhanced Knowledge Base, Analogical Reasoning
+ */
+import { Tool } from '@modelcontextprotocol/sdk/types.js';
+export declare class PsychoSymbolicTools {
+    private knowledgeBase;
+    private domainEngine;
+    private creativeEngine;
+    private analogicalEngine;
+    private performanceCache;
+    private toolLearningHooks;
+    constructor();
+    getTools(): Tool[];
+    handleToolCall(name: string, args: any): Promise<any>;
+    private performCompleteReasoning;
+    private extractAdvancedEntities;
+    private enhancedKnowledgeTraversal;
+    private synthesizeAdvancedAnswer;
+    private advancedKnowledgeQuery;
+    private addEnhancedKnowledge;
+    private registerToolInteraction;
+    private getCrossToolInsights;
+    private getLearningStatus;
+}