feat: RSSI visualization, RuVector attention WASM, cache-bust fixes

- Add animated RSSI Signal Strength panel with sparkline history
- Fix RuVector WasmMultiHeadAttention retptr calling convention
- Wire up RuVector Multi-Head + Flash Attention in CNN embedder
- Add ambient temporal drift to CSI simulator for visible heatmap animation
- Fix embedding space projection (sparse projection replaces cancelling sum)
- Add auto-scaling to embedding space renderer
- Add cache busters (?v=4) to all ES module imports to prevent stale caches
- Add diagnostic logging for module version verification
- Add RSSI tracking with quality labels and color-coded dBm display
- Includes ruvector-attention-wasm v2.0.5 browser ESM wrapper

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

ui/pose-fusion/pkg/ruvector-attention/LICENSE

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2025 rUv
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

ui/pose-fusion/pkg/ruvector-attention/README.md

@@ -0,0 +1,220 @@
+# ruvector-attention-wasm
+
+WebAssembly bindings for the ruvector-attention package, providing high-performance attention mechanisms for browser and Node.js environments.
+
+## Features
+
+- **Multiple Attention Mechanisms**:
+  - Scaled Dot-Product Attention
+  - Multi-Head Attention
+  - Hyperbolic Attention (for hierarchical data)
+  - Linear Attention (Performer-style)
+  - Flash Attention (memory-efficient)
+  - Local-Global Attention
+  - Mixture of Experts (MoE) Attention
+  - **CGT Sheaf Attention** (coherence-gated via Prime-Radiant)
+
+- **Training Utilities**:
+  - InfoNCE contrastive loss
+  - Adam optimizer
+  - AdamW optimizer (with decoupled weight decay)
+  - Learning rate scheduler (warmup + cosine decay)
+
+- **TypeScript Support**: Full type definitions and modern API
+
+## Installation
+
+```bash
+npm install ruvector-attention-wasm
+```
+
+## Usage
+
+### TypeScript/JavaScript
+
+```typescript
+import { initialize, MultiHeadAttention, utils } from 'ruvector-attention-wasm';
+
+// Initialize WASM module
+await initialize();
+
+// Create multi-head attention
+const attention = new MultiHeadAttention({ dim: 64, numHeads: 8 });
+
+// Prepare inputs
+const query = new Float32Array(64);
+const keys = [new Float32Array(64), new Float32Array(64)];
+const values = [new Float32Array(64), new Float32Array(64)];
+
+// Compute attention
+const output = attention.compute(query, keys, values);
+
+// Use utilities
+const similarity = utils.cosineSimilarity(query, keys[0]);
+```
+
+### Advanced Examples
+
+#### Hyperbolic Attention
+
+```typescript
+import { HyperbolicAttention } from 'ruvector-attention-wasm';
+
+const hyperbolic = new HyperbolicAttention({
+  dim: 128,
+  curvature: 1.0
+});
+
+const output = hyperbolic.compute(query, keys, values);
+```
+
+#### MoE Attention with Expert Stats
+
+```typescript
+import { MoEAttention } from 'ruvector-attention-wasm';
+
+const moe = new MoEAttention({
+  dim: 64,
+  numExperts: 4,
+  topK: 2
+});
+
+const output = moe.compute(query, keys, values);
+
+// Get expert utilization
+const stats = moe.getExpertStats();
+console.log('Load balance:', stats.loadBalance);
+```
+
+#### Training with InfoNCE Loss
+
+```typescript
+import { InfoNCELoss, Adam } from 'ruvector-attention-wasm';
+
+const loss = new InfoNCELoss(0.07);
+const optimizer = new Adam(paramCount, {
+  learningRate: 0.001,
+  beta1: 0.9,
+  beta2: 0.999,
+});
+
+// Training loop
+const lossValue = loss.compute(anchor, positive, negatives);
+optimizer.step(params, gradients);
+```
+
+#### Learning Rate Scheduling
+
+```typescript
+import { LRScheduler, AdamW } from 'ruvector-attention-wasm';
+
+const scheduler = new LRScheduler({
+  initialLR: 0.001,
+  warmupSteps: 1000,
+  totalSteps: 10000,
+});
+
+const optimizer = new AdamW(paramCount, {
+  learningRate: scheduler.getLR(),
+  weightDecay: 0.01,
+});
+
+// Training loop
+for (let step = 0; step < 10000; step++) {
+  optimizer.learningRate = scheduler.getLR();
+  optimizer.step(params, gradients);
+  scheduler.step();
+}
+```
+
+## Building from Source
+
+### Prerequisites
+
+- Rust 1.70+
+- wasm-pack
+
+### Build Commands
+
+```bash
+# Build for web (ES modules)
+wasm-pack build --target web --out-dir pkg
+
+# Build for Node.js
+wasm-pack build --target nodejs --out-dir pkg-node
+
+# Build for bundlers (webpack, vite, etc.)
+wasm-pack build --target bundler --out-dir pkg-bundler
+
+# Run tests
+wasm-pack test --headless --firefox
+```
+
+## API Reference
+
+### Attention Mechanisms
+
+- `MultiHeadAttention` - Standard multi-head attention
+- `HyperbolicAttention` - Attention in hyperbolic space
+- `LinearAttention` - Linear complexity attention (Performer)
+- `FlashAttention` - Memory-efficient attention
+- `LocalGlobalAttention` - Combined local and global attention
+- `MoEAttention` - Mixture of Experts attention
+- `CGTSheafAttention` - Coherence-gated via Prime-Radiant energy
+- `scaledDotAttention()` - Functional API for basic attention
+
+### CGT Sheaf Attention (Prime-Radiant Integration)
+
+The CGT (Coherence-Gated Transformer) Sheaf Attention mechanism uses Prime-Radiant's sheaf Laplacian energy to gate attention based on mathematical consistency:
+
+```typescript
+import { CGTSheafAttention } from 'ruvector-attention-wasm';
+
+const cgtAttention = new CGTSheafAttention({
+  dim: 128,
+  numHeads: 8,
+  coherenceThreshold: 0.3,  // Block if energy > threshold
+});
+
+// Attention is gated by coherence energy
+const result = cgtAttention.compute(query, keys, values);
+console.log('Coherence energy:', result.energy);
+console.log('Is coherent:', result.isCoherent);
+```
+
+**Key features:**
+- Energy-weighted attention: Lower coherence energy → higher attention
+- Automatic hallucination detection via residual analysis
+- GPU-accelerated with wgpu WGSL shaders (vec4 optimized)
+- SIMD fallback (AVX-512/AVX2/NEON)
+
+### Training
+
+- `InfoNCELoss` - Contrastive loss function
+- `Adam` - Adam optimizer
+- `AdamW` - AdamW optimizer with weight decay
+- `LRScheduler` - Learning rate scheduler
+
+### Utilities
+
+- `utils.cosineSimilarity()` - Cosine similarity between vectors
+- `utils.l2Norm()` - L2 norm of a vector
+- `utils.normalize()` - Normalize vector to unit length
+- `utils.softmax()` - Apply softmax transformation
+- `utils.attentionWeights()` - Compute attention weights from scores
+- `utils.batchNormalize()` - Batch normalization
+- `utils.randomOrthogonalMatrix()` - Generate random orthogonal matrix
+- `utils.pairwiseDistances()` - Compute pairwise distances
+
+## Performance
+
+The WASM bindings provide near-native performance for attention computations:
+
+- Optimized with `opt-level = "s"` and LTO
+- SIMD acceleration where available
+- Efficient memory management
+- Zero-copy data transfer where possible
+
+## License
+
+MIT OR Apache-2.0

ui/pose-fusion/pkg/ruvector-attention/package.json

@@ -0,0 +1,28 @@
+{
+  "name": "ruvector-attention-wasm",
+  "collaborators": [
+    "Ruvector Team"
+  ],
+  "description": "High-performance WebAssembly attention mechanisms: Multi-Head, Flash, Hyperbolic, MoE, CGT Sheaf Attention with GPU acceleration for transformers and LLMs",
+  "version": "2.0.5",
+  "license": "MIT",
+  "repository": {
+    "type": "git",
+    "url": "https://github.com/ruvnet/ruvector"
+  },
+  "files": [
+    "ruvector_attention_wasm_bg.wasm",
+    "ruvector_attention_wasm.js",
+    "ruvector_attention_wasm.d.ts"
+  ],
+  "main": "ruvector_attention_wasm.js",
+  "homepage": "https://ruv.io/ruvector",
+  "types": "ruvector_attention_wasm.d.ts",
+  "keywords": [
+    "wasm",
+    "attention",
+    "transformer",
+    "flash-attention",
+    "llm"
+  ]
+}

ui/pose-fusion/pkg/ruvector-attention/ruvector_attention_browser.js

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+/**
+ * Browser ESM wrapper for ruvector-attention-wasm v2.0.5
+ *
+ * The upstream pkg/ was built with wasm-pack --target nodejs (CJS + fs.readFileSync).
+ * This wrapper loads the same WASM binary via fetch() for browser use.
+ *
+ * Usage:
+ *   import initWasm, { WasmMultiHeadAttention, ... } from './ruvector_attention_browser.js';
+ *   await initWasm();
+ *   const attn = new WasmMultiHeadAttention(dim, heads);
+ */
+
+let _wasm;
+let _initialized = false;
+
+// The entire CJS module runs inside this IIFE to avoid polluting global scope.
+// We capture all exports in _mod.
+const _mod = {};
+
+(function(exports, wasm_getter) {
+
+// ── wasm-bindgen heap management ──────────────────────────────────
+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 dropObject(idx) {
+  if (idx < 132) return;
+  heap[idx] = heap_next;
+  heap_next = idx;
+}
+function takeObject(idx) {
+  const ret = getObject(idx);
+  dropObject(idx);
+  return ret;
+}
+function isLikeNone(x) { return x === undefined || x === null; }
+
+// ── Memory views ──────────────────────────────────────────────────
+let cachedDataViewMemory0 = null;
+let cachedUint8ArrayMemory0 = null;
+let cachedFloat32ArrayMemory0 = null;
+
+function wasm() { return wasm_getter(); }
+
+function getDataViewMemory0() {
+  if (cachedDataViewMemory0 === null || cachedDataViewMemory0.buffer !== wasm().memory.buffer)
+    cachedDataViewMemory0 = new DataView(wasm().memory.buffer);
+  return cachedDataViewMemory0;
+}
+function getUint8ArrayMemory0() {
+  if (cachedUint8ArrayMemory0 === null || cachedUint8ArrayMemory0.buffer !== wasm().memory.buffer)
+    cachedUint8ArrayMemory0 = new Uint8Array(wasm().memory.buffer);
+  return cachedUint8ArrayMemory0;
+}
+function getFloat32ArrayMemory0() {
+  if (cachedFloat32ArrayMemory0 === null || cachedFloat32ArrayMemory0.buffer !== wasm().memory.buffer)
+    cachedFloat32ArrayMemory0 = new Float32Array(wasm().memory.buffer);
+  return cachedFloat32ArrayMemory0;
+}
+function getArrayF32FromWasm0(ptr, len) {
+  ptr = ptr >>> 0;
+  return getFloat32ArrayMemory0().subarray(ptr / 4, ptr / 4 + len);
+}
+function getArrayU8FromWasm0(ptr, len) {
+  ptr = ptr >>> 0;
+  return getUint8ArrayMemory0().subarray(ptr, ptr + len);
+}
+
+let WASM_VECTOR_LEN = 0;
+
+function passArrayF32ToWasm0(arg, malloc) {
+  const ptr = malloc(arg.length * 4, 4) >>> 0;
+  getFloat32ArrayMemory0().set(arg, ptr / 4);
+  WASM_VECTOR_LEN = arg.length;
+  return ptr;
+}
+
+const cachedTextEncoder = new TextEncoder();
+const cachedTextDecoder = new TextDecoder('utf-8', { ignoreBOM: true, fatal: true });
+cachedTextDecoder.decode();
+
+function getStringFromWasm0(ptr, len) {
+  ptr = ptr >>> 0;
+  return cachedTextDecoder.decode(getUint8ArrayMemory0().subarray(ptr, ptr + len));
+}
+
+function passStringToWasm0(arg, malloc, realloc) {
+  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;
+}
+
+function debugString(val) {
+  const type = typeof val;
+  if (type == 'number' || type == 'boolean' || val == null) return `${val}`;
+  if (type == 'string') return `"${val}"`;
+  if (type == 'symbol') return val.description ? `Symbol(${val.description})` : 'Symbol';
+  if (type == 'function') return 'Function';
+  if (Array.isArray(val)) return `[${val.map(debugString).join(', ')}]`;
+  try {
+    const keys = Object.keys(val);
+    return `{${keys.map(k => `${k}: ${debugString(val[k])}`).join(', ')}}`;
+  } catch (_) { return Object.prototype.toString.call(val); }
+}
+
+function handleError(f, args) {
+  try { return f.apply(this, args); }
+  catch (e) { wasm().__wbindgen_export3(addHeapObject(e)); }
+}
+
+// ── FinalizationRegistry ──────────────────────────────────────────
+const FR = typeof FinalizationRegistry !== 'undefined'
+  ? FinalizationRegistry
+  : class { register() {} unregister() {} };
+
+const WasmMultiHeadAttentionFinalization = new FR(ptr => wasm().__wbg_wasmmultiheadattention_free(ptr >>> 0, 1));
+const WasmFlashAttentionFinalization = new FR(ptr => wasm().__wbg_wasmflashattention_free(ptr >>> 0, 1));
+const WasmHyperbolicAttentionFinalization = new FR(ptr => wasm().__wbg_wasmhyperbolicattention_free(ptr >>> 0, 1));
+const WasmMoEAttentionFinalization = new FR(ptr => wasm().__wbg_wasmmoeattention_free(ptr >>> 0, 1));
+const WasmLinearAttentionFinalization = new FR(ptr => wasm().__wbg_wasmlinearattention_free(ptr >>> 0, 1));
+const WasmLocalGlobalAttentionFinalization = new FR(ptr => wasm().__wbg_wasmlocalglobalattention_free(ptr >>> 0, 1));
+
+// ── Classes ───────────────────────────────────────────────────────
+
+class WasmMultiHeadAttention {
+  constructor(dim, num_heads) {
+    const retptr = wasm().__wbindgen_add_to_stack_pointer(-16);
+    try {
+      wasm().wasmmultiheadattention_new(retptr, dim, num_heads);
+      var r0 = getDataViewMemory0().getInt32(retptr + 0, true);
+      var r1 = getDataViewMemory0().getInt32(retptr + 4, true);
+      var r2 = getDataViewMemory0().getInt32(retptr + 8, true);
+      if (r2) throw takeObject(r1);
+      this.__wbg_ptr = r0 >>> 0;
+      WasmMultiHeadAttentionFinalization.register(this, this.__wbg_ptr, this);
+    } finally {
+      wasm().__wbindgen_add_to_stack_pointer(16);
+    }
+  }
+  free() {
+    const ptr = this.__wbg_ptr; this.__wbg_ptr = 0;
+    WasmMultiHeadAttentionFinalization.unregister(this);
+    wasm().__wbg_wasmmultiheadattention_free(ptr, 0);
+  }
+  get dim() { return wasm().wasmmultiheadattention_dim(this.__wbg_ptr); }
+  get num_heads() { return wasm().wasmmultiheadattention_num_heads(this.__wbg_ptr); }
+  compute(query, keys, values) {
+    const retptr = wasm().__wbindgen_add_to_stack_pointer(-16);
+    try {
+      const ptr0 = passArrayF32ToWasm0(query, wasm().__wbindgen_export);
+      const len0 = WASM_VECTOR_LEN;
+      wasm().wasmmultiheadattention_compute(retptr, this.__wbg_ptr, ptr0, len0, addHeapObject(keys), addHeapObject(values));
+      var r0 = getDataViewMemory0().getInt32(retptr + 0, true);
+      var r1 = getDataViewMemory0().getInt32(retptr + 4, true);
+      var r2 = getDataViewMemory0().getInt32(retptr + 8, true);
+      var r3 = getDataViewMemory0().getInt32(retptr + 12, true);
+      if (r3) throw takeObject(r2);
+      var v1 = getArrayF32FromWasm0(r0, r1).slice();
+      wasm().__wbindgen_export4(r0, r1 * 4, 4);
+      return v1;
+    } finally {
+      wasm().__wbindgen_add_to_stack_pointer(16);
+    }
+  }
+}
+
+class WasmFlashAttention {
+  constructor(dim, block_size) {
+    const ret = wasm().wasmflashattention_new(dim, block_size);
+    this.__wbg_ptr = ret >>> 0;
+    WasmFlashAttentionFinalization.register(this, this.__wbg_ptr, this);
+  }
+  free() {
+    const ptr = this.__wbg_ptr; this.__wbg_ptr = 0;
+    WasmFlashAttentionFinalization.unregister(this);
+    wasm().__wbg_wasmflashattention_free(ptr, 0);
+  }
+  compute(query, keys, values) {
+    const retptr = wasm().__wbindgen_add_to_stack_pointer(-16);
+    try {
+      const ptr0 = passArrayF32ToWasm0(query, wasm().__wbindgen_export);
+      const len0 = WASM_VECTOR_LEN;
+      wasm().wasmflashattention_compute(retptr, this.__wbg_ptr, ptr0, len0, addHeapObject(keys), addHeapObject(values));
+      var r0 = getDataViewMemory0().getInt32(retptr + 0, true);
+      var r1 = getDataViewMemory0().getInt32(retptr + 4, true);
+      var r2 = getDataViewMemory0().getInt32(retptr + 8, true);
+      var r3 = getDataViewMemory0().getInt32(retptr + 12, true);
+      if (r3) throw takeObject(r2);
+      var v1 = getArrayF32FromWasm0(r0, r1).slice();
+      wasm().__wbindgen_export4(r0, r1 * 4, 4);
+      return v1;
+    } finally {
+      wasm().__wbindgen_add_to_stack_pointer(16);
+    }
+  }
+}
+
+class WasmHyperbolicAttention {
+  constructor(dim, curvature) {
+    const ret = wasm().wasmhyperbolicattention_new(dim, curvature);
+    this.__wbg_ptr = ret >>> 0;
+    WasmHyperbolicAttentionFinalization.register(this, this.__wbg_ptr, this);
+  }
+  free() {
+    const ptr = this.__wbg_ptr; this.__wbg_ptr = 0;
+    WasmHyperbolicAttentionFinalization.unregister(this);
+    wasm().__wbg_wasmhyperbolicattention_free(ptr, 0);
+  }
+  get curvature() { return wasm().wasmhyperbolicattention_curvature(this.__wbg_ptr); }
+  compute(query, keys, values) {
+    const retptr = wasm().__wbindgen_add_to_stack_pointer(-16);
+    try {
+      const ptr0 = passArrayF32ToWasm0(query, wasm().__wbindgen_export);
+      const len0 = WASM_VECTOR_LEN;
+      wasm().wasmhyperbolicattention_compute(retptr, this.__wbg_ptr, ptr0, len0, addHeapObject(keys), addHeapObject(values));
+      var r0 = getDataViewMemory0().getInt32(retptr + 0, true);
+      var r1 = getDataViewMemory0().getInt32(retptr + 4, true);
+      var r2 = getDataViewMemory0().getInt32(retptr + 8, true);
+      var r3 = getDataViewMemory0().getInt32(retptr + 12, true);
+      if (r3) throw takeObject(r2);
+      var v1 = getArrayF32FromWasm0(r0, r1).slice();
+      wasm().__wbindgen_export4(r0, r1 * 4, 4);
+      return v1;
+    } finally {
+      wasm().__wbindgen_add_to_stack_pointer(16);
+    }
+  }
+}
+
+class WasmMoEAttention {
+  constructor(dim, num_experts, top_k) {
+    const ret = wasm().wasmmoeattention_new(dim, num_experts, top_k);
+    this.__wbg_ptr = ret >>> 0;
+    WasmMoEAttentionFinalization.register(this, this.__wbg_ptr, this);
+  }
+  free() {
+    const ptr = this.__wbg_ptr; this.__wbg_ptr = 0;
+    WasmMoEAttentionFinalization.unregister(this);
+    wasm().__wbg_wasmmoeattention_free(ptr, 0);
+  }
+  compute(query, keys, values) {
+    const retptr = wasm().__wbindgen_add_to_stack_pointer(-16);
+    try {
+      const ptr0 = passArrayF32ToWasm0(query, wasm().__wbindgen_export);
+      const len0 = WASM_VECTOR_LEN;
+      wasm().wasmmoeattention_compute(retptr, this.__wbg_ptr, ptr0, len0, addHeapObject(keys), addHeapObject(values));
+      var r0 = getDataViewMemory0().getInt32(retptr + 0, true);
+      var r1 = getDataViewMemory0().getInt32(retptr + 4, true);
+      var r2 = getDataViewMemory0().getInt32(retptr + 8, true);
+      var r3 = getDataViewMemory0().getInt32(retptr + 12, true);
+      if (r3) throw takeObject(r2);
+      var v1 = getArrayF32FromWasm0(r0, r1).slice();
+      wasm().__wbindgen_export4(r0, r1 * 4, 4);
+      return v1;
+    } finally {
+      wasm().__wbindgen_add_to_stack_pointer(16);
+    }
+  }
+}
+
+// ── Standalone functions ──────────────────────────────────────────
+
+function cosine_similarity(a, b) {
+  const retptr = wasm().__wbindgen_add_to_stack_pointer(-16);
+  try {
+    const ptr0 = passArrayF32ToWasm0(a, wasm().__wbindgen_export);
+    const len0 = WASM_VECTOR_LEN;
+    const ptr1 = passArrayF32ToWasm0(b, wasm().__wbindgen_export);
+    const len1 = WASM_VECTOR_LEN;
+    wasm().cosine_similarity(retptr, ptr0, len0, ptr1, len1);
+    var r0 = getDataViewMemory0().getFloat64(retptr + 0, true);
+    var r1 = getDataViewMemory0().getInt32(retptr + 8, true);
+    var r2 = getDataViewMemory0().getInt32(retptr + 12, true);
+    if (r2) throw takeObject(r1);
+    return r0;
+  } finally {
+    wasm().__wbindgen_add_to_stack_pointer(16);
+  }
+}
+
+function normalize(vec) {
+  const ptr0 = passArrayF32ToWasm0(vec, wasm().__wbindgen_export);
+  const len0 = WASM_VECTOR_LEN;
+  wasm().normalize(ptr0, len0, addHeapObject(vec));
+}
+
+function l2_norm(vec) {
+  const retptr = wasm().__wbindgen_add_to_stack_pointer(-16);
+  try {
+    const ptr0 = passArrayF32ToWasm0(vec, wasm().__wbindgen_export);
+    const len0 = WASM_VECTOR_LEN;
+    wasm().l2_norm(retptr, ptr0, len0);
+    var r0 = getDataViewMemory0().getFloat64(retptr + 0, true);
+    var r1 = getDataViewMemory0().getInt32(retptr + 8, true);
+    var r2 = getDataViewMemory0().getInt32(retptr + 12, true);
+    if (r2) throw takeObject(r1);
+    return r0;
+  } finally {
+    wasm().__wbindgen_add_to_stack_pointer(16);
+  }
+}
+
+function softmax(vec) {
+  const ptr0 = passArrayF32ToWasm0(vec, wasm().__wbindgen_export);
+  const len0 = WASM_VECTOR_LEN;
+  wasm().softmax(ptr0, len0, addHeapObject(vec));
+}
+
+function rv_init() { wasm().init(); }
+
+function rv_version() {
+  let d0, d1;
+  const retptr = wasm().__wbindgen_add_to_stack_pointer(-16);
+  try {
+    wasm().version(retptr);
+    d0 = getDataViewMemory0().getInt32(retptr + 0, true);
+    d1 = getDataViewMemory0().getInt32(retptr + 4, true);
+    return getStringFromWasm0(d0, d1);
+  } finally {
+    wasm().__wbindgen_add_to_stack_pointer(16);
+    if (d0 !== undefined) wasm().__wbindgen_export4(d0, d1, 1);
+  }
+}
+
+// ── Collect exports ───────────────────────────────────────────────
+exports.WasmMultiHeadAttention = WasmMultiHeadAttention;
+exports.WasmFlashAttention = WasmFlashAttention;
+exports.WasmHyperbolicAttention = WasmHyperbolicAttention;
+exports.WasmMoEAttention = WasmMoEAttention;
+exports.cosine_similarity = cosine_similarity;
+exports.normalize = normalize;
+exports.l2_norm = l2_norm;
+exports.softmax = softmax;
+exports.init = rv_init;
+exports.version = rv_version;
+
+// ── Build WASM import object ──────────────────────────────────────
+exports.__wbg_get_imports = function() {
+  const import0 = {
+    __proto__: null,
+    __wbg_Error_4577686b3a6d9b3a: (arg0, arg1) => addHeapObject(Error(getStringFromWasm0(arg0, arg1))),
+    __wbg_String_8564e559799eccda: (arg0, arg1) => {
+      const ret = String(getObject(arg1));
+      const ptr1 = passStringToWasm0(ret, wasm().__wbindgen_export, wasm().__wbindgen_export2);
+      const len1 = WASM_VECTOR_LEN;
+      getDataViewMemory0().setInt32(arg0 + 4, len1, true);
+      getDataViewMemory0().setInt32(arg0, ptr1, true);
+    },
+    __wbg___wbindgen_boolean_get_18c4ed9422296fff: (arg0) => {
+      const v = getObject(arg0);
+      const ret = typeof v === 'boolean' ? v : undefined;
+      return isLikeNone(ret) ? 0xFFFFFF : ret ? 1 : 0;
+    },
+    __wbg___wbindgen_copy_to_typed_array_5294f8e46aecc086: (arg0, arg1, arg2) => {
+      new Uint8Array(getObject(arg2).buffer, getObject(arg2).byteOffset, getObject(arg2).byteLength).set(getArrayU8FromWasm0(arg0, arg1));
+    },
+    __wbg___wbindgen_debug_string_ddde1867f49c2442: (arg0, arg1) => {
+      const ret = debugString(getObject(arg1));
+      const ptr1 = passStringToWasm0(ret, wasm().__wbindgen_export, wasm().__wbindgen_export2);
+      const len1 = WASM_VECTOR_LEN;
+      getDataViewMemory0().setInt32(arg0 + 4, len1, true);
+      getDataViewMemory0().setInt32(arg0, ptr1, true);
+    },
+    __wbg___wbindgen_is_function_d633e708baf0d146: (arg0) => typeof getObject(arg0) === 'function',
+    __wbg___wbindgen_is_object_4b3de556756ee8a8: (arg0) => {
+      const val = getObject(arg0);
+      return typeof val === 'object' && val !== null;
+    },
+    __wbg___wbindgen_jsval_loose_eq_1562ceb9af84e990: (arg0, arg1) => getObject(arg0) == getObject(arg1),
+    __wbg___wbindgen_number_get_5854912275df1894: (arg0, arg1) => {
+      const obj = getObject(arg1);
+      const ret = typeof obj === 'number' ? obj : undefined;
+      getDataViewMemory0().setFloat64(arg0 + 8, isLikeNone(ret) ? 0 : ret, true);
+      getDataViewMemory0().setInt32(arg0, !isLikeNone(ret), true);
+    },
+    __wbg___wbindgen_string_get_3e5751597f39a112: (arg0, arg1) => {
+      const obj = getObject(arg1);
+      const ret = typeof obj === 'string' ? obj : undefined;
+      var ptr1 = isLikeNone(ret) ? 0 : passStringToWasm0(ret, wasm().__wbindgen_export, wasm().__wbindgen_export2);
+      var len1 = WASM_VECTOR_LEN;
+      getDataViewMemory0().setInt32(arg0 + 4, len1, true);
+      getDataViewMemory0().setInt32(arg0, ptr1, true);
+    },
+    __wbg___wbindgen_throw_39bc967c0e5a9b58: (arg0, arg1) => { throw new Error(getStringFromWasm0(arg0, arg1)); },
+    __wbg_call_73af281463ec8b58: function() { return handleError(function(arg0, arg1) {
+      return addHeapObject(getObject(arg0).call(getObject(arg1)));
+    }, arguments); },
+    __wbg_done_5aad55ec6b1954b1: (arg0) => getObject(arg0).done,
+    __wbg_error_a6fa202b58aa1cd3: (arg0, arg1) => {
+      try { console.error(getStringFromWasm0(arg0, arg1)); }
+      finally { wasm().__wbindgen_export4(arg0, arg1, 1); }
+    },
+    __wbg_error_ad28debb48b5c6bb: (arg0) => console.error(getObject(arg0)),
+    __wbg_get_4920fefd3451364b: function() { return handleError(function(arg0, arg1) {
+      return addHeapObject(Reflect.get(getObject(arg0), getObject(arg1)));
+    }, arguments); },
+    __wbg_get_unchecked_3d0f4b91c8eca4f0: (arg0, arg1) => addHeapObject(getObject(arg0)[arg1 >>> 0]),
+    __wbg_instanceof_ArrayBuffer_15859862b80b732d: (arg0) => {
+      try { return getObject(arg0) instanceof ArrayBuffer; } catch (_) { return false; }
+    },
+    __wbg_instanceof_Uint8Array_2240b7046ac16f05: (arg0) => {
+      try { return getObject(arg0) instanceof Uint8Array; } catch (_) { return false; }
+    },
+    __wbg_isArray_fad08a0d12828686: (arg0) => Array.isArray(getObject(arg0)),
+    __wbg_iterator_fc7ad8d33bab9e26: () => addHeapObject(Symbol.iterator),
+    __wbg_length_5855c1f289dfffc1: (arg0) => getObject(arg0).length,
+    __wbg_length_a31e05262e09b7f8: (arg0) => getObject(arg0).length,
+    __wbg_log_3c5e4b64af29e724: (arg0) => console.log(getObject(arg0)),
+    __wbg_new_09959f7b4c92c246: (arg0) => addHeapObject(new Uint8Array(getObject(arg0))),
+    __wbg_new_227d7c05414eb861: () => addHeapObject(new Error()),
+    __wbg_new_cbee8c0d5c479eac: () => addHeapObject(new Array()),
+    __wbg_next_a5fe6f328f7affc2: (arg0) => addHeapObject(getObject(arg0).next),
+    __wbg_next_e592122bb4ed4c67: function() { return handleError(function(arg0) {
+      return addHeapObject(getObject(arg0).next());
+    }, arguments); },
+    __wbg_prototypesetcall_f034d444741426c3: (arg0, arg1, arg2) => {
+      Uint8Array.prototype.set.call(getArrayU8FromWasm0(arg0, arg1), getObject(arg2));
+    },
+    __wbg_random_2b7bed8995d680fb: () => Math.random(),
+    __wbg_set_4c81cfb5dc3a333c: (arg0, arg1, arg2) => { getObject(arg0)[arg1 >>> 0] = takeObject(arg2); },
+    __wbg_stack_3b0d974bbf31e44f: (arg0, arg1) => {
+      const ret = getObject(arg1).stack;
+      const ptr1 = passStringToWasm0(ret, wasm().__wbindgen_export, wasm().__wbindgen_export2);
+      const len1 = WASM_VECTOR_LEN;
+      getDataViewMemory0().setInt32(arg0 + 4, len1, true);
+      getDataViewMemory0().setInt32(arg0, ptr1, true);
+    },
+    __wbg_value_667dcb90597486a6: (arg0) => addHeapObject(getObject(arg0).value),
+    __wbindgen_cast_0000000000000001: (arg0, arg1) => addHeapObject(getStringFromWasm0(arg0, arg1)),
+    __wbindgen_object_drop_ref: (arg0) => takeObject(arg0),
+  };
+  return { __proto__: null, "./ruvector_attention_wasm_bg.js": import0 };
+};
+
+})(_mod, () => _wasm);
+
+
+// ── Async WASM init (fetch-based for browsers) ───────────────────
+
+export default async function initWasm() {
+  if (_initialized) return;
+  const wasmUrl = new URL('ruvector_attention_wasm_bg.wasm', import.meta.url);
+  const imports = _mod.__wbg_get_imports();
+  let result;
+  if (typeof WebAssembly.instantiateStreaming === 'function') {
+    try {
+      result = await WebAssembly.instantiateStreaming(fetch(wasmUrl), imports);
+    } catch (e) {
+      // Fallback if streaming fails (e.g. wrong MIME type)
+      const bytes = await (await fetch(wasmUrl)).arrayBuffer();
+      result = await WebAssembly.instantiate(bytes, imports);
+    }
+  } else {
+    const bytes = await (await fetch(wasmUrl)).arrayBuffer();
+    result = await WebAssembly.instantiate(bytes, imports);
+  }
+  _wasm = result.instance.exports;
+  _wasm.__wbindgen_start();
+  _initialized = true;
+}
+
+// ── ESM re-exports ────────────────────────────────────────────────
+export const WasmMultiHeadAttention = _mod.WasmMultiHeadAttention;
+export const WasmFlashAttention = _mod.WasmFlashAttention;
+export const WasmHyperbolicAttention = _mod.WasmHyperbolicAttention;
+export const WasmMoEAttention = _mod.WasmMoEAttention;
+export const cosine_similarity = _mod.cosine_similarity;
+export const normalize = _mod.normalize;
+export const l2_norm = _mod.l2_norm;
+export const softmax = _mod.softmax;
+export const init = _mod.init;
+export const version = _mod.version;

ui/pose-fusion/pkg/ruvector-attention/ruvector_attention_wasm.d.ts

@@ -0,0 +1,359 @@
+/* tslint:disable */
+/* eslint-disable */
+
+/**
+ * Adam optimizer
+ */
+export class WasmAdam {
+    free(): void;
+    [Symbol.dispose](): void;
+    /**
+     * Create a new Adam optimizer
+     *
+     * # Arguments
+     * * `param_count` - Number of parameters
+     * * `learning_rate` - Learning rate
+     */
+    constructor(param_count: number, learning_rate: number);
+    /**
+     * Reset optimizer state
+     */
+    reset(): void;
+    /**
+     * Perform optimization step
+     *
+     * # Arguments
+     * * `params` - Current parameter values (will be updated in-place)
+     * * `gradients` - Gradient values
+     */
+    step(params: Float32Array, gradients: Float32Array): void;
+    /**
+     * Get current learning rate
+     */
+    learning_rate: number;
+}
+
+/**
+ * AdamW optimizer (Adam with decoupled weight decay)
+ */
+export class WasmAdamW {
+    free(): void;
+    [Symbol.dispose](): void;
+    /**
+     * Create a new AdamW optimizer
+     *
+     * # Arguments
+     * * `param_count` - Number of parameters
+     * * `learning_rate` - Learning rate
+     * * `weight_decay` - Weight decay coefficient
+     */
+    constructor(param_count: number, learning_rate: number, weight_decay: number);
+    /**
+     * Reset optimizer state
+     */
+    reset(): void;
+    /**
+     * Perform optimization step with weight decay
+     */
+    step(params: Float32Array, gradients: Float32Array): void;
+    /**
+     * Get current learning rate
+     */
+    learning_rate: number;
+    /**
+     * Get weight decay
+     */
+    readonly weight_decay: number;
+}
+
+/**
+ * Flash attention mechanism
+ */
+export class WasmFlashAttention {
+    free(): void;
+    [Symbol.dispose](): void;
+    /**
+     * Compute flash attention
+     */
+    compute(query: Float32Array, keys: any, values: any): Float32Array;
+    /**
+     * Create a new flash attention instance
+     *
+     * # Arguments
+     * * `dim` - Embedding dimension
+     * * `block_size` - Block size for tiling
+     */
+    constructor(dim: number, block_size: number);
+}
+
+/**
+ * Hyperbolic attention mechanism
+ */
+export class WasmHyperbolicAttention {
+    free(): void;
+    [Symbol.dispose](): void;
+    /**
+     * Compute hyperbolic attention
+     */
+    compute(query: Float32Array, keys: any, values: any): Float32Array;
+    /**
+     * Create a new hyperbolic attention instance
+     *
+     * # Arguments
+     * * `dim` - Embedding dimension
+     * * `curvature` - Hyperbolic curvature parameter
+     */
+    constructor(dim: number, curvature: number);
+    /**
+     * Get the curvature
+     */
+    readonly curvature: number;
+}
+
+/**
+ * InfoNCE contrastive loss for training
+ */
+export class WasmInfoNCELoss {
+    free(): void;
+    [Symbol.dispose](): void;
+    /**
+     * Compute InfoNCE loss
+     *
+     * # Arguments
+     * * `anchor` - Anchor embedding
+     * * `positive` - Positive example embedding
+     * * `negatives` - Array of negative example embeddings
+     */
+    compute(anchor: Float32Array, positive: Float32Array, negatives: any): number;
+    /**
+     * Create a new InfoNCE loss instance
+     *
+     * # Arguments
+     * * `temperature` - Temperature parameter for softmax
+     */
+    constructor(temperature: number);
+}
+
+/**
+ * Learning rate scheduler
+ */
+export class WasmLRScheduler {
+    free(): void;
+    [Symbol.dispose](): void;
+    /**
+     * Get learning rate for current step
+     */
+    get_lr(): number;
+    /**
+     * Create a new learning rate scheduler with warmup and cosine decay
+     *
+     * # Arguments
+     * * `initial_lr` - Initial learning rate
+     * * `warmup_steps` - Number of warmup steps
+     * * `total_steps` - Total training steps
+     */
+    constructor(initial_lr: number, warmup_steps: number, total_steps: number);
+    /**
+     * Reset scheduler
+     */
+    reset(): void;
+    /**
+     * Advance to next step
+     */
+    step(): void;
+}
+
+/**
+ * Linear attention (Performer-style)
+ */
+export class WasmLinearAttention {
+    free(): void;
+    [Symbol.dispose](): void;
+    /**
+     * Compute linear attention
+     */
+    compute(query: Float32Array, keys: any, values: any): Float32Array;
+    /**
+     * Create a new linear attention instance
+     *
+     * # Arguments
+     * * `dim` - Embedding dimension
+     * * `num_features` - Number of random features
+     */
+    constructor(dim: number, num_features: number);
+}
+
+/**
+ * Local-global attention mechanism
+ */
+export class WasmLocalGlobalAttention {
+    free(): void;
+    [Symbol.dispose](): void;
+    /**
+     * Compute local-global attention
+     */
+    compute(query: Float32Array, keys: any, values: any): Float32Array;
+    /**
+     * Create a new local-global attention instance
+     *
+     * # Arguments
+     * * `dim` - Embedding dimension
+     * * `local_window` - Size of local attention window
+     * * `global_tokens` - Number of global attention tokens
+     */
+    constructor(dim: number, local_window: number, global_tokens: number);
+}
+
+/**
+ * Mixture of Experts (MoE) attention
+ */
+export class WasmMoEAttention {
+    free(): void;
+    [Symbol.dispose](): void;
+    /**
+     * Compute MoE attention
+     */
+    compute(query: Float32Array, keys: any, values: any): Float32Array;
+    /**
+     * Create a new MoE attention instance
+     *
+     * # Arguments
+     * * `dim` - Embedding dimension
+     * * `num_experts` - Number of expert attention mechanisms
+     * * `top_k` - Number of experts to use per query
+     */
+    constructor(dim: number, num_experts: number, top_k: number);
+}
+
+/**
+ * Multi-head attention mechanism
+ */
+export class WasmMultiHeadAttention {
+    free(): void;
+    [Symbol.dispose](): void;
+    /**
+     * Compute multi-head attention
+     */
+    compute(query: Float32Array, keys: any, values: any): Float32Array;
+    /**
+     * Create a new multi-head attention instance
+     *
+     * # Arguments
+     * * `dim` - Embedding dimension
+     * * `num_heads` - Number of attention heads
+     */
+    constructor(dim: number, num_heads: number);
+    /**
+     * Get the dimension
+     */
+    readonly dim: number;
+    /**
+     * Get the number of heads
+     */
+    readonly num_heads: number;
+}
+
+/**
+ * SGD optimizer with momentum
+ */
+export class WasmSGD {
+    free(): void;
+    [Symbol.dispose](): void;
+    /**
+     * Create a new SGD optimizer
+     *
+     * # Arguments
+     * * `param_count` - Number of parameters
+     * * `learning_rate` - Learning rate
+     * * `momentum` - Momentum coefficient (default: 0)
+     */
+    constructor(param_count: number, learning_rate: number, momentum?: number | null);
+    /**
+     * Reset optimizer state
+     */
+    reset(): void;
+    /**
+     * Perform optimization step
+     */
+    step(params: Float32Array, gradients: Float32Array): void;
+    /**
+     * Get current learning rate
+     */
+    learning_rate: number;
+}
+
+/**
+ * Compute attention weights from scores
+ */
+export function attention_weights(scores: Float32Array, temperature?: number | null): void;
+
+/**
+ * Get information about available attention mechanisms
+ */
+export function available_mechanisms(): any;
+
+/**
+ * Batch normalize vectors
+ */
+export function batch_normalize(vectors: any, epsilon?: number | null): Float32Array;
+
+/**
+ * Compute cosine similarity between two vectors
+ */
+export function cosine_similarity(a: Float32Array, b: Float32Array): number;
+
+/**
+ * Initialize the WASM module with panic hook
+ */
+export function init(): void;
+
+/**
+ * Compute L2 norm of a vector
+ */
+export function l2_norm(vec: Float32Array): number;
+
+/**
+ * Log a message to the browser console
+ */
+export function log(message: string): void;
+
+/**
+ * Log an error to the browser console
+ */
+export function log_error(message: string): void;
+
+/**
+ * Normalize a vector to unit length
+ */
+export function normalize(vec: Float32Array): void;
+
+/**
+ * Compute pairwise distances between vectors
+ */
+export function pairwise_distances(vectors: any): Float32Array;
+
+/**
+ * Generate random orthogonal matrix (for initialization)
+ */
+export function random_orthogonal_matrix(dim: number): Float32Array;
+
+/**
+ * Compute scaled dot-product attention
+ *
+ * # Arguments
+ * * `query` - Query vector as Float32Array
+ * * `keys` - Array of key vectors
+ * * `values` - Array of value vectors
+ * * `scale` - Optional scaling factor (defaults to 1/sqrt(dim))
+ */
+export function scaled_dot_attention(query: Float32Array, keys: any, values: any, scale?: number | null): Float32Array;
+
+/**
+ * Compute softmax of a vector
+ */
+export function softmax(vec: Float32Array): void;
+
+/**
+ * Get the version of the ruvector-attention-wasm crate
+ */
+export function version(): string;

ui/pose-fusion/pkg/ruvector-attention/ruvector_attention_wasm_bg.wasm.d.ts

@@ -0,0 +1,71 @@
+/* tslint:disable */
+/* eslint-disable */
+export const memory: WebAssembly.Memory;
+export const __wbg_wasmadam_free: (a: number, b: number) => void;
+export const __wbg_wasmadamw_free: (a: number, b: number) => void;
+export const __wbg_wasmflashattention_free: (a: number, b: number) => void;
+export const __wbg_wasmhyperbolicattention_free: (a: number, b: number) => void;
+export const __wbg_wasminfonceloss_free: (a: number, b: number) => void;
+export const __wbg_wasmlinearattention_free: (a: number, b: number) => void;
+export const __wbg_wasmmoeattention_free: (a: number, b: number) => void;
+export const __wbg_wasmmultiheadattention_free: (a: number, b: number) => void;
+export const __wbg_wasmsgd_free: (a: number, b: number) => void;
+export const attention_weights: (a: number, b: number, c: number, d: number) => void;
+export const available_mechanisms: () => number;
+export const batch_normalize: (a: number, b: number, c: number) => void;
+export const cosine_similarity: (a: number, b: number, c: number, d: number, e: number) => void;
+export const l2_norm: (a: number, b: number) => number;
+export const log: (a: number, b: number) => void;
+export const log_error: (a: number, b: number) => void;
+export const normalize: (a: number, b: number, c: number, d: number) => void;
+export const pairwise_distances: (a: number, b: number) => void;
+export const random_orthogonal_matrix: (a: number, b: number) => void;
+export const scaled_dot_attention: (a: number, b: number, c: number, d: number, e: number, f: number) => void;
+export const softmax: (a: number, b: number, c: number) => void;
+export const version: (a: number) => void;
+export const wasmadam_learning_rate: (a: number) => number;
+export const wasmadam_new: (a: number, b: number) => number;
+export const wasmadam_reset: (a: number) => void;
+export const wasmadam_set_learning_rate: (a: number, b: number) => void;
+export const wasmadam_step: (a: number, b: number, c: number, d: number, e: number, f: number) => void;
+export const wasmadamw_new: (a: number, b: number, c: number) => number;
+export const wasmadamw_reset: (a: number) => void;
+export const wasmadamw_step: (a: number, b: number, c: number, d: number, e: number, f: number) => void;
+export const wasmadamw_weight_decay: (a: number) => number;
+export const wasmflashattention_compute: (a: number, b: number, c: number, d: number, e: number, f: number) => void;
+export const wasmflashattention_new: (a: number, b: number) => number;
+export const wasmhyperbolicattention_compute: (a: number, b: number, c: number, d: number, e: number, f: number) => void;
+export const wasmhyperbolicattention_curvature: (a: number) => number;
+export const wasmhyperbolicattention_new: (a: number, b: number) => number;
+export const wasminfonceloss_compute: (a: number, b: number, c: number, d: number, e: number, f: number, g: number) => void;
+export const wasminfonceloss_new: (a: number) => number;
+export const wasmlinearattention_compute: (a: number, b: number, c: number, d: number, e: number, f: number) => void;
+export const wasmlinearattention_new: (a: number, b: number) => number;
+export const wasmlocalglobalattention_compute: (a: number, b: number, c: number, d: number, e: number, f: number) => void;
+export const wasmlocalglobalattention_new: (a: number, b: number, c: number) => number;
+export const wasmlrscheduler_get_lr: (a: number) => number;
+export const wasmlrscheduler_new: (a: number, b: number, c: number) => number;
+export const wasmlrscheduler_reset: (a: number) => void;
+export const wasmlrscheduler_step: (a: number) => void;
+export const wasmmoeattention_compute: (a: number, b: number, c: number, d: number, e: number, f: number) => void;
+export const wasmmoeattention_new: (a: number, b: number, c: number) => number;
+export const wasmmultiheadattention_compute: (a: number, b: number, c: number, d: number, e: number, f: number) => void;
+export const wasmmultiheadattention_dim: (a: number) => number;
+export const wasmmultiheadattention_new: (a: number, b: number, c: number) => void;
+export const wasmmultiheadattention_num_heads: (a: number) => number;
+export const wasmsgd_learning_rate: (a: number) => number;
+export const wasmsgd_new: (a: number, b: number, c: number) => number;
+export const wasmsgd_reset: (a: number) => void;
+export const wasmsgd_set_learning_rate: (a: number, b: number) => void;
+export const wasmsgd_step: (a: number, b: number, c: number, d: number, e: number, f: number) => void;
+export const init: () => void;
+export const wasmadamw_set_learning_rate: (a: number, b: number) => void;
+export const wasmadamw_learning_rate: (a: number) => number;
+export const __wbg_wasmlocalglobalattention_free: (a: number, b: number) => void;
+export const __wbg_wasmlrscheduler_free: (a: number, b: number) => void;
+export const __wbindgen_export: (a: number, b: number) => number;
+export const __wbindgen_export2: (a: number, b: number, c: number, d: number) => number;
+export const __wbindgen_export3: (a: number) => void;
+export const __wbindgen_export4: (a: number, b: number, c: number) => void;
+export const __wbindgen_add_to_stack_pointer: (a: number) => number;
+export const __wbindgen_start: () => void;