2 Commits

Author	SHA1	Message	Date
rUv	29de574e63	Beyond-SOTA engine/signal/train improvements: mesh partition guard, FFT CIR solver, canonical frame decoder, falsifiable occupancy benchmark, governed streaming, adapter provenance (#1018) ... * docs(research): add RuView beyond-SOTA system review (00) First document of the beyond-SOTA research series: capability audit of the current RuView engine with role-to-crate maturity matrix, ruvsense module inventory, gap analysis, and risk register. https://claude.ai/code/session_01MjBucx95K4BuUxZi8NWwRH * docs(research): add beyond-SOTA architecture design (02, in progress) https://claude.ai/code/session_01MjBucx95K4BuUxZi8NWwRH * docs(research): finalize beyond-SOTA architecture (02) https://claude.ai/code/session_01MjBucx95K4BuUxZi8NWwRH * docs(research): add benchmark/validation methodology snapshot (03) https://claude.ai/code/session_01MjBucx95K4BuUxZi8NWwRH * docs(research): add beyond-SOTA series index with validation results; changelog README index ties the 5 research docs together with the session's measured validation evidence: 2,797 workspace tests / 0 failed, Python proof PASS (bit-exact), and paired pre/post criterion CIR benchmarks. https://claude.ai/code/session_01MjBucx95K4BuUxZi8NWwRH * perf(signal): precompute CIR warm-start system; hoist tomography solver allocs Exact, determinism-safe optimizations (bit-identical float results): - cir.rs: diag(PhiH Phi)+lambda*I and its CSR matrix depend only on Phi and lambda (fixed at CirEstimator::new) but were rebuilt every frame (O(K*G) pass + CSR allocation). Now built once in new() via build_warm_start_system; summation order unchanged. - tomography.rs: ISTA gradient buffer hoisted out of the 100-iteration loop (fill(0.0) reset) and the Frobenius Lipschitz bound moved from per-reconstruct to construction. Verified: signal 456 tests green; engine 11/11 green including cycle_is_deterministic and witness-stability tests. Criterion paired pre/post: cir_estimate/he40 -3.9% (p<0.01), multiband -1.2/-1.4%. https://claude.ai/code/session_01MjBucx95K4BuUxZi8NWwRH * fix(worldgraph): bound SemanticState growth with deterministic retention StreamingEngine::process_cycle appended one SemanticState belief per cycle with no eviction — ~1.7M nodes/day at 20 Hz (beyond-SOTA roadmap finding #6). Add WorldGraph::prune_semantic_states(max): deterministic eviction of the oldest beliefs by (valid_from_unix_ms, id); structural nodes (rooms, zones, sensors, anchors, tracks, events) are never eligible. Wire it into the engine after each belief append (DEFAULT_SEMANTIC_RETENTION = 7,200, ~6 min at 20 Hz; set_semantic_retention to tune). The WorldGraph holds current beliefs; durable history is the recorder's job, so no audit data is lost. 3 new tests: end-to-end bounded growth, oldest-only eviction, deterministic equal-timestamp tie-break. Workspace gate: 2,865 passed, 0 failed. https://claude.ai/code/session_01MjBucx95K4BuUxZi8NWwRH * feat(sensing-server): route live frames through the governed StreamingEngine Closes the live-trust-path gap (ADR-136 section 8, beyond-SOTA system review): the running server fused live CSI with the bare MultistaticFuser, while the privacy/provenance/witness control plane (ADR-135..146) only ever ran on synthetic in-test frames. The privacy control plane was therefore bypassable on the real path. New engine_bridge module drives StreamingEngine::process_cycle from the server's live NodeState map, reusing the existing NodeState -> MultiBandCsiFrame conversion. It lazily wires each contributing node as a WorldGraph sensor (idempotent), bounds belief growth via the retention cap, and forwards explicit timestamps/calibration ids so the path stays deterministic and replayable. Wired additively into both live ESP32/WiFi fusion sites in main.rs via a split-borrow off the write guard, so person-count behavior is unchanged; the latest BLAKE3 witness is stored on AppState. Every published belief now carries evidence + model + calibration + privacy decision and a deterministic witness. Adds wifi-densepose-engine/-worldgraph/-bfld/-geo deps. 6 new bridge tests (witnessed belief with full provenance, cross-run determinism, idempotent node registration, retention bound, privacy-mode propagation). sensing-server suite 430+128 green; workspace gate 2,904 passed / 0 failed. https://claude.ai/code/session_01MjBucx95K4BuUxZi8NWwRH * feat(train): falsifiable occupancy benchmark with anti-overfitting gate Makes the presence/person-count "beyond SOTA" claim falsifiable in code instead of aspirational (the unfalsifiability gap from the beyond-SOTA system review). occupancy_bench grades predictions vs ground truth and gates a SOTA claim behind one claim_allowed invariant requiring ALL of: - DataProvenance::Measured — synthetic/mock data is scorable for regression but never claimable (anti-mock-contamination; the CLAUDE.md Kconfig-bug lesson made structural). - A leak-free EvalSplit — validate() refuses any split where a subject OR environment id appears in both train and test (subject leakage / per-environment overfitting). - n_test >= min_test_samples (small-N guard). - Presence F1 whose bootstrap-CI lower bound (deterministic seeded splitmix64) clears the threshold — not the point estimate. - Count MAE within threshold. The claim string is unreadable except through the gate (NO_CLAIM otherwise), same discipline as the ruview-gamma acceptance gate. What remains is data, not method: a frozen, SHA-pinned, subject/environment-disjoint measured replay set turns the claim into a passing/failing test. Lives in wifi-densepose-train (the eval bounded context, alongside ablation/ eval/metrics). 10 tests cover each refusal path; warning-clean under the crate's missing_docs lint. Workspace gate 2,914 passed / 0 failed. Doc 03 updated. https://claude.ai/code/session_01MjBucx95K4BuUxZi8NWwRH * feat(engine): per-room adapter provenance + drift-to-recalibration advisor Closes the trust-chain gap where an ~11 KB per-room LoRA adapter (ADR-150 section 3.4) could silently change inference without the witness noticing: provenance carried only "rfenc-v<N>" with no notion of adapter identity. - StreamingEngine::set_room_adapter(AdapterInfo): pins the adapter's content-derived id into provenance model_version ("rfenc-v1+adapter:<id>") — and therefore into the BLAKE3 witness — so swapping or clearing adapter weights always shifts the witness. Engine test proves base -> adapter -> other-adapter -> cleared all witness differently and cleared == base. - RecalibrationAdvisor: recommends re-running the ADR-135 empty-room baseline / refitting the room adapter on sustained low fusion coherence (streak threshold, default 60 cycles ~ 3 s at 20 Hz) or an ADR-142 change-point. Surfaced as TrustedOutput::recalibration_recommended, stored on the sensing-server AppState alongside the witness at both live fusion sites. - Bridge plumbing: EngineBridge::{set_room_adapter, clear_room_adapter} + live-path test that the adapter id flows into the live witness. Scope note (honest): this is the deployable provenance/trigger half of the "retrained model" roadmap item. Fitting the adapter itself runs in the existing external calibration service (aether-arena/calibration/); a trained RF-encoder checkpoint still does not exist in-tree. Engine 15 tests, bridge 7 tests. Workspace gate: 2,918 passed / 0 failed. https://claude.ai/code/session_01MjBucx95K4BuUxZi8NWwRH * fix(mat): gate api module behind its feature — standalone no-default-features builds pub mod api was unconditional while its only dependency, serde, is optional behind the 'api' feature, so any build without default features failed with 101 unresolved-serde errors (masked in --workspace runs by feature unification). The api module and its create_router/AppState re-export are now cfg(feature = "api")-gated with docsrs annotations. All combos compile: bare --no-default-features (was 101 errors, now 0), --no-default-features --features api, and full default (177 tests pass). Workspace gate: 2,918 passed / 0 failed. https://claude.ai/code/session_01MjBucx95K4BuUxZi8NWwRH * perf(signal): opt-in FFT operator for the CIR ISTA solver (8-14x measured) Phi is a sub-DFT, so each ISTA mat-vec can run as one length-G FFT (O(G log G)) instead of a dense O(K*G) product — the dominant-latency-hazard finding from the beyond-SOTA optimization roadmap. New CirConfig::fft_operator, default FALSE: the dense path stays the bit-exact witness default. The FFT evaluates the same sums in a different order, so enabling it shifts float results in the last bits and requires regenerating any pinned witness — strictly opt-in per deployment. FftOperator (rustfft, planned once at CirEstimator::new, scratch buffers reused across the ISTA loop) dispatches inside ista_solve: Phi x = scale * forward-FFT(x) sampled at bins (k_idx mod G) Phi^H v = scale * unnormalised inverse-FFT of v scattered into those bins Warm-start and Lipschitz estimation stay dense at construction. Measured (criterion, same run, same machine): ht20: 2.22 ms -> 265 us (8.4x) ht40: 10.26 ms -> 717 us (14.3x) The real HE40 grid (K=484, G=1452) scales further per the O(K*G)/O(G log G) ratio. 3 new tests: FFT<->dense matvec equivalence to float tolerance on ht20 and he40 grids; end-to-end dominant-tap agreement on a single-path frame; all default configs keep FFT off. New cir_estimate_fft bench group. Workspace gate: 2,921 passed / 0 failed (default path bit-exact, witnesses unchanged). https://claude.ai/code/session_01MjBucx95K4BuUxZi8NWwRH * feat(core): canonical frame decoder — capture-to-claim replay (ADR-136) The encode half of the ADR-136 frame contract existed (ComplexSample, to_canonical_bytes, witness_hash) but there was no decoder: a captured canonical frame could be witnessed but never reconstructed, blocking replay-from-capture. CsiFrame::from_canonical_bytes is the exact inverse: same id, metadata, complex payload, and witness hash (tested as the round-trip law AC7 — the replayed frame re-encodes byte-identically). Amplitude/phase are recomputed from the payload (projections, not independent state). Every malformed-input class fails closed (AC8): header truncation -> Truncated, payload truncation -> PayloadMismatch, unknown discriminants, non-UTF-8 device id, trailing bytes. Nil calibration uuid decodes as None per the documented encoding. Core: 36 tests pass. Workspace gate: 2,937 passed / 0 failed. https://claude.ai/code/session_01MjBucx95K4BuUxZi8NWwRH * feat(engine): dynamic min-cut mesh partition guard (ruvector-mincut) Maintains an exact min-cut over the live mesh coupling graph — nodes are sensing nodes, coupling is the product of fusion attention weights — and surfaces per cycle, as TrustedOutput::mesh: - cut value: the global "how close is the array to partitioning" number, a structural measure per-node heuristics miss; - weak side: which specific nodes would split off (failure/jamming triage, feeds ADR-032 posture); - at-risk flag: counts as a structural event for the drift->recalibration advisor (alongside ADR-142 change-points). Degenerate cases fail toward risk: a node with zero coupling is reported as already partitioned (cut 0, that node as the weak side). Measured cost policy (criterion, 12-node mesh — the honest part): - weights quantized (1/64) + change-gated: steady-state cycles do ZERO graph work and reuse the cached cut (~7.3 us, ~23x cheaper than building); - on any real change a full exact rebuild (~171 us) is used, because ONE DynamicMinCut delete+insert measured ~240 us — the subpolynomial machinery amortizes on much larger graphs, so rebuild-on-change is the measured optimum at mesh scale (one-edge case -28% after switching policy); - full process_cycle with the guard: ~33 us for 4 nodes vs the 50 ms budget. 9 mesh_guard tests (weak-node detection, steady-state zero updates, sub-quantum gating, join/drop rebuild, determinism, disconnection) + an engine-level wiring test (down-weighted node -> weak side -> recalibration). Engine 24 tests; workspace gate 2,946 passed / 0 failed. https://claude.ai/code/session_01MjBucx95K4BuUxZi8NWwRH * feat(engine): mesh partition risk demotes privacy + enters the witness (ADR-032) Completes the mesh-guard integration: its at_risk signal was advisory-only (fed the recalibration advisor). It now also contributes to the ADR-141 privacy demotion alongside fusion- and array-level contradictions — a mesh close to partitioning makes the fused belief less trustworthy, so the cycle emits at a more restricted class (monotonic; information only removed). Because effective_class feeds the BLAKE3 witness, a fragmenting array now shifts the witness: partition risk is auditable, not just logged. The mesh computation moved ahead of the demotion step in process_cycle; mesh_guard_mut exposes risk-threshold tuning. Test: a forced-risk 3-node cycle demotes PrivateHome Anonymous->Restricted and shifts the witness vs a clean baseline. Engine 25 tests; workspace gate 2,947 passed / 0 failed. https://claude.ai/code/session_01MjBucx95K4BuUxZi8NWwRH * fix: public-PR review findings — privacy-path honesty, gate holes, mesh-guard cliff - sensing-server: engine errors logged+counted (no silent swallow), trust state exposed via status surface, privacy-demotion claims aligned with the actual parallel-audit-path behavior - occupancy_bench: vacuous-F1 hole closed (degenerate test sets fail with their own criterion); CI-lower-bound test made probative - mesh_guard: quantization scaled to observed coupling range — >=65-node balanced meshes no longer permanently at_risk (regression test) - engine: both wiring tests made probative (same-topology witness compare, deterministic risk-crossing fixture) - mat: axum/tokio optional behind api; real serde feature (api enables it) - core: canonical decoder strict (non-zero reserved bytes and nil UUID rejected — injective on accepted domain, forged-bytes tests) - CHANGELOG: un-spliced the FFT/adapter bullet mangle Co-Authored-By: claude-flow <ruv@ruv.net> * chore: strip private-track references for public PR Reword the occupancy-benchmark changelog bullet to drop a cross-reference to the private research track, and restore the WorldGraph retention bullet header that was glued onto the preceding MAT bullet. Co-Authored-By: claude-flow <ruv@ruv.net> * chore: lockfile refresh for cherry-picked feature set Co-Authored-By: claude-flow <ruv@ruv.net> --------- Co-authored-by: Claude <noreply@anthropic.com>	2026-06-11 16:08:54 -04:00
rUv	9e7fa83210	feat(signal): ADR-134 CSI→CIR via ISTA + NeumannSolver warm-start (#837) ... * feat(signal): ADR-134 — CSI→CIR via ISTA + NeumannSolver warm-start End-to-end first-class Channel Impulse Response estimation in the Rust workspace. Bridges CSI (frequency domain) to CIR (delay domain) so multistatic coherence gating, NLOS/LOS classification, and (at HT40+) ToF ranging become tractable in `wifi-densepose-signal`. Algorithm: ISTA L1 sparse recovery over a normalized DFT sub-matrix sensing operator Φ ∈ ℂ^(K×G) with G = 3K (3× super-resolution). The Tikhonov-regularised warm start re-uses `ruvector_solver::neumann:: NeumannSolver` — same call pattern as `fresnel.rs:280` and `train/subcarrier.rs:225` — so no new crate dependencies. Tiers supported: HT20 / HT40 / HE20 (Tier A-HE, C6) / HE40. The C6 HE-LTF tier is the preferred Tier A target whenever an 11ax AP is in range; firmware substrate already shipped at v0.7.0-esp32 per ADR-110. Measured performance (release, single CirEstimator shared across 12 links): HT20 2.72 ms / HE20 3.20 ms / HT40 13.43 ms / HE40 9.71 ms per estimate(). HT20 12-link multistatic 17.7 ms — fits the 50 ms RuvSense cycle; HT40 12-link 74 ms exceeds it and is flagged in ADR-134 §2.7 as requiring Rayon parallelism or G=2K super-res reduction. Measured Φ conditioning: κ(Φ) ≈ 1.00 identically across all tiers. ADR-134 §2.3 was corrected — the C6 advantage is statistical SNR gain (√(242/52) ≈ 2.16×) from more independent measurements, not improved conditioning. Witness: bit-deterministic SHA-256 over CirEstimator output on the synthetic ADR-028 reference signal (100 frames, top-5 taps, 1e-6 quantization). Hash committed to expected_cir_features.sha256; verify-cir-proof.sh wires the check into the existing witness bundle. CI: cargo test --features cir + verify-cir-proof.sh added as separate steps under the Rust Workspace Tests job; regressions are unambiguously attributable. Files: - ADR + WITNESS-LOG-028 row 34 + CLAUDE.md module count (14 → 15) - src/ruvsense/cir.rs (~540 LOC) + lib.rs re-exports + multistatic.rs wire-up (reversible via `use_cir_gate=false`) - 3 integration tests + Criterion bench + 3 deterministic fixtures - cir_proof_runner binary + sha256 + verify-cir-proof.sh Test rate: 395 pass / 6 ignored (P2 ISTA hyperparameter tuning; see #[ignore] reasons) / 0 fail. cargo check clean; verify-cir-proof.sh VERDICT: PASS. Co-Authored-By: claude-flow <ruv@ruv.net> * fix(signal): make CIR witness cross-platform-deterministic The first witness (Windows-generated hash 89704bfd…) failed on Linux CI with a different hash (b36741bf…). Root cause: hashing `re`/`im` parts of top-5 taps at 1e-6 precision is too tight against libm differences in sin/cos/sqrt across glibc, MSVC, and Apple-clang. The previous "top-5 sorted by magnitude" form also suffered from rank instability when taps are near-tied — libm jitter could shuffle the ordering even when the algorithm is unchanged. New canonical form: full per-tap quantised-magnitude profile in natural index order, no sort. - 156 taps × 2 bytes (u16 le) per frame = 312 bytes/frame. - Quantisation 1e-2 — robust to ~1e-3 float drift while still tripping on real algorithmic changes (e.g., a 10× lambda shift moves magnitudes by >1e-2). - No top-K selection — eliminates the unstable magnitude-sort step. Regenerated expected_cir_features.sha256 — new hash 120bd7b1… If the next CI run still mismatches, the cause is structural (rustfft SIMD code path selection or NeumannSolver internal ordering), not magnitudes, and the witness needs further coarsening or to be made platform-tagged. Co-Authored-By: claude-flow <ruv@ruv.net>	2026-05-28 16:24:37 -04:00