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https://github.com/ruvnet/RuView
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20 Commits
| Author | SHA1 | Message | Date | |
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 rUv
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0f64d23516 |
feat(bench): int8 quantization of WiFlow-STD half pose model — MEASURED trade-off (ADR-175, honest negative) (#1095)
Sub-deliverable 8.2 of the benchmark/optimization milestone. Quantizes the 843,834-param "half" WiFlow-STD pose model (half_best.pth) to int8 two ways and MEASURES the accuracy/size trade-off vs fp32 under ONE locked normalization (ADR-173 torso-diameter PCK, upstream calculate_pck use_torso_norm=True), on the same seed-42 file-level 70/15/15 test split that produced the fp32 sweep numbers. MEASURED on ruvultra (RTX 5080, torch 2.11.0+cu128, fbgemm; clean test, torso-PCK): fp32 96.62% pck@20 99.47% pck@50 0.008981 mpjpe 3.351 MB int8 PTQ static 40.98% pck@20 94.98% pck@50 0.038262 mpjpe 1.046 MB (-55.64pp) int8 QAT (3 ep) 67.48% pck@20 98.69% pck@50 0.026548 mpjpe 1.043 MB (-29.15pp) Verdict (honest no): int8 is NOT a win at the strict PCK@20 edge target. Static PTQ collapses; QAT recovers a large share but still loses 29 pp @20 for a 3.2x size win — keep fp32/fp16 on the edge. Disclosed: QAT fake-quant val pck@20 was 83.45% but converted int8 scores 67.48% (~16pp convert_fx gap, reported honestly). Deliverables: - v2/crates/wifi-densepose-train/scripts/quantize_half_int8.py (reproducible: header carries the exact ssh command + run date; QAT primary, static PTQ fallback) - docs/adr/ADR-175-int8-quantization-half-pose-model-measured.md (MEASURED table, locked normalization, QAT-vs-PTQ labeling, verdict, reproduction, limitations) - CHANGELOG [Unreleased] ### Added entry No production Rust or signal-pipeline change. Python deterministic proof unchanged (f8e76f21a0f9852b70b6d9dd5318239f6b20cbcb4cdd995863263cecdc446f7a, bit-exact). |
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rUv
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90a88ada9a |
feat(train): metric-locked PCK/MPJPE accuracy harness + ADR-173 (resolve PCK-definition ambiguity) (#1092)
* feat(train): metric-locked PCK/MPJPE accuracy harness — resolve PCK-definition ambiguity
The SOTA brief (docs/research/sota-nn-train-benchmark-brief.md §1/§3.1/§4)
identifies metric ambiguity as the single biggest threat to any beyond-SOTA
claim: three PCK@20 numbers (96.09% WiFlow-STD image-normalized, 81.63%
AetherArena torso-PCK, 61.1% GraphPose-Fi standard PCK) cannot be lined up
because each silently uses a different normalization. The project was retracted
twice over this (a withdrawn 92.9% used absolute pixels, not torso).
New src/accuracy.rs makes the normalizer explicit, selectable, and carried with
every reported number:
- PckNormalization enum: TorsoDiameter (standard MM-Fi/GraphPose-Fi hip↔hip),
BoundingBoxDiagonal (looser WiFlow-STD image-normalized), AbsolutePixels(t)
(retracted convention, reproducible + clearly non-comparable).
- pck_at(pred, gt, vis, k, normalization) — one canonical PCK reusing the
metrics_core geometric primitives (no duplicate kernel).
- mpjpe(pred, gt, vis) — 2D/3D, mm.
- PoseAccuracy { pck_at: BTreeMap<u8,f32>, mpjpe, normalization, n_keypoints,
n_frames } via accuracy_report(frames, ks, normalization) — an unlabeled PCK
number is structurally impossible.
17 hand-computed deterministic tests (no GPU, no datasets) prove the harness
arithmetic, including the key proof that identical predictions score
0.50 / 1.00 / 0.75 under the three normalizations, plus graceful degenerate
handling (zero torso, empty frames, NaN coords — no panic, never false-perfect).
This is measurement infrastructure, NOT an accuracy claim. Public API worth an
ADR — needs ADR slot 173 (parent to write).
wifi-densepose-train lib 191→206, test_metrics 12→14, 0 failed; full workspace
green (exit 0); Python deterministic proof unchanged
(f8e76f21a0f9852b70b6d9dd5318239f6b20cbcb4cdd995863263cecdc446f7a).
Co-Authored-By: claude-flow <ruv@ruv.net>
* docs(adr): ADR-173 — metric-locked PCK/MPJPE accuracy harness
Documents the accuracy harness (committed
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rUv
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1d12e8831a |
refactor(beyond-sota): ADR-155 M2 — host-verifiable §8 closeout (7 de-magic, 9 boundary tests, native-conv honest-null) (#1059)
* refactor(train): ADR-155 M2 §8 — de-magic train non-tch tuning constants + boundary tests Lift bare numeric literals used as thresholds / guard epsilons in the non-tch (host-verifiable) train surface into named, documented consts and pin each set with a *_consts_unchanged_from_literals test. Values are bit-identical to the prior inline literals — cleanup, no behaviour change. De-magicked (const + pin test): - metrics_core.rs: VISIBILITY_THRESHOLD (0.5), MIN_REFERENCE_EXTENT (1e-6), OKS_FALLBACK_SIGMA (0.07) - ruview_metrics.rs: NUM_KEYPOINTS (17), VISIBILITY_THRESHOLD (0.5), PCK_THRESHOLD (0.2), MIN_BBOX_DIAG (1e-3), MIN_DURATION_MINUTES (1e-6) - subcarrier.rs: SPARSE_BASIS_SIGMA (0.15), SPARSE_BASIS_THRESHOLD (1e-4), SPARSE_REGULARIZATION_LAMBDA (0.1), SPARSE_COO_PRUNE_EPS (1e-8), SPARSE_SOLVER_TOL (1e-5 f64), SPARSE_SOLVER_MAX_ITERS (500) - eval.rs: MIN_POSITIVE_MPJPE (1e-10) - domain.rs: LAYER_NORM_EPS (1e-5) - virtual_aug.rs: BOX_MULLER_U1_FLOOR (1e-10), MIN_ROOM_SCALE (1e-10) Boundary / characterization tests (pin CURRENT behaviour): - visibility_threshold_boundary_is_inclusive (>= 0.5 at the edge) - degenerate_extent_below_floor_is_unscoreable ((0,0,0.0)/0.0, not perfect) - tracking_zero_duration_does_not_divide_by_zero - oks_short_array_is_bounded_at_keypoint_count (16 rows, no panic) - compute_interp_weights_single_target_is_index_zero (target_sc==1) - sparse_interp_single_target_is_finite - domain_gap_infinite_when_in_domain_perfect_but_cross_nonzero - domain_gap_unity_when_everything_perfect - augment_frame_zero_room_scale_passes_amplitude_finite Doc-only (no behaviour change): - rapid_adapt.rs: correct module-doc O(eps) -> O(eps^2) for central differences - geometry.rs: add # Panics to DeepSets::encode (documents existing assert!) train --no-default-features: 191 lib (was 176), 303 total (was 288), 0 failed. Co-Authored-By: claude-flow <ruv@ruv.net> * feat(nn): ADR-155 M2 §3 — pure-Rust LinearHead::try_new input guard + de-magic softplus threshold ADR-155 §3 found rf_encoder.rs has no adversarial checkpoint-deserialization assert — its assert_eq!s in LinearHead::new are construction-time API contracts on programmer-supplied vectors. This adds the honest, in-scope improvement the M2 task allows: a pure-Rust *fallible* constructor so weights from an untrusted / deserialized checkpoint can be shape-validated without panicking. - Add RfHeadError (WeightShape / BiasShape / VarWeightShape) + Display + Error. - Add LinearHead::try_new returning Result<Self, RfHeadError>; on success the head is byte-identical to LinearHead::new. new() is unchanged (still asserts; now documents # Panics and points to try_new) — no behaviour change for existing callers. - De-magic softplus's bare 20.0 overflow threshold into SOFTPLUS_LINEAR_THRESHOLD (value unchanged) + pin test. Tests: try_new_accepts_valid_and_rejects_each_bad_shape (valid == new forward; each bad shape → typed error), softplus_threshold_unchanged_from_literal. nn --no-default-features lib: 37 passed (was 35), 0 failed. Co-Authored-By: claude-flow <ruv@ruv.net> * perf(nn): ADR-155 M2 §4 — native-conv bench-first → MEASURED-INCONCLUSIVE (no perf change shipped) The §8 "native-conv naive-loop rewrite" backlog item: DensePoseHead:: apply_conv_layer is a pure-Rust 6-nested-loop conv (benchable on this host, not tch/ort-gated). Bench-first per the §0 PROOF discipline. - Add committed criterion bench benches/native_conv_bench.rs measuring forward() through the naive conv on representative single-layer configs (--no-default- features; no ort download). - Prototyped a bit-identical range-clamped variant (hoist the per-tap in-bounds branch by pre-clamping kh/kw ranges; same ic→kh→kw MAC order ⇒ bit-identical). MEASURED before/after on this host: ~35% faster on padding-heavy small-channel maps (4.40→2.84 ms) but a ~3% *regression* on channel-heavy maps (11.09→11.48 ms), all inside a ±20% run-to-run noise floor. Verdict: INCONCLUSIVE — the benefit is not robustly positive, so the rewrite is NOT shipped and NOT a fabricated speedup. Reverted to the naive loop; honestly deferred (ADR-155 §8). - Add native_conv_matches_reference: a hand-computed characterization anchor (1×1 = scalar MAC; same-padded 3×3 ones = truncated-window sums 9/6/4) pinning CURRENT conv behaviour for any future rewrite. nn --no-default-features lib: 38 passed (was 37), 0 failed. No behaviour change. Co-Authored-By: claude-flow <ruv@ruv.net> * docs(adr-155): M2 §8.2 — enumerated host-verifiable P3 backlog clearance + CHANGELOG Replace the §8 bulk "~40 lower-severity findings" line with the real, enumerated M2 resolution (§8.2): 7 de-magicked (const + pin == prior literal), 9 boundary tests, 1 input guard (rf_encoder try_new), 2 doc-only, 1 perf bench-first MEASURED-INCONCLUSIVE (not shipped). Mark native-conv + rf_encoder RESOLVED; state which §8 items stay data-gated (GraphPose-Fi/INT4/CSI-JEPA) or tch-gated (proof/trainer/model panic sites, metrics *_v2 dead code) and ONNX read-lock upstream-gated — blocked, not dropped. Declare the non-tch-verifiable subset of §8 cleared. Validation: train --no-default-features 303 passed (was 288); nn lib 38 (was 35); workspace --no-default-features 3,293 passed, 0 failed; Python proof VERDICT PASS, hash f8e76f21…46f7a UNCHANGED bit-exact. Co-Authored-By: claude-flow <ruv@ruv.net> |
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rUv
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9b07dff298 |
feat(beyond-sota): ADR-155 metric unification + ADR-156 RaBitQ Pass-2 (honest negative + latent topk bugfix) (#1053)
* refactor(train): hoist canonical PCK/OKS to un-gated metrics_core; fold test_metrics onto production (ADR-155 M1 §8) ADR-155 §8 deferred item: test_metrics.rs reference kernels validated production against their OWN reimplementation — a test that cannot catch a canonical-impl bug (both could be wrong the same way). - Extract canonical_torso_size / pck_canonical / oks_canonical / sigmas / bounding_box_diagonal into a new NON-tch-gated `metrics_core` module, so the single metric definition is reachable under `cargo test --no-default-features` (the `metrics` module is tch-gated). `metrics` re-exports every item → still exactly ONE implementation. - Rewrite tests/test_metrics.rs to assert the PRODUCTION pck_canonical / oks_canonical equal hand-computed fixtures (not a reimplementation): canonical_pck_matches_hand_computed_fixture (corr=3/total=4/pck=0.75), hip↔hip normalizer pin, zero-visible⇒0.0, OKS perfect⇒1.0, fake-Gold pin. - Keep an INDEPENDENT raw-threshold reference kernel only as a differential cross-check: test_kernel_agrees_with_canonical asserts it AGREES with canonical where torso==1.0 (genuine cross-check, not duplication). Grade: MEASURED. test_metrics 10→12 tests, 0 failed. Co-Authored-By: claude-flow <ruv@ruv.net> * fix(sensing-server): relabel divergent live PCK/OKS so they're never conflated with canonical (ADR-155 M1 §2.1/§8 Goal C) Goal C named training_api.rs:804 (torso-HEIGHT PCK). Auditing it surfaced TWO findings the ADR-155 §1 table missed: 1. training_api.rs is an ORPHAN file — not declared `mod` in lib.rs OR main.rs, so it does NOT compile into the crate. It does not drive the live server. 2. The REAL live `best_pck`/`best_oks` (main.rs training path → RVF metadata JSON read by model_manager.rs) come from trainer.rs: - `pck_at_threshold` = RAW-threshold PCK, NO torso normalization (the most divergent kind), printed/serialized as bare "PCK@0.2". - `oks_map` calls `oks_single(area=1.0)` = the EXACT fake-Gold pattern ADR-155 §2.1 claimed closed elsewhere — still live here, inflating best_oks. Resolution = RELABEL (torso/raw math is load-bearing on different data; the pub fns can't be renamed without breaking API; sensing-server has no train/ ndarray dep). Honest unify is a tracked §8 backlog item. - training_api.rs: `compute_pck` → `compute_pck_torso_height` + divergence doc; val_pck/best_pck/val_oks struct fields documented as torso-HEIGHT proxies; logs say `pck_torso_h@0.2`. Test torso_pck_is_labelled_distinctly_from_canonical. - trainer.rs (LIVE): `pck_at_threshold` documented raw-unnormalized; `oks_map` area=1.0 flagged fake-Gold; test pck_at_threshold_is_raw_unnormalized_not_canonical. - main.rs: live print relabelled `pck_raw@0.2` / `oks_map(area=1.0 proxy)`. No wire-format field renames (back-compat); no pub-API rename (no silent break). Grade: MEASURED (relabel + divergence pinned). sensing-server 450→451 lib tests, 0 failed. Co-Authored-By: claude-flow <ruv@ruv.net> * docs(adr-155): mark §8 metric items RESOLVED + audit map + honest §1 under-count correction (M1b Goals A/D) - §8.1: full PCK/OKS audit map (every def: file:line, basis, canonical/ legacy/distinct), the two §8 items marked RESOLVED with resolution+why. - Honest finding: §1's "seven divergent metrics" was an UNDER-count — sensing-server's LIVE trainer.rs has a raw-unnormalized PCK and an area=1.0 fake-Gold OKS the table omitted, and the file §8 named (training_api.rs) is orphaned dead code. §9 honest-limits updated. - Goal D: metrics.rs *_v2 variants confirmed caller-less + deprecated; noted for future cleanup, NOT deleted (public API, tch-gated). - CHANGELOG [Unreleased] Fixed entry. Co-Authored-By: claude-flow <ruv@ruv.net> * feat(ruvector): RaBitQ Pass-2 randomized rotation + topk bugfix (ADR-156 §8) Implements the deferred "Multi-bit / Extended RaBitQ Pass 2" backlog item from ADR-156 §8: a deterministic randomized orthogonal rotation applied before sign-quantization, the published RaBitQ construction (Gao & Long, SIGMOD 2024). Rotation construction: Fast Hadamard Transform + seeded ±1 sign flips ("HD" / randomized Hadamard), O(d log d) time and O(d) memory — a dense d×d rotation is O(d²) and infeasible at the 65,535-d the wire format provisions for. Pads to the next power of two; SplitMix64 seeds the sign stream so index-time and query-time rotations are bit-identical. API is additive and backward-compatible: Pass 1 (`from_embedding`) is untouched; Pass 2 is opt-in via `Sketch::from_embedding_rotated` and `SketchBank::with_rotation` (+ `insert_embedding` / `topk_embedding` / `novelty_embedding` helpers that rotate consistently). Default behaviour is unchanged. While building the Pass-2 coverage harness, found and fixed a PRE-EXISTING correctness bug in `SketchBank::topk`: the n>k heap path used `BinaryHeap<Reverse<(d,id)>>` (a min-heap) but treated its peek as the max, so it returned the k FARTHEST sketches as "nearest". The shipped unit tests only exercised the n≤k fast path, so it went unnoticed. Fixed to a plain max-heap; pinned by `topk_heap_path_returns_nearest` and `tight_clusters_give_high_coverage_with_overfetch` (the latter measured 0.072 on the old code). New tests (+17, 100→117 in the crate): rotation determinism/norm-preservation (`rotation_is_deterministic_for_seed`, `rotation_preserves_norm`), Pass-2 shape-compatibility, `pass2_coverage_not_worse_than_pass1`, and a deterministic coverage report. MEASURED top-K coverage (anisotropic planted-cluster fixture, cosine ground truth; dim=128 N=2048 K=8 64 clusters noise=0.35 128 queries): candidate_k=K=8 : Pass1 36.13% -> Pass2 46.39% (both << 90% bar) candidate_k=24 : Pass1 83.89% -> Pass2 91.60% (Pass2 clears 90%) candidate_k=32 : Pass1/Pass2 100% Honest result: rotation consistently helps (+10pp at strict K), but neither pass clears the ADR-084 90% bar at candidate_k==K on this distribution. Pass 2 reaches 90% only with ~3x over-fetch (the ADR-084 "candidate set" deployment pattern). Multi-bit Pass 3 evaluated separately. Co-Authored-By: claude-flow <ruv@ruv.net> * feat(ruvector): multi-bit Pass-3 experiment + ADR-156/084 measured results Adds the multi-bit half of the ADR-156 §8 "Multi-bit / Extended RaBitQ" item as a MEASURED experiment (coverage::measure_multibit): rotate, then b-bit uniform scalar-quantize each coord, rank by L1 over codes — the natural multi-bit generalization of hamming. Measures the bit/coverage tradeoff the backlog item asked for. MEASURED at the strict bar (candidate_k=K=8, anisotropic planted-cluster fixture, cosine ground truth): Pass1 (1-bit, no rot) 36.13% 16 B/vec Pass2 (1-bit, rot) 46.39% 16 B/vec Pass3 (rot, 2-bit) 54.39% 32 B/vec Pass3 (rot, 3-bit) 66.70% 48 B/vec Pass3 (rot, 4-bit) 74.22% 64 B/vec Honest: multi-bit monotonically helps but even 4-bit (4x memory) reaches only 74% at the strict bar — neither rotation nor <=4-bit multi-bit clears the strict-K 90% bar on this distribution. The bar is met via over-fetch (Pass2 @ candidate_k=24). Tests: multibit_tradeoff_report, multibit_1bit_matches_pass2_approx (+ sanity that 1-bit ~= Pass-2). Docs: - ADR-156 §8 item #2 marked RESOLVED-PARTIAL; §5 #2 grade CLAIMED -> MEASURED-on-our-hardware; new §10 with full measured tables, the topk bugfix disclosure, and graded deferred sub-items. - ADR-084: "Pass 2" section answering the rotation open-question with measured numbers + the topk bug note. - CHANGELOG [Unreleased]: Added (Pass-2 milestone) + Fixed (topk heap). Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
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2e4461d64d |
release: bump 9 crates changed in the beyond-SOTA sweep for crates.io
vitals/wifiscan/hardware/nn 0.3.0->0.3.1, ruvector 0.3.1->0.3.2, signal 0.3.2->0.3.3, train 0.3.1->0.3.2, mat 0.3.0->0.3.1, sensing-server 0.3.1->0.3.2. Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
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aa3a6725a6 |
fix(train,nn): Tier-2 correctness/security — metric scale, OOM bounds, panics (ADR-155 §Tier-2)
Each fix ships a test that would have caught the bug: - ruview_metrics OKS: derive scale from GT extent (no s=1.0 fake-Gold), reject s<=0, bound the loop to array extents (no panic on short/adversarial input). - config.validate(): UPPER bounds on window_frames/subcarriers/backbone_channels/ heatmap_size/keypoints/body_parts/batch_size + reject negative gpu_device_id (closes the config-OOM class); defaults+presets still validate. - subcarrier.rs: graceful fallback instead of panic on non-contiguous input. - ablation.rs latency_percentiles: total_cmp + NaN guard (no partial_cmp unwrap). - tensor.rs softmax(axis): normalize per-lane along the given axis (was whole- tensor), out-of-range axis -> NnError; fixes densepose per-pixel probs. - translator.rs apply_attention: real scaled-dot-product attention (was a uniform 1/seq_len stub that made any "with attention" ablation == without); mis-shaped checkpoint projections rejected. Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
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84e2c920fd |
fix(train): proof margin + committed-hash requirement (ADR-155 §Tier-1.4)
The deterministic proof self-certified: PASS on any loss decrease (incl. 1e-9 noise) and a missing expected hash defaulted to PASS. - MIN_LOSS_DECREASE=1e-4: a run counts as learning only above float noise; a noise-only pipeline now FAILS. - is_pass() requires hash_matches==Some(true); no-hash -> SKIP (exit 2), never PASS. verify-training fails fast on a sub-margin loss before the hash compare, so a missing baseline cannot mask a non-learning pipeline. Documented honestly: the proof certifies reproducibility/determinism on a synthetic dataset, NOT that real data produced the weights nor that any accuracy claim is met. Tests: no_committed_hash_is_skip_not_pass, submargin_loss_change_fails_even_without_hash, committed_matching_hash_with_real_decrease_passes. Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
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7fb3e33557 |
fix(train): rapid_adapt real finite-difference gradients, not a fake step (ADR-155 §Tier-1.3)
contrastive_step/entropy_step wrote a fake gradient (grad += v*0.01) unrelated to the stated objective, so any "TTA improves the metric" was unsupported. The *_loss functions are now pure evaluators of the real objective; adapt() descends them with a central finite-difference gradient of that exact loss, so "the adaptation loss decreases" is now a real, reproducible measurement. Honest scope caveat (documented): this minimizes a self-supervised proxy over a LoRA bottleneck on raw CSI; it is NOT wired to the pose model and there is NO measured end-to-end PCK gain on WiFi pose from this path. Tests: contrastive_loss_decreases, entropy_loss_decreases (real gradient steps don't increase the loss), reported_loss_is_the_real_objective_not_a_placeholder. Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
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2a2a2c5b06 |
fix(train): leak-free subject-disjoint split + synthetic-val disclosure (ADR-155 §Tier-1.2)
MM-Fi windows are stride-1 (~99% overlap), so an index-level split leaks; and
bin/train.rs validated real training against a SYNTHETIC val set, making any
printed PCK meaningless on two counts.
- MmFiDataset::subject_disjoint_split partitions whole subjects -> the two views
share no subject and no window (leak-free by construction, deterministic per
seed). assert_split_leak_free verifies subject- AND window-disjointness and is
called inside the split so a leaky split is never handed out.
- bin/train.rs now prefers the real split; the synthetic path is a labelled
run_smoke_test ("[SMOKE-TEST] DO NOT REPORT") reachable only as a fallback.
- New DatasetError::InvalidSplit.
Tests prove disjointness, determinism, single-subject/bad-fraction rejection,
and that the validator catches an injected subject leak.
Co-Authored-By: claude-flow <ruv@ruv.net>
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ruv
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50b657459f |
fix(train): unify 7 divergent PCK/OKS into one canonical metric (ADR-155 §Tier-1.1)
Collapse the four PCK and three OKS implementations into a single source of
truth — pck_canonical (torso hip↔hip, COCO/ADR-152 convention validated at
~96% PCK@20 in benchmarks/wiflow-std) and oks_canonical (scale from GT pose
extent). MetricsAccumulator, compute_pck/_per_joint/_oks, aggregate_metrics and
the deprecated *_v2 path all route through them, so Trainer::evaluate() and the
bench definition agree.
Fixes two claim-inflating bugs, each pinned by a regression test:
- zero-visible-joint PCK was 1.0 (false-perfect) -> now 0.0
- OKS s=1.0 on normalized coords made OKS~=1.0 for any pose ("fake Gold tier")
-> scale now derived from the pose; a 3x-torso-wrong pose yields OKS<0.2
Divergent local kernels (training_bench raw-threshold, sensing-server
torso-height) annotated "DO NOT USE for reported metrics". Legitimately changed
test expectations (all-coincident "perfect" fixtures are correctly unscoreable;
all-invisible -> 0.0) updated with comments citing the finding.
Co-Authored-By: claude-flow <ruv@ruv.net>
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rUv
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17471e93ff |
ADR-152: WiFi-Pose SOTA 2026 intake — WiFlow-STD benchmark, Rust integrations, ADR-153 802.11bf layer, efficiency frontier (#1008)
* feat(calibration): NodeGeometry transceiver-geometry recording (ADR-152 §2.1.1) PerceptAlign-motivated geometry capture at enrollment: per-node optional records (position, antenna orientation, inter-node distances, acquisition method) — recorded when known, never required. Event-sourced via EnrollmentEvent::GeometryRecorded (latest recording wins); persisted on SpecialistBank with serde defaults so pre-ADR-152 bank JSON loads cleanly (fixture-proven, and geometry-free banks serialize byte-shape-identical to the old schema); threaded through MultiNodeMixture as data only — the learned geometry embeddings and algorithmic fusion use are §2.1.2, deliberately deferred until the ADR-151 P6 LoRA heads exist. Geometry recorded from now on means banks captured today remain usable for layout-conditioned training later — you can't retroactively add geometry to data you didn't record. 8 new tests (3 geometry, 2 anchor, 2 bank, 1 multistatic) + full-loop extension (2-node geometry, one tape-measured + one unknown, surviving the bank JSON round-trip the runtime loads from). 50/50 calibration (both feature configs) + 23 CLI tests green. Co-Authored-By: RuFlo <ruv@ruv.net> * feat(training): two-checkerboard camera↔room calibration for ADR-079 labels (ADR-152 §2.1.3) Defends the camera-supervised pipeline against PerceptAlign's "coordinate overfitting": MediaPipe keypoints were emitted in raw camera coordinates with no shared frame and no transceiver-geometry metadata — the exact label shape that memorizes deployment layout and collapses cross-layout. - scripts/calibrate-camera-room.py + calibration_lib.py: OpenCV two-checkerboard calibration → versioned bundle JSON (intrinsics, camera→room extrinsics, checkerboard spec, transceiver geometry, sha256 calibration_id). Intrinsics resolve from file > cache > multi-view computation > loud-warning 2-view fallback. - collect-ground-truth.py --calibration <bundle>: every sample gains keypoints_room (unit bearing rays from the camera center in the room frame — documented projective alignment; raw image coords preserved so training chooses), camera_origin_room, calibration_id, and the transceiver geometry stamp. Without the flag, output is byte-identical to before (tested) + a one-line ADR-152 warning. Design finding (recorded for ADR-152): a single planar checkerboard's corner grid is centrosymmetric — the reversed corner ordering fits a ghost camera pose with IDENTICAL reprojection error, so per-board flip disambiguation is mathematically ill-posed. solve_two_board_extrinsics solves the joint wall+floor set over all 4 flip combinations, where the minimum is unique — an independent reason the TWO-checkerboard method is required, beyond what PerceptAlign states. 15 headless pytest tests green (synthetic corners: extrinsics recovery incl. ghost resolution, bundle round-trip + hash stability, ray transforms w/ distortion + cross-resolution, no-calibration byte identity). Co-Authored-By: RuFlo <ruv@ruv.net> * feat(benchmarks): WiFlow-STD reproduction harness + measurement (a) results (ADR-152 §2.2) Shipped checkpoint REFUTED (0.08% PCK@20, wrong keypoint normalization); 6 reproducibility defects documented (broken imports, corrupted dataset tail with float32-max garbage that NaN-poisons fp16 BatchNorm, unreachable test phase). After repairs, retraining with upstream defaults reproduces 96.09% PCK@20 full-test / 96.61% corruption-free (published 97.25%) on RTX 5080. Claims graded MEASURED-EQUIVALENT; 2.23M params + ~0.055 GFLOPs verified. Third-party code/weights/data stay out of tree (gitignored). Co-Authored-By: claude-flow <ruv@ruv.net> * feat: ADR-152 Rust integrations + ADR-153 802.11bf protocol model - calibration: GeometryEmbedding — 32-slot permutation-invariant NodeGeometry featurization for future LoRA-head conditioning (ADR-152 §2.1.2); derived SpecialistBank::geometry_embedding() accessor; 59 tests - train: MaePretrainConfig + patchify/random-mask with UNSW measured recipe (80% masking, (30,3) patches; ADR-152 §2.3, arXiv 2511.18792); strict no-truncate/no-NaN policy; proptest properties - train: WiFlowStdModel — tch-gated port of the verified ~96%-PCK@20 WiFlow-STD architecture (ADR-152 §2.2 beyond-SOTA); ungated param formula pinned to 2,225,042; 15/17-keypoint support; 239 crate tests - hardware: ieee80211bf forward-compatibility protocol model (ADR-153): SpecProfile gates, SensingCapabilities negotiation, required ConsentMode, session FSM, SensingTransport + SimTransport + OpportunisticCsiBridge; full acceptance checklist covered; 156+4 tests - deps: ruvector bumps per ADR-152 §2.6 survey (mincut/solver 2.0.6, attention 2.1.0, gnn 2.2.0); vendor/ruvector synced to a083bd77f - docs: ADR-153 accepted; ADR-152 §2.2 status, §2.4 amendment, §2.6 added Workspace: 162 test suites green (--no-default-features); Python proof PASS. Known pre-existing flake: homecore-api env_empty_falls_back_to_defaults (unserialized env-var mutation) — untouched, follow-up. Co-Authored-By: claude-flow <ruv@ruv.net> * docs: CHANGELOG + CLAUDE.md entries for ADR-152 integrations and ADR-153 Co-Authored-By: claude-flow <ruv@ruv.net> * fix(train): repair tch-backend bit-rot — gated path compiles and tests run again Mechanical API refresh against current tch: Vec::from(Tensor) -> try_from (+ explicit flatten), numel() usize cast, Rem/div ops -> remainder() / divide_scalar_mode(floor) — the latter fixed a silent true-division bug in heatmap argmax decoding; clamp(1.0, f64::MAX) -> clamp_min (torch 2.x scalar overflow panic); petgraph EdgeRef import; missing EvalMetrics and verify_checkpoint_dir APIs that tests documented. wiflow_std roundtrip test uses safetensors (.pt _save_parameters roundtrip broken in torch 2.11 Windows). Gated: 349 passed (incl. all 20 wiflow_std); ungated: unchanged. Known pre-existing: gaussian-heatmap convention mismatch (2 tests), proof seed race under parallel threads — documented, deliberate follow-ups. Co-Authored-By: claude-flow <ruv@ruv.net> * feat(train): WiFlow-STD PyTorch->tch weight import + numerical parity proof export_to_safetensors.py maps the retrained checkpoint (295 tensors -> 248 mapped, param sum exactly 2,225,042; num_batches_tracked dropped) into a tch-loadable safetensors plus a deterministic parity fixture. Gated #[ignore] integration test loads it strictly and asserts forward-pass agreement: max abs diff 1.192e-7 on the seed-42 fixture. dump_variable_names test makes the tch name layout authoritative. Zero architecture discrepancies found. Co-Authored-By: claude-flow <ruv@ruv.net> * fix: workflow-review findings — BN gamma init, ThresholdParams serde, init docs Concurrent validation workflow (2 review lanes + adversarial verification, 13 agents): 5 confirmed findings, 3 refuted. Fixes: - wiflow_std: pin BatchNorm gamma to 1.0 (tch default draws Uniform(0,1) — silently halves activations in from-scratch training; loaded checkpoints unaffected, parity re-verified after the change) - wiflow_std: document the conv-init divergences vs the reference's effective kaiming_normal(fan_out) re-init (from-scratch dynamics only) - ieee80211bf: ThresholdParams deserialization validates via try_from so the <=100 invariant holds for untrusted payloads (+ rejection test) Benchmarks (release, ruvzen): GeometryEmbedding 1.84us/call (542k/s), MAE tokenization 7.38us/window (135k/s), 802.11bf FSM 8.9M events/s — nothing suspicious. Co-Authored-By: claude-flow <ruv@ruv.net> * docs(adr): ADR-152 §2.1.4 gate resolved — PerceptAlign repo MIT, dataset on HF Co-Authored-By: claude-flow <ruv@ruv.net> * feat(benchmarks): edge optimization measured + measurement (b) blocked + 92.9% retraction Edge optimization (ADR-152 optimize track): ONNX Runtime fp32 is the CPU latency win (3.2 ms/window, ~3.4x faster than torch, parity 2.4e-7); ORT dynamic int8 reaches 2.44 MB (paper's ~2.2 MB claim plausible only via conv-capable toolchains; -0.16pt PCK@20, +18% MPJPE, 2x slower); torch dynamic quant converts 0% of this conv-only model; fp16 halves storage free but is slower on CPU. Measurement (b) BLOCKED-ON-DATA: only 1,077 paired ESP32 windows exist (stop rule <2k). Forensic recheck of the surviving April holdout RETRACTS the ADR-079 '92.9% PCK@20' figure: constant-output model, absolute (not torso) threshold, 69 near-static frames — mean predictor scores 100% under that protocol; torso-PCK@20 is 19.1%. Corroborates PR #535. Stale citations removed from user-guide, readme-details, ADR-152 §2.1.3; no-citation rule extended to ADR-079 accuracy claims. Unblock: >=2k-window multi-pose paired session + torso-PCK re-baseline. Co-Authored-By: claude-flow <ruv@ruv.net> * docs(user-guide): corrected camera-supervised collection tutorial Step 0 CSI-rate check + session-length math (window yield = frames/20 — the May session's 8x under-delivery was a ~12 Hz CSI rate, not an aligner bug); two-checkerboard calibration step (ADR-152 §2.1.3); pose-variety and confidence guidance; torso-normalized PCK + temporal-split + pred-variance eval protocol (lessons from the 92.9% retraction); scale presets re-keyed to realistic window counts. Co-Authored-By: claude-flow <ruv@ruv.net> * feat(benchmarks): static PTQ int8 (calibrated) results + overnight capture script Conv-only static QDQ beats dynamic int8 on accuracy (PCK@20 96.61-96.63% vs 96.52%, MPJPE +10% vs +18% over fp32) at ~equal size/latency; all-ops QDQ strictly worse (int8 activations through attention glue). Entropy calibration verified bit-identical to MinMax on this data. Deployment: ONNX fp32 for speed (3.2ms), static conv-only QDQ for smallest (2.53MB). Also: scripts/overnight-empty-capture.py — segmented UDP CSI recorder for empty-room baselines (no glob collisions, detach-safe). Co-Authored-By: claude-flow <ruv@ruv.net> * feat(benchmarks): measurement (b) MEASURED — optimization transfer only, mean-pose baseline wins WiFlow-STD fine-tuned on 2,046 fresh single-room ESP32 paired windows (temporal 70/15/15, 70->540 adapter, K=17): pretrained-init 65% PCK@20 vs scratch 0% (optimization transfer) but frozen-trunk ~0% (no feature transfer), and NOTHING beats the mean-pose baseline (95.9% PCK@20 — single subject, near-static normalized coords). Honesty gates held: pred std 0.0113 (non-constant model) but mean-baseline dominance means no citable CSI->pose capability from this data. ADR-152 open question 1 answered partially; definitive answer needs multi-subject/position data. Two new aligner findings: heterogeneous csi_shape with silent zero-padding (~20%), and extractCsiMatrix's transposed shape label (frame-major data, [nSc, nFrames] label) — fixes pending. Co-Authored-By: claude-flow <ruv@ruv.net> * feat(benchmarks): efficiency sweep MEASURED — half model dominates full reference Compact WiFlow-STD variants on the same data/split/protocol: half (843,834 params, 0.38x) strictly dominates the 2.23M reference (PCK@20 96.62 vs 96.61, PCK@50 99.47 vs 99.11, MPJPE 0.00898 vs 0.0094) — the published architecture is over-parameterized for its own benchmark. quarter (338k) 96.05%; tiny (56,290 params, 1/39.5) holds 94.11% — a ~220KB fp32 edge candidate. In-domain caveats recorded; cross-domain untested. Co-Authored-By: claude-flow <ruv@ruv.net> * feat(train): compact WiFlow-STD presets in Rust + tiny edge artifact (ADR-152) WiFlowStdConfig gains half()/quarter()/tiny() mirroring the overnight sweep exactly: TcnGroupsMode (Fixed/Gcd/Depthwise), input_pw_groups, derived stride schedule and decoder-mid (all default to upstream behavior; legacy serde JSON unaffected). Param formulas pin to trained ground truth first try: 843,834 / 338,600 / 56,290; default 2,225,042 pin and 1.192e-7 parity unchanged. 248 tests green. Tiny edge artifact (tiny_edge_bench.py): ONNX fp32 = 295 KB, 0.66 ms/win (~1,500/s CPU), 94.11% PCK@20 (matches sweep clean-test exactly; parity 1.49e-7). Static int8 is a bad trade at this scale (-1.43pt, +19% MPJPE, -16% size, slower) — recorded as negative result. Export note: width-16 breaks AdaptiveAvgPool((15,1)) TorchScript export; replaced by exact mean+matmul equivalent, proven by parity. Co-Authored-By: claude-flow <ruv@ruv.net> * fix: resolve all 10 confirmed code-review findings (7-angle review, 20/20 verified) wiflow_std: min_feature_width (default 15) replaces the keypoints->stride coupling — for_keypoints(17) now provably builds the trained [2,2,2,2] graph and pools 15->17, matching the validated Python protocol (pinned by tests); param_count() total on invalid configs; random_mask returns Result and rejects non-finite/out-of-range ratios; trainer checkpoints switched to safetensors (.pt VarStore roundtrip broken on Windows torch 2.11). ieee80211bf: SBP proxy now re-triggers instances and relays reports via Action::RelaySbpReport -> SensingFrame::SbpReport (clients consume via their existing path); missed_instances reset on success = consecutive semantics; SessionTable gains a guarded SBP entry point + unknown-id drop counter; initiator-role sessions reject inbound setup/SBP requests (RejectedNotSupported) closing the idle hijack; StartSetup/StartSbp outside Idle return InvalidStateForCommand; SBP validation unified through evaluate_setup with a 1:1 SetupStatus->SbpStatus mapping. events.rs split out to honor the 500-line cap. calibration/cli: enrollment geometry now actually reaches trained banks — both production call sites attach .with_geometry; --geometry flag on train-room and POST /enroll/geometry + train-body geometry on calibrate-serve give production a recording surface; geometry-free banks log the ADR-152 §2.1.2 note. benchmarks: corruption masks committed as ground truth (unregenerable after in-place cleaning; verified bit-identical regeneration from the pristine copy) + generate_corruption_masks.py producer; _bench_common.py dedups the 5x-copied shim/evaluate/seed/remap (post-refactor PCK@20 re-verified equal to the last digit); remote scripts get the mmap patch; tiny_edge --calib validated multiple-of-64; onnx_bench --help no longer executes (and overwrote) the export — artifact restored byte-exact. Workspace: 2,963 tests passed, 0 failed; Python proof PASS. Co-Authored-By: claude-flow <ruv@ruv.net> * ci: build workspace tests without debuginfo — runner disk exhaustion The combined 38-crate debug target exceeds the GitHub runner's disk ('final link failed: No space left on device'); the same tree measured 151GB locally with full debuginfo. CARGO_PROFILE_{DEV,TEST}_DEBUG=0 shrinks the target ~5-10x; debuginfo serves no purpose in CI test runs. Co-Authored-By: claude-flow <ruv@ruv.net> |
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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> |
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483bfa4660 |
feat(aether-arena): benchmark-first scorer + witness chain + repeatability (M2/M5/M7)
Per direction "remove the initial number, optimize for benchmark first" + "include witness chain capabilities for proof and repeatability analysis": - Empty board, no seeded numbers: ledger seeds to genesis only. Every result is a real scoring-pipeline witness; RuView gets no hand-entered baseline. - Real model scoring: aa_score_runner now loads predictions + an eval split (--split/--pred) and scores them through the real ruview_metrics pose harness — not just a synthetic fixture. Committed public smoke split (fixtures/smoke_*.json). - Witness chain: each score emits a witness = inputs_sha256 (binds it to the exact inputs) + proof_sha256 (cross-platform-stable score hash) + harness_version. - Repeatability analysis: --repeat N runs the harness N× and fails if it ever yields >=2 distinct proof hashes (16/16 identical locally). - Witness ledger: ledger/ledger_tools.py — append-only, hash-chained, tamper- evident (seed/append/verify); editing any past row breaks the chain. - CI gate extended: determinism + repeatability(16) + real-scoring smoke + ledger chain verify on every PR. Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
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a6808568a2 |
feat(aether-arena): ADR-149 spatial-intelligence benchmark — scorer + CI harness gate (M1-M4)
AetherArena ("AA") — the official, project-agnostic Spatial-Intelligence Benchmark
(ADR-149, Accepted). Iteration 1 of the long-horizon build:
- ADR-149 accepted: name locked (ruvnet/aether-arena), v0 metrics locked
(pose/presence/latency/determinism), dataset legality resolved (MM-Fi CC BY-NC
only; Wi-Pose excluded). Adds four-part framing, threat model, arena_score
formula, submission state machine, neutrality/governance, and the §7 acceptance test.
- aa_score_runner: deterministic scorer bin reusing the real ruview_metrics pose
harness on a fixed seed=42 fixture → RuViewTier-style verdict + cross-platform
SHA-256 proof hash. Builds --no-default-features (no torch/GPU). VERDICT: PASS.
- CI harness gate: .github/workflows/aether-arena-harness.yml runs the scorer on
every PR — the "PR that runs the harness as part of the build" requirement.
- Scaffold: aether-arena/{README,VERIFY,STATUS}.md + schema/aa-submission.toml.
- Horizon record persisted (.claude-flow/horizons/aether-arena-aa.json).
Infra = the deliverable; model SOTA (MM-Fi PCK@20) is a separate effort blocked on
ADR-079 data collection, tracked as a stretch goal, not an infra exit.
Co-Authored-By: claude-flow <ruv@ruv.net>
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ruv
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0f336b7d36 |
feat(train): ADR-145 ablation eval harness + privacy-leakage/latency metrics (#849)
- train/ablation.rs: FeatureSet matrix (CSI/CIR/CSI+CIR/+Doppler/+BFLD/+UWB); AblationMetrics (presence acc, loc err, FP/FN, latency p50/p95, privacy leakage, cross-room degradation) derived deterministically from VariantRun - membership_inference_leakage(): MIA proxy = |AUC-0.5|*2 (0 indistinguishable, 1 perfectly separable); latency_percentiles_ms (nearest-rank); confusion_rates - AblationReport.to_markdown() (deterministic), csi_cir_beats_csi_only() acceptance check - 5 tests; workspace 0 errors Co-Authored-By: claude-flow <ruv@ruv.net> |
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rUv
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004a63e82d |
fix(security): audit — fix RUSTSEC vulns, clippy warnings, dead code (#769)
- Upgrade openssl to 0.10.78 (CVE-2026-41676), jsonwebtoken to 9.4 - Suppress unmaintained-only/no-CVE advisories in .cargo/audit.toml with per-entry rationale - Fix all `cargo clippy --all-targets -- -D warnings` errors across 35 crates: derivable_impls, needless_range_loop, map_or→is_some_and/ is_none_or, await_holding_lock (drop MutexGuard before .await), ptr_arg (&mut Vec→&mut [T]), useless_conversion, approximate_constant (2.718→E, 3.14→PI), field_reassign_with_default, manual_inspect, useless_vec, lines_filter_map_ok, print_literal, dead_code - Apply `cargo fmt --all` - Pre-existing test failure in wifi-densepose-signal (test_estimate_occupancy_noise_only) is not introduced by this PR |
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ruv
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6f77b37f5e |
chore(release): wifi-densepose-train 0.3.0 -> 0.3.1
Publishing the additive changes from PRs #536/#537 to crates.io: - `signal_features` module — wires `wifi-densepose-signal` into the pipeline (audit #1/#2) - `TrainingConfig::for_subcarriers` / `ht40_192()` / `multiband_168()` presets + the real `MmFiDataset` loader integration test (audit #4/#6/#7) No public API removals or changes — additive only, so 0.3.0 -> 0.3.1 is semver-correct. No other workspace crate depends on `wifi-densepose-train`, so this is a standalone bump. Co-Authored-By: claude-flow <ruv@ruv.net> |
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rUv
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c604ca1150 |
feat(train): TrainingConfig subcarrier-layout presets + real MmFiDataset loader test (#537)
Closes the remaining doable items from the 2026-05-11 training-pipeline audit: #6 (CSI format default = 56-sc / 1 NIC) + #7 (multi-band 168-sc mesh not in config): new `TrainingConfig::for_subcarriers(native, target)` plus named presets `mmfi()` (114→56), `ht40_192()` (≈192-sc ESP32 HT40 → 56) and `multiband_168()` (168-sc ADR-078 multi-band mesh → 56). Non-MM-Fi CSI shapes are now first-class instead of requiring manual `native_subcarriers` / `num_subcarriers` overrides; the field docs list the supported source counts and the multi-NIC mapping (a 2–3-node mesh currently rides on `n_rx` until a dedicated node dimension lands). Model input width stays `num_subcarriers`; the presets only vary the resampling input. #4 (proof.rs uses synthetic data): reframed — a deterministic proof *must* use a reproducible source, so `verify-training` correctly stays on `SyntheticCsiDataset`. The real gap was that nothing exercised the on-disk `MmFiDataset` path. New `tests/test_real_loader.rs` writes synthetic CSI to `.npy` files in the `MmFiDataset::discover` layout, loads it back, and checks the resulting `CsiSample` — covering the no-interp case, the subcarrier-interpolation branch, and the empty-root case. Adds `ndarray` / `ndarray-npy` as dev-deps for the fixture writing. cargo check + cargo test -p wifi-densepose-train --no-default-features: clean, all existing tests green, 3 new loader tests + the updated config doctest pass. Purely additive — no model-shape change, no tch-module change. |
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rUv
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eaedfded6f |
fix(train): wire wifi-densepose-signal into the pipeline; correct MODEL_CARD env-sensor claim (#536)
Addresses three findings from the 2026-05-11 training-pipeline audit: #1/#2 — `wifi-densepose-signal` was a phantom dependency of `wifi-densepose-train` (listed in Cargo.toml, never imported), and vitals/CSI signal features were absent from the pipeline. New module `wifi_densepose_train::signal_features`: `extract_signal_features(&Array4<f32>, &Array4<f32>) -> Array1<f32>` (and the convenience method `CsiSample::signal_features()`) runs a windowed observation's centre frame through `wifi_densepose_signal::features::FeatureExtractor`, producing a fixed-length (FEATURE_LEN=12) amplitude / phase-coherence / PSD feature vector — the hook for a future vitals / multi-task supervision head (breathing- and heart-rate-band power are read off the PSD summary). The vector is produced on demand and is not yet fed back into the loss; wiring it as a training target is the documented follow-up. `wifi-densepose-signal` is now an actually-used dependency. 5 new tests (2 unit in signal_features.rs, 3 integration in tests/test_dataset.rs); existing wifi-densepose-train tests unchanged and green. #3 — `docs/huggingface/MODEL_CARD.md` presented PIR/BME280 environmental-sensor weak-label fine-tuning as a current capability; there is no env-sensor ingestion in the training pipeline. Marked that path as planned/not-implemented in the training-steps list and the data-provenance section. (#5 — README's "92.9% PCK@20" overclaim — fixed separately in PR #535.) CHANGELOG updated. |
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rUv
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f49c722764 |
chore(repo): rename rust-port/wifi-densepose-rs → v2/ (flatten to one level) (#427)
The Rust port lived two directories deep (rust-port/wifi-densepose-rs/) without any sibling under rust-port/ that warranted the extra level. Move the whole workspace up to v2/ to match v1/ (Python) at the same depth and shorten every cd / build command across the repo. git mv preserves history for all tracked files. 60 files updated for path references (CI workflows, ADRs, docs, scripts, READMEs, internal .claude-flow state). Two manual fixes for relative-cd paths in CLAUDE.md and ADR-043 that became wrong after the depth change (cd ../.. → cd ..). Validated: - cargo check --workspace --no-default-features → clean (after target/ nuke; the gitignored target/ was carried by the OS rename and had hard-coded old paths in build scripts) - cargo test --workspace --no-default-features → 1,539 passed, 0 failed, 8 ignored (same totals as pre-rename) - ESP32-S3 on COM7 → still streaming live CSI (cb #40300, RSSI -64 dBm) After-merge follow-up: contributors should `rm -rf v2/target` once and let cargo regenerate from the new path. |