mirror of
https://github.com/ruvnet/RuView
synced 2026-06-19 11:53:19 +00:00
c84ea39e62d14dcafe61fc80d357dfe0349462cd
15 Commits
| Author | SHA1 | Message | Date | |
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 rUv
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306d009e72 |
feat(rufield): rufield-viewer live-ingest mode (submodule bump) (#1072)
Bumps vendor/rufield to add --source live --upstream: the dashboard ingests RuView's /ws/field events, verifies each ed25519 receipt on ingest (forged events flagged, never fused), and renders real RuView FieldEvents through the same display path. Honest SYNTHETIC/LIVE/DISCONNECTED banner, mutually exclusive, never mislabeled (409 on /api/run in live mode). Closes the RuView↔RuField visual loop (ADR-262 surfaces). 26 tests, 0 failed. Co-authored-by: ruv <ruvnet@gmail.com> |
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rUv
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6f6c867629 |
feat(rufield): CsiReplayAdapter — first real WiFi-CSI adapter (submodule bump) (#1068)
Bumps vendor/rufield to include CsiReplayAdapter: RuField now ingests real captured WiFi CSI (.csi.jsonl) → FieldTensor → CSI-variance motion/presence proxy → signed FieldEvents → fusion. Measured on 199 real frames: 182 fused inferences (115 breathing, 67 person_present) from real signal. Replay-from-file, unlabeled (proxy not validated accuracy) — live streaming + labeled accuracy remain roadmap; mmWave/thermal stay synthetic. Co-authored-by: ruv <ruvnet@gmail.com> |
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rUv
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95a5ecc746 |
feat(rufield): rufield-viewer dashboard — completes ADR-260 §27.9 (#1067)
Bumps the vendor/rufield submodule to include the new rufield-viewer crate (Axum + vanilla JS read-only dashboard streaming the deterministic SyntheticSim→fusion camera-free room-intelligence demo: live room state, P0–P5 privacy-badged event log, fusion graph, signed-receipt viewer, behind a permanent SYNTHETIC banner). All ADR-260 §27 criteria 1–10 now PASS. Read-only demo viewer, not device management (real-adapter milestone later). rufield repo now 7 crates / 72 tests. Co-authored-by: ruv <ruvnet@gmail.com> |
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rUv
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261ce80a72 |
feat(adr-260): RuField MFS spec + vendor/rufield submodule (#1061)
ADR-260 (Accepted — v0.1 reference stack): RuField, the open specification for camera-free multimodal field sensing — one FieldEvent/FieldTensor/ FusionGraph/PrivacyClass/ProvenanceReceipt model above WiFi CSI/CIR/BFLD, UWB, BLE Channel Sounding, mmWave radar, ultrasound, subsonic, infrared, and quantum sensors. Published standalone as github.com/ruvnet/rufield and vendored here as the vendor/rufield submodule (the vendor/rvcsi pattern — not a v2/ workspace member). v0.1 reference stack: 6 crates, 60 tests/0 failed, clippy-clean. All benchmark metrics SYNTHETIC (simulator ground truth, no hardware). Co-authored-by: ruv <ruvnet@gmail.com> |
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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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rUv
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2bccdf5065 |
ADR-125 APPLE-FABRIC: RuView <-> Apple Home native HAP bridge (e2e on real C6) (#797)
* feat(adr-125 iter 3): BFLD PrivacyGate + semantic-event naming at HAP boundary
Inserts a Python equivalent of `wifi-densepose-bfld::PrivacyClass` +
`PrivacyGate` between the rv_feature_state parser and the HAP toggle
file. ADR-125 §2.1.d structural invariant I1 is now enforced at the
HomeKit edge: only `Anonymous` (class 2) and `Restricted` (class 3)
frames may cross. `Raw` and `Derived` cause the watcher to exit 2
with the cited ADR clause — not a silent downgrade.
Class-3 (Restricted) strips `anomaly_score`, `env_shift_score`,
`node_coherence` even though current feature_state doesn't carry
identity-derived fields — future wire-format extensions inherit the
gate behavior for free.
Operator-facing semantic naming follows ADR-125 §2.1.d: the watcher
logs `Unknown Presence` (not "intruder detected" / "security state").
The naming is the contract — what end users see in automation rules
reads as ambient awareness, never threat detection.
Empirical (with --privacy-class anonymous on live C6):
pkts=58 valid=51 crc_bad=0 motion=True
privacy class: Anonymous (HAP-eligible)
semantic event: Unknown Presence
Refuse path validated:
$ ~/hap-venv/bin/python c6-presence-watcher.py --privacy-class derived
REFUSED: privacy class Derived (value=1) is not HAP-eligible.
ADR-125 §2.1.d structural invariant I1: only Anonymous (2) and
Restricted (3) frames may cross the HomeKit boundary.
$ echo $?
2
Branch: feat/adr-125-apple-fabric (kept off main while docker build
for sha
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github-actions[bot]
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5d6e50d8a0 |
chore: update vendor submodules (#634)
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> |
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github-actions[bot]
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540ecb4538 |
chore: update vendor submodules (#604)
Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> |
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ruv
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f2525d7a0d |
chore(vendor): add rvcsi as a vendor submodule (github.com/ruvnet/rvcsi)
rvCSI — the edge RF sensing runtime incubated here as `v2/crates/rvcsi-*` (ADR-095, ADR-096, PR #542) — now has a standalone home at github.com/ruvnet/rvcsi (9 crates published to crates.io, @ruv/rvcsi on npm, a Claude Code plugin). This vendors it under `vendor/rvcsi`, alongside `vendor/ruvector` / `vendor/midstream` / `vendor/sublinear-time-solver`. Follow-up: migrate the workspace to consume `vendor/rvcsi/crates/rvcsi-*` and drop the inline `v2/crates/rvcsi-*` copies (kept for now so this change is a pure addition). Co-Authored-By: claude-flow <ruv@ruv.net> |
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rUv
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5e5781b28a |
feat: RuVector all phases — temporal smoothing + kinematic constraints + coherence
* chore: update vendored ruvector to latest main (v2.1.0-40) Was at v2.0.5-172 (f8f2c600a), now at v2.1.0-40 (050c3fe6f). 316 commits with new crates: ruvector-coherence, sona, ruvector-core, ruvector-gnn improvements, and security hardening. Co-Authored-By: claude-flow <ruv@ruv.net> * feat: RuVector Phases 2+3 — temporal smoothing, kinematic constraints, coherence gating Phase 2 (sensing server): - Temporal keypoint smoothing via EMA (alpha=0.3) with coherence-adaptive blending - Coherence scoring: running variance of motion_energy over 20 frames - Low coherence → reduce alpha to 0.1 (trust measurements less) - Per-node prev_keypoints for frame-to-frame smoothing - Bone length clamping (±20%) in derive_single_person_pose Phase 3 (signal crate): - SkeletonConstraints: Jakobsen relaxation (3 iterations) on 12-bone COCO-17 kinematic tree — prevents impossible skeletons - CompressedPoseHistory: two-tier storage (hot f32 + warm i16 quantized) for trajectory matching and re-ID - 8 new tests for constraints + history Vendored ruvector updated to v2.1.0-40 (latest main, 316 commits). Workspace deps remain at v2.0.4 (crates.io) until v2.1.0 is published. 647 tests pass across both crates (0 failures). Refs #296 Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
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f771cf8461 |
docs: add vendor README with submodule setup instructions
Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
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c6f061a191 |
refactor: convert vendor/ directories to git submodules
Replace 9,608 tracked vendor files (~737MB) with git submodule pointers to their upstream repositories: - vendor/midstream -> https://github.com/ruvnet/midstream - vendor/ruvector -> https://github.com/ruvnet/ruvector - vendor/sublinear-time-solver -> https://github.com/ruvnet/sublinear-time-solver This dramatically reduces repo size and ensures vendor code stays in sync with upstream. New clones should use: git clone --recurse-submodules Existing clones should run: git submodule update --init --recursive Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
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4b1005524e |
feat: complete vendor repos, add edge intelligence and WASM modules
- Add 154 missing vendor files (gitignore was filtering them) - vendor/midstream: 564 files (was 561) - vendor/sublinear-time-solver: 1190 files (was 1039) - Add ESP32 edge processing (ADR-039): presence, vitals, fall detection - Add WASM programmable sensing (ADR-040/041) with wasm3 runtime - Add firmware CI workflow (.github/workflows/firmware-ci.yml) - Add wifi-densepose-wasm-edge crate for edge WASM modules - Update sensing server, provision.py, UI components Co-Authored-By: claude-flow <ruv@ruv.net> |
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rUv
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407b46b206 |
feat: vendor midstream and sublinear-time-solver libraries (#109)
Add ruvnet/midstream (AIMDS real-time inference) and ruvnet/sublinear-time-solver (sublinear optimization algorithms) as vendored dependencies under vendor/. |
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ruv
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cd5943df23 | Merge commit 'd803bfe2b1fe7f5e219e50ac20d6801a0a58ac75' as 'vendor/ruvector' |