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819 Commits

Author SHA1 Message Date
ruv 4db727649a feat(calibration): RuView per-room calibration service (reference impl) 
Operationalizes the campaign's central finding (ADR-150 §3.3-3.6): a frozen
shared base + a ~11KB per-room LoRA adapter from ~100-200 labeled samples
recovers SOTA-level pose in any new room/person. Verified end-to-end:
source-only base zero-shot 3.09% on unseen room -> 74.29% after 200-sample
calibration. Files: model.py (PoseNet+LoRA), calibrate.py, infer.py, README
with measured calibration budget.

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-05-31 02:22:10 -04:00
ruv 5533ffe43e docs(adr-150): cross-env few-shot — no unsolved deployment case 
Decisive capstone: cross-environment (unseen room+people) zero-shot
10.6%, but 5 calibration samples/person -> 60%, 200 -> 73%. The hard
frontier is calibration-soluble, MORE dramatically than cross-subject
(+62.5 vs +12 at K=200). The unsolved-frontier framing was a zero-shot
artifact. Reframes generalization: ship few-shot calibration, not
zero-shot invariance. Recommend accepting ADR-150 re-scoped around the
calibration mechanism.

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-05-31 02:09:03 -04:00
ruv ef4344f0f9 docs(adr-150): LoRA calibration data requirement — completes calibration spec 
11KB adapter needs ~100-200 labeled samples/room for ~72% (knee ~50->70%);
below ~20 it hurts. Evidence-complete calibration-service spec: base +
~100-200 samples -> 11KB LoRA -> ~72% cross-subject. Encoder goal now
precisely posed: cut the sample requirement / lift the per-budget ceiling.

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-05-31 02:04:37 -04:00
ruv ed1294a176 docs(adr-150): deployable adapter calibration — 11KB LoRA = calibration service 
Compared per-room calibration methods at K=200: LoRA rank-8 recovers
63.6->72.5% (SOTA-level) with just 11K params (~11KB), 0.5% the model
size. Validates the ship-base-once + tiny-per-room-adapter mechanism for
the RuView calibration service. Accuracy/size knob documented.

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-05-31 01:54:23 -04:00
ruv 898aaef053 docs(adr-150): few-shot adaptation resolves the cross-subject frontier 
Decisive result: 50 labeled frames/subject of in-room calibration ->
72.2% (reaches SOTA), 200 -> 76.1%, 1000 -> 78.3%. Few-shot target
adaptation dominates source volume (+24 subjects bought +6pt; 200 target
frames bought +12.4pt). Re-scopes the deployment story: ship a ~30s on-site
calibration, not a mass corpus. Foundation encoder's role shifts to making
that calibration cheaper. Supersedes the earlier data-bound pessimism.

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-05-31 01:47:00 -04:00
ruv 70bf9e41fe docs(adr-150): subject-scaling study — capture diversity, not volume 
Measured cross-subject PCK vs N training subjects: 4->8 = +21pts, but
24->32 = +0.45pt. Saturates ~64%, ~19pt below in-domain. Correction to
'more data': subject-count returns vanish past ~16-20; the residual is
device/room/protocol shift. Re-scope phase-1 capture around DIVERSITY
(rooms/devices/protocols) + few-shot target adaptation, not headcount.

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-05-31 01:43:49 -04:00
ruv 96ccfa58fb bench: ship int4 edge artifact + CPU latency 
Published deployable int4-QAT micro (verified 74.08%, ~20KB) at
ruvnet/wifi-densepose-mmfi-pose/edge. Runs 0.135ms single-thread x86 CPU
(no GPU) - real-time pose without an accelerator. ARM on-device validation
pending fleet availability.

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-05-31 01:30:29 -04:00
ruv 92d433523d bench: deployed quantized accuracy + QAT for micro edge model 
int8 PTQ lossless (74.70%, 73.5KB); int4 naive PTQ drops below SOTA
(70.21%) but QAT recovers to 74.46% (36.7KB) - still beats MultiFormer.
A SOTA-beating WiFi-pose model genuinely runs in ~37KB int4 (QAT) /
73KB int8. Distillation negative noted.

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-05-31 01:23:30 -04:00
ruv d64323c2d6 bench: add quantized footprint — SOTA-beating WiFi pose in 37KB int4 
micro (74.87%, beats MultiFormer 72.25%) = 36.7KB int4 / 73.5KB int8;
nano (~72%) = 19.5KB int4. Distillation tested, no gain (direct training
wins). A SOTA-beating pose model fits on the sensing node itself.

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-05-31 01:16:16 -04:00
ruv 9c64d90054 bench: WiFi-CSI pose efficiency frontier — 75K-param model beats SOTA 
Swept model size on MM-Fi random_split: every config from micro (75,237
params, 0.22ms, 74.30%) up beats MultiFormer (72.25%); nano (40K, 0.13ms)
within 0.5pt. Pareto-dominant (smaller AND more accurate than prior SOTA).
Orthogonal to the data-bound accuracy frontier (ADR-150).

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-05-31 01:10:33 -04:00
ruv 5d1fb48eb5 docs(adr-150): empirical cross-subject findings — pose-contrastive pretrain refuted 
Measured all near-term levers on the official MM-Fi cross-subject split:
- mixup+TTA+ensemble = best at 64.92% (+0.9 over doc 64.04)
- pose-contrastive foundation pretrain: estimated +5..+12, MEASURED -2.3
  (SupCon loss pinned at ln(B) across K/BS/seeds -> same-pose CSI is not
  contrastively alignable across subjects)
- instance-norm+SpecAugment -4.6; CORAL/DANN ~0

Conclusion: the 18-pt in-domain<->cross-subject gap is fundamental subject
shift, not algorithmic. Promotes multi-subject data collection to the primary
lever; recommends re-scoping ADR-150 phase 1 around capture.

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-05-31 00:33:43 -04:00
ruv b4cb1384de docs(readme): honest re-benchmark of ESP32 presence model (retract single-class 100%) 
v1 '100% presence accuracy' was on a single-class overnight recording
(6062/6063 'present'). Replaced with v2 encoder's honest label-free
held-out temporal-triplet accuracy (66.4% raw -> 82.3% trained).
Models published to HF; tracking ruvnet/RuView#882.

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-05-30 23:52:11 -04:00
ruv 66e917ea86 bench: HOMECORE vs Home Assistant — measured perf + capability matrix 
Head-to-head on the wire-compatible HA API surface:
- Cold start 0.55s vs 9.7s (18x), idle RSS 10.1MB vs 359MB (35x),
  binary 4.7MB vs 610MB image (130x), throughput 1599 vs 716 rps.
- Honest caveats: latency endpoints differ (auth /api/states vs
  unauth /manifest.json); HA wins integration breadth + UI maturity.
- Repro harnesses in aether-arena/staging/.

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-05-30 23:41:15 -04:00
ruv 7738370b18 docs(readme): link SOTA MM-Fi pose model (82.69% torso-PCK@20) on HF 
Published ruvnet/wifi-densepose-mmfi-pose — beats MultiFormer (72.25%)
and CSI2Pose (68.41%) on matched MM-Fi random_split torso-PCK@20.
Tracking: ruvnet/RuView#880

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-05-30 23:32:12 -04:00
ruv 7bad51aca6 publish: best MM-Fi benchmark set (in-domain 83.59, x-subject 64.0, x-env 17.5 CORAL) 
Append best witness rows to ledger (seq 2-4) + update HF Space leaderboard banner.
In-domain 83.59% torso-PCK@20 (graph+ensemble+TTA) supersedes the 81.63 single-model entry,
+11.34 over MultiFormer 72.25. Cross-subject 64.04% (official split). Cross-environment 17.51%
(CORAL domain alignment, the cross-room DG win). Gist + issue #876 updated with frontier map.

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-05-30 22:22:53 -04:00
ruv eb3509e9ab reframe(aether-arena): vendor-neutral industry benchmark, RuView is one entrant 2026-05-30 19:59:10 -04:00
ruv 046b2564b8 feat(aether-arena): publish RuView MM-Fi SOTA result + ADR-150 RF Foundation Encoder 
- Ledger witness row (seq 1, Gold): RuView CSI-Transformer 81.63% torso-PCK@20 on
  MM-Fi random_split, exceeding MultiFormer 72.25% (CSI2Pose 68.41%) — protocol- and
  metric-matched, self-corrected from inflated 91.86% bbox. Hash-chained, verifiable.
- HF Space updated with the controlled SOTA claim + caveat (cross-subject is the frontier).
- Proof/replay/witness gist: gist.github.com/ruvnet/af2fbc1c7674dddf09c15509b3c7f785
- Tracking issue #876 (result + Generalization Track roadmap).
- ADR-150: RuView RF Foundation Encoder — pose-preserving, subject/room/device-invariant
  SSL embedding (masked CSI + pose-contrast-across-subjects + coherence head); the
  principled attack on the cross-subject frontier. DANN failed; this is the corrected design.

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-05-30 19:55:58 -04:00
ruv 4f7ab8e4f0 docs(aether-arena): v0 infrastructure complete — Space live, harness gate passing (M8) 2026-05-30 17:15:08 -04:00
ruv de6715d958 fix(aether-arena): move HF Space to gradio 5.9.1 (4.44.1 jinja2 cache bug) 2026-05-30 17:14:21 -04:00
ruv c1c04441e9 fix(aether-arena): Space launch on 0.0.0.0:7860 2026-05-30 17:10:17 -04:00
ruv 5284591770 fix(aether-arena): pin huggingface_hub 0.25.2 for gradio 4.44.1 Space 2026-05-30 17:07:08 -04:00
ruv 3f93fcd4ea fix(aether-arena): pin HF Space to python 3.12 (gradio pydub pyaudioop 3.13 removal) 2026-05-30 17:03:14 -04:00
ruv 644b4ba816 docs(aether-arena): mark M6 HF Space deployed 2026-05-30 17:02:03 -04:00
ruv 9359bf5d04 feat(aether-arena): HF Space (Gradio) v0 — deployed to ruvnet/aether-arena (M6) 
Public face of the benchmark: empty-board leaderboard from the witness ledger,
chain-integrity display, submit/verify/about tabs. Presentation layer per ADR-149
§2.2 (heavy scoring stays in the pinned RuView harness / CI).
Live: https://huggingface.co/spaces/ruvnet/aether-arena

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-05-30 17:01:10 -04:00
ruv 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>
 2026-05-30 16:59:11 -04:00
ruv 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>
 2026-05-30 16:47:22 -04:00
ruv 9ad550d95f feat(worldmodel): Candle Rust port + GCP GPU scripts (ADR-147 Phase 4+6) 
Candle native port — wifi-densepose-occworld-candle v0.3.0:
- config.rs: OccWorldConfig (14 params matching occworld.py)
- vqvae.rs: ClassEmbedding(18→64), VQCodebook(512×512, squared-L2),
  QuantConv/PostQuantConv(1×1 Conv2d), fold_3d_to_2d helpers
  ResNet encoder/decoder are documented stubs (Phase 5 checkpoint pending)
- transformer.rs: full Candle MHA transformer (2 layers, temporal+spatial
  cross-attention, FFN, pre-norm residuals)
- inference.rs: OccWorldCandle::dummy() + ::load() + predict()
  InferenceOutput: sem_pred(1,15,200,200,16) + trajectory_priors
- 14/14 tests pass (12 lib + 2 doctests)

GCP GPU scripts — scripts/gcp/:
- provision_training.sh: a2-highgpu-8g (8×A100 40GB) for Phase 5 retraining
- run_training.sh: rsync + torchrun 8-GPU train + checkpoint download
- provision_cosmos.sh: a2-ultragpu-1g (A100 80GB) for Cosmos evaluation
- cosmos_eval.sh: run Cosmos-Transfer2.5 inference, download results
- teardown.sh: safe checkpoint download + instance delete

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-05-29 20:52:51 -04:00
ruv da40503a9e docs(adr-147): add real CSI benchmark — 208ms median, 3.98GB VRAM, 72 frames/sec 
Real data: archive/v1 CSI proof dataset (seed=42, 3rx, 56sc, 100Hz, 1000 frames)
Pipeline: CSI amplitude → presence → ENU position → voxels → OccWorld inference
20 inference windows, no mocks.

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-05-29 19:56:28 -04:00
ruv bb7de84cb4 docs: add Phase 3+5 scripts to user guide and README world model row 
- User guide: full retrain workflow (record → vqvae → transformer → serve)
  with checkpoint path usage
- README: note fine-tune capability in world model capability row

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-05-29 19:50:21 -04:00
ruv cd1c391afc feat(worldmodel): ADR-147 Phase 3+5 — RuViewOccDataset domain adapter + retraining pipeline 
Phase 3 — scripts/ruview_occ_dataset.py:
- RuViewOccDataset: WorldGraph JSON snapshots → OccWorld (F,H,W,D) tensors
- Indoor class remapping: person→7, floor→9, wall→11, furniture→16, free→17
- Zero ego-poses (fixed indoor sensor, no ego-motion)
- record_snapshot() helper for training data accumulation
- Validated: 5 windows, (16,200,200,16) tensor, person+floor voxels confirmed

Phase 5 — scripts/occworld_retrain.py:
- record: stream WorldGraph snapshots from sensing server REST API
- vqvae: fine-tune VQVAE tokenizer on RuView occupancy (200 epochs, AdamW)
- transformer: fine-tune autoregressive transformer with frozen VQVAE

wifi-densepose-worldmodel v0.3.0 published to crates.io

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-05-29 18:46:56 -04:00
ruv 28a27bbfd8 fix(worldmodel): use published worldgraph v0.3.0 instead of path dep (crates.io publish prep) 
Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-05-29 18:43:35 -04:00
rUv c7ddb2d7d1
feat(worldmodel): ADR-147 — OccWorld world model integration, wifi-densepose-worldmodel v0.3.0 (#856) 
* feat(worldmodel): ADR-147 — OccWorld integration, wifi-densepose-worldmodel v0.3.0 (#854)

- New crate `wifi-densepose-worldmodel` v0.3.0: async Unix-socket bridge
  to OccWorld Python inference server; `OccWorldBridge`, `OccupancyGrid3D`,
  `TrajectoryPrior`, `worldgraph_to_occupancy` encoder (14/14 tests pass)
- `scripts/occworld_server.py`: long-lived Python inference server for
  OccWorld TransVQVAE (72.4M params); applies API-bug patches; dummy mode
  for CI testing; graceful SIGTERM shutdown
- `pose_tracker.rs`: `trajectory_prior` soft-blend injection (80/20
  Kalman/prior) on torso keypoint; `set_trajectory_prior()` public method
- CI: added `Run ADR-147 worldmodel tests` step
- ADR-147: accepted — OccWorld primary (209 ms, 3.37 GB VRAM, RTX 5080);
  Cosmos deferred to ADR-148 (32.54 GB VRAM exceeds hardware)
- Benchmark proof: 208.7 ms P50, 3.37 GB peak VRAM, 12.1 GB headroom

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

* chore: update ruvector.db state

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

* chore: ruvector.db sync

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

* fix(cli): add missing min_frames field to CalibrateArgs test helper

E0063 in calibrate.rs:448 — CalibrateArgs gained min_frames in ADR-135
but the default_args() test helper was not updated. min_frames=0 means
'use tier default', matching the existing runtime behaviour.

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-05-29 16:53:51 -04:00
rUv 2cc9f8acb3
Merge pull request #853 from ruvnet/feat/adr-136-146-streaming-engine 
RuView Streaming Engine (ADR-135..146): auditable environmental intelligence
 2026-05-29 09:42:46 -04:00
ruv d24bf36110 release: version bumps for crates.io publish (streaming-engine cascade) 
- core 0.3.0->0.3.1 (ComplexSample/CanonicalFrame/provenance + blake3 dep)
- ruvector 0.3.0->0.3.1 (ClockQualityGate)
- bfld 0.3.0->0.3.1 (privacy control plane)
- signal 0.3.1->0.3.2 (fuse_scored_calibrated/ArrayCoordinator/evolution/rf_slam)
- geo: add license/repository for first publish; worldgraph/engine pin geo version
- new: geo 0.1.0, worldgraph 0.3.0, engine 0.3.0

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-05-29 09:26:38 -04:00
ruv c60a55ca6e docs: RuView streaming-engine v0.3.0 release notes (intro + usage) 
Introduction (auditable environmental intelligence / trust throughline), what's
new per ADR-135..146, quick-start usage for StreamingEngine, the 4 validated
acceptance paths, ~6.35us/cycle benchmark, build/test, and honest status.

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-05-29 08:46:12 -04:00
ruv 95bdd37e76 bench+test: engine per-cycle benchmark + ADR-142 acceptance path 
- engine: criterion benchmark engine_cycle — full process_cycle (4 nodes / 56
  subcarriers) measured at ~6.35 us/cycle, ~7800x under the 50ms (20Hz) budget.
- signal: ADR-142 acceptance test — 3 links drift 30 frames -> ChangePoint ->
  VoxelMap accumulates -> low-confidence voxels suppressed -> VoxelGate
  Restricted emits histogram only -> ADR-137 contradiction recorded.

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-05-29 08:42:46 -04:00
ruv 020aa08049 test(sensing-server): ADR-140 live acceptance — snapshot to expired-rejection 
Drives a real SemanticBus: raw snapshot (fall_detected, past warmup) ->
FallRisk primitive -> SemanticStateRecord (provenance) -> single-signal rule
fires / multi-signal agreement rule does NOT (no false escalation) -> expired
record rejected. Proves the ADR-140 credibility path end to end.

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-05-29 08:37:28 -04:00
ruv 5878868060 feat(signal,engine): ADR-137 calibration-mismatch contradiction + trust witness 
- signal: MultistaticFuser::fuse_scored_calibrated() threads per-node
  CalibrationId; agreeing epochs → calibration_id set + CalibrationApplied
  evidence; disagreeing → calibration_id None + CalibrationIdMismatch flag
  (forces demotion). +2 tests.
- engine: process_cycle_calibrated() per-node calibration path; process_cycle
  delegates with a uniform epoch. TrustedOutput gains a deterministic BLAKE3
  witness over (provenance || class). calibration_version='cal:none' on mismatch.
- ADR-137 acceptance test: two frames + mismatched calibration -> QualityScore
  contradiction -> Restricted -> calibration_id None -> witness stable. +happy path.
- 11 engine tests, signal 411+ lib tests; workspace 0 errors.

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-05-29 08:35:40 -04:00
ruv 2517a16d88 feat(engine): compose ADR-138/142/143 + ADR-139 live loop 
- ADR-138: process_cycle runs ArrayCoordinator when node geometry is registered;
  array contradictions (CoherenceDrop/GeometryInsufficient) fold into the
  privacy demotion; DirectionalEvidence surfaced in TrustedOutput
- ADR-142: per-node mean-amplitude → EvolutionTracker; cross-link change-point
  recorded as a WorldGraph Event node
- ADR-143: ingest_reflectors() runs Rf-SLAM discovery, writes stable
  Wall/Furniture reflectors as ObjectAnchor nodes
- ADR-139 live loop: update_person_track(), apply_active_privacy_mode()
  (PrivacyRollup suppresses person_track under identity-strict modes),
  snapshot_json()
- Acceptance test live_frame_to_reload_same_contents: full path
  fusion->worldgraph->privacy_rollup->persist->reload->same contents, no raw RF
- 9 engine tests; workspace 0 errors

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-05-29 08:31:05 -04:00
ruv 2eada40e3b feat(engine): integrate ADR-135..141 into an end-to-end trust pipeline 
- signal/calibration.rs: BaselineCalibration gains calibration_id()/
  calibration_uuid()/apply() — the ADR-135->136 link that stamps
  FrameMeta.calibration_id (deterministic id, no serialization change). +1 test.
- NEW crate wifi-densepose-engine: StreamingEngine::process_cycle() composes
  fuse_scored (137) -> calibration provenance (135/136) -> privacy demotion on
  contradiction (141) -> WorldGraph SemanticState with mandatory provenance +
  DerivedFrom edge (139). Returns TrustedOutput (the trust chain made concrete).
- Validates the throughline: every output names evidence + model + calibration
  + privacy decision; calibration_id flows input->QualityScore->provenance;
  contradiction demotes class; deterministic; privacy mode attested.
- 4 integration tests; workspace 0 errors; signal 410 lib tests pass.

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-05-29 08:21:48 -04:00
ruv f2e9e2f2bd docs(adr): add Implementation Status & Integration to ADR-136..146 
Weaves the three framing points into every ADR in the series:
- skeleton/scaffolding (data contracts + trust/privacy/audit machinery +
  algorithms; real, tested, compiling) that existing sensing code plugs into
- Built (tested building block) vs Integration glue (not yet on the live 20 Hz
  path) — per-ADR, with commit + issue references
- trust throughline (traceable evidence, sensor agreement, calibration
  provenance, auditable privacy)
ADR-136 §8 carries the full series framing; 137-146 carry per-ADR status.

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-05-29 08:09:23 -04:00
ruv f18b096f2f feat(nn): ADR-146 RF encoder multi-task heads + uncertainty (#850) 
- nn/rf_encoder.rs (forward-looking; extends ADR-024 AETHER):
  - RfEmbedding (256-d pure-Rust f32 ABI), TaskKind (7 heads)
  - LinearHead: W*emb+b + separate log-variance projection → HeadOutput with
    softplus uncertainty + confidence(); MultiTaskHeads.forward_subset() for
    ADR-145 ablation toggling
  - calibration_robustness_loss (ADR-135 invariance), triplet_loss (ADR-024)
  - ContrastiveBatcher: deterministic cross-environment positive / different-
    state negative triplet sampling (ADR-027 MERIDIAN)
- 7 tests; workspace 0 errors

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-05-28 23:41:25 -04:00
ruv 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>
 2026-05-28 23:38:43 -04:00
ruv b10bc2e9ab feat(mat): ADR-144 UWB range-constraint fusion (#848) 
- mat/localization/range_constraint.rs (forward-looking; no UWB hw yet):
  - RangeConstraint domain model (anchor_id/pos/measured_range/uncertainty/
    signal_quality); predicted_range/residual/mahalanobis/is_consistent
  - RangeConstraintFusion::refine() — Newton-normalized weighted least-squares
    that constrains a CSI/CIR prior toward range spheres, Mahalanobis-gates
    inconsistent (NLOS/multipath) ranges; returns RefineResult with rejected
    anchors + RMS residual
  - associate() disambiguates which track a range belongs to (re-ID hook)
- 4 tests (converges to truth, absurd range gated, consistency math, track
  association); workspace 0 errors

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-05-28 23:35:30 -04:00
ruv 2d4f3dea53 feat(signal): ADR-143 RF-SLAM reflector discovery + anchor learning (#847) 
- ruvsense/rf_slam.rs (forward-looking, ships v1 fixed-map first):
  - RfSlam::fixed_map() — discovery disabled (v1); with_discovery() — v2
  - ReflectorObservation (CIR-tap sighting), PersistentReflector (per-axis
    Welford position, migration_m_per_day, classify Wall/Furniture/Mobile)
  - observe(): nearest-reflector association within assoc_radius or seed new;
    coherence-gated; static_anchors() rejects Mobile → ADR-139 ObjectAnchor set
  - persistent_count() for topology-change detection
- 6 tests (fixed-map no-op, persistence, low-coherence reject, cluster split,
  mobile excluded, static→Wall); workspace 0 errors

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-05-28 23:29:14 -04:00
ruv 1f8e180d69 feat(signal): ADR-142 evolution tracker + temporal VoxelMap (#846) 
- ruvsense/evolution.rs (extends ADR-030):
  - TemporalVoxel: Bayesian log-odds occupancy update, evidence_count,
    confidence = 1-exp(-count/5) (5-frame low-confidence floor), Welford
    variance, doppler attribution, last_update_ns
  - TemporalVoxelMap: persistent grid, observe(), low_confidence_indices()
  - EvolutionTracker: per-link Welford baselines + cross-link change-point
    (>=3 links beyond 2sigma in one window); divergence checked vs prior baseline
  - VoxelGate: privacy demotion (Anonymous clears doppler+confidence, keeps
    occupancy; Restricted → occupancy histogram only, raw map cleared)
- reuses field_model::WelfordStats; 6 tests; workspace 0 errors

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-05-28 23:26:28 -04:00
ruv 7d88eb84c7 feat(bfld): ADR-141 privacy control plane — modes, actions, attestation (#845) 
- privacy_mode.rs: PrivacyMode (RawResearch/PrivateHome/EnterpriseAnonymous/
  CareWithConsent/StrictNoIdentity) layered over the existing 4-class
  PrivacyClass; each mode pins target_class + enforced PrivacyAction bitset +
  soul_signature_enabled
- PrivacyAction enum (Allow/SuppressIdentity/ReduceResolution/DropRaw/AggregateOnly)
- PrivacyModeRegistry (std-gated, heap audit log per ESP32 no_std convention):
  active-mode source of truth, is_action_enforced(), set_mode() appends
  hash-chained PrivacyAttestationProof (BLAKE3, ADR-010), verify_chain()
- no_std-safe: PrivacyMode/Action/AttestationProof are heap-free; registry
  std-gated. Builds --no-default-features AND --features std.
- 6 tests incl. tamper-detection; workspace 0 errors

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-05-28 23:23:01 -04:00
ruv 169a355bde feat(sensing-server): ADR-140 semantic state record + Ruflo agent bridge (#844) 
- semantic/record.rs: SemanticStateRecord (kind/room/node/timestamp/expiry/
  confidence/model_version/calibration_version/privacy_action/evidence_refs) —
  the auditable wire form of an ADR-139 SemanticState node, enriched from the
  existing SemanticEvent via RecordContext
- PrivacyAction enum (Allow/AnonymizeByRoom/StripBiometrics); StripBiometrics
  removes HR/BR evidence tags at the record boundary
- Ruflo agent bridge: MultiSignalRule.evaluate() fires AgentRoute only on
  multi-signal agreement (fall_risk + elderly_anomaly → caregiver_escalation);
  route_all() sorts by severity + dedups
- 4 tests; workspace 0 errors

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-05-28 23:17:53 -04:00
ruv 521a012d84 feat(worldgraph): ADR-139 WorldGraph environmental digital twin (#843) 
New crate wifi-densepose-worldgraph:
- model.rs: WorldNode (10 kinds) + WorldEdge (7 relations) as serde enums (no
  trait objects → deterministic RVF persistence); WorldId, EnuPoint,
  ZoneBoundsEnu (with point-in-bounds), SemanticProvenance (house-rule tuple)
- graph.rs: WorldGraph over petgraph StableDiGraph; upsert/add_edge/neighbors,
  room_for_area (HomeCore area_id linkage), observed_by/contents_of queries,
  add_semantic_state (append-with-provenance DerivedFrom), add_contradiction
  (both beliefs retained), apply_privacy_mode → PrivacyRollup, JSON persistence
- 7 tests (upsert/replace, linkage, unknown-endpoint, location, provenance+
  contradiction, privacy rollup, deterministic JSON round-trip)
- workspace 0 errors

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-05-28 23:14:29 -04:00
ruv fc7674bde9 feat(signal,ruvector): ADR-138 LinkGroup/ArrayCoordinator clock-quality gating (#842) 
- ruvector viewpoint/coherence.rs: ClockQualityScore, ClockQualityGate,
  ClockGateDecision (Admit/MonitorOnly/Reject), ClockRejectReason. 200us floor,
  9s staleness ceiling per ADR-110.
- signal ruvsense/array_coordinator.rs: ArrayCoordinator domain service +
  DirectionalEvidence. Gates nodes, computes GDI + Cramer-Rao credence, builds
  attention weights (real node_attention_weights when amplitudes present, else
  clock-quality softmax), emits CoherenceDrop + GeometryInsufficient flags.
- Cycle resolution: ArrayCoordinator lives in signal (depends on ruvector), not
  ruvector, so it can emit ADR-137 canonical ContradictionFlag. Documented.
- 8 tests (5 coordinator + 3 clock gate); workspace 0 errors.

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-05-28 23:09:06 -04:00