Beyond-SOTA sweep M5–M6 (ADR-159/160): appliance + edge-skill honesty + crates.io publish#1024
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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>
pose_v1 has no confidence head, so infer() emits a constant 0.185 per frame. The config default_min_confidence was 0.3 and the runtime gates on confidence >= min_confidence, so a default install silently emitted ZERO pose.frame events while health reported healthy. - Add inference::MODEL_TYPICAL_CONFIDENCE (0.185, the validation PCK@50) as the single published per-frame confidence. - Pin default_min_confidence() to MODEL_TYPICAL_CONFIDENCE so a default install clears its own gate and emits. - Warn at run.started when min_confidence exceeds the model typical confidence (disclosed, not silent); document the trade-off in the config field, the JSON schema, and inference.rs. Failing-on-old test: default_config_emits_frames_with_real_model (with old 0.3 it panics: "default install would emit zero pose.frame events"). Co-Authored-By: claude-flow <ruv@ruv.net>
…R-159 A2) The count head has 8 classes but count_train_results.json only has support for classes 0/1 (presence, not multi-occupant counting). An argmax on classes 2..=7 is out-of-distribution, yet the cog emitted it as a confident headcount and the crate billed itself a "multi-person counter". - Add MAX_TRAINED_CLASS=1, CountPrediction::is_low_confidence() and clamped_count(). - person.count events now carry low_confidence + raw_count, downgrade to level "warn" when OOD, and clamp the reported count to the trained range (no fabricated headcount). - run.started discloses count_max_trained_class / count_classes. - Cargo.toml description: "multi-person counter" -> "presence detector + (data-gated) person count". Multi-occupant accuracy stays DATA-GATED (not fabricated). Failing-on-old test: untrained_class_argmax_is_flagged_low_confidence. Co-Authored-By: claude-flow <ruv@ruv.net>
cmd_manifest emitted a null skeleton (binary_sha256: null) while the real signed manifest existed on disk at cog/artifacts/manifests/<arch>/manifest.json. - New manifest module include_str!-embeds the real signed manifests (x86_64 + arm), selected by build target arch. - cmd_manifest parses-then-emits the embedded signed manifest, mirroring cog-pose-estimation manifest_roundtrips. CLI now reports the real binary_sha256, weights_sha256, Ed25519 signature, and honest build_metadata (training_class1_accuracy = 0.343). Failing-on-old test: manifest::tests::embedded_manifest_has_non_null_binary_sha256 (+ embedded_manifest_is_signed, embedded_manifest_id_matches_cog). Verified end-to-end: cog-person-count manifest -> non-null sha256. Co-Authored-By: claude-flow <ruv@ruv.net>
… A3)
RemoteIdBroadcast::update stored NED metres (state.position.x/.y) into
drone_lat/drone_lon, so the ASTM F3411 broadcast would carry physically
-impossible coordinates ("latitude = 37.5 m"). The module doc claimed a
Location/Vector message but only encode_basic_id() exists.
- Rename drone_lat/drone_lon -> drone_north_m/drone_east_m (NED metres
relative to the operator/takeoff datum), documented as non-geodetic.
operator_lat/lon stay true WGS84.
- Correct the module doc to claim Basic ID only; Location/Vector encoding
is deferred until a datum-anchored NED->WGS84 transform lands.
Never broadcast physically-impossible coordinates.
Failing-on-old test:
security::remote_id::tests::test_ned_offset_stored_as_metres_not_latlon.
Co-Authored-By: claude-flow <ruv@ruv.net>
Matter commissioning is deferred to v0.8 (TlsConfig::Off, LAN-only, per tls_defaults_to_off_for_v1_lan_only). Soften the Cargo.toml description from "Home Assistant + Matter integration" to "Home Assistant (MQTT) integration ... Matter Bridge commissioning is deferred to v0.8 and not yet implemented" (honest-absence, ADR-158 pattern). No code change. Co-Authored-By: claude-flow <ruv@ruv.net>
Records the anti-AI-slop sweep over cog-person-count, cog-pose-estimation, cog-ha-matter, ruview-swarm. HEADLINE: the "never identified anyone" accusation is REFUTED (real SHA-pinned Ed25519-signed trained Candle models, honest 34%/3% accuracy in manifests). Documents claim-surface fixes A1-A5 (MEASURED), NO-ACTION positives (witness chain, fusion, PPO + randn audit), graded SOTA landscape (counting/pose DATA-GATED, swarm MARL untrained-at-runtime by design), and the deferred backlog (benches, Location/Vector, Matter v0.8, wasm-edge accuracy). Co-Authored-By: claude-flow <ruv@ruv.net>
…ls (ADR-160) The wasm-edge skill library runs real DSP with 0 stubs / 0 theater; the exposure is an over-confident claim surface on unvalidated skills plus a latent static-mut soundness issue. Make the labels TRUE (do not pretend to validate the capability) and fix the soundness mechanically: - A1 (HIGH): med_seizure/cardiac/respiratory/sleep_apnea/gait -- add mandatory "EXPERIMENTAL / NOT VALIDATED AGAINST CLINICAL DATA / NOT A MEDICAL DEVICE" disclaimers, soften assertive verbs to "flags candidate <X>-like signatures", and gate all 5 behind a NON-default medical-experimental cargo feature so they cannot be silently shipped. DSP kept. - A2 (HIGH): exo_happiness_score/exo_emotion_detect -- delete the uncited "~12% faster" stat, add "speculative, unvalidated affect heuristic; outputs are NOT measurements of emotion" disclaimers, reframe HAPPINESS_SCORE as a gait-energy proxy. Math kept. - A3 (MEDIUM): sec_weapon_detect -- rename EVENT_WEAPON_ALERT -> EVENT_HIGH_METAL_REFLECTIVITY and WEAPON_RATIO_THRESH -> HIGH_REFLECTIVITY_THRESH (a variance ratio measures reflectivity, not weapons). Registry updated. - A4 (MEDIUM): exo_dream_stage/exo_gesture_language -- add experimental disclaimers, promote the Exotic/Research tag into the header. - A5 (MEDIUM, soundness): replace ~61 `static mut EVENTS`/EV/TE/EMPTY per-call scratch buffers (60 modules) with owned per-instance `events` fields returned as `&self.events[..n]`. Public signature unchanged; behavior preserved. Only the two legitimate single-threaded WASM module singletons (lib.rs STATE, ghost_hunter DETECTOR) remain as static mut. Removes the static_mut_refs source. NO-ACTION positives (cited, labels untouched): qnt_* (quantum-/Grover-inspired, disclosed), exo_time_crystal, exo_ghost_hunter, sig_*/lrn_* algorithm-named skills. Co-Authored-By: claude-flow <ruv@ruv.net>
…59 backlog now TRUE) - tests/honest_labeling.rs: 10 source-presence tests asserting the A1-A5 claim invariants (disclaimers present, uncited stat removed, WEAPON_ALERT no longer exported, med_* feature-gated, no static-mut event buffers). Each is designed to FAIL on the pre-fix source (ADR-159 A5 manifest-roundtrip style). - ADR-160: records the headline (0 stubs/0 theater, all real DSP -> claim-surface honesty debt), the graded A1-A5 fixes, NO-ACTION positives, per-prefix classification, and the DATA-GATED deferred backlog (criterion benches, per-skill accuracy validation, wasm32 static_mut_refs CI confirmation). - ADR-159: its deferred-backlog line "wasm-edge ... honestly labelled, not claimed" is now actually TRUE. Validation (all 0 failed, host --features std): DEFAULT 615 | MEDICAL (+medical-experimental) 653 | NO-DEFAULT 615; 0 warnings. Co-Authored-By: claude-flow <ruv@ruv.net>
…er concurrent runs) checkpoint_round_trip / rvf_test / rvf_pipeline_test shared fixed temp_dir paths and remove_dir at teardown, so two concurrent/repeated test runs raced (one's teardown wiped the other's file -> NotFound). Make each dir process-unique. Test-only; no public API change. Co-Authored-By: claude-flow <ruv@ruv.net>
One-command harness: clone, run scripts/prove.sh, and every headline claim is either verified on your machine (re-runs the bug-catching tests) or printed as 'CLAIMED — not reproduced here' with the exact prerequisite. Hard gate = workspace tests + deterministic Python proof; section 3 re-runs 7 anti-slop assertion tests (each fails on pre-fix code); gated claims (GPU/dataset/hardware/ trained-checkpoint/named-identity) are honestly listed, never faked. Co-Authored-By: claude-flow <ruv@ruv.net>
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Beyond-SOTA sweep M5–M6 (ADR-159, ADR-160) — appliance + edge-skill honesty
Continuation of #1023 (M0–M4). Same discipline: real fixes, MEASURED, tests that fail on the old code. 3,128 tests / 0 failed (
cargo test --workspace --no-default-features), Python proof PASS.M5 — Cognitum appliance / detection (ADR-159)
The external audit's "the Seed never identified anyone" accusation is REFUTED, byte-level:
cog-person-countandcog-pose-estimationship real, trained, SHA-pinned, Ed25519-signed Candle models (trained weight distributions, not zeros/random) with honest manifests (they publish 34% / 3.0 PCK accuracy — not inflated) and an honest"stub"fallback.cog-ha-matter's witness chain is genuinely SOTA-grade. Theruview-swarmrandnuses are all legitimate. The real defects were claim-surface overclaims, all fixed:M6 — Edge-skill library honest labeling (ADR-160)
0 stubs, 0 theater — all ~70 skills run real DSP. Exposure was the claim surface:
med_*modules stated clinical detection as fact → mandatory "experimental / NOT a medical device" disclaimers + gated behind a non-defaultmedical-experimentalfeature.WEAPON_ALERT→HIGH_METAL_REFLECTIVITY(honest physical quantity).static mutevent buffers → owned per-instance (fixes latent aliasing UB +static_mut_refsdeny). 0 warnings.Also
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