mirror of
https://github.com/ruvnet/RuView
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c84ea39e62d14dcafe61fc80d357dfe0349462cd
262 Commits
| Author | SHA1 | Message | Date | |
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 ruv
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d4170ad159 |
fix: revert config-dependent cargo-fix changes (kept only always-safe edits)
cargo fix ran under --no-default-features and removed an import/mut that are 'unused' ONLY in the minimal build but genuinely USED in CI's full build (error[E0596]: cannot borrow result as mutable in desktop discovery.rs). Those are false-positive warnings in the minimal config. Reverted bridge.rs/ commissioning.rs/discovery.rs to origin/main; kept the always-safe edits (dead-code #[allow] notes + ClockGateDecision doc fields + camera macOS-only allow). Full-features build of all four crates: Finished, 0 errors. Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
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0d6c20c278 |
chore(v2): zero-warnings hygiene — clear 13 build warnings across 4 crates
Removed unused Matter imports (sensing-server bridge/commissioning), dropped needless mut (bridge, desktop discovery), documented ClockGateDecision variant fields (ruvector coherence), and marked deferred-P2/platform-only helpers #[allow(dead_code)] with honest notes (entity_on_matter/next_endpoint = Matter-publisher API deferred per ADR-159 §A5; decode_jpeg_to_rgb = macOS-only). Behavior-neutral; touched-crate tests green. Remaining 1 warning is a benign Windows .pdb filename collision inherent to the Tauri lib+bin desktop crate (renaming the bin would break Tauri bundling — won't-fix for a cosmetic warning). Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
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7c13ec6a00 |
bench(cogs): steady-state CPU infer latency benches (ADR-163 T2)
Criterion benches over InferenceEngine::infer for cog-person-count and cog-pose-estimation, on Device::Cpu with the real shipped safetensors weights (asserts candle backend so the stub is never silently benched), over a fixed CSI window after a warm-up forward. HOST-MEASURED steady-state medians (idle box): ~305us each. This is the recurring per-frame cost and is explicitly NOT the pose manifest's cold_start_ms_avg=5.4 (a different measurement, weight-load included, taken on ruvultra/RTX 5080) -- the two are labelled and not conflated. Closes the ADR-159/160 deferred cog inference-latency item. No production- code behavior change. Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
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d3606d51a7 |
bench(wasm-edge): host process_frame latency benches (ADR-163 T1)
Criterion benches over the M6-audit-named heaviest hot paths: exo_time_crystal 256x128 autocorrelation, exo_ghost_hunter periodicity, sec_weapon_detect per-subcarrier Welford, med_seizure_detect clonic rhythm (medical-experimental-gated). Drives each through the public process_frame on a fixed synthetic CSI frame after warming the relevant buffers. Crate is workspace-excluded: run from the crate dir with --features std. Set lib bench=false so libtest does not intercept criterion CLI flags. HOST-MEASURED medians (Intel Core Ultra 9 285H, native --release), NOT the ESP32/WASM3 doc budget (that needs hardware): time_crystal 17.3us, ghost_hunter 1.44us, weapon 0.42us, seizure 0.10us. Closes the ADR-160 deferred 'criterion benches for process_frame budget claims' item on host. No production-code behavior change. Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
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3292bd2c5d |
feat(homecore-automation): implement bounded RunModes Restart/Queued/max (ADR-162, completes ADR-161 §A5)
ADR-161 implemented RunMode::Single (AtomicBool re-entrancy guard) + Parallel but honestly left Restart/Queued/max as "ACCEPTED-FUTURE / unbounded parallel" — every non-Single mode spawned an unbounded task. This makes them real. New `runmode` module — per-automation RunState owns the machinery: - Restart: aborts the in-flight action task (tokio::task::AbortHandle) and starts a fresh one. - Queued: serializes runs in arrival order via a per-automation async Mutex — sequential, never concurrent, nothing dropped. - max: N: caps concurrency at N via a per-automation Semaphore; triggers beyond N queue (await a permit) rather than running concurrently (HA bounded semantics). Documented in the module table. - Single/IgnoreFirst/Parallel preserved. engine.rs now holds a RunState per registration and calls run_state.dispatch() at all three trigger sites (event loop, timer, fire_time_for_test); the old spawn_run is removed. engine.rs trimmed to 433 lines. Tests (tests/engine_behaviors.rs) — verified to FAIL on the old unbounded- parallel dispatch (simulated and confirmed each panics), pass on the new: - restart_mode_cancels_prior_run (old: both runs complete → 2; new: 1) - queued_mode_runs_sequentially_not_concurrently (old: max concurrency 3; new: all 3 run, max concurrency 1) - max_two_caps_concurrency_at_two (old: 4 concurrent; new: all 4 run, max 2) homecore-automation --no-default-features: 45 passed (lib 37, engine_behaviors 8), 0 failed. Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
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0ca903b497 |
feat(homecore-plugins): enforce plugin signature + capability isolation (ADR-162 P4/P5)
ADR-161 honestly relabelled the manifest's wasm_module_hash / wasm_module_sig / publisher_key as "(P4 — not yet enforced)" and the homecore_permissions claims as deferred P5 authority isolation. This makes both real and tested. P4 (signature/integrity verification, SECURITY): - New `verify` module: SHA-256 module-hash check + Ed25519 signature verification over the digest against publisher_key, with a PluginPolicy trust allowlist and an explicit AllowUnsigned dev escape hatch (loud warn). Secure default rejects unsigned / unknown-publisher / tampered modules. - Reuses the in-repo cog-ha-matter::witness_signing Ed25519 pattern; sha2 is a workspace dep, ed25519-dalek/hex/base64 already in the lock — no new external dep tree (only new edges in homecore-plugins). - WasmtimeRuntime::load_plugin verifies before instantiation; legacy load_wasm retained for trusted/test modules. P5 (authority/capability isolation, SECURITY): - New `permissions` module: PermissionSet distilled from homecore_permissions (state:write:<glob> or bare entity glob). hc_state_set now consults it and returns a typed -3 to the guest on an undeclared write (no host panic). Tests (fail on old code, which had no load_plugin/verify and an unchecked hc_state_set): tampered module rejected; valid sig from trusted key loads; valid sig from untrusted key rejected; unsigned rejected by default and loads only under AllowUnsigned; light.* plugin writes light.kitchen but is denied lock.front_door; no-permission plugin can write nothing. Real deterministic keypair signs real bytes. Manifest doc updated: P4/P5 now ENFORCED (was "not yet enforced"). homecore-plugins --features wasmtime: 32 passed (lib 23, integration 9), 0 failed. Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
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d1328b0299 |
test(homecore-api): serialize HOMECORE_CORS_ORIGINS env tests (fix parallel race)
env_override_* and env_empty_* both set_var/remove_var the same process-global HOMECORE_CORS_ORIGINS; under full-workspace parallelism they raced (one's remove_var wiped the other's value mid-assert). Serialize via a poison-tolerant module Mutex. Test-only. Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
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e51704cd25 |
docs(homecore-plugins): label sig/hash fields '(P4 - not yet enforced)' (ADR-161 B5)
manifest.rs documented wasm_module_hash as 'verified before execution' but wasm_module_hash/wasm_module_sig/publisher_key are never read for verification (only set to None in tests). Re-doc'd the three fields as P4-not-yet-enforced so the doc matches the code. No verification code added (that is P4); no false capability claimed. Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
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dff75a479e |
fix(homecore-automation): start engine + implement time/run-mode/choose/template (ADR-161 A3-A7)
A3 (HIGH): homecore-server constructed AutomationEngine then dropped it immediately while the doc claimed automation was active. Now .start()s the engine into a long-lived binding (event loop + timer task). A4 (HIGH): Trigger::Time was hard-coded false with no timer. Added a 1 Hz wall-clock timer task that fires time: automations when local HH:MM:SS matches 'at' (HH:MM or HH:MM:SS); matches_sync(Time)=false is now correct + documented. A5 (HIGH): RunMode was documented as AtomicBool-enforced but every trigger spawned unbounded parallel. Each automation now carries a running AtomicBool; Single/IgnoreFirst skip re-entrant triggers, Parallel fires every time. (Bounded Queued/Restart/max → ACCEPTED-FUTURE, honestly stated in the doc.) A6 (HIGH): Action::Choose discarded choices and always ran default. Now deserialises each branch's conditions, evaluates them, and runs the first matching branch; default only if none match. A7 (MEDIUM): template: conditions were always false in the engine path (EvalContext built with template_env: None). The engine now builds a TemplateEnvironment over the state machine and threads it into every EvalContext (event loop, timer, Choose). Tests (fail on old source): - engine_behaviors::time_trigger_fires_via_timer_path (A4) - engine_behaviors::single_mode_does_not_double_fire_on_rapid_triggers (A5; old fired 2x) - engine_behaviors::parallel_mode_does_fire_concurrently (A5) - action::choose_runs_matching_branch_not_default (A6; old ran default) - engine_behaviors::template_condition_evaluates_true_in_engine (A7; old always false) engine.rs kept <500 lines; behavioral tests moved to tests/engine_behaviors.rs. Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
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9d52d49c0b |
fix(homecore-api): close WS auth bypass + reply-theater, harden dev bin (ADR-161 A1/A2/A8)
A1 (CRITICAL): the /api/websocket handshake accepted any non-empty token, ignoring the LongLivedTokenStore whitelist the REST path enforces — a full WS auth bypass. Now validates via state.tokens().is_valid() before auth_ok; wrong tokens get auth_invalid + close. A2 (HIGH): WS command replies were pushed into an mpsc whose only consumer logged and discarded them — no result/pong/event reached the client. Split the socket with futures StreamExt::split; a dedicated writer task drains the response channel onto the wire. A8 (HIGH): the homecore-api dev bin bound 0.0.0.0 with unconditional allow-any auth and no env path. Wired the HOMECORE_TOKENS env path (dev fallback warn-logged when unset) and defaulted the bind to 127.0.0.1 (HOMECORE_BIND to opt into LAN). Tests (fail on old source): - ws_handshake::wrong_token_is_rejected (old → auth_ok) - ws_handshake::result_reply_is_received / ping_pong_reply_is_received (old → timeout) - server_bin_auth::provisioned_bin_rejects_wrong_bearer / from_env_path_enforces_whitelist Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
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d120cc2278 |
test(sensing-server): unique per-process temp dirs (deterministic under 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> |
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ruv
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8ad0d0f91c |
test+docs(wasm-edge): honest-labeling presence tests + ADR-160 (ADR-159 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> |
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ruv
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36af09a4a8 |
feat(wasm-edge): honest labeling + static-mut soundness for edge skills (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> |
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ruv
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48b002fa7e |
docs(cog-ha-matter): stop claiming Matter until it exists (ADR-159 A5)
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> |
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ruv
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8d9c5994db |
fix(ruview-swarm): honest NED metres in Remote ID, not WGS84 (ADR-159 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>
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ruv
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6b5fd3cf25 |
fix(cog-person-count): emit real signed manifest from CLI (ADR-159 A4)
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> |
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ruv
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2400216920 |
fix(cog-person-count): flag untrained-class counts low_confidence (ADR-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> |
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ruv
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98bf8c4726 |
fix(cog-pose-estimation): emit frames under default config (ADR-159 A1)
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> |
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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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d79c22e03a |
fix(homecore-assist): exact in-memory cosine k-NN, drop fragile :memory: HNSW
The semantic recognizer built a ruvector-core VectorDB at ":memory:"; under full-workspace feature unification the file-storage backend is enabled and ":memory:" is an invalid Windows filename (os error 123), panicking via .expect(). Replace the external index with an exact in-memory cosine k-NN over the enrolled exemplars (embeddings are L2-normalised, so cosine = dot product). For HOMECORE's small intent vocabularies this is faster, fully deterministic, and removes the storage backend + cross-crate feature coupling entirely. ruvector-core dropped from the crate (only used here). Workspace 3122 passed/0 failed. Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
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e1dc6e05ab |
feat(mat): wire real ESP32/UDP/PCAP CSI ingest; honest typed errors for gated adapters (ADR-158 §3)
hardware_adapter read_esp32_csi/read_udp_csi/read_pcap_csi returned 'not yet implemented'. Wired them to the real CsiParser/PcapCsiReader that already live in csi_receiver: - UDP: bind + recv + parse (auto-detect) -> CsiReadings. End-to-end test sends a real JSON datagram on the wire and parses it. - PCAP: load + read_next + parse. End-to-end test writes a real little-endian .pcap with one record and reads it back. - ESP32: parse CSI_DATA CSV via the real parser; live serial byte I/O behind an optional feature (native serialport gated off the default/appliance build) — without it, live reads return a typed UnsupportedAdapter while the byte parser still works (tested). Intel5300/Atheros/PicoScenes now return typed HardwareUnavailable/UnsupportedAdapter (no device/driver/validatable-format here) instead of fake CSI — added AdapterError::HardwareUnavailable and ::UnsupportedAdapter. Test asserts the gated adapters error honestly. Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
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982994ca3c |
fix(mat): real dimensionless GDOP = sqrt(trace((HtH)^-1)), not ad-hoc angle factor (ADR-158 §5)
estimate_gdop returned an average-pair-angle factor merely labelled GDOP (the same class of defect ADR-156 §2.3 fixed). Replaced with the genuine Geometric Dilution of Precision computed from the range-measurement Jacobian H (unit target->sensor bearings): GDOP = sqrt(trace((HtH)^-1)), dimensionless, returning None for singular (collinear) geometry which the caller treats as factor 1.0. Tests assert a well-spread array yields lower GDOP than a near-collinear one, cross-check the closed form, and confirm singular geometry returns None. Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
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c9a8ca758a |
feat(mat): real 3-point parabolic peak interpolation in find_dominant_frequency (ADR-158 §4)
The comment claimed interpolation but the function returned the bin center, capping breathing-rate resolution at +/-half a bin. Implemented quadratic (3-point parabolic) peak interpolation: delta = 0.5*(yL-yR)/(yL-2y0+yR), clamped to [-0.5,0.5], with an edge fallback to bin center. For a parabola-shaped peak the recovery is exact (delta=0.4 for a true peak at bin 10.4). Test asserts the result lands within half a bin of truth and strictly beats the old bin-center estimate. Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
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650e2b5c52 |
fix(mat): real RSSI localization + vitals-signature dedup, kill count inflation (ADR-158 §2)
simulate_rssi_measurements always returned vec![], so every survivor got location: None, which disabled spatial dedup — one person re-detected across N scan cycles became N survivors, fabricating a mass-casualty event. Two fixes: 1. Real RSSI source: SensorPosition gains an optional last_rssi (populated by the hardware layer from actual signal-strength readings). collect_rssi_measurements reads only real per-sensor RSSI and feeds the existing triangulator; it NEVER fabricates a value. <min_sensors real readings -> None location (honest). 2. Zone + vitals-signature dedup: when no usable location exists, record_detection matches an existing active, un-located survivor in the same zone whose latest vital signature (breathing presence + START rate band, heartbeat presence, movement class) is compatible — collapsing repeat detections of one person while keeping genuinely distinct survivors (different rate bands) separate. Tests (fail on old code): 3x identical-vitals/None-location -> 1 survivor (was 3); distinct vitals stay 2; real-RSSI path yields a position; no-RSSI path yields None. Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
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78821f1657 |
fix(mat): unify divergent triage engines to single canonical source (ADR-158 §1)
The ensemble gate (EnsembleClassifier::determine_triage) and the survivor record (Survivor::new -> TriageCalculator::calculate) used two different START-protocol approximations with different rate bands and movement handling. The pipeline gated on the ensemble triage then discarded it and recomputed via TriageCalculator, so a survivor could be admitted as one priority and recorded as another (e.g. 28 bpm + Tremor: gate said Delayed, record said Immediate). In a mass-casualty tool that divergence is a life-safety defect. determine_triage now delegates to TriageCalculator (the single source of truth), retaining only the ensemble confidence gate (low confidence -> Unknown, except Immediate which is never suppressed). Updated unit + integration tests to the canonical expectations and added a divergent-boundary regression asserting gate triage == survivor-record triage. Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
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67dd539e68 |
bench(pointcloud): sweep points-per-cell density for splats bench
Realistic depth backprojection is dense (many points per 8 cm voxel). Sweep
points-per-cell {4,16,64,256} at n=50k instead of point-count, so the
measurement reflects where the 9-pass→2-pass reduction actually applies.
Parity guard (old≡new, bit-for-bit) holds at every density.
Co-Authored-By: claude-flow <ruv@ruv.net>
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ruv
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2754af804e |
feat(occworld): real conv encoder/decoder forward pass + honesty flag
Replace the `Tensor::randn` stubs in occworld-candle's VQVAE encoder (`encode_occupancy`) and decoder (`decode_to_logits`) with a real, deterministic, input-dependent convolutional forward pass. Previously `predict()` emitted trajectory waypoints + confidence that were a function of RANDOM NOISE, independent of the input and silently presented as model output — the exact "AI slop" the project must eliminate. occworld-candle: - New `cnn.rs`: `Encoder2D` (3× Conv2d + GELU, interpolate2d to pin the token grid) and `Decoder2D` (upsample_nearest2d + Conv2d + 1×1 head). Both are deterministic functions of the input — same input → identical output; different input → different output. No randn in any forward path. - Deterministic weight init (`det_fill`, seeded xorshift64*) across all `dummy()` constructors (encoder/decoder, VQ codebook, quant-convs, transformer), so untrained engines are bit-for-bit reproducible. - `InferenceOutput.weights_trained: bool` — honest disclosure flag. `false` for `dummy()` (real but untrained net), `true` only after `load()` reads a real checkpoint. Priors are always from the real forward pass, never faked. - VQ codebook + quant/post-quant convs kept and wired encoder→VQ→decoder. - Centerpiece tests in `tests/predict_honesty.rs` (input-dependence, run-to-run + cross-engine determinism, untrained flag). All three FAIL on the old randn stub (verified by temporarily reinstating randn). pointcloud: - Optimize `to_gaussian_splats` hot path: 9 separate `.iter().sum()` passes per voxel → 2 fused accumulation passes. Bit-identical output. - `benches/splats_bench.rs` (criterion) measures old 9-pass vs new 2-pass with a parity guard. ~1.3× faster on representative cloud sizes. - Confirmed: no `randn`/placeholder in any claimed production path. The remaining synthetic generators (`send_test_frames`, `demo_depth_cloud`) and honestly-flagged heuristics (`heuristic_pose_from_amplitude`, luminance pseudo-depth fallback) are explicitly disclosed, not faked output. DATA-GATED: a trained checkpoint. An untrained-but-real net is the honest deliverable; accuracy is flagged via `weights_trained`, never claimed. Tests: occworld 16 unit + 3 integration + 2 doc, pointcloud 18 — all pass (CPU `Device::Cpu`; CUDA feature is GPU-gated and untouched). Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
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7c80711454 |
feat(homecore-assist,homecore-recorder): replace stubs with real impls (ADR-132/133)
Implements the three placeholder paths with real, tested behaviour and an honest typed result wherever a capability is genuinely data-gated. homecore-assist: - runner.rs: add LocalRunner — runs the real IntentRecognizer pipeline and returns a fully-formed RufloResponse (resolved intent + speech). NoopRunner is now honest: typed NotStarted before spawn, explicit empty after (never a silent fabricated response). A live ruflo-agent.js subprocess remains the data-gated future path. - recognizer.rs / semantic_recognizer.rs: real SemanticIntentRecognizer — embeds the utterance (deterministic feature-hash embedding, new embedding.rs) and runs ruvector-core HNSW nearest-neighbour search over enrolled exemplars, accepting matches above a configurable cosine-similarity threshold (default 0.75) and falling back to regex below it. Measured: paraphrase "turn on the kitchen light" vs exemplar "turn on the light" -> sim 0.855 (match); "schedule a dentist appointment" -> sim 0.106 (no-match). `semantic` feature on by default. homecore-recorder: - db.rs: search_states_by_text — real SQL LIKE query over entity_id/state/attrs returning real rows (newest-first, k-capped, LIKE-escaped). search_semantic now falls back to it when the vector index yields no hits, so it is no longer always-empty under the default NullSemanticIndex. Tests (real behaviour; each fails on the old always-empty stub, verified): - homecore-assist: 39 passed / 0 failed - homecore-recorder (P1, no features): 19 passed / 0 failed - homecore-recorder (P2, --features ruvector): 25 passed / 0 failed All files < 500 lines; homecore-server consumer still builds. Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
|
a0e72eef50 |
feat(wifiscan,sensing): native wlanapi.dll FFI + real Matter manual code
wifiscan (Tier 2 wlanapi adapter ONLY): - Real native wlanapi.dll BSS-list FFI (new adapter/wlanapi_native.rs): WlanOpenHandle -> WlanEnumInterfaces -> WlanGetNetworkBssList -> WlanFreeMemory/WlanCloseHandle via windows-sys 0.59 (already in lock tree). Per-BSSID RSSI(dBm)/channel/band/radio-type/SSID + CSI-capable filter. #[cfg(windows)] real path; #[cfg(not(windows))] returns typed WifiScanError::Unsupported (honest, never fabricated). - wlanapi_scanner now native-first with documented netsh fallback, native_scans metric, scan_native()/scan_native_csi_capable(), and a benchmark() that MEASURES real Hz (no hardcoded "10x" claim). - MEASURED 9.74 Hz native on ruvzen (30 iters, Native backend) vs netsh ~2 Hz baseline. Live measurement kept as an #[ignore] test. - Cargo.toml: unsafe_code forbid->deny so only the audited wlan_ffi module opts into unsafe; all unsafe confined + null-checked + freed. sensing-server (Matter commissioning): - Replaced the lossy modulo placeholder in matter/commissioning.rs with the real Matter Core Spec 1.3 §5.1.4.1.1 field-packing. Canonical vector (20202021, 3840) now encodes to the published 34970112332. - Added ManualPairingCode::decode + DecodedManualCode proving the code is real/lossless (passcode round-trips bit-for-bit; short discriminator = top 4 bits) with Verhoeff integrity, incl. proptest. Tests: wifi-densepose-wifiscan 145 passed (real FFI exercised on Windows); wifi-densepose-sensing-server 614 passed. 0 failed. Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
|
e2864bbd52 |
test(bfld): measured §3.6 separability + audit's cardiac-alone negative result
Deterministic synthetic-data tests producing reproducible, honestly-labeled numbers (MEASURED-on-synthetic, explicitly NOT real-person identification): - same_person_scores_higher_than_cross_person: self-match ≈1.0000, cross-person ≈0.8088 (full channels) — a real but modest ~0.19 margin. - cardiac_alone_cannot_separate_identity_matches_audit (centerpiece): with the decisive channels (AETHER 0.35, subcarrier 0.20) absent, cardiac (0.15) + respiratory (0.10) alone give same=1.0000 cross=0.9995, gap=0.0005 — no threshold fits, so the matcher correctly refuses to lock identity. Proves the audit's claim 'your heartbeat alone overlaps too much' with real numbers. - Graceful degradation, zero-norm/NaN safety, insufficient-channels typed result, empty-enrolled-set, threshold boundary, min-channels gate. 13 new tests; full crate suite 364 passed / 0 failed. Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
|
b08e49e47c |
feat(bfld): implement §3.6 Soul Signature matcher + real SoulMatchOracle
First running implementation of the spec's §3.6 per-channel weighted-cosine matcher (docs/research/soul/specification.md). Replaces reliance on NullOracle (which always returns NotEnrolled) with a real EnrolledMatcher oracle. - soul_channels.rs: 8-channel SoulChannels container (AETHER reuses IdentityEmbedding, preserving invariant I2 — no Clone/Serialize, zeroized on Drop), MatchWeights with the §3.6 default table (unvalidated design intent), heapless FeatureVector. no_std-compatible. - soul_match.rs: match_score() implementing the exact formula Σ w·cos / Σ w·availability, with graceful degradation, zero-norm/NaN safety, and a typed 'insufficient channels' result (never a default-high score). EnrolledMatcher (std) satisfies the existing SoulMatchOracle trait, gated on a score threshold AND a minimum shared-channel count (so a single low-weight channel can never lock identity). NullOracle retained as the disabled default. Named-identity locking remains data-gated: it requires real AETHER enrollment + body-resonance data, which has not been provided. Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
|
0b78eb6e03 |
fix(hardware): drop-instead-of-truncate subcarrier count in 802.11bf bridge (ADR-157 §B1)
OpportunisticCsiBridge::ingest built CsiReportPayload.n_subcarriers via `self.amp_accum.len() as u16`, which would silently wrap a count above 65_535. Replace with `u16::try_from(...).ok()?` (drop-instead-of-truncate). Disclosed honestly as defense-in-depth on an UNREACHABLE path: ingest already gates subcarrier_count > MAX_REPORT_SUBCARRIERS (484) at entry and report.validate() rejects oversized counts downstream, so the cast can never wrap in practice. Correct-by-construction rather than gate-dependent; no behavior change, no new test (the gate prevents the input that would exercise it). Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
|
8fb6ef6547 |
fix(vitals): renormalize partial-weight fusion + clamp IIR resonator (ADR-157 §A2/§A3)
§A2 (correctness): BreathingExtractor weighted fusion was an un-normalized sum.
When `weights` was supplied shorter than n, supplied entries were used raw while
the missing tail defaulted to uniform 1/n -- two scales summed with no
renormalization, silently mis-scaling the breathing signal by a factor of
weights.len(). Extract to fuse_weighted_residuals() and normalize by
Sigma(effective weights), mirroring heartrate::compute_phase_coherence_signal.
Tests: partial_weights_are_renormalized_not_scale_mixed,
partial_weights_fusion_is_weighted_average (both fail on old code).
§A3 (stability): the IIR resonator pole radius r = 1 - bw/2 diverges when the
pole MAGNITUDE |r| >= 1 (i.e. bw >= 4: a very low fs relative to band width) --
NOT merely when r is negative, as the research report stated (a negative r with
|r| < 1 is still stable; the comments/tests are corrected accordingly). On
divergence the filter overflows to +/-inf within ~600 frames, NaN-poisons acf0,
and the extractor stalls permanently. Clamp r to [0, 0.9999] AND finite-guard
the filter output before the history push (defense-in-depth, mirrors ADR-154 §3).
Applied to both heartrate.rs and breathing.rs. Tests:
{heartrate,breathing}::low_sample_rate_filter_stays_finite (fs=0.5, 0.1-0.9 Hz
band, 600-frame unit step -> all-finite; both panic on old code).
These files also carry the §A1 VecDeque window conversion (bit-identical).
Co-Authored-By: claude-flow <ruv@ruv.net>
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ruv
|
a7f7adfabc |
perf(vitals,wifiscan): O(1) VecDeque sliding windows + vitals bench (ADR-157 §A1/§D1)
Replace Vec::remove(0) (O(n) per-sample buffer shift -> O(n^2) full-window sweep) with VecDeque push_back/pop_front (O(1) eviction) in the fixed-length sliding/ring buffers of the vital-sign and wifiscan extractors. Where the autocorrelation / zero-crossing / Pearson loop needs a contiguous slice, make_contiguous() is called once per extract(), matching the idiom already used in wifiscan/pipeline/orchestrator.rs. Output is bit-identical. Sites: anomaly.rs (rr/hr history), store.rs (readings ring; history() now takes &mut self to hand back a contiguous slice, no external callers), wifiscan breathing_extractor.rs (filtered history), wifiscan correlator.rs (per-BSSID histories -> Vec<VecDeque<f32>>). (heartrate.rs/breathing.rs windows land with the §A2/§A3 fixes in a separate commit.) New criterion bench crates/wifi-densepose-vitals/benches/vitals_bench.rs drives each extractor over a full-window fill. Honest MEASURED result: end-to-end win is NULL within noise at realistic ESP32 window sizes (1500-3000) because the per-frame DSP dominates the eviction (heartrate 42.8ms->44.4ms, breathing 7.95ms->7.86ms, overlapping CIs). In isolation the eviction collapses O(n^2) -> O(n) (34.6x at window=3000, 3158x at window=100000); A1 lands as the correct data structure removing a latent O(n^2), NOT a claimed hot-path speedup. Reproduce: cargo bench -p wifi-densepose-vitals --bench vitals_bench Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
|
a92b043143 |
perf(ruvector): eliminate fuse() double-clone (~2.17x marshalling) + bench (ADR-156 §2.4, §4)
MultistaticArray::fuse / fuse_ungated cloned every viewpoint embedding twice per fusion (once into `extracted`, again when building the attention input). Now the embeddings are MOVED out of `extracted` (one clone per viewpoint instead of two), capturing geometry/ids by Copy in the same pass. Correctness-neutral — all 100 viewpoint/mat lib tests pass unchanged. MEASURED (new benches/fusion_bench.rs, embedding_extract A/B, 8 vp x 128-d): before_double_clone 1.0029 us -> after_single_clone 461.6 ns (~2.17x) End-to-end fusion_pipeline (8 vp): 202 us — marshalling is <1% of fusion (n*n attention dominates), so end-to-end win is modest; the A/B isolates the clone elimination. Reproduce: cargo bench -p wifi-densepose-ruvector --bench fusion_bench Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
|
a2daa2e443 |
fix(ruvector): crafted-input DoS — no panic on out-of-range indices (ADR-156 §2.2)
Security fix: two functions on a fusion/localisation path that can carry network-sourced multistatic frames panicked on crafted input (remote DoS). - triangulation::solve_triangulation indexed ap_positions[0] (empty table) and ap_positions[i]/[j] (crafted out-of-range AP index in a TDoA tuple). Now uses .first()? / .get(i)? / .get(j)? — returns None, never panics. - heartbeat::band_power computed n_freq_bins-1 (usize underflow on a zero-bin spectrogram) and did not clamp low_bin. Now guards n_freq_bins==0 and clamps both bounds into [0,last]; returns 0.0 for empty/inverted ranges. Tests (each panics on old code, verified by revert): triangulation_out_of_range_index_returns_none_no_panic, triangulation_empty_ap_positions_returns_none_no_panic, heartbeat_band_power_zero_bins_no_panic, heartbeat_band_power_out_of_range_bounds_no_panic. Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
|
5b3e337c6d |
fix(ruvector): honest GDOP + canonical wrapped angular distance (ADR-156 §2.1, §2.3)
Two correctness/integrity fixes on the cross-viewpoint fusion geometry path, each pinned by a regression test that fails on the old code. - GDOP mislabel (§2.3): CramerRaoBound.gdop was `sqrt(crb_x+crb_y)` — identical to rmse_lower_bound (metres, noise-dependent), NOT a dimensionless GDOP. Now computes true GDOP = sqrt(trace(G^-1)) on the unit-variance bearing geometry, in both estimate() and estimate_regularised(); INFINITY (not NaN) for degenerate collinear geometry. Test gdop_is_dimensionless_and_noise_independent asserts GDOP is unchanged under 10x noise while RMSE scales 10x (old code failed: it scaled with noise, proving it was RMSE). - Angular wrap (§2.1): GeometricBias::build_matrix used raw |delta-azimuth| (can exceed pi, mis-states the 0/2pi seam) instead of the wrapped distance. angular_distance made pub and reused as the single canonical helper. HONEST: under the current cos() kernel this is a NUMERIC NO-OP (cos is even/periodic, cos(raw)==cos(wrapped)); landed for contract correctness + single-source-of- truth + future non-even kernels, not as a behaviour change. Tests pin the contract (wrapped value in [0,pi], seam symmetry). ruvector lib tests: 100 passed / 0 failed (+ new tests). Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
|
5cacb5fe0a |
perf(nn): zero-copy ORT input (~1.48x) + dynamic-dim guard + concurrency bench (ADR-155 §Tier-3)
- onnx.rs ORT input: arr.as_slice() single-memcpy fast path with iterator fallback for strided views. MEASURED [1,256,64,64]: 1.972ms -> 1.336ms (~1.48x). Repro: cargo bench -p wifi-densepose-nn --no-default-features --features onnx --bench onnx_bench -- onnx_input_copy - onnx.rs checked_output_dims: reject ONNX dim <= 0 (incl. unresolved -1) before allocation (config-OOM class) + test. - onnx_concurrency bench: empirically proves the per-inference write lock serializes (throughput drops with more threads). The intended read-lock win is NOT landable on ort 2.0.0-rc.11 (safe Session::run is &mut self, verified) and is deferred to the backlog with the upgrade path documented in-code. New committed fixture tests/fixtures/tiny_conv.onnx (666 B, not gitignored). Co-Authored-By: claude-flow <ruv@ruv.net> |
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ruv
|
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
|
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
|
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
|
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
|
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
|
4d384cb884 |
perf(signal): cache PSD FFT planner (2.0–3.1x) + honor DTW band (2.4–4.1x) (ADR-154 M0)
Two measured, bit-equivalent perf wins. Each ships a criterion bench
(benches/features_bench.rs, new) with before/after numbers and a committed
bit-identity test — no perf claim without a measured before/after.
PSD FFT-planner caching (features.rs)
PowerSpectralDensity::from_csi_data re-planned a FftPlanner on EVERY frame,
and FeatureExtractor::extract calls it per frame on the hot path. New
from_csi_data_with_fft(csi, n, &Arc<dyn Fft>) reuses a plan cached in
FeatureExtractor (built once in new()). Bit-identical output
(psd_cached_fft_bit_identical_to_fresh, f64::to_bits over 6 sizes).
MEASURED (median ns/frame, criterion):
fft=64 5.84µs -> 1.89µs (3.09x)
fft=128 9.31µs -> 3.61µs (2.58x)
fft=256 13.77µs -> 6.73µs (2.04x)
DTW Sakoe-Chiba band (gesture.rs)
dtw_distance computed j_start/j_end but iterated the FULL 1..=m row,
continue-ing out-of-band — band constrained the path, not the work (O(n*m)).
Now iterates j_start..=j_end (O(n*band)), resetting only the two boundary
guard cells the recurrence reads, with endpoint reachability (|n-m|<=band)
at the return. Bit-identical across 12 shapes x 8 bands
(dtw_banded_bit_identical_to_fullrow).
MEASURED (median, criterion):
n=m=100 band=5 33.45µs -> 13.77µs (2.43x)
n=m=200 band=5 122.32µs -> 29.55µs (4.14x)
n=m=200 band=10 159.98µs -> 60.19µs (2.66x)
Reproduce:
cd v2 && cargo bench -p wifi-densepose-signal --no-default-features \
--bench features_bench
Co-Authored-By: claude-flow <ruv@ruv.net>
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ruv
|
be068748b3 |
fix(signal): revive dead CIR coherence gate + NaN bypass + window div0 (ADR-154 M0)
Milestone-0 correctness/security fixes for the beyond-SOTA signal/DSP sweep.
Every fix ships with a committed regression test (proof, not adjectives).
CRITICAL — ADR-134 CIR coherence gate was DEAD in production
MultistaticFuser fuses canonical-56 frames (hardware_norm.rs resamples every
chipset onto a 56-tone grid), but the gate was wired to CirConfig::ht20()
which expects 64/52. Every estimate() returned SubcarrierMismatch and
cir_gate_coherence silently fell back to freq-domain coherence — use_cir_gate
was indistinguishable from false. Fixes:
- new CirConfig::canonical56() (64-bin HT20 framing, 56 active tones, 168 taps)
- new MultistaticFuser::with_cir_canonical56() (correct default); ht20 kept,
now doc-warned
- active_indices() handles (64,56) + length-matched fallback (no silent
fall-through to the 52-index slice)
- SubcarrierMismatch in the gate now debug_assert!s loudly (config error can
no longer hide as a graceful degrade)
- cir_estimate_first() exposes the Ok/Err verdict for tests
PROOF (ruvsense::multistatic::tests): ht20 → 8/8 Err (dead); canonical56 →
8/8 Ok (alive); coherence(gate on) != coherence(gate off).
CRITICAL — adversarial.rs NaN/inf detector bypass
One non-finite link energy bypassed the whole detector (every `e>thresh`
false on NaN; score clamp returns NaN). A non-finite input is itself the
strongest spoof — now short-circuits to a definite anomaly (score 1.0,
affected link reported) and does not poison the temporal-continuity state.
PROOF: nan_link_energy_flags_anomaly, inf_link_energy_flags_anomaly.
CORRECTNESS — divide-by-(n-1) window trio
csi_processor hamming_window (n=0 usize underflow, n=1 div0), bvp Hann,
spectrogram make_window all guarded for n<=1 (empty / constant-1.0 window).
Python deterministic proof still PASS, same pipeline hash (reference uses n>=2).
PROOF: *_degenerate_sizes / *_size_one_is_finite / make_window_size_0_and_1.
CLARITY — calibration.rs subtract_in_place
Removed the vacuous `if active_input {ki} else {ki}` branch that implied a
full-FFT->bin remap that never existed; documented the sequential
active-index convention (matches sibling extract_first_stream). No behavior
change.
Tests: cargo test -p wifi-densepose-signal --no-default-features (+--features cir)
green; full workspace green; verify.py VERDICT: PASS.
Co-Authored-By: claude-flow <ruv@ruv.net>
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rUv
|
07b6bf8084 |
chore: extract ruv-neural to ruvnet/ruv-neural, wire as submodule (#1019)
The 12-crate brain-topology analysis ecosystem (v2/crates/ruv-neural) was a self-contained nested workspace with no inbound deps from the v2 workspace (verified: zero path references outside its own tree). Published standalone at github.com/ruvnet/ruv-neural and re-attached here as a submodule at the same path, so the build layout is unchanged while the project gets its own repo/CI/release cadence. |
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rUv
|
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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d0e27e652e |
fix(firmware): C6 IDF v5.5 guard + HE-LTF host ingest + WITNESS-LOG-110 B1 resolution (#1005) (#1011)
* fix(firmware): c6_sync_espnow IDF v5.5 send-callback guard + B1 HE-LTF resolution (#1005)
Espressif backported the esp_now_send_cb_t signature change to v5.5
(esp_now_send_info_t = wifi_tx_info_t there), so the #944 guard must be
ESP_IDF_VERSION >= VAL(5,5,0), not MAJOR >= 6.
Validated on this repo's hardware toolchain:
- WITHOUT fix, IDF v5.5.2 esp32c6 build fails with the reporter's exact
incompatible-pointer error at c6_sync_espnow.c:199 (reproduced)
- WITH fix, clean build on IDF v5.5.2 (esp32c6) AND IDF v5.4 (regression)
Docs: WITNESS-LOG-110 §B1 marked RESOLVED WITH MEASUREMENT (external,
@stuinfla, issue #1005): IDF v5.4 driver downconverts HE->HT; v5.5.2
delivers true HE-LTF (532B / 256 bins / 242 tones, PPDU 0x01 HE-SU).
ADR-110 capability table updated accordingly.
Co-Authored-By: claude-flow <ruv@ruv.net>
* docs: WITNESS-LOG-110 §B1 — in-house HE-LTF replication on the original COM12 C6
84% of 1,525 frames at 532B/PPDU 0x01 (HE-SU) with IDF v5.5.2 + the #1005
guard fix, AP ruv.net 11ax 2.4GHz. Two independent rigs now confirm:
v5.4 downconverts, v5.5.2 delivers 242-tone HE20.
Co-Authored-By: claude-flow <ruv@ruv.net>
* fix(host): 256-bin HE-LTF ingest end-to-end + latent offset bugs (#1005)
Audit of every ADR-018 consumer against live C6 HE20 frames (532B/256-bin):
- sensing-server + CLI calibrate parsers read n_subcarriers from one byte
(256 decoded as 0) with stale seq/rssi offsets (rssi always 0 — latent,
pre-existing, confirmed vs firmware csi_collector.c). Fixed to the real
ADR-018 layout; n_subcarriers u8->u16; byte 18 surfaced as typed PpduType.
- sensing-server probe buffer 256B -> 2048B (532B datagram errored on Windows)
- per-node grid gate: lock densest (n_subcarriers, ppdu_type) grid, re-warm
on upgrade, skip sparser minority frames — HT-64 never mixes into an
HE-256 baseline window
- hardware parser: HE-aware bandwidth classification (256-FFT HE20 = 20MHz,
was Bw160); PpduType/Adr018Flags re-exported
- verbatim live frames (532B HE-SU, 148B HT) embedded as regression fixtures
- archive python parser: bandwidth heuristic mirror fix
Live-validated: calibrate --tier he20 consumed 600x 256-bin frames into an
ADR-135 He20 baseline (242 tones) skipping 94 HT frames; sensing-server
shows node 12 active with real RSSI (-40dBm). 765 tests green across the
three crates; workspace check clean; Python proof PASS.
Co-Authored-By: claude-flow <ruv@ruv.net>
* test(fuzz): esp_netif/ping_sock/ip_addr stubs — un-break ADR-061 fuzz build after #954
csi_collector.c gained esp_netif.h / ping/ping_sock.h / lwip/ip_addr.h
includes for the #954 gateway self-ping; the host-fuzz stub env lacked
them, breaking the fuzz build on main since
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rUv
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2a307138f2 |
feat: per-room calibration system (ADR-151) + cognitum-v0 appliance integration spec (#989)
* docs(adr): ADR-151 — Per-Room Calibration & Specialized Model Training Room-first calibration -> bank of small specialised ruVector models (breathing, heartbeat, restlessness, posture, presence, anomaly) distilled from the frozen Hugging-Face-published RF Foundation Encoder (ADR-150). Four-stage local-first pipeline: baseline (ADR-135 environmental fingerprint) -> guided enrollment (NEW EnrollmentProtocol, clean anchors not hours) -> feature extraction (reuse signal_features + ruvsense) -> specialist bank training (rapid_adapt LoRA heads, RVF storage, HNSW prototypes). Invariants: specialisation over scale; local heads over a shared public base; honest STALE degradation on baseline drift. Indexes ADR-149/150/151. Co-Authored-By: claude-flow <ruv@ruv.net> * feat(cli): calibration HTTP API for UI-driven baseline capture (ADR-135/151) Adds `wifi-densepose calibrate-serve` — an Axum HTTP API that wraps the ADR-135 CalibrationRecorder so a UI (or any client) can drive an empty-room baseline capture remotely. Stage 1 ("teach the room") of the ADR-151 room calibration & training pipeline. A single background task owns the UDP socket (ESP32 0xC511_0001 frames) and the optional active recorder; HTTP handlers talk to it over an mpsc command channel and read a shared status snapshot, keeping the &mut recorder lock-free. CORS permissive so a browser UI can call it. Endpoints (/api/v1/calibration/*): GET /health liveness + UDP ingest stats (frames_seen, streaming) POST /start { tier?, duration_s?, room_id?, min_frames? } GET /status live progress (state, frames, progress, z, eta) — poll for UI POST /stop finalize the current session early GET /result finalized baseline summary (amp/phase-dispersion averages) GET /baselines list persisted baseline .bin files Reuses the existing calibrate.rs ESP32 wire parser (made pub(crate)); honest abort when <10 frames arrive in the window (e.g. ESP32 not streaming). Verified end-to-end over loopback: start -> 300 replayed HT20 frames -> state=complete, 52-subcarrier baseline, phase_dispersion_avg=0.00096 (concentrated/valid), persisted to disk; all 6 endpoints exercised. CLI: 19 tests pass; crate builds clean. Co-Authored-By: claude-flow <ruv@ruv.net> * test(cli): firewall-free CSI UDP relay for local Windows ESP32 testing Windows Defender blocks inbound LAN UDP to a freshly-built binary without an admin allow-rule; python.exe is already allowed. This relay binds the public CSI port and forwards each datagram verbatim to a loopback port where `calibrate-serve --udp-bind 127.0.0.1 --udp-port 5006` listens (loopback is firewall-exempt). No admin required. Validated: ESP32-format 0xC5110001 frames -> :5005 -> relay -> :5006 -> calibrate-serve -> state=complete, 52-subcarrier baseline, phase_dispersion_avg=0.00098 (clean). Completes the no-admin live-test path. Co-Authored-By: claude-flow <ruv@ruv.net> * docs(changelog): record ADR-151 calibration API (calibrate-serve) Co-Authored-By: claude-flow <ruv@ruv.net> * feat(calibration): ADR-151 Stages 2–5 — enrollment, extraction, specialist bank, runtime New crate wifi-densepose-calibration implementing the per-room pipeline beyond Stage-1 baseline: - anchor.rs: guided-anchor sequence + event-sourced EnrollmentSession (Stage 2) - enrollment.rs: AnchorQualityGate + AnchorRecorder — gates anchors against the ADR-135 baseline deviation (presence/motion), re-prompts bad captures - extract.rs: Features + AnchorFeature — autocorrelation periodicity (breathing/ HR bands), variance/motion (Stage 3) - specialist.rs: 6 small room-calibrated models — presence (learned threshold), posture (nearest-prototype), breathing/heartbeat (band periodicity), restlessness (calm/active normalization), anomaly (novelty vs anchors) (Stage 4) - bank.rs: SpecialistBank — train/persist + baseline-drift STALE invalidation - runtime.rs: MixtureOfSpecialists — presence short-circuit + anomaly veto + stale flagging (Stage 5) Statistical heads make the pipeline runnable/validatable today; the ADR-150 HF RF Foundation Encoder backbone is the documented upgrade path. 29 unit tests pass. Co-Authored-By: claude-flow <ruv@ruv.net> * feat(cli): wire ADR-151 enroll / train-room / room-status / room-watch Integrates the wifi-densepose-calibration crate into the CLI as four subcommands driving the full Stage 2–5 pipeline against a live ESP32 raw-CSI stream (edge_tier=0): - enroll: walks the guided anchor sequence, gates each capture against the ADR-135 baseline deviation (re-prompts bad anchors), writes labelled features - train-room: fits the SpecialistBank from the enrollment, persists JSON - room-status: prints a trained bank's summary - room-watch: live mixture-of-specialists readout (presence/posture/breathing/ heart/restless) over a rolling window, with anomaly veto + STALE flagging Per-frame scalar is the mean CSI amplitude (carries presence/motion + breathing modulation). Validated end-to-end on the live ESP32 (COM8, edge_tier=0): the real parser → feature extraction → runtime detected breathing (~16–31 BPM) on hardware. Full multi-anchor enrollment accuracy requires the operator to perform the poses; phase-based breathing extraction is a noted refinement. 48 tests pass (29 calibration + 19 CLI). Co-Authored-By: claude-flow <ruv@ruv.net> * docs(adr-151): mark Stages 1–5 implemented; expand CHANGELOG Co-Authored-By: claude-flow <ruv@ruv.net> * fix(cli): keep proven mean-amplitude carrier for room features The max-variance-subcarrier carrier locked onto motion artifacts (not breathing) and also had an out-of-bounds bug on variable CSI subcarrier counts. Reverted to the mean-amplitude carrier, which is validated live to detect breathing. Phase-based extraction on a stable subcarrier remains the proper higher-SNR refinement (ADR-151 §4). Co-Authored-By: claude-flow <ruv@ruv.net> * feat(calibration): multistatic fusion of co-located nodes (ADR-029/151) MultiNodeMixture fuses several co-located nodes (each with its own room-calibrated SpecialistBank) into one RoomState: - presence: OR across nodes (any node seeing a person wins) - posture/breathing/heartbeat: highest-confidence node (best viewpoint) - restlessness/anomaly: max across nodes - veto: any node's physically-implausible signal vetoes the room's vitals (anti-hallucination, same as single-node runtime) + presence short-circuit - stale: any node's STALE flag propagates Same-room multistatic only; cross-room is federation (ADR-105), not fusion. 6 unit tests (presence OR, best-confidence breathing, single-node veto, staleness). 35 calibration tests pass. Co-Authored-By: claude-flow <ruv@ruv.net> * feat(cli): multistatic room-watch — fuse co-located nodes (ADR-029/151) `room-watch --node-bank N:path` (repeatable) groups live CSI frames by node_id and fuses per-node banks via MultiNodeMixture. Validated live on COM8 (node 9, edge_tier=0): frames grouped + fused end-to-end. True 2-node fusion is covered by unit tests; a second raw-CSI node is the hardware blocker. 54 tests pass. Co-Authored-By: claude-flow <ruv@ruv.net> * docs(integration): calibration → cognitum-v0 appliance integration overview Detailed cross-repo integration spec for cognitum-one/v0-appliance: data contracts (CSI wire format, ADR-135 baseline binary, enrollment/bank/RoomState JSON schemas), calibrate-serve HTTP API, public crate API, Pi5+Hailo tiering, and a 5-step appliance integration plan. Grounded in the verified cognitum-v0 inventory (aarch64, cargo 1.96, HAILO10H, ruview-vitals-worker:50054). Co-Authored-By: claude-flow <ruv@ruv.net> * fix(calibration): address PR review — aarch64 decouple, API auth, path traversal, throttle Resolves the review on #989: - **Cross-compile (the appliance blocker):** make wifi-densepose-mat optional and feature-gate it (`mat`), so `cargo build -p wifi-densepose-cli --no-default-features` excludes the mat→nn→ort(ONNX)→openssl-sys chain. Verified: `cargo tree --no-default-features` shows 0 ort/openssl deps → calibration cross-compiles clean for the Pi. - **Security (must-fix before LAN):** - `--token` / CALIBRATE_TOKEN bearer-auth middleware on every route; warns if bound non-loopback without a token. - sanitize client-supplied `room_id` to [A-Za-z0-9_-] (≤64) before it reaches the baseline write path — kills the `../` file-write primitive. + test. - **Perf:** stop locking shared status + cloning SessionStatus on every UDP frame — counters/snapshot flush on the 200 ms tick instead (no CPU starvation under flood). finalize write moved to async `tokio::fs::write`. - **Docs:** ADR-151 STALE wording matches the impl (baseline-id change; drift-threshold = P6 refinement); integration doc gets the `--no-default-features` build + auth/sanitize notes. 35 calibration + 15 CLI tests (no-default) / 20 CLI (default) pass. Co-Authored-By: claude-flow <ruv@ruv.net> * docs(worldgraph,worldmodel): add crates.io READMEs Plain-language overviews + feature lists, comparison tables (symbolic graph vs predictive occupancy; graph vs grid vs event-log), usage, and technical details. Adds readme = "README.md" to both manifests so they render on crates.io on the next release. Co-Authored-By: claude-flow <ruv@ruv.net> * release: worldgraph & worldmodel 0.3.1 (READMEs on crates.io) Co-Authored-By: claude-flow <ruv@ruv.net> * docs: precise calibration validation scope (capture+API+auth proven; clean enroll→train→infer not yet on-target) Aligns ADR-151 §7 + the appliance integration doc with the PR #989 scope clarification: nothing has run a clean baseline → enroll → train → infer on live CSI; the live breathing read used the stateless head, not a trained bank. Adds --source-format adr018v6 to the backlog. Co-Authored-By: claude-flow <ruv@ruv.net> * feat(calibrate-serve): live GET /room/state endpoint (mixture over CSI window) Adds a live RoomState readout over HTTP — the appliance UI's main need. The ingest task maintains a rolling per-frame scalar window (flushed on the 200 ms tick, no per-frame lock); the handler loads a bank (resolved as a sanitized name under output_dir — same path-traversal defense as room_id), runs the MixtureOfSpecialists over the window, returns RoomState JSON. Validated live (ESP32-S3 via relay): breathing 14-19 BPM over HTTP; a bank=../../etc/passwd query is neutralized to 'etcpasswd' (no traversal). Co-Authored-By: claude-flow <ruv@ruv.net> * feat(calibrate-serve): POST /room/train + fix AnchorLabel JSON to snake_case - POST /api/v1/room/train: { room_id, baseline_id, anchors[] } → trains a SpecialistBank and persists it as <output_dir>/<room_id>.json (path-sanitized), readable via /room/state?bank=<room_id>. Completes the HTTP train→infer loop. - Fix data-contract bug: AnchorLabel serialized as PascalCase variant names (serde default) while as_str() + the integration doc used snake_case. Added #[serde(rename_all = "snake_case")] so the JSON wire format matches the documented contract (empty/stand_still/…). Locked with a roundtrip test. Validated live (ESP32-S3): POST train (4 anchors → 6 specialists, persisted) → GET /room/state returns RoomState with the trained presence/restlessness; the synthetic-vs-real scale mismatch correctly triggers the anomaly veto. 36 calibration tests pass. Co-Authored-By: claude-flow <ruv@ruv.net> * feat(calibrate-serve): live enroll-over-HTTP (POST /enroll/anchor + /enroll/status) Closes the last HTTP gap — the appliance can now drive the ENTIRE calibration pipeline over HTTP without the CLI: baseline (start/stop) -> enroll/anchor x8 -> room/train -> room/state - POST /enroll/anchor { room_id, baseline, label, duration_s? }: the ingest task loads the baseline (sanitized name under output_dir), captures the anchor for the duration against it (AnchorRecorder + per-frame series), runs the quality gate, and on completion replies with the verdict + accumulates the AnchorFeature in an in-server enrollment map keyed by room_id. Re-prompts on rejection. - GET /enroll/status?room=<id>: accepted anchors, next, complete. - POST /room/train now falls back to the in-server enrollment when anchors[] is omitted. Validated live (ESP32-S3): capture baseline -> enroll stand_still (271 frames, 6s) -> gate correctly rejects "no person detected (presence_z 0.90 < 1.50)" relative to a same-occupancy baseline (a clean empty-room baseline is the documented on-target prerequisite). Builds clean; CLI tests pass. Co-Authored-By: claude-flow <ruv@ruv.net> * test(calibrate-serve): HTTP integration tests for the room/enroll endpoints Factor the router into build_router() (shared by execute + tests) and add tower-oneshot integration tests (no network/ingest needed): - health + descriptor → 200 - POST /room/train persists the bank; GET /room/state → 200; train with no anchors/enrollment → 400 - path-traversal: /room/state?bank=../../etc/passwd → 404 (sanitized, never reads outside output_dir) - enroll/status empty; /enroll/anchor with an unknown label → 400 CI regression coverage for the endpoints added this session. 18 CLI tests pass. Co-Authored-By: claude-flow <ruv@ruv.net> * fix(mat): make serde non-optional — unblocks `cargo test --workspace --no-default-features` Making wifi-densepose-mat optional in the CLI (for the aarch64/ort decouple) exposed a latent feature bug: mat's `api` module compiles unconditionally and uses serde, but `serde` was an optional dep enabled only via the `api`/`serde` features. Previously the CLI's *unconditional* mat dependency enabled those features transitively, so `--workspace --no-default-features` still got serde; once mat became optional+gated, the workspace build lost it → `error[E0432]: unresolved import serde` across mat's api/* (CI red). mat already pulls serde_json + axum unconditionally, so making `serde` non-optional has no real cost and restores the workspace build. Does NOT affect the aarch64 CLI build (mat isn't built there at all): verified `cargo tree -p wifi-densepose-cli --no-default-features` still shows 0 ort/openssl deps, and `cargo test --workspace --no-default-features` compiles clean. Co-Authored-By: claude-flow <ruv@ruv.net> * docs(claude.md): add wifi-densepose-calibration to crate table (pre-merge) Co-Authored-By: claude-flow <ruv@ruv.net> * docs(adr): ADR-152 — WiFi-pose SOTA 2026 intake (geometry-conditioned calibration, external benchmarks, encoder recipe) Records the 2026-06-10 deep-research run (22 sources, 110 claims, 25 adversarially verified: 24 confirmed / 1 refuted) and the decisions it implies: - §2.1 ACCEPTED: geometry-condition the ADR-151 calibration system — NodeGeometry at enrollment, geometry embeddings for future LoRA heads, PerceptAlign-style two-checkerboard camera↔WiFi alignment for the ADR-079 supervised path. PerceptAlign (MobiCom'26) names the failure mode ("coordinate overfitting") that matches our own ADR-150 cross- subject collapse. - §2.2 ACCEPTED: benchmark protocol vs external "WiFlow-STD (DY2434)" (claimed 97.25% PCK@20, Apache-2.0 weights+dataset) with a no-citation rule until measured on our 17-keypoint ESP32 eval set. Name collision with our internal WiFlow is disambiguated. - §2.3 ACCEPTED: amend ADR-150 training recipe per UNSW MAE study — 80% masking, (30,3) patches, data-over-capacity priority (log-linear, unsaturated at 1.3M samples). - §2.4 watch items: IEEE 802.11bf-2025 published 2025-09-26; esp_wifi_sensing as external presence baseline (drop-in claim REFUTED 0-3); ZTECSITool 160MHz/512-subcarrier anchor node (procurement-gated). - §2.5 NOT adopted: non-WiFi "foundation model" papers; DensePose-UV (no 2025-2026 work does UV regression from commodity WiFi). Every number is evidence-graded CLAIMED vs MEASURED in the source register. Re-check horizon 2026-12. Co-Authored-By: RuFlo <ruv@ruv.net> * test(calibration): full-loop integration test — baseline→enroll→train→infer proven in-process (ADR-151 §7 gap, software half) Closes the software half of PR #989's headline validation gap: the complete calibration loop had never run end-to-end anywhere, even in-process. tests/full_loop.rs (412 lines, deterministic xorshift32 room simulator, HT20/52-subcarrier/20Hz, same fingerprint family as the ADR-135 roundtrip test) now drives the CLI's exact stage order through the public API: 1. baseline — 600 static frames, zero motion flags post-warmup, calibration_uuid() exactly as the CLI derives it 2. enroll — all 8 AnchorLabel::SEQUENCE anchors through AnchorQualityGate::default(), session is_complete() 3. extract — AnchorFeature::from_series recovers injected 0.25Hz and 0.125Hz breathing within ±0.04Hz 4. train — SpecialistBank::train fits all 6 specialists; JSON round-trip and the runtime consumes the RELOADED bank 5. infer — positive: never-enrolled 0.30Hz subject reads present, 18±2 BPM; negative: empty window reads absent; degradation: foreign baseline_id flags STALE Seed-robust (5 seeds), passes with and without default features: 36 unit + 1 integration green. Validation docs updated (ADR-151 §7 + integration doc §7 matrix): what remains is strictly the on-target hardware session (real CSI, physically empty room, operator performing the guided anchors). Three behavioral findings from building the test are recorded for pre-session triage: z-band squeeze between baseline motion flagging (z>2.0) and the still- anchor gate (presence_z≥1.5) — likeliest on-hardware enroll failure; variance-only PresenceSpecialist missing motionless-person mean shift; ungated breathing_hz/heart_hz in noise-window embeddings. Co-Authored-By: RuFlo <ruv@ruv.net> * fix(calibration): close all four ADR-152 behavioral findings pre-hardware-session The full-loop integration test surfaced three findings; fixing the third exposed a fourth. All four are fixed and regression-guarded: 1. z-band squeeze (enrollment.rs) — anchor motion is now measured from frame-to-frame deltas of the deviation series (|Δz| > Z_DELTA_MOTION 0.5 ∨ |Δφ| > π/6), not from the absolute motion_flagged, which fires at amplitude_z_median > 2.0 vs the EMPTY baseline and so conflated presence strength with motion. A strongly-reflecting still person (z = 3.0 — every frame flagged by the old heuristic) now enrolls. The old unit tests mocked (z=3.0, motion=false), a combination the real deviation() can never emit — which is exactly how the squeeze hid; tests now derive the flag from z the way the producer does. 2. variance-only presence (specialist.rs) — PresenceSpecialist gains a mean-shift channel: present when variance > threshold OR |mean − empty_mean| > mean_dist_threshold (trained at half the empty→occupied mean distance, None when the means don't separate). Detects the motionless person whose body raises the scalar mean but not its variance. Old persisted banks deserialize with the channel inert (serde default None) — variance-only behavior preserved, proven by a fixture test against pre-change JSON. 3. ungated hz embedding (extract.rs) — Features::embedding() zeroes breathing_hz/heart_hz below EMBED_MIN_SCORE (0.25), keeping the random in-band peaks of noise windows out of the posture/anomaly prototype space. Raw fields stay ungated (specialists have their own stricter gates). 4. heart-band lag-floor leakage (extract.rs, found while fixing 3) — a pure 0.30 Hz breathing signal scored 0.67 in the heart band at 3.33 Hz: out-of-band rhythm leaks as a monotonic slope whose max sits at the band's lag floor, so score gating alone cannot stop it. autocorr_dominant now requires the winning lag to be an interior local maximum; band-edge "peaks" are rejected, true in-band peaks (interior by definition) are preserved. full_loop.rs strengthened to drive the fixes end-to-end: the StandStill anchor is now a z=3.0 strong reflector (unenrollable pre-fix), and a new motionless-person runtime case proves mean-channel detection at empty- level variance. Validation: 41 calibration unit + 1 full-loop integration + 23 CLI tests green; cargo test --workspace --no-default-features exit 0. Co-Authored-By: RuFlo <ruv@ruv.net> |