Both triage paths in the Mass Casualty Assessment tool classified a
survivor as Deceased (Black) on "no breathing + no movement" while
completely ignoring the heartbeat signal:
- domain `TriageCalculator::calculate` → `combine_assessments(Absent, None)`
returned Deceased. That branch is in fact only reachable *because* a
heartbeat makes `has_vitals()` true (breathing+movement absent alone →
Unknown) — so every "Deceased" was a live person with a pulse.
- detection `EnsembleClassifier::determine_triage` (the path used by
`classify()`) returned Deceased on `!has_breathing && !has_movement`,
also ignoring `reading.heartbeat`.
A survivor with a detectable pulse but no sensed breathing/movement is in
respiratory arrest — the most time-critical *savable* state. Reporting them
Deceased would deprioritize a rescuable person. WiFi-CSI also cannot confirm
death (no airway-repositioning step), so a pulse must override.
Fix: in both paths, if the result would be Deceased but a heartbeat is
present, return Immediate. Total absence of breathing, movement AND heartbeat
is unchanged (domain → Unknown, ensemble → Deceased).
2 safety regression tests added. Full MAT suite: 168 + 6 + 3 passed, 0 failed
(existing test_no_vitals_is_deceased still green — no heartbeat → Deceased).
The --export-rvf handler ran *before* the --train/--pretrain handlers and
unconditionally wrote placeholder sine-wave weights, then returned. So the
documented `--train --dataset … --export-rvf <path>` workflow
(user-guide.md) short-circuited to a PLACEHOLDER model and never trained —
printing "exported successfully" for a non-functional model. Given the
project's anti-"is it fake" stance, silently emitting a fake model is the
wrong default.
Fix:
- Only emit the placeholder container-format demo when --export-rvf is used
*standalone* (new `export_emits_placeholder_demo` guard). With
--train/--pretrain, fall through so the real training pipeline runs and
exports calibrated weights.
- The standalone path now prints a clear WARNING that it writes a
container-format demo with placeholder weights — not a trained model —
pointing to --train / a pretrained encoder (#894).
- Docs: flag --export-rvf as a placeholder demo in the flag table, and fix
the Docker training example to use --save-rvf (consistent with the
from-source example) instead of the placeholder --export-rvf.
3 unit tests for the guard. Full crate unit suite: 429 + 117 passed, 0 failed.
Users who downloaded ruvnet/wifi-densepose-pretrained and passed
model.safetensors / model-q4.bin / model.rvf.jsonl to --model hit a bare
"Progressive loader init failed: invalid magic at offset 0: expected
0x52564653, got 0x77455735" and were stuck — the server then silently fell
back to signal heuristics (which over-count, feeding "is it fake" reports).
The HF files are a different *format* and encoder architecture than the RVF
binary container the progressive loader expects, so they can't load directly.
Now the load-failure path detects the common cases (safetensors header,
JSONL manifest, quantized .bin blob) and emits a plain explanation naming the
format, what --model actually expects (RVF `RVFS` container from
wifi-densepose-train), and that it's continuing with heuristics — with a
pointer to #894.
Pure, testable `diagnose_model_load_error()` + 4 unit tests (run under the
default `--no-default-features` CI). Full crate unit suite: 429 + 114 passed,
0 failed.
The MQTT bridge fanned out one Home-Assistant device per node (#898) but
applied the *room-level aggregate* classification to every node — so in a
multi-node setup a node in an empty corner inherited another node's
"present", and `motion_level: "absent"` was mis-mapped to full motion
(the aggregate match fell through `Some(_) => 1.0`).
Each node in the sensing broadcast's `nodes` array already carries its own
`classification` (`motion_level`/`presence`/`confidence`, see
PerNodeFeatureInfo) and RSSI. Now each per-node snapshot reads that node's
own classification, deferring to the room aggregate only for fields a node
omits. Vitals (breathing/heart rate) and person count stay room-level.
Extracted the JSON→VitalsSnapshot mapping into a pure, testable function
(`vitals_snapshots_from_sensing_json`) and added 4 unit tests covering
per-node divergence, partial-field fallback, the no-nodes aggregate path,
and the absent→zero-motion fix.
Supersedes #899, which targeted the right bug but read non-existent fields
(`node["motion_level"]` / `node["status"]` instead of the nested
`node["classification"]` + `stale`).
Verified: builds with `--features mqtt`; new tests pass; full crate unit
suite 432 + 114 passed, 0 failed.
field_bridge::occupancy_or_fallback returned FieldModel::estimate_occupancy
unbounded (internal ceiling 10), while the perturbation fallback below it
and score_to_person_count both cap at 3 ("1-3 for single ESP32"). On noisy
or under-calibrated CSI the eigenvalue count inflated → "10 persons when 1
present" (#894, seen when --model fails to load → heuristic mode). Bound the
eigenvalue path to a shared MAX_SINGLE_LINK_OCCUPANCY const (3) so every
single-link estimator agrees. Genuine higher counts come from the
multistatic fusion path. Build clean, field_bridge tests pass.
After the per-node discovery change, discovery configs are published the
first time a snapshot for a node_id arrives (not eagerly at startup). The
two discovery integration tests (discovery_topics_appear_on_broker,
privacy_mode_suppresses_biometric_discovery) spawned the publisher with an
empty broadcast channel and never sent a snapshot, so they collected []
and failed ("missing presence discovery topic in []").
Drive snapshots for the test node_id throughout the capture window (same
pattern as state_messages_published_on_snapshot_broadcast) so the per-node
device's discovery lands. Verified against a local mosquitto: 3 passed.
After the #872 MQTT wiring, the JSON->VitalsSnapshot bridge hard-coded a
single node_id (the MQTT client id) and the publisher used one
OwnedDiscoveryBuilder, so every physical node collapsed into a single
Home-Assistant device (identifiers:["wifi_densepose_wifi-densepose-1"]),
contradicting the one-device-per-node docs.
- Bridge (main.rs): emit one VitalsSnapshot per node in the sensing
update's nodes[] (each carries its own node_id + RSSI; shared aggregate
presence/vitals), falling back to a single aggregate snapshot when
there is no per-node data (wifi/simulate sources).
- Publisher (publisher.rs): add OwnedDiscoveryBuilder::for_node(), and
publish discovery + availability lazily on first sight of each node_id,
routing state to per-node topics. Heartbeat/refresh/offline-LWT iterate
all known nodes. Result: N distinct HA devices, one per node.
3 new unit tests (distinct nodes -> distinct wifi_densepose_<node>
identifiers); full MQTT suite 71 passed, example builds.
CI `cargo test --no-default-features (baseline regression)` failed with
`error: associated function compute is never used` under -D warnings.
compute() is only reachable via PrivacyModeRegistry (#[cfg(feature =
"std")]); without std there is no caller. Gate the impl to match its only
callers. Verified clean under --no-default-features, default, and
--features mqtt with RUSTFLAGS=-D warnings.
Co-Authored-By: claude-flow <ruv@ruv.net>
CI `clippy (-D warnings, --no-deps)` failed on patterns.rs:131 —
`row % 2 == 0` is flagged by clippy::manual_is_multiple_of. Use
`row.is_multiple_of(2)` (identical even-row check). Both CI clippy
variants (--no-default-features and --features full,train) now pass.
Co-Authored-By: claude-flow <ruv@ruv.net>
The pure-CSI per-node path clamped its own occupancy estimate before the
aggregator could read it. estimate_persons_from_correlation (DynamicMinCut)
returns 0-3, but it was mapped to a score via `corr_persons / 3.0`, putting
2 people at 0.667 — just under the 0.70 up-threshold of
score_to_person_count — so the per-node count never climbed past 1, leaving
node_max stuck at 1 for CSI-only nodes even when the min-cut cleanly
separated two people.
Replace the lossy /3.0 mapping with a threshold-aligned corr_persons_to_score
(1->0.40, 2->0.74, 3->0.96) whose steady state round-trips back to the same
count through the EMA + hysteresis bands, while still gating transient noise.
A convergence test replays the exact CSI-loop EMA and asserts min-cut=2 now
reports 2 / 3 reports 3 / 1 reports 1, plus a regression test documenting
that the old /3.0 mapping pinned two people to 1.
Full suite: 586 passed, 0 failed.
Co-Authored-By: claude-flow <ruv@ruv.net>
Person count was pinned to 1 because the aggregate was derived from
`smoothed_person_score`, an EMA-smoothed *activity* score (amplitude
variance / motion / spectral energy) that saturates near a single
occupant and cannot discriminate count. The count-aware per-node
estimates the ESP32 paths already compute (firmware n_persons, mincut
corr_persons) were stored in NodeState::prev_person_count then discarded
by the aggregator — the same dead-wiring class as #872.
Add `aggregate_person_count(activity_count, node_states)` = max(activity,
node_max) and use it at both ESP32 aggregation sites (edge-vitals + CSI
loop, Some + fallback arms). It can only raise the count when a node
positively reports more occupants, so the lone-occupant case is provably
never inflated (regression-guarded).
5 new unit tests + full suite: 582 passed, 0 failed.
Co-Authored-By: claude-flow <ruv@ruv.net>
#872 reported '--mqtt: unexpected argument' on the Docker image; prior
attempts chased a Docker *rebuild*, but the real cause was disconnected
*code*: the --mqtt* flags lived only in cli::Args (dead code — referenced
nowhere), while the binary parses a separate main::Args with no mqtt fields,
and main.rs never declared/started the mqtt:: publisher. So MQTT was fully
unwired: flags didn't parse, and the publisher never ran.
Fix:
- Extract the mqtt + privacy flags into a shared
(#[derive(clap::Args)]); retarget mqtt::config::{from_args,build_tls} to it.
- #[command(flatten)] MqttArgs into the binary's main::Args (using the *lib*
crate's type so it matches from_args), so --mqtt* now parse.
- Spawn the publisher on --mqtt: build MqttConfig, validate, and bridge the
existing JSON sensing broadcast into the typed VitalsSnapshot stream the
publisher consumes (defensive serde_json::Value mapping — absent fields
default, never wrong values). #[cfg(feature=mqtt)]-gated; without the
feature --mqtt WARNs and no-ops (documented contract). Fix the
mqtt_publisher example for the new signature.
Verified end-to-end against local mosquitto: publisher connects and emits
20 HA auto-discovery entities + live state (presence ON, person_count, …).
Tests: 577 pass default / 580 pass --features mqtt / 0 fail; both configs
build.
Co-Authored-By: claude-flow <ruv@ruv.net>
The cir_pipeline end-to-end test was gated on the same dominant_tap_ratio
floor; the windowed-ratio fix resolves it. All 6 ADR-134 P2 CIR tests
(cir_synthetic 5 + cir_pipeline 1) now pass. signal+cir: 472 pass / 0 fail.
Co-Authored-By: claude-flow <ruv@ruv.net>
Found the principled fix for the rms-delay-spread inflation (superseding my
prior 'needs ISTA work' note): the spurious ~15-20% tap at ~bin 150 is an
ALIAS of the near-zero dominant tap — the ISTA delay grid is circular (Φ is
DFT-like), so bins >= G/2 are non-causal negative delays. Computing the delay
spread over only the causal half [0, G/2) drops rms from 389ns to 65ns (true
value), cleanly and robustly (no fragile magnitude threshold). Un-ignores
should_produce_positive_rms_delay_spread.
ADR-134 P2 cir_synthetic now FULLY resolved: all 5 previously-ignored tests
pass via two physics-justified fixes (windowed dominant-ratio for super-
resolution leakage + causal-window rms for circular-grid aliasing). signal+cir:
471 pass / 0 fail / 0 ignored in cir_synthetic.
Co-Authored-By: claude-flow <ruv@ruv.net>
Diagnosed the one still-ignored CIR test: ISTA emits a spurious ~15-20%-of-
dominant tap at an implausible far delay (~bin 150 / ~3us) that inflates
rms_delay_spread to ~390ns (vs ~53ns true). It sits too close to the real
weakest tap (~30% of dominant) for a safe magnitude cutoff, so the proper fix
is ISTA recovery-quality work (grid de-aliasing / far-tap suppression), not a
band-aid threshold. Sharpened the #[ignore] note accordingly. signal+cir:
470 pass / 0 fail.
Co-Authored-By: claude-flow <ruv@ruv.net>
The CIR estimator's dominant_tap_ratio measured a single grid bin, but on the
3x super-resolved ISTA grid a single physical tap leaks across ~3 adjacent
bins — so the ratio under-counted the dominant tap and sat far below the
per-tier floors (HT20 0.158<0.30, HT40 0.133<0.35, HE20 0.102<0.40), forcing
the 3-tap recovery + 40MHz-ToF tests to be #[ignore]d.
Fix (data-backed via a lambda sweep): (1) compute dominant_tap_ratio over a
+/-1-bin window around the peak — the physical tap's true footprint; (2) tune
L1 lambda for sparse multipath (HT20 .05->.08, HT40 .03->.08, HE20 .03->.18).
Result: ratios 0.367/0.406/0.474, comfortably above floors with all 3 taps
preserved. Un-ignores should_recover_3tap_channel_{ht20,ht40,he20} and
should_return_tof_at_40mhz. signal crate: 470 pass / 0 fail; change isolated
to CIR (no external consumers). The rms-delay-spread test stays ignored with a
re-scoped note (far-tap robustness is separate remaining work).
Co-Authored-By: claude-flow <ruv@ruv.net>
Closes the last verification gap in the calibration feature: previously the
Python producer and Rust consumer were proven compatible only by format
matching. Now a real ~11KB adapter fitted by cog_calibrate.py on the in-repo
pose_v1.safetensors is committed as a fixture, and a Rust test loads it via
the engine and asserts is_calibrated() + that it changes inference output.
The full Python->Rust calibration contract is verified with a real artifact.
7/7 cog-pose tests pass.
Co-Authored-By: claude-flow <ruv@ruv.net>
The --adapter docs claimed the adapter is produced by
aether-arena/calibration/calibrate.py, but that reference tool targets the
MM-Fi *transformer* model and emits .npz with proj/head LoRA keys, while
this cog runs a *conv+MLP* model expecting safetensors with fc1.a/fc1.b/
fc2.a/fc2.b. Same LoRA mechanism, different model -> adapters are
model-specific and NOT interchangeable. Clarify the expected key layout and
that the Python tool is a mechanism reference, not a drop-in producer.
6/6 tests pass.
Co-Authored-By: claude-flow <ruv@ruv.net>
readme_quickstart_uses_canonical_public_api checked a multi-line needle
'pipeline\n .process' against the include_str! README. On a CRLF
checkout (Windows / core.autocrlf) the content is 'pipeline\r\n .process',
so the LF needle never matched and the test failed deterministically (only
surfaced once the worldmodel fix let cargo test --workspace run on Windows;
the test is #[cfg(feature=std)]-gated, enabled via workspace feature
unification). Normalize CRLF->LF before the check. Full workspace now green
3/3 runs on Windows.
Co-Authored-By: claude-flow <ruv@ruv.net>
bridge.rs imported tokio::net::UnixStream unconditionally, so the whole
workspace failed to build on Windows (E0432) — blocking cargo test
--workspace and the pre-merge gate there. The OccWorld Unix-socket bridge
is a Linux-appliance feature (Python inference server on the GPU host), so
gate it #[cfg(unix)] and add a #[cfg(not(unix))] send_recv that fails fast
with a clear 'unsupported on this target' Protocol error. Workspace now
builds on Windows; worldmodel 12 tests pass.
Co-Authored-By: claude-flow <ruv@ruv.net>
Completes the end-to-end product path: cog-pose-estimation run --config
<cfg> --adapter <room.safetensors> loads the shared base + a per-room LoRA
adapter for calibrated inference. Adds InferenceEngine::with_adapter()
(default weights + adapter) and logs when a calibration adapter is active.
6/6 tests pass.
Co-Authored-By: claude-flow <ruv@ruv.net>
Ports the calibration mechanism (ADR-150 §3.5-3.6, reference impl in
aether-arena/calibration/) into the real product pose engine. The Candle
InferenceEngine now loads an optional per-room adapter safetensors and
applies low-rank deltas (y + (x.A).B) on the fc1/fc2 head at inference.
Architecture-agnostic LoRA; base behaviour unchanged when no adapter.
New API: with_weights_and_adapter(), is_calibrated(). Tested: adapter
detection + output-change integration test (6/6 pass).
Co-Authored-By: claude-flow <ruv@ruv.net>
Per direction "remove the initial number, optimize for benchmark first" + "include
witness chain capabilities for proof and repeatability analysis":
- Empty board, no seeded numbers: ledger seeds to genesis only. Every result is a
real scoring-pipeline witness; RuView gets no hand-entered baseline.
- Real model scoring: aa_score_runner now loads predictions + an eval split
(--split/--pred) and scores them through the real ruview_metrics pose harness —
not just a synthetic fixture. Committed public smoke split (fixtures/smoke_*.json).
- Witness chain: each score emits a witness = inputs_sha256 (binds it to the exact
inputs) + proof_sha256 (cross-platform-stable score hash) + harness_version.
- Repeatability analysis: --repeat N runs the harness N× and fails if it ever
yields >=2 distinct proof hashes (16/16 identical locally).
- Witness ledger: ledger/ledger_tools.py — append-only, hash-chained, tamper-
evident (seed/append/verify); editing any past row breaks the chain.
- CI gate extended: determinism + repeatability(16) + real-scoring smoke + ledger
chain verify on every PR.
Co-Authored-By: claude-flow <ruv@ruv.net>
AetherArena ("AA") — the official, project-agnostic Spatial-Intelligence Benchmark
(ADR-149, Accepted). Iteration 1 of the long-horizon build:
- ADR-149 accepted: name locked (ruvnet/aether-arena), v0 metrics locked
(pose/presence/latency/determinism), dataset legality resolved (MM-Fi CC BY-NC
only; Wi-Pose excluded). Adds four-part framing, threat model, arena_score
formula, submission state machine, neutrality/governance, and the §7 acceptance test.
- aa_score_runner: deterministic scorer bin reusing the real ruview_metrics pose
harness on a fixed seed=42 fixture → RuViewTier-style verdict + cross-platform
SHA-256 proof hash. Builds --no-default-features (no torch/GPU). VERDICT: PASS.
- CI harness gate: .github/workflows/aether-arena-harness.yml runs the scorer on
every PR — the "PR that runs the harness as part of the build" requirement.
- Scaffold: aether-arena/{README,VERIFY,STATUS}.md + schema/aa-submission.toml.
- Horizon record persisted (.claude-flow/horizons/aether-arena-aa.json).
Infra = the deliverable; model SOTA (MM-Fi PCK@20) is a separate effort blocked on
ADR-079 data collection, tracked as a stretch goal, not an infra exit.
Co-Authored-By: claude-flow <ruv@ruv.net>
Drives a real SemanticBus: raw snapshot (fall_detected, past warmup) ->
FallRisk primitive -> SemanticStateRecord (provenance) -> single-signal rule
fires / multi-signal agreement rule does NOT (no false escalation) -> expired
record rejected. Proves the ADR-140 credibility path end to end.
Co-Authored-By: claude-flow <ruv@ruv.net>
Adds a `--min-frames N` flag to `wifi-densepose calibrate` that overrides
the ADR-135 tier minimum (default 600 frames at 20 Hz for HT20).
Motivation: validated end-to-end against a live ESP32-S3 on COM9, freshly
re-provisioned with target-ip = 192.168.1.50 (this host). The firmware
emits CSI at roughly 0.5 Hz in the current quiet RF environment (most
UDP packets are 0xC511_0006 status, not 0xC511_0001 CSI). Waiting 20 min
to collect 600 frames at install time is operator-hostile; raising the
firmware's CSI rate is a separate concern.
When `--min-frames > 0`, the CLI prints a WARN line stating the override
relaxes the phase-concentration guarantee and should not be used in
production. ADR-135 defaults are preserved unchanged.
Live-hardware validation with `--min-frames 10` over 32 s captured 10
real CSI frames from the ESP32, finalised a baseline-real.bin (860 B)
with correct magic 0xCA1B_0001, version 1, tier HT20, and 52 active
subcarriers. End-to-end pipeline confirmed against real hardware, not
just synthetic UDP.
Co-Authored-By: claude-flow <ruv@ruv.net>
rUv