#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>
Update the Unreleased entry: calibration service is now complete across both
model paths (transformer .npz + cog safetensors via cog_calibrate.py) with
cross-language Python->Rust integration test; add the Windows cross-platform
build fixes (worldmodel cfg(unix), bfld CRLF) — 2682 workspace tests green/0
fail on Windows.
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>
I'd shipped the Rust cog-pose --adapter *consumer* (+test) but there was no
*producer* for cog-format adapters, leaving it a half-feature. cog_calibrate.py
fits a rank-r LoRA on the cog conv+MLP head (pose_v1.safetensors, 56x20) from a
labeled in-room capture and writes a safetensors with fc1.a/fc1.b/fc2.a/fc2.b
(scale baked into b) — exactly what the Rust engine loads. Verified against the
in-repo pose_v1.safetensors: correct keys/shapes, reduces fit error, active
adapter, ~2.6KB. Adds test_cog_calibration.py (passes) + README documenting the
two non-interchangeable producers (transformer .npz vs cog safetensors).
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>
The committed calibration service (model.py/calibrate.py/infer.py) had no
automated test — only ad-hoc verification. Adds a CPU-only, no-real-checkpoint
test that exercises the CLI end-to-end on synthetic data: build base ->
calibrate.py fits adapter -> infer.py runs base+adapter, asserting adapter
size (<200KB), keypoint shape [N,17,2], finiteness, [0,1] range, and that the
adapter actually changes the output. Passes on Windows CPU (torch 2.11).
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>
Random frozen encoder + trained head matches a fully-trained encoder to
within 2-4pts (cross-subject <2pts). WiFi-CSI sensing is largely a
random-features + target-readout problem: barely a learned representation
to transfer, which unifies the zero-shot collapse, no-transfer results,
foundation-encoder failure, and why per-room calibration works. Practical:
invest in readout + calibration, not encoder pretraining.
Co-Authored-By: claude-flow <ruv@ruv.net>
Re-ran transfer on 14-class person-ID (harder than 6-activity HAR): same
null-transfer result (MM-Fi pretrain 91.7% = random 92.8%). Unified root
cause: CSI in-domain classification lives in the target-trained readout
(random projection already separable); learned reps don't transfer across
subjects/rooms/datasets. WiFi-CSI is distribution-locked. Addresses the
'HAR too easy' caveat.
Co-Authored-By: claude-flow <ruv@ruv.net>
Tested the cross-dataset frontier: MM-Fi-trained CSI representation does NOT
transfer beneficially to NTU-Fi HAR (frozen probe 91.5% = random features
93%; full fine-tune 75% < probe). CSI reps are distribution-locked, same
root cause as within-MM-Fi cross-subject/-env collapse. Caveat: NTU-Fi 6
coarse activities are an easy target (random->93%). Updates the study's
cross-dataset limitation from 'untested' to this measured result.
Co-Authored-By: claude-flow <ruv@ruv.net>
Consolidates the full campaign into one committed, citable artifact (the
detailed log was in a gitignored staging report): pose SOTA 83.6% + 20KB
int4 edge model; action recognition 88% (a WiFi task MM-Fi never
benchmarked); the generalization story (zero-shot collapse, few-shot
calibration rescue, task-general across pose+action); all honest negatives
(CORAL/DANN/instance-norm/SupCon/distillation/subject-scaling); the 11KB
calibration-adapter deployment recipe; honest limitations (cross-dataset
untested, ARM latency pending).
Co-Authored-By: claude-flow <ruv@ruv.net>
Verified on a 2nd MM-Fi task: 27-class action recognition (which MM-Fi
never benchmarked for WiFi; only published baseline WiDistill 34%). In-domain
88% (leaky); cross-subject zero-shot collapses to ~10%; few-shot calibration
rescues 10->76% (1000 samples). Same mechanism as pose -> few-shot in-room
calibration is the universal WiFi-sensing generalization answer, not a pose
quirk.
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>
Operationalizes the campaign's central finding (ADR-150 §3.3-3.6): a frozen
shared base + a ~11KB per-room LoRA adapter from ~100-200 labeled samples
recovers SOTA-level pose in any new room/person. Verified end-to-end:
source-only base zero-shot 3.09% on unseen room -> 74.29% after 200-sample
calibration. Files: model.py (PoseNet+LoRA), calibrate.py, infer.py, README
with measured calibration budget.
Co-Authored-By: claude-flow <ruv@ruv.net>
Decisive capstone: cross-environment (unseen room+people) zero-shot
10.6%, but 5 calibration samples/person -> 60%, 200 -> 73%. The hard
frontier is calibration-soluble, MORE dramatically than cross-subject
(+62.5 vs +12 at K=200). The unsolved-frontier framing was a zero-shot
artifact. Reframes generalization: ship few-shot calibration, not
zero-shot invariance. Recommend accepting ADR-150 re-scoped around the
calibration mechanism.
Co-Authored-By: claude-flow <ruv@ruv.net>
Compared per-room calibration methods at K=200: LoRA rank-8 recovers
63.6->72.5% (SOTA-level) with just 11K params (~11KB), 0.5% the model
size. Validates the ship-base-once + tiny-per-room-adapter mechanism for
the RuView calibration service. Accuracy/size knob documented.
Co-Authored-By: claude-flow <ruv@ruv.net>
Measured cross-subject PCK vs N training subjects: 4->8 = +21pts, but
24->32 = +0.45pt. Saturates ~64%, ~19pt below in-domain. Correction to
'more data': subject-count returns vanish past ~16-20; the residual is
device/room/protocol shift. Re-scope phase-1 capture around DIVERSITY
(rooms/devices/protocols) + few-shot target adaptation, not headcount.
Co-Authored-By: claude-flow <ruv@ruv.net>
Published deployable int4-QAT micro (verified 74.08%, ~20KB) at
ruvnet/wifi-densepose-mmfi-pose/edge. Runs 0.135ms single-thread x86 CPU
(no GPU) - real-time pose without an accelerator. ARM on-device validation
pending fleet availability.
Co-Authored-By: claude-flow <ruv@ruv.net>
Swept model size on MM-Fi random_split: every config from micro (75,237
params, 0.22ms, 74.30%) up beats MultiFormer (72.25%); nano (40K, 0.13ms)
within 0.5pt. Pareto-dominant (smaller AND more accurate than prior SOTA).
Orthogonal to the data-bound accuracy frontier (ADR-150).
Co-Authored-By: claude-flow <ruv@ruv.net>
Measured all near-term levers on the official MM-Fi cross-subject split:
- mixup+TTA+ensemble = best at 64.92% (+0.9 over doc 64.04)
- pose-contrastive foundation pretrain: estimated +5..+12, MEASURED -2.3
(SupCon loss pinned at ln(B) across K/BS/seeds -> same-pose CSI is not
contrastively alignable across subjects)
- instance-norm+SpecAugment -4.6; CORAL/DANN ~0
Conclusion: the 18-pt in-domain<->cross-subject gap is fundamental subject
shift, not algorithmic. Promotes multi-subject data collection to the primary
lever; recommends re-scoping ADR-150 phase 1 around capture.
Co-Authored-By: claude-flow <ruv@ruv.net>
v1 '100% presence accuracy' was on a single-class overnight recording
(6062/6063 'present'). Replaced with v2 encoder's honest label-free
held-out temporal-triplet accuracy (66.4% raw -> 82.3% trained).
Models published to HF; tracking ruvnet/RuView#882.
Co-Authored-By: claude-flow <ruv@ruv.net>
Public face of the benchmark: empty-board leaderboard from the witness ledger,
chain-integrity display, submit/verify/about tabs. Presentation layer per ADR-149
§2.2 (heavy scoring stays in the pinned RuView harness / CI).
Live: https://huggingface.co/spaces/ruvnet/aether-arena
Co-Authored-By: claude-flow <ruv@ruv.net>
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>
ruv