feat(aether-arena): ADR-149 Spatial-Intelligence Benchmark — scorer + CI harness gate#874
Merged
Conversation
… CI harness gate (M1-M4)
AetherArena ("AA") — the official, project-agnostic Spatial-Intelligence Benchmark
(ADR-149, Accepted). Iteration 1 of the long-horizon build:
- ADR-149 accepted: name locked (ruvnet/aether-arena), v0 metrics locked
(pose/presence/latency/determinism), dataset legality resolved (MM-Fi CC BY-NC
only; Wi-Pose excluded). Adds four-part framing, threat model, arena_score
formula, submission state machine, neutrality/governance, and the §7 acceptance test.
- aa_score_runner: deterministic scorer bin reusing the real ruview_metrics pose
harness on a fixed seed=42 fixture → RuViewTier-style verdict + cross-platform
SHA-256 proof hash. Builds --no-default-features (no torch/GPU). VERDICT: PASS.
- CI harness gate: .github/workflows/aether-arena-harness.yml runs the scorer on
every PR — the "PR that runs the harness as part of the build" requirement.
- Scaffold: aether-arena/{README,VERIFY,STATUS}.md + schema/aa-submission.toml.
- Horizon record persisted (.claude-flow/horizons/aether-arena-aa.json).
Infra = the deliverable; model SOTA (MM-Fi PCK@20) is a separate effort blocked on
ADR-079 data collection, tracked as a stretch goal, not an infra exit.
Co-Authored-By: claude-flow <ruv@ruv.net>
…ility (M2/M5/M7) Per direction "remove the initial number, optimize for benchmark first" + "include witness chain capabilities for proof and repeatability analysis": - Empty board, no seeded numbers: ledger seeds to genesis only. Every result is a real scoring-pipeline witness; RuView gets no hand-entered baseline. - Real model scoring: aa_score_runner now loads predictions + an eval split (--split/--pred) and scores them through the real ruview_metrics pose harness — not just a synthetic fixture. Committed public smoke split (fixtures/smoke_*.json). - Witness chain: each score emits a witness = inputs_sha256 (binds it to the exact inputs) + proof_sha256 (cross-platform-stable score hash) + harness_version. - Repeatability analysis: --repeat N runs the harness N× and fails if it ever yields >=2 distinct proof hashes (16/16 identical locally). - Witness ledger: ledger/ledger_tools.py — append-only, hash-chained, tamper- evident (seed/append/verify); editing any past row breaks the chain. - CI gate extended: determinism + repeatability(16) + real-scoring smoke + ledger chain verify on every PR. Co-Authored-By: claude-flow <ruv@ruv.net>
…arena (M6) Public face of the benchmark: empty-board leaderboard from the witness ledger, chain-integrity display, submit/verify/about tabs. Presentation layer per ADR-149 §2.2 (heavy scoring stays in the pinned RuView harness / CI). Live: https://huggingface.co/spaces/ruvnet/aether-arena Co-Authored-By: claude-flow <ruv@ruv.net>
…gate passing (M8)
…ndation Encoder - Ledger witness row (seq 1, Gold): RuView CSI-Transformer 81.63% torso-PCK@20 on MM-Fi random_split, exceeding MultiFormer 72.25% (CSI2Pose 68.41%) — protocol- and metric-matched, self-corrected from inflated 91.86% bbox. Hash-chained, verifiable. - HF Space updated with the controlled SOTA claim + caveat (cross-subject is the frontier). - Proof/replay/witness gist: gist.github.com/ruvnet/af2fbc1c7674dddf09c15509b3c7f785 - Tracking issue #876 (result + Generalization Track roadmap). - ADR-150: RuView RF Foundation Encoder — pose-preserving, subject/room/device-invariant SSL embedding (masked CSI + pose-contrast-across-subjects + coherence head); the principled attack on the cross-subject frontier. DANN failed; this is the corrected design. Co-Authored-By: claude-flow <ruv@ruv.net>
…-env 17.5 CORAL) Append best witness rows to ledger (seq 2-4) + update HF Space leaderboard banner. In-domain 83.59% torso-PCK@20 (graph+ensemble+TTA) supersedes the 81.63 single-model entry, +11.34 over MultiFormer 72.25. Cross-subject 64.04% (official split). Cross-environment 17.51% (CORAL domain alignment, the cross-room DG win). Gist + issue #876 updated with frontier map. Co-Authored-By: claude-flow <ruv@ruv.net>
Published ruvnet/wifi-densepose-mmfi-pose — beats MultiFormer (72.25%) and CSI2Pose (68.41%) on matched MM-Fi random_split torso-PCK@20. Tracking: #880 Co-Authored-By: claude-flow <ruv@ruv.net>
Head-to-head on the wire-compatible HA API surface: - Cold start 0.55s vs 9.7s (18x), idle RSS 10.1MB vs 359MB (35x), binary 4.7MB vs 610MB image (130x), throughput 1599 vs 716 rps. - Honest caveats: latency endpoints differ (auth /api/states vs unauth /manifest.json); HA wins integration breadth + UI maturity. - Repro harnesses in aether-arena/staging/. Co-Authored-By: claude-flow <ruv@ruv.net>
…ngle-class 100%) v1 '100% presence accuracy' was on a single-class overnight recording (6062/6063 'present'). Replaced with v2 encoder's honest label-free held-out temporal-triplet accuracy (66.4% raw -> 82.3% trained). Models published to HF; tracking #882. Co-Authored-By: claude-flow <ruv@ruv.net>
…etrain refuted Measured all near-term levers on the official MM-Fi cross-subject split: - mixup+TTA+ensemble = best at 64.92% (+0.9 over doc 64.04) - pose-contrastive foundation pretrain: estimated +5..+12, MEASURED -2.3 (SupCon loss pinned at ln(B) across K/BS/seeds -> same-pose CSI is not contrastively alignable across subjects) - instance-norm+SpecAugment -4.6; CORAL/DANN ~0 Conclusion: the 18-pt in-domain<->cross-subject gap is fundamental subject shift, not algorithmic. Promotes multi-subject data collection to the primary lever; recommends re-scoping ADR-150 phase 1 around capture. Co-Authored-By: claude-flow <ruv@ruv.net>
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>
micro (74.87%, beats MultiFormer 72.25%) = 36.7KB int4 / 73.5KB int8; nano (~72%) = 19.5KB int4. Distillation tested, no gain (direct training wins). A SOTA-beating pose model fits on the sensing node itself. Co-Authored-By: claude-flow <ruv@ruv.net>
int8 PTQ lossless (74.70%, 73.5KB); int4 naive PTQ drops below SOTA (70.21%) but QAT recovers to 74.46% (36.7KB) - still beats MultiFormer. A SOTA-beating WiFi-pose model genuinely runs in ~37KB int4 (QAT) / 73KB int8. Distillation negative noted. Co-Authored-By: claude-flow <ruv@ruv.net>
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>
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>
Decisive result: 50 labeled frames/subject of in-room calibration -> 72.2% (reaches SOTA), 200 -> 76.1%, 1000 -> 78.3%. Few-shot target adaptation dominates source volume (+24 subjects bought +6pt; 200 target frames bought +12.4pt). Re-scopes the deployment story: ship a ~30s on-site calibration, not a mass corpus. Foundation encoder's role shifts to making that calibration cheaper. Supersedes the earlier data-bound pessimism. Co-Authored-By: claude-flow <ruv@ruv.net>
…on service Compared per-room calibration methods at K=200: LoRA rank-8 recovers 63.6->72.5% (SOTA-level) with just 11K params (~11KB), 0.5% the model size. Validates the ship-base-once + tiny-per-room-adapter mechanism for the RuView calibration service. Accuracy/size knob documented. Co-Authored-By: claude-flow <ruv@ruv.net>
…ion spec 11KB adapter needs ~100-200 labeled samples/room for ~72% (knee ~50->70%); below ~20 it hurts. Evidence-complete calibration-service spec: base + ~100-200 samples -> 11KB LoRA -> ~72% cross-subject. Encoder goal now precisely posed: cut the sample requirement / lift the per-budget ceiling. Co-Authored-By: claude-flow <ruv@ruv.net>
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>
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>
…ce path 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>
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>
…service Document the beyond-SOTA efficiency frontier (75K params beats SOTA, int4 edge model 20KB@74%), few-shot calibration resolving generalization (cross-env 10->73%), and the calibration service (Python ref + Rust cog-pose --adapter integration). 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>
…egative) 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>
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>
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>
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>
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>
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>
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>
… feature 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>
…> Rust engine) 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>
…ixes 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>
…CIR tests
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>
…d failure 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>
…34 P2 cir test 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>
…fully resolved 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>
#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>
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>
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>
- README: replace retracted "100% presence" claim with honest 82.3% held-out temporal-triplet; correct stale "pose model not in this release" (now live at ruvnet/wifi-densepose-mmfi-pose, 82.69% torso-PCK@20 SOTA); add a Results & proof table (HF models, AetherArena, benchmark study, deterministic verify.py proof, witness). - user-guide: same 100%->82.3% correction in two places; add Results & proof pointers and the SOTA pose model + AetherArena links. - docs/proof-of-capabilities.md (new): evidence-first rebuttal to the "fake / misleading" claims. Concedes what was fair (over-stated early metrics, AI-doc tone), refutes the category errors (simulate-mode mistaken for fraud; missing weights mistaken for missing pipeline), and gives copy-paste "prove it yourself" steps (verify.py VERDICT: PASS + published SHA-256, cargo test, HF model pull, ESP32 CSI). Emphasizes built-in-public history (git, 96 ADRs, CHANGELOG, issues incl. #803/#872 bug->fix arcs) as the anti-facade evidence. - aether-arena/VERIFY.md: cross-link the whole-platform proof doc. Verified: python archive/v1/data/proof/verify.py -> VERDICT: PASS (hash ca58956c...9199 matches published expected_features.sha256). Co-Authored-By: claude-flow <ruv@ruv.net>
The MM-Fi benchmark environment archives (E01-E04.zip) are large data files fetched separately for evaluation — they must never be committed. Also keeps the existing aether-arena/staging/ private-staging exclusion. Co-Authored-By: claude-flow <ruv@ruv.net>
…rena # Conflicts: # CHANGELOG.md
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>
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>
The clippy job failed with "cargo-clippy is not installed for the toolchain '1.89'". v2/rust-toolchain.toml pins channel "1.89" (profile "minimal", no clippy); dtolnay@stable installed clippy on the floating "stable" toolchain, but the override makes cargo use the separate "1.89" toolchain in working-directory v2. Pin the toolchain input to "1.89" so clippy lands on the toolchain cargo actually runs. (The real clippy lint it then catches — manual_is_multiple_of — was fixed in 29e698a.) Co-Authored-By: claude-flow <ruv@ruv.net>
Rust Workspace Tests failed the CIR determinism guard: expected 120bd7b1… (from the original ADR-134, #837) vs actual 304d5469…. The later CIR fixes on this branch (windowed dominant-tap ratio, λ tuning, causal-delay-window rms — ADR-134 P2) intentionally changed the CirEstimator output but never regenerated the witness hash. The new output is bit-deterministic and cross-platform stable: the Rust cir_proof_runner produces 304d5469… on both Linux CI and local Windows. Regenerated via the sanctioned `--generate-hash` path; verify-cir-proof.sh now prints "VERDICT: PASS (CIR hash matches)". Co-Authored-By: claude-flow <ruv@ruv.net>
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…9-aether-arena feat(aether-arena): ADR-149 Spatial-Intelligence Benchmark — scorer + CI harness gate f850d46
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Implements ADR-149 (Accepted) — AetherArena ("AA"), the official, project-agnostic Spatial-Intelligence Benchmark. Iteration 1 of the long-horizon build (
.claude-flow/horizons/aether-arena-aa.json).What's here (M1–M4)
ruvnet/aether-arena), v0 metrics locked (pose/presence/latency/determinism), dataset legality resolved (MM-Fi CC BY-NC only; Wi-Pose excluded). Includes four-part framing, threat model,arena_scoreformula, submission state machine, neutrality/governance, §7 acceptance test.aa_score_runner— deterministic scorer bin reusing the realruview_metricspose harness on a fixed seed=42 fixture → RuViewTier verdict + cross-platform SHA-256 proof hash. Pure Rust,--no-default-features(no torch/GPU). Locally VERDICT: PASS..github/workflows/aether-arena-harness.ymlruns the scorer on every PR (the "a PR that runs the harness as part of the build" requirement). This PR triggers it.aether-arena/{README,VERIFY,STATUS}.md+schema/aa-submission.toml.Verify locally (the open scorer)
Scope honesty
Infrastructure is the deliverable. Model SOTA (MM-Fi PCK@20 ~72%) is a separate ML effort blocked on ADR-079 camera-ground-truth collection — tracked as a stretch goal, not an infra exit.
Remaining (tracked in STATUS.md)
ruvnet/aether-arenaSpace (noHF_TOKENin env)🤖 Generated with claude-flow