1d9c0b3d4c
docs(study): sharpest finding — the encoder barely matters for CSI pose
...
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>
2026-05-31 03:43:14 -04:00
c95dd308fd
docs(study): cross-dataset confirmed on harder NTU-Fi-HumanID task
...
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>
2026-05-31 03:37:19 -04:00
af68bd68d8
docs(study): cross-dataset transfer tested (MM-Fi -> NTU-Fi, honest negative)
...
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>
2026-05-31 03:27:38 -04:00
695b5fb700
docs: complete MM-Fi WiFi-sensing study (pose + action, the honest picture)
...
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>
2026-05-31 03:06:54 -04:00
dac40e5df2
docs(adr-150): calibration thesis is task-general (action recognition)
...
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>
2026-05-31 03:01:50 -04:00
17ff2433bc
docs(changelog): WiFi-CSI efficiency frontier + per-room calibration 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>
2026-05-31 02:38:07 -04:00
83299b4d04
feat(cog-pose): --adapter CLI flag for per-room calibration
...
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>
2026-05-31 02:28:16 -04:00
3760db6c9a
feat(cog-pose): per-room LoRA calibration adapter in the Rust inference 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>
2026-05-31 02:26:48 -04:00
4db727649a
feat(calibration): RuView per-room calibration service (reference impl)
...
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>
2026-05-31 02:22:10 -04:00
5533ffe43e
docs(adr-150): cross-env few-shot — no unsolved deployment case
...
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>
2026-05-31 02:09:03 -04:00
ef4344f0f9
docs(adr-150): LoRA calibration data requirement — completes calibration 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>
2026-05-31 02:04:37 -04:00
ed1294a176
docs(adr-150): deployable adapter calibration — 11KB LoRA = calibration 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>
2026-05-31 01:54:23 -04:00
898aaef053
docs(adr-150): few-shot adaptation resolves the cross-subject frontier
...
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>
2026-05-31 01:47:00 -04:00
70bf9e41fe
docs(adr-150): subject-scaling study — capture diversity, not volume
...
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>
2026-05-31 01:43:49 -04:00
96ccfa58fb
bench: ship int4 edge artifact + CPU latency
...
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>
2026-05-31 01:30:29 -04:00
92d433523d
bench: deployed quantized accuracy + QAT for micro edge model
...
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>
2026-05-31 01:23:30 -04:00
d64323c2d6
bench: add quantized footprint — SOTA-beating WiFi pose in 37KB int4
...
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>
2026-05-31 01:16:16 -04:00
9c64d90054
bench: WiFi-CSI pose efficiency frontier — 75K-param model beats SOTA
...
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>
2026-05-31 01:10:33 -04:00
5d1fb48eb5
docs(adr-150): empirical cross-subject findings — pose-contrastive pretrain 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>
2026-05-31 00:33:43 -04:00
b4cb1384de
docs(readme): honest re-benchmark of ESP32 presence model (retract single-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 ruvnet/RuView#882 .
Co-Authored-By: claude-flow <ruv@ruv.net>
2026-05-30 23:52:11 -04:00
66e917ea86
bench: HOMECORE vs Home Assistant — measured perf + capability matrix
...
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>
2026-05-30 23:41:15 -04:00
7738370b18
docs(readme): link SOTA MM-Fi pose model (82.69% torso-PCK@20) on HF
...
Published ruvnet/wifi-densepose-mmfi-pose — beats MultiFormer (72.25%)
and CSI2Pose (68.41%) on matched MM-Fi random_split torso-PCK@20.
Tracking: ruvnet/RuView#880
Co-Authored-By: claude-flow <ruv@ruv.net>
2026-05-30 23:32:12 -04:00
7bad51aca6
publish: best MM-Fi benchmark set (in-domain 83.59, x-subject 64.0, x-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>
2026-05-30 22:22:53 -04:00
eb3509e9ab
reframe(aether-arena): vendor-neutral industry benchmark, RuView is one entrant
2026-05-30 19:59:10 -04:00
046b2564b8
feat(aether-arena): publish RuView MM-Fi SOTA result + ADR-150 RF Foundation 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>
2026-05-30 19:55:58 -04:00
8d64434d21
feat(swarm): ADR-149 evaluation harness — GDOP, IQM+bootstrap CI, noise sweep ( #875 )
...
Stage-1 kinematic evaluator per ADR-149 (peer-reviewed). Pure Rust, no new deps.
evals/:
- gdop.rs: 2D Geometric Dilution of Precision ((HᵀH)⁻¹ trace-sqrt); None for
<2 observers or collinear/singular geometry
- stats.rs: IQM (Agarwal 2021) + 95% stratified-bootstrap CI (deterministic LCG)
+ probability_of_improvement
- metrics.rs: EpisodeMetrics + AggregateMetrics::from_strata (IQM±CI, seed-stratified)
- runner.rs: seeded kinematic rollout (FlightPattern-driven), seed×episode matrix,
3σ×3κ default noise sweep (Gaussian amplitude × von Mises phase)
- report.rs + eval_swarm bin: generates evals/RESULTS.md leaderboard
RESULTS.md surfaces the real coverage-vs-localization-precision trade-off via GDOP:
partitioned wins coverage (100%) but single-drone sightings (GDOP 0 → 7.0m);
pheromone gets multistatic fusion (GDOP 1.6 → 4.1m). Wi2SAR 5m paper-baseline row included.
Stage-2 (Gazebo/PX4 SITL false-alarm + collision on median seeds) is documented follow-on.
Tests: 116 default / 133 full+train (+13 eval tests), 0 failed. Clippy clean (-D warnings).
2026-05-30 17:38:49 -04:00
4f7ab8e4f0
docs(aether-arena): v0 infrastructure complete — Space live, harness gate passing (M8)
2026-05-30 17:15:08 -04:00
de6715d958
fix(aether-arena): move HF Space to gradio 5.9.1 (4.44.1 jinja2 cache bug)
2026-05-30 17:14:21 -04:00
c1c04441e9
fix(aether-arena): Space launch on 0.0.0.0:7860
2026-05-30 17:10:17 -04:00
5284591770
fix(aether-arena): pin huggingface_hub 0.25.2 for gradio 4.44.1 Space
2026-05-30 17:07:08 -04:00
3f93fcd4ea
fix(aether-arena): pin HF Space to python 3.12 (gradio pydub pyaudioop 3.13 removal)
2026-05-30 17:03:14 -04:00
644b4ba816
docs(aether-arena): mark M6 HF Space deployed
2026-05-30 17:02:03 -04:00
9359bf5d04
feat(aether-arena): HF Space (Gradio) v0 — deployed to ruvnet/aether-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>
2026-05-30 17:01:10 -04:00
483bfa4660
feat(aether-arena): benchmark-first scorer + witness chain + repeatability (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>
2026-05-30 16:59:11 -04:00
a6808568a2
feat(aether-arena): ADR-149 spatial-intelligence benchmark — scorer + 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>
2026-05-30 16:47:22 -04:00
0d3d835bf8
feat(swarm): add ruview-swarm crate — drone swarm control system (ADR-148) ( #862 )
...
* feat(swarm): add wifi-densepose-swarm crate implementing ADR-148 drone swarm control system
New crate `wifi-densepose-swarm` with hierarchical-mesh swarm topology,
Raft consensus, MAPPO MARL, CSI sensing integration, and ITAR-gated
coordination features. Closes 3 of 7 milestones (M1, M2, M5) with 5/5
ADR-148 SOTA performance targets met.
## Modules (45 source files, 14 modules)
- types: NodeId, DroneState, Position3D, SwarmTask, SwarmError, FailSafeState
- topology: Raft consensus (leader election, log replication, quorum), Gossip, Mesh
- formation: VirtualStructure, LeaderFollower, Reynolds flocking (itar-gated)
- planning: RRT-APF hybrid planner, 3-phase coverage, Bayesian grid, pheromone
- allocation: Auction + FNN bid scorer (itar-gated)
- sensing: CsiPayloadPipeline (Live/Synthetic/Replay), MultiViewFusion, OccWorldBridge
- marl: MAPPO actor (3-layer MLP), LocalObservation (64-dim), RewardCalculator, PPO loop
- security: MAVLink v2 HMAC-SHA256, UWB anti-spoofing, geofence, Remote ID, FHSS
- failsafe: 10-state onboard machine, GCS-independent safety transitions
- config: TOML SwarmConfig with SAR/inspection/agriculture/mine/demo/wi2sar_reference
- demo: SyntheticCsiGenerator, DemoScenario (SAR/open-field/mine)
- integration: FlightController trait, MAVLink dialect (50000-50005), SwarmSim
- orchestrator: SwarmOrchestrator wiring all subsystems end-to-end
- bench_support: Criterion fixture generators
## ITAR compliance
Swarming coordination features gated behind `itar-unrestricted` feature
per USML Category VIII(h)(12). Default build compiles clean stubs.
## Benchmark results (criterion, release mode)
- MARL actor inference: 3.3 µs (target ≤ 5 ms — 1,516× headroom)
- RRT-APF planning (100 iter): 0.043 ms (target < 300 ms — 6,946× headroom)
- MultiView CSI fusion (3 UAVs): 58.5 ns (target < 10 ms — 171,000× headroom)
- 3-view localization: 1.732 m (target ≤ 2 m — beats Wi2SAR SOTA)
- 4-drone SAR coverage (400×400 m): 223 s (target ≤ 240 s — PASS)
## Tests
- --no-default-features: 73/73 passing
- --features itar-unrestricted: 85/85 passing
Closes #861
Co-Authored-By: claude-flow <ruv@ruv.net>
* refactor(swarm): rename wifi-densepose-swarm → ruview-swarm
The swarm control system is a RuView-level capability (drone coordination,
Raft consensus, MARL) that operates above the wifi-densepose sensing layer
rather than being a sub-component of it. Rename aligns with the project
identity and separates coordination infrastructure from sensing modules.
Co-Authored-By: claude-flow <ruv@ruv.net>
* fix(swarm): resolve all clippy warnings + add MARL convergence test
- planning/probability_grid: map_or(true,…) → is_none_or (clippy::unnecessary_map_or)
- planning/pheromone: &mut Vec<T> → &mut [T] on evaporate+deposit (clippy::ptr_arg)
- marl/observation: fix doc lazy-continuation warning on TOTAL line
- marl/trainer: manual Default impl → #[derive(Default)] + #[default] on Demo variant
Also adds test_marl_convergence_improves_mean_return: fills 64-transition
ReplayBuffer with mixed rewards (steps 0-31: negative, 32-63: positive),
runs ppo_update, asserts mean_return is finite and non-zero.
Result: 0 clippy warnings · 74/74 tests (default) · 86/86 (itar-unrestricted)
Co-Authored-By: claude-flow <ruv@ruv.net>
* feat(swarm): integrate Ruflo AI-agent capabilities into ruview-swarm
Adds a feature-gated Ruflo integration layer connecting ruview-swarm to the
claude-flow daemon's AgentDB, AIDefence, and SONA intelligence subsystems.
Default build is unaffected (all paths behind `Option<Box<dyn RufloBackend>>`).
## New module: src/ruflo/
- backend.rs: RufloBackend trait (9 async methods) + RufloError, MissionMemoryEntry,
PatternEntry, MavlinkScanResult types (always compiled)
- mock_backend.rs: MockRufloBackend in-memory impl for testing (always compiled, 5 tests)
- http_backend.rs: HttpRufloBackend — JSON-RPC 2.0 → claude-flow daemon localhost:3000
(gated behind `ruflo` feature, requires reqwest)
- mission_summary.rs: MissionSummary serializer with pattern description + confidence
scoring from victim recall, coverage %, collision penalty (always compiled, 3 tests)
## 4 capability areas
1. MissionMemory → memory_store / memory_search (cross-mission victim memory)
2. PatternLearner → agentdb_pattern-store / -search (HNSW SONA trajectory patterns)
3. MavlinkDefence → aidefence_is_safe / aidefence_scan (scan MAVLink before accepting)
4. IntelligenceHooks → trajectory-start/step/end (SONA learning loop)
## SwarmOrchestrator integration
- with_ruflo(backend): builder to attach a backend
- start_trajectory(task) / finish_trajectory(success, key): SONA mission lifecycle
- receive_peer_detection_checked(): AIDefence scan before accepting peer detections
## Cargo feature
`ruflo = ["dep:reqwest", "dep:serde_json"]` — optional, not in default
## Tests
- --no-default-features: 82/82 pass (8 new ruflo tests)
- --features ruflo,itar-unrestricted: 94/94 pass
Co-Authored-By: claude-flow <ruv@ruv.net>
* feat(swarm): M7 mission profiles with victim confirmation reports + pre-merge docs
Adds end-to-end mission runners producing structured MissionReport output,
and updates project docs (CHANGELOG, README, CLAUDE.md) per pre-merge checklist.
## M7 Mission Profiles (integration/mission_report.rs + swarm_sim.rs)
- MissionReport / VictimReport / SotaComparison types (serde-serializable)
- run_mission_with_report(): full mission → detailed report with per-victim
localization error, fusion uncertainty, contributing drones, detection time
- run_inspection_mission(): leader-follower power-line corridor inspection
- run_mine_mission(): GPS-denied underground (2-drone, slow, UWB-only)
- SotaComparison embeds Wi2SAR baseline (5m / 810s) vs achieved metrics
## Docs (pre-merge checklist)
- CHANGELOG.md: ruview-swarm + Ruflo integration + performance entries
- README.md: ruview-swarm row
- CLAUDE.md: Key Rust Crates table row + ADR-148 in ADR list
## Tests
- --no-default-features: 86/86 pass
- --features ruflo,itar-unrestricted: 98/98 pass
Co-Authored-By: claude-flow <ruv@ruv.net>
* fix(swarm): convergence-assist for victim fusion + 5s Ruflo HTTP timeout
Follow-up to 13b08927
2026-05-30 16:00:59 -04:00
9ad550d95f
feat(worldmodel): Candle Rust port + GCP GPU scripts (ADR-147 Phase 4+6)
...
Candle native port — wifi-densepose-occworld-candle v0.3.0:
- config.rs: OccWorldConfig (14 params matching occworld.py)
- vqvae.rs: ClassEmbedding(18→64), VQCodebook(512×512, squared-L2),
QuantConv/PostQuantConv(1×1 Conv2d), fold_3d_to_2d helpers
ResNet encoder/decoder are documented stubs (Phase 5 checkpoint pending)
- transformer.rs: full Candle MHA transformer (2 layers, temporal+spatial
cross-attention, FFN, pre-norm residuals)
- inference.rs: OccWorldCandle::dummy() + ::load() + predict()
InferenceOutput: sem_pred(1,15,200,200,16) + trajectory_priors
- 14/14 tests pass (12 lib + 2 doctests)
GCP GPU scripts — scripts/gcp/:
- provision_training.sh: a2-highgpu-8g (8×A100 40GB) for Phase 5 retraining
- run_training.sh: rsync + torchrun 8-GPU train + checkpoint download
- provision_cosmos.sh: a2-ultragpu-1g (A100 80GB) for Cosmos evaluation
- cosmos_eval.sh: run Cosmos-Transfer2.5 inference, download results
- teardown.sh: safe checkpoint download + instance delete
Co-Authored-By: claude-flow <ruv@ruv.net>
2026-05-29 20:52:51 -04:00
da40503a9e
docs(adr-147): add real CSI benchmark — 208ms median, 3.98GB VRAM, 72 frames/sec
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Real data: archive/v1 CSI proof dataset (seed=42, 3rx, 56sc, 100Hz, 1000 frames)
Pipeline: CSI amplitude → presence → ENU position → voxels → OccWorld inference
20 inference windows, no mocks.
Co-Authored-By: claude-flow <ruv@ruv.net>
2026-05-29 19:56:28 -04:00
bb7de84cb4
docs: add Phase 3+5 scripts to user guide and README world model row
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- User guide: full retrain workflow (record → vqvae → transformer → serve)
with checkpoint path usage
- README: note fine-tune capability in world model capability row
Co-Authored-By: claude-flow <ruv@ruv.net>
2026-05-29 19:50:21 -04:00
cd1c391afc
feat(worldmodel): ADR-147 Phase 3+5 — RuViewOccDataset domain adapter + retraining pipeline
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Phase 3 — scripts/ruview_occ_dataset.py:
- RuViewOccDataset: WorldGraph JSON snapshots → OccWorld (F,H,W,D) tensors
- Indoor class remapping: person→7, floor→9, wall→11, furniture→16, free→17
- Zero ego-poses (fixed indoor sensor, no ego-motion)
- record_snapshot() helper for training data accumulation
- Validated: 5 windows, (16,200,200,16) tensor, person+floor voxels confirmed
Phase 5 — scripts/occworld_retrain.py:
- record: stream WorldGraph snapshots from sensing server REST API
- vqvae: fine-tune VQVAE tokenizer on RuView occupancy (200 epochs, AdamW)
- transformer: fine-tune autoregressive transformer with frozen VQVAE
wifi-densepose-worldmodel v0.3.0 published to crates.io
Co-Authored-By: claude-flow <ruv@ruv.net>
2026-05-29 18:46:56 -04:00
28a27bbfd8
fix(worldmodel): use published worldgraph v0.3.0 instead of path dep (crates.io publish prep)
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Co-Authored-By: claude-flow <ruv@ruv.net>
2026-05-29 18:43:35 -04:00
c7ddb2d7d1
feat(worldmodel): ADR-147 — OccWorld world model integration, wifi-densepose-worldmodel v0.3.0 ( #856 )
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* feat(worldmodel): ADR-147 — OccWorld integration, wifi-densepose-worldmodel v0.3.0 (#854 )
- New crate `wifi-densepose-worldmodel` v0.3.0: async Unix-socket bridge
to OccWorld Python inference server; `OccWorldBridge`, `OccupancyGrid3D`,
`TrajectoryPrior`, `worldgraph_to_occupancy` encoder (14/14 tests pass)
- `scripts/occworld_server.py`: long-lived Python inference server for
OccWorld TransVQVAE (72.4M params); applies API-bug patches; dummy mode
for CI testing; graceful SIGTERM shutdown
- `pose_tracker.rs`: `trajectory_prior` soft-blend injection (80/20
Kalman/prior) on torso keypoint; `set_trajectory_prior()` public method
- CI: added `Run ADR-147 worldmodel tests` step
- ADR-147: accepted — OccWorld primary (209 ms, 3.37 GB VRAM, RTX 5080);
Cosmos deferred to ADR-148 (32.54 GB VRAM exceeds hardware)
- Benchmark proof: 208.7 ms P50, 3.37 GB peak VRAM, 12.1 GB headroom
Co-Authored-By: claude-flow <ruv@ruv.net>
* chore: update ruvector.db state
Co-Authored-By: claude-flow <ruv@ruv.net>
* chore: ruvector.db sync
Co-Authored-By: claude-flow <ruv@ruv.net>
* fix(cli): add missing min_frames field to CalibrateArgs test helper
E0063 in calibrate.rs:448 — CalibrateArgs gained min_frames in ADR-135
but the default_args() test helper was not updated. min_frames=0 means
'use tier default', matching the existing runtime behaviour.
Co-Authored-By: claude-flow <ruv@ruv.net>
2026-05-29 16:53:51 -04:00
2cc9f8acb3
Merge pull request #853 from ruvnet/feat/adr-136-146-streaming-engine
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RuView Streaming Engine (ADR-135..146): auditable environmental intelligence
2026-05-29 09:42:46 -04:00
d24bf36110
release: version bumps for crates.io publish (streaming-engine cascade)
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- core 0.3.0->0.3.1 (ComplexSample/CanonicalFrame/provenance + blake3 dep)
- ruvector 0.3.0->0.3.1 (ClockQualityGate)
- bfld 0.3.0->0.3.1 (privacy control plane)
- signal 0.3.1->0.3.2 (fuse_scored_calibrated/ArrayCoordinator/evolution/rf_slam)
- geo: add license/repository for first publish; worldgraph/engine pin geo version
- new: geo 0.1.0, worldgraph 0.3.0, engine 0.3.0
Co-Authored-By: claude-flow <ruv@ruv.net>
2026-05-29 09:26:38 -04:00
c60a55ca6e
docs: RuView streaming-engine v0.3.0 release notes (intro + usage)
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Introduction (auditable environmental intelligence / trust throughline), what's
new per ADR-135..146, quick-start usage for StreamingEngine, the 4 validated
acceptance paths, ~6.35us/cycle benchmark, build/test, and honest status.
Co-Authored-By: claude-flow <ruv@ruv.net>
2026-05-29 08:46:12 -04:00
95bdd37e76
bench+test: engine per-cycle benchmark + ADR-142 acceptance path
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- engine: criterion benchmark engine_cycle — full process_cycle (4 nodes / 56
subcarriers) measured at ~6.35 us/cycle, ~7800x under the 50ms (20Hz) budget.
- signal: ADR-142 acceptance test — 3 links drift 30 frames -> ChangePoint ->
VoxelMap accumulates -> low-confidence voxels suppressed -> VoxelGate
Restricted emits histogram only -> ADR-137 contradiction recorded.
Co-Authored-By: claude-flow <ruv@ruv.net>
2026-05-29 08:42:46 -04:00
020aa08049
test(sensing-server): ADR-140 live acceptance — snapshot to expired-rejection
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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>
2026-05-29 08:37:28 -04:00
5878868060
feat(signal,engine): ADR-137 calibration-mismatch contradiction + trust witness
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- signal: MultistaticFuser::fuse_scored_calibrated() threads per-node
CalibrationId; agreeing epochs → calibration_id set + CalibrationApplied
evidence; disagreeing → calibration_id None + CalibrationIdMismatch flag
(forces demotion). +2 tests.
- engine: process_cycle_calibrated() per-node calibration path; process_cycle
delegates with a uniform epoch. TrustedOutput gains a deterministic BLAKE3
witness over (provenance || class). calibration_version='cal:none' on mismatch.
- ADR-137 acceptance test: two frames + mismatched calibration -> QualityScore
contradiction -> Restricted -> calibration_id None -> witness stable. +happy path.
- 11 engine tests, signal 411+ lib tests; workspace 0 errors.
Co-Authored-By: claude-flow <ruv@ruv.net>
2026-05-29 08:35:40 -04:00
2517a16d88
feat(engine): compose ADR-138/142/143 + ADR-139 live loop
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- ADR-138: process_cycle runs ArrayCoordinator when node geometry is registered;
array contradictions (CoherenceDrop/GeometryInsufficient) fold into the
privacy demotion; DirectionalEvidence surfaced in TrustedOutput
- ADR-142: per-node mean-amplitude → EvolutionTracker; cross-link change-point
recorded as a WorldGraph Event node
- ADR-143: ingest_reflectors() runs Rf-SLAM discovery, writes stable
Wall/Furniture reflectors as ObjectAnchor nodes
- ADR-139 live loop: update_person_track(), apply_active_privacy_mode()
(PrivacyRollup suppresses person_track under identity-strict modes),
snapshot_json()
- Acceptance test live_frame_to_reload_same_contents: full path
fusion->worldgraph->privacy_rollup->persist->reload->same contents, no raw RF
- 9 engine tests; workspace 0 errors
Co-Authored-By: claude-flow <ruv@ruv.net>
2026-05-29 08:31:05 -04:00
2eada40e3b
feat(engine): integrate ADR-135..141 into an end-to-end trust pipeline
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- signal/calibration.rs: BaselineCalibration gains calibration_id()/
calibration_uuid()/apply() — the ADR-135->136 link that stamps
FrameMeta.calibration_id (deterministic id, no serialization change). +1 test.
- NEW crate wifi-densepose-engine: StreamingEngine::process_cycle() composes
fuse_scored (137) -> calibration provenance (135/136) -> privacy demotion on
contradiction (141) -> WorldGraph SemanticState with mandatory provenance +
DerivedFrom edge (139). Returns TrustedOutput (the trust chain made concrete).
- Validates the throughline: every output names evidence + model + calibration
+ privacy decision; calibration_id flows input->QualityScore->provenance;
contradiction demotes class; deterministic; privacy mode attested.
- 4 integration tests; workspace 0 errors; signal 410 lib tests pass.
Co-Authored-By: claude-flow <ruv@ruv.net>
2026-05-29 08:21:48 -04:00
ruv