A-B-A capture from the overnight run, ten 30 s windows per arm: pairing
fraction rises 0.5668 -> 0.7287 (Welch t=13.26, p<0.0001) when the fleet is
filtered to one transmitter, but paired observations/s falls 44% because
tx/s falls 56%. Fraction was the wrong figure of merit; not adopted.
Firmware changes (persistent on-node log, live filter_mac, and the node-3
pilot fixes) that produced this capture are held back pending a port against
main's restructured firmware/main/ tree (config_api.c and ota_update.c are
gone there).
Claude-Session: https://claude.ai/code/session_01PVWMiHQifoYXL7uL3bphrZ
Co-authored-by: Joe <joe@GitDev.hedgehog>
Co-authored-by: claude-flow <ruv@ruv.net>
scripts/train-ruvllm.js ran two separate computations during contrastive
pretraining: ContrastiveTrainer.train() (a baseline pass that computes loss
but never updates the encoder) and a hand-written loop that actually applies
gradient updates to encoder.w2. The exported training-metrics.json took
lossHistory and contrastive.durationMs from the first (no-op) pass instead
of the second (real) one, so the published curve was flat/meaningless
(~0.1352 for all 20 epochs) and the duration was ~30x too short.
This did not affect finalLoss/improvement or the held-out temporal-triplet
accuracy metric published for this model on HuggingFace
(ruvnet/wifi-densepose-pretrained) -- those already came from the real loop.
It only affected the diagnostic curve/duration anyone auditing convergence
from the published JSON would see.
Also: config.json's training.steps field (epochs * triplets, technically
consistent but reads like an optimizer-step count) is now reported as
separate epochs/tripletUpdatesPerEpoch fields, and the exported version
bumped 1.0.0 -> 2.0.1 to match the "v2" naming already used elsewhere for
this file set.
Verified by re-running against the real capture
(data/recordings/overnight-1775217646.csi.jsonl): lossHistory now shows a
genuine decreasing curve (0.1037 -> 0.0654 across 20 epochs, matching the
already-published finalLoss exactly), durationMs reports the real ~40s
gradient-loop time instead of ~12s.
Model card updated with the same disclosure (docs/huggingface/MODEL_CARD.md)
plus a real, previously-undisclosed EWC forgetting-rate figure (32.97%,
present in training-metrics.json but not surfaced in the human-readable
card) and a corrected repro-script pointer.
Records why device-free position kept failing here, measured rather than
argued, and the join key built in response. The implementing PRs land without
this and are harder to read for it.
**Phase is unusable on this hardware.** The bistatic tier needed a signed
per-link radial velocity, which requires the common-mode component of CSI
phase. Measured across three ESP32-C6 nodes over ~40,000 frames, the
frame-to-frame change in common-mode phase is statistically indistinguishable
from uniform random: resultant length R = 0.011-0.032 against a 0.000
reference, sd 1.78-1.81 against pi/sqrt(3) = 1.814, no lag-1 autocorrelation.
A second, unwrap-free estimator agreed.
The mechanism is packet-detection timing quantization. Common-mode phase is
-2*pi*f_c*tau, and one ADC sample of jitter at 20 MHz is about 758 rad -- some
120 full wraps, re-randomized every packet. Not calibratable. The bistatic tier
is default-off as a result.
**Amplitude works but had no parallax.** The three AP->node links leave a single
distant transmitter inside a 36.5 degree fan; scalar per-node energy weighting
closed with a clean negative result -- magnitude responds to proximity,
direction does not.
What follows from both: a position tier needs several transmitters seen from
several receivers, and fusing those requires knowing that two nodes observed
the SAME transmission. Arrival timestamps cannot say so -- the sink sees network
jitter, not capture time -- and the per-node sequence counter is private to each
node. Hence (tx_mac, rx_seq): the 802.11 sequence control field is assigned by
the transmitter and is identical at every receiver, so it names one
transmission without any coordination between receivers, no shared clock and no
negotiation.
Status is Proposed, deliberately. The measurement layer is implemented and
validated on hardware; localization is NOT built, and the ADR says so along
with the rest of its known limitations -- peer links are ~10x weaker than data
frames, three links is below the threshold for tomographic localization
regardless of quality, and no position tier consumes the link table.
References only ADR-018, ADR-060 and ADR-110, all already present here.
Co-Authored-By: claude-flow <ruv@ruv.net>
Claude-Session: https://claude.ai/code/session_01BHNNWbmKvxXrVaEDwXEBNo
The mesh gate uses mesh-time buckets only while c6_sync_espnow_is_valid(); an
unsynced node falls back to the elapsed gate. Three of nine nodes never
synchronised during these runs, so for those three the mesh and mesh+seq arms
silently ran the elapsed gate.
+0.5 pp is therefore not a clean fleet-wide measurement of window alignment.
Reported as observed rather than reconstructed: the arms were assigned before
we understood the fallback, and a post-hoc per-node reanalysis would not be the
experiment we ran.
Names the cause as environmental rather than a defect. Sync is leader-elected
and hub-and-spoke, so whether every node hears the leader is a property of the
building -- and a house that fragments into sync domains degrades any
shared-time option quietly, for exactly the nodes whose data you would most
want.
Co-Authored-By: claude-flow <ruv@ruv.net>
Claude-Session: https://claude.ai/code/session_01BHNNWbmKvxXrVaEDwXEBNo
How a node rate-limits CSI callbacks decides whether cross-node fusion works at
all, because it decides whether two nodes keep the SAME frames or merely the
same NUMBER of frames. We measured three gates on a nine-node ESP32-C6 fleet as
a 2x2 factorial, three Latin-square blocks, every arm verified live on every
node.
Selecting frames by 802.11 rx_seq roughly doubled cross-node pairing over the
elapsed-time gate: 0.5605 against 0.2434, +31.7 pp, reproduced in all three
blocks at ~70x the measured drift floor.
Two results we did not want and are reporting anyway:
- Mesh-time alignment -- a gate we built and expected to be the answer -- bought
+0.5 pp, about one standard deviation of noise. Indistinguishable from doing
nothing.
- We predicted rx_seq selection would win most on node pairs spanning a broken
sync domain. Per-pair yield says the opposite: never-synced nodes gained
LEAST. So it is not a workaround for broken time sync, and repairing our mesh
split would probably not have closed the gap.
The document leads with the method rather than the numbers, because the numbers
are one house. It records the three mistakes that made an earlier attempt
unreadable -- comparing different node subsets, not verifying the gate was
actually live, and using frame rate as the check that a gate is working when a
bucket gate makes frame rate nearly insensitive to it -- and the null run that
establishes the noise floor without which none of the contrasts can be judged.
run_arms.py is included so a reader can measure their own environment instead
of trusting ours. That is the point of the contribution; our figures are the
worked example.
Stated as a trade, not a win: pairing is bought with raw frame rate. On-device
edge processing was unaffected here (separately rate-limited), so the cost fell
on the uplink -- but whether that is acceptable depends on what consumes the
stream.
Co-Authored-By: claude-flow <ruv@ruv.net>
Points v2/crates/ruforecast at ruvnet/RuForecast@a802067f, the tip of
fix/governance-and-spend-enforcement (ruvnet/RuForecast PR #2), which closes
14 code-review findings against the training stack merged as #1766 -- most
critically, ADR-349's governance-verification and spend-approval-record
requirements were specified but never actually enforced by the fal.ai
hosted path; they are now.
Adds ADR-354 recording this as a RuView-level integration decision and
pointing to the submodule's own ADR-350 for the full technical record,
since that repository is now the source of truth for the forecast
subsystem's decision history.
Depends on ruvnet/RuForecast#2 merging with green CI first; see that PR for
status before merging this one.
Trains a real fal.ai LoRA fine-tune (fal-ai/flux-lora-fast-training) on
frames extracted from the real fal.ai-generated room-visualization MP4s
already captured this session, so display-layer generation has a specific
trained style target instead of prompt engineering against a stock model.
Architecture boundary: this model only ever produces supplementary
synthetic visual/pixel output. It has no path into measured/derived RF,
pose, vitals, identity, or confidence values, which remain local and
authoritative.
Real run: request 01a060b2-c940-7972-abf9-ea48847eb2ae, 1000 steps, 27
training images, ~$2.04 spend, validated with a real inference call
against the trained LoRA (assets/fal-visual-teacher-sample.png).
Chronos-Bolt (base/small), Chronos-T5-small, and TimesFM-2.5-200M run
zero-shot (no fine-tuning) on the same BIDMC cross-entity test windows,
baselines, and WQL/MAE metrics as the prior from-scratch RuForecast
BIDMC result. Unlike every from-scratch RuForecast configuration tried
this session, all four pretrained checkpoints beat both last-value and
seasonal-naive baselines on WQL, on both independent patient-partition
judges (12-42% margin), consistent with the massive-pretraining-corpus
hypothesis from the prior literature pass.
* feat(forecast): add independent Rust training stack
Signed-off-by: Codex <codex@openai.com>
* docs(forecast): record informal HPO exploration note (unaccepted)
Records an exploratory Darwin Mode numeric-genome hyperparameter search
against the tiny_ci synthetic accuracy protocol: WQL improved 0.257 -> 0.161
-> 0.153 over two search rounds, staying ahead of both baselines throughout.
Explicitly scoped as informal/exploratory (not a frozen leakage-free report,
not eligible for the evidence ledger, not validated for large_linux or any
real dataset) per this doc's own evidence-tagging discipline.
* feat(forecast-core): add per-horizon weighted quantile loss
weighted_quantile_loss collapses the whole horizon into one aggregate
number, hiding whether error grows with lead time. Add a sibling
function using the identical per-cell pinball formula and domain
checks, just reduced per horizon step instead of globally. A unit
test reconstructs the aggregate from the per-step numerators/
denominators to prove the two functions agree exactly, not just
approximately.
* feat(forecast-model): add evaluation-only local activation
Adds activate_for_evaluation and build_eval_input, gated behind the
cpu feature. activate_for_evaluation self-signs an unsigned candidate
with a fixed, publicly-known, non-secret Ed25519 key so a CLI operator
can run inference against their own just-trained candidate without a
real release signature -- explicitly never a production trust path,
documented as such in the module doc comment. The schema-digest check
in ArtifactActivationPolicy still applies in full; only signing is
relaxed. build_eval_input constructs a single-window ModelInput for
CPU inference from raw context-major values/mask arrays, matching the
training batch builders exact time-feature/descriptor encoding.
Six unit tests cover: malformed-candidate rejection, digest-mismatch
rejection (proving the schema check has teeth even on this relaxed
path), getting past the policy gate with a matching digest, and
eval-input shape validation.
* feat(forecast-train): add evaluate and prepare-synthetic-dataset CLI
Turns this sessions throwaway HPO scripts into real, tested CLI
surfaces, replacing two example binaries that bypassed the CLI/TOML
training path entirely.
prepare-synthetic-dataset generates a larger, configurable synthetic
training shard (default 24 windows) plus a matching train-local.toml
with the given OptimizerSpec hyperparameters baked in, and a separate
held-out test.jsonl -- the same synthetic-only, local-validation-only
posture as prepare-local-example, just bigger and configurable.
evaluate scores a trained (unsigned) candidate against held-out
windows and the LastValue/SeasonalNaive baselines: overall weighted
quantile loss, a per-horizon breakdown, 80% interval coverage, and
missingness, for the model and both baselines. Explicitly lists what
docs/benchmarks/ruforecast.mds full accuracy protocol additionally
asks for that this does not cover (abstention coverage, selective
risk, site/device slices, interference regime, RuVector-retrieval
ablation) rather than silently omitting them -- each needs
infrastructure a single-entity synthetic fixture does not have.
Together the three real commands (prepare-synthetic-dataset,
train-local, evaluate) reproduce the exact WQL numbers the old
library-bypassing example scripts produced for the same
hyperparameters, confirmed by re-running the harness/ruview HPO
dry-run end to end against the new CLI path.
New integration test trains a real candidate via smoke, evaluates it
against held-out synthetic windows, and asserts the full report shape
and an empty-input rejection.
* feat(forecast-train): add prepare-synthetic-dataset --seed + Autogenous bridge
--seed (default 0, backward compatible) makes prepare-synthetic-dataset's
generator deterministic per offset: same seed -> byte-identical corpus
across runs (needed to train two genomes on the SAME corpus for a fair
comparison); different seeds -> genuinely independent corpora (needed for
honest multi-seed/multi-judge evaluation upstream, both in
harness/ruview/flywheel/ruforecast/gate.mjs's fitness function and in the
new v2/crates/ruforecast-autogenous-bridge crate added here).
ruforecast-autogenous-bridge is a LOCAL-DEV-ONLY (excluded from the v2
workspace, publish = false) bridge from this crate's real evaluate CLI
into ruvnet/autogenous's new regression-candidate promotion path (separate,
unpushed branch feat/regression-candidate-kind): runs N independent
train+evaluate judges on genuinely distinct synthetic corpora, signs
receipts, and gets a real cryptographically-verified PROMOTE/REJECT
decision -- defense in depth on top of, not a replacement for, Darwin's own
promotion gate. Path-depends on a sibling autogenous checkout that does not
exist in CI; build/run directly via `cargo build --manifest-path
crates/ruforecast-autogenous-bridge/Cargo.toml`.
cargo test -p ruview-forecast-train --no-default-features --features
cpu,cli: all green (unaffected existing tests + this backward-compatible
addition).
* docs(forecast): retract round-2 HPO result, record honest multi-seed finding
The earlier "Informal HPO exploration note" claimed a real improvement
(WQL 0.257 -> 0.153) from a 3-round hyperparameter search. That search
evaluated every candidate against one fixed synthetic corpus (seed 0) for
every round -- textbook overfitting. Independent verification against two
fresh corpora (via the new ruforecast-autogenous-bridge crate) showed the
"winner" losing to the baseline on both.
Fixed the root cause in harness/ruview/flywheel/ruforecast/gate.mjs:
candidates are now scored against three independent corpora, worst-case
across them, not one fixed corpus.
Re-ran the search under the fix. It found a new winner that genuinely beat
baseline on all three of its own search seeds -- and that winner ALSO lost
independent verification on fresh seeds. Two independent search rounds,
pre- and post-fix, both produced an illusory "winner." The honest reading:
at this dataset scale (24 synthetic windows) held-out WQL varies enormously
by which corpus is drawn, regardless of hyperparameters -- confirmed
directly by the baseline genome's own primary swinging from 0.83 to a full
regression across the three fixed search seeds with unchanged
hyperparameters. No RuForecast hyperparameter configuration has been shown
to reliably beat the trivial baselines out-of-sample at this scale.
Also fixes a real design inconsistency surfaced by this exploration: the
Autogenous regression-candidate promotion verifier
(envelope::regression::verify_regression_promotion, separate unpushed
ruvnet/autogenous branch feat/regression-candidate-kind) required all
judges to share one corpus_id, which conflicts with this kind's
intentional cross-corpus judge design. Corrected there (commit bfa4c48);
did not change either REJECT verdict, which were already driven by the
real NotBetterThanParent signal on their own.
Append-only: the original round-2 row is kept, read together with this
new amendment section, per this doc's own evidence-ledger discipline.
* refactor(forecast): extract RuForecast to ruvnet/RuForecast submodule
Mirrors the v2/crates/worldgraph pattern: RuForecast becomes its own
independent public repo/workspace (ruforecast-core/model/train), mounted
at v2/crates/ruforecast as a git submodule, with v2/Cargo.toml path-depping
into its sub-crates and excluding the submodule from the v2 workspace.
Full v2 --workspace check passes; the ruforecast CLI builds and runs
correctly from its new location. Real git history for the extracted
crates (7 commits) was preserved via git-filter-repo, not squashed.
* feat(autogenous-bridge): real-data independent verification tool
real_data_verify.rs: signs two real, temporally-independent judge
measurements (different train/test split boundaries on the same real
household vitals corpus, not synthetic seeds) through the real
Autogenous regression-candidate promotion path, and gets a genuine
cryptographically-checked PROMOTE/REJECT verdict.
Result recorded (see docs/benchmarks/ruforecast.md): REJECT. One judge
nominally beat the trivial baseline but by less than the 0.01
non-inferiority margin; the other judge lost outright. Consistent with
every synthetic-data search this session -- no configuration has yet
been shown to reliably beat trivial baselines out-of-sample, now
including a real 6390-sample household corpus.
Bumps the ruforecast submodule to pick up the evaluate real-gap fix.
* docs(forecast): record real-household-data result (REJECT, signed)
6390 real vital-signs samples, two independent temporal splits,
independently verified via the real Autogenous regression-candidate
promotion path. Signed verdict: REJECT -- neither split cleared the
non-inferiority margin. Same conclusion as every synthetic search this
session, now confirmed with real data too: more real data is the
credible next lever, not further search on this scale of fixture.
* chore(forecast): point ruforecast submodule at published main (post gap-tolerance fix)
* ci(forecast): fix ruforecast-ci.yml for the new v2/crates/ruforecast submodule layout
The RuForecast crates were extracted into a standalone submodule (v2/crates/ruforecast, mirroring the existing v2/crates/worldgraph pattern) and are no longer members of the v2 workspace. The CI workflow still referenced the old in-tree package names/paths (ruview-forecast-core/model/train under v2/), which broke every forecast job with "cannot specify features for packages outside of workspace".
Fixed every job to target the submodule: working-directory -> v2/crates/ruforecast, -p ruview-forecast-* -> -p ruforecast-*, all hardcoded Cargo.toml/source paths in the Python assertion and clean-room-scan blocks, the Swatinem/rust-cache workspaces input, the artifact-tree output paths, and the two levels of relative path (../ -> ../../) in the informational-benchmark steps whose working directory moved one level deeper.
Also collapsed the paths: trigger filters internal-crate globs (v2/crates/ruview-forecast-*/**) down to the single v2/crates/ruforecast path, since GitHub Actions path filters only ever see a submodule gitlink change as one entry in the parent tree, never its internal file paths -- the old globs could never have matched anything.
Includes a real cargo fmt fix (submodule commit d9902f9) for formatting drift left over from the earlier real-data gap-tolerance fix, which the contract job's fmt --check step would otherwise have failed on.
All four forecast CI jobs' real commands verified locally before push: cargo test/check for CPU, CUDA/WGPU compile-check, the contract job's Python assertion block, the hosted-boundaries clean-room scan, and cargo fmt --check.
* docs(forecast): record real BIDMC cross-entity holdout result (REJECT)
Two independent real-patient partitions (34/16 and 24/26 splits, 53 total
ICU patients from PhysioNet BIDMC, Open Data Commons Attribution License
v1.0) both show the model losing to trivial baselines by a decisive margin
(+69% and +601% worse WQL). First genuine cross-entity real-data test this
project has run; same conclusion as every prior synthetic and single-household
test. Full Autogenous signed verification was not run for this entry -
flagged explicitly in the note.
* chore(forecast): point ruforecast submodule at published main (example feature-gate fix)
* chore(forecast): point ruforecast submodule at published main (clippy fix)
---------
Signed-off-by: Codex <codex@openai.com>
ADR-262 §8 question 5 left the second modality open, asking whether it should
be rvcsi. This answers ultrasonic instead, because the cost collapsed:
rufield-adapters now ships UltrasonicReplayAdapter, the first adapter for
Modality::Ultrasonic (registry code 7, empty since v0.1), which parses,
validates and signs BatVu range profiles upstream. RuView only has to decide
what it will put on a wire.
Bumps vendor/rufield 43b1df3 -> 9955672. Two struct literals in bridge.rs gain
fields added upstream (Observation: track_id, attributes, identity_evidence,
channel_sounding_provenance; SensorDescriptor: coordinate_frame, position_m,
orientation_xyzw). All left empty, each for a stated reason rather than a
convenient default — the pose fields in particular, because a CSI link has no
boresight and §6 makes no validated room-coordinate claim. The nine existing
P1 gates pass unchanged.
The decision this module makes is structural rather than a runtime refusal.
The adapter's full per-bin frame is P0 and would be dropped by the egress gate
after all the work of parsing and signing it; its 32-bin coarse reduction is
P1 and egress-safe. So the module does not offer the choice — it configures
the coarse mode, because a consumer cannot un-coarsen a coarse profile whereas
a check can be reordered. The gate still runs and is asserted to drop nothing.
12 gates in tests/ultrasonic_gates.rs, including the honest negative result: an
ultrasonic scan produces no fused inferences at all, and both independent
reasons are pinned. The adapter declines to populate `presence` — one
transducer pair cannot distinguish a person from a coat over the back of a
chair — and the engine's feature vocabulary is entirely statements about a
body, so range_m has nothing to drive.
The fixture is BatVu's own emitter output, byte-identical to the one in
ruvnet/rufield, so schema drift fails a build in one of three repositories
rather than an ingest in a deployment.
Not wired into the running server; P1 shipped as a library before P3 wired it
in, and this follows the same staging.
Use mesh-aligned capture timestamps, remediate Rust advisories, harden the audit gate, and correct deployment claims. Includes the live MQTT subscriber lifetime fix verified against Mosquitto.
New leaf crate v2/crates/ruview-offaxis implementing ADR-324's projection
core from the published math only (Kooima 2008 generalized perspective
projection; Casiez 2012 one-euro filter) — no code from the unlicensed
prior-art repo. Dependency-free native core; wasm-bindgen surface gated to
wasm32 (cdylib+rlib, ~54 KB wasm after bindgen).
- projection: Screen (3 corners, any orientation) + off_axis() -> typed
errors, never NaN matrices; column-major f64 (three.js Matrix4 layout).
Tests pin the defining invariants: screen corners -> NDC corners for a
grid of eye positions and tilted screens; screen-plane points are
eye-invariant; centered eye reduces to the symmetric frustum; near/far
map to NDC -1/+1.
- filter: one-euro with injected timestamps (no clock in crate);
monotonicity/convergence/NaN-rejection tests.
- rf: field-peak extraction mirroring field_localize.rs constants
(X_SCALE 0.6, Z_SCALE 0.5, PEAK_THRESHOLD 0.35) and the Tier B
coarse-parallax stage (deadband, gain, hard clamp) so over-claiming is
impossible at the API level. No accuracy numbers asserted.
- wasm: OffAxisCamera + RfParallax bindgen classes; per-frame updates hold
last good state instead of throwing.
- benches (criterion): full Tier B frame ~598 ns; argmax scan optimized
-18%/-33% (20x20/100x100). MEASURED table + reproducer in README.
- examples/three.js/demos/07-off-axis-window.html: demo with SYNTHETIC
mouse simulator and labeled RF Tier B mode ('coarse body parallax - not
head tracking'), physical calibration panel, /ws/sensing input, and a
build-instructions overlay when the local pkg/ output is missing
(generated artifacts stay uncommitted; .gitignore entry added).
- Validated 23 unit tests + doctest, clippy clean, wasm32 release build,
Node smoke test of the bindgen output, and a headless-Chromium run of
the demo (engine load, eye response, mode labels, ws failure path).
- ADR-324: header + section 2.5 amendment recording the Rust/WASM core.
Co-Authored-By: claude-flow <ruv@ruv.net>
Claude-Session: https://claude.ai/code/session_01BU3NcEgTpAVvu5QGtw4czT
Deep-research ADR answering whether icurtis1/off-axis-sneaker can be used
with RuView. Adopts the head-coupled perspective technique (Kooima
generalized off-axis projection) via a clean-room implementation — the
upstream repo is unlicensed, so no code or assets are reused. Defines a
tiered integration: webcam-fine tracking with RF presence gating and
multi-person arbitration (Tier A), an explicitly labeled RF-only coarse
body-parallax mode (Tier B), and evidence-gated future metric RF head
positioning (Tier C). No server changes; existing /ws/sensing and
/api/v1/stream/pose streams only. Indexed in the ADR README.
Co-Authored-By: claude-flow <ruv@ruv.net>
Claude-Session: https://claude.ai/code/session_01BU3NcEgTpAVvu5QGtw4czT
Fix breathing confidence, Windows desktop launch and clean UI builds, contributor-harness line-ending integrity, runtime secret hygiene, and remove the six ADR-299 CSI/person-data files from the current tree.
Reviews and fixes RuView's 10 most recent substantive issues, plus closes
the wiring gap flagged in the perception-substrate review (docs/adr,
gist, release notes from the PR #1579 review).
## Fixed
- #1526: UI falsely claimed "LIVE — ESP32 Hardware Connected" whenever the
unauthenticated /api/v1/status probe 401'd. Now sends the bearer token
on the probe (the fallback direction was already fixed by ADR-295).
- #1554: top-level `classification` (GET /api/v1/sensing/latest) was the
last UDP packet's single node, not the fused room aggregate, so it
flapped at packet rate with 2+ disagreeing nodes. Now derived from
`RoomInference` (ADR-297's `fuse_room`) at both call sites.
- #1541: per-node MQTT `presence`/`presence_score` read a `classification`
JSON key that does not exist on `NodeInfo` (the real field is
`node_inference`), silently falling back to the room aggregate for
every node — reproducing the exact "lockstep publish" the field report
measured. Also fixed the *existing* regression test for this bug,
which used the same wrong key in its own fixture and so never caught it.
- #1557: pose-fusion's `wsPortMap` only knew port 3000, so a remapped host
port fell through to `localhost:8765` (nothing there for a remote
viewer). WS port is now derived from `location.port` instead of a
2-entry lookup table (the "never render simulated as live" half was
already fixed by ADR-295's `onVerifiedFrame` gate).
- #1525: the no-model pose path already clamps keypoint confidence to a
0.1 floor, but the renderer's own threshold is also 0.1 compared with
`<=`/`>` — a keypoint at exactly the floor was still invisible. Floor
raised to 0.15 to clear the client's gate.
- #1556: `--mqtt-ca-file`/`--mqtt-client-cert`/`--mqtt-client-key` were
parsed and stored but never applied — TLS always used the system trust
store, so a self-signed broker always failed UnknownIssuer. Now builds
`rumqttc::TlsConfiguration::Simple` from the real PEM files (no new TLS
dependency needed). A file that can't be read logs why and falls back
to system trust instead of failing opaquely later.
- #1555: the MQTT availability heartbeat asserted "online" for every known
node on a fixed 30s timer regardless of whether that node's data was
still arriving. Now tracks each node's last-seen broadcast snapshot and
only reports "online" within a 10s freshness window, otherwise
"offline" — a frozen sensor can no longer look available. (The other
half — restarting a publisher that goes permanently silent — needs a
reproduction the reporter themselves weren't certain of; left for a
follow-up rather than guessing at the trigger.)
- #1540: already fixed on main (node-keyed RateLimiter, ADR-297).
- #1521/#1522: not fixable in this repo (published HF model artifact);
replied with the ADR-298 gate status and the exact byte-level fix for
the safetensors header, and corrected the README row that claimed the
file loads with the reference loader.
- #1542, #1527: replied — #1542 is a real, larger firmware+server feature
left open for follow-up; #1527's suggested fixes were already applied,
the one residual sample is an inherent first-frame paint gap.
## Certificate spine wiring (closes the gap flagged in the PR #1579 review)
`ruview-certify` and `ruview-policy` now depend on `ruview-ood` and provide
real `From<ruview_ood::DomainState>` adapters plus a composed entry point,
`ruview_policy::authorize_from_certificate`, matching the adapter contract
`ruview-policy`'s own doc comment already described but that no code
actually implemented. A new cross-crate integration test
(`acceptance_test_b_real_integration`) mints a real signed
`CapabilityCertificate` and proves a real post-drift `ruview_ood::Unknown`
denies a `SafetyCritical` action through the composed pipeline — not two
disconnected unit tests hand-setting the same enum value.
Also:
- Wires `evaluate_linear_head` (ADR-298 model-release gate) into a new CI
job so the checker itself can't silently regress; documents that gating
an actual model publish is still a manual step (no HF automation here).
- Wires `SourceState::export_watermark()` into `start_recording`: a
recording captured while the source is synthetic is now stamped in its
metadata (not the filename or per-line JSON, to avoid breaking
`delete_recording`'s path reconstruction or the training dataset
loader's schema).
- Updates docs/user-guide.md's "Developer Preview" section to describe
what's now genuinely wired vs. still not (no live continuous
calibration/OOD loop in the running server yet).
## Validation
- cargo test --workspace --no-default-features: 4391 passed, 0 failed
- cargo build --release -p wifi-densepose-sensing-server --features mqtt:
clean
- Server smoke-tested end-to-end against the simulator (real startup,
UDP/WS/HTTP listeners, /api/v1/sensing/latest stable across calls)
- Real ESP32-S3 hardware was NOT reachable this session (no COM port
present, zero UDP frames received after 40s bound to 0.0.0.0:5005) —
the multi-node fixes are validated by the new unit/integration tests
and full-workspace regression, not by live hardware.
Co-Authored-By: claude-flow <ruv@ruv.net>
Documents the 9 new spine crates (calibrate -> certify -> govern) as a
composable library, explicit about current status: each crate is tested in
isolation but not yet wired together or into the live sensing-server
request path (DomainState is 3 separate enum types across
ruview-ood/certify/policy with no automatic bridge). Contrasts against the
two ADR-292-296 remediation items that ARE genuinely enforced today: UDP
source allowlisting (checked on every packet) and the CSI data-policy CI
guard.
The higher-ceiling primitives on the fused world state. Six crates, all
deterministic SYNTHETIC/L0 model scaffolds (a twin predicts, it never measures);
70 tests + 6 doctests, verified green independently.
ruview-twin (ADR-312): per-deployment RF twin — radio geometry, a documented
synthetic log-distance + wall-attenuation propagation model, per-link expected
distributions, and the load-bearing delta(observed,expected) that localizes a
physical change (moved node / new reflector) to specific links. 8 tests.
ruview-infogain (ADR-311): Value(sensor) = expected uncertainty reduction /
weighted cost; pure bounded-greedy selection under a multi-dimension budget;
unknown-value candidates handled explicitly (defer/probe, never silent zero). 15.
ruview-active (ADR-306): closed-loop control vocabulary (channel/bandwidth/
cadence/antenna as validated ranges); step() proposes the next measurement to
reduce uncertainty, widening exploration when the last response is UNKNOWN;
emits a plan, never RF. 13.
ruview-placement (ADR-305): floorplan + inventory -> ranked placement via the
twin's propagation model; blind-spot flags; predicted-vs-observed adjustment. 11.
ruview-memory (ADR-309): learns per-zone normal physics; anomalies are
significant deltas vs baseline emitted as evidence records; UNKNOWN before a
baseline exists (no false positives). 14.
ruview-counterfactual (ADR-310): scores hypotheses under the twin — empty-room
vs occupied, one person vs two; UNKNOWN when indistinguishable. 8.
Flips ADR-305/306/309/310/311/312 to implemented. Completes all three phases of
the ADR-297 perception-substrate program. No hardware/MEASURED claims.
Co-Authored-By: claude-flow <ruv@ruv.net>
Claude-Session: https://claude.ai/code/session_015TcKegTS7QqhWPC2L2SzaS
The layer that turns the certificate spine into a modality-agnostic perception
substrate. Four crates, all deterministic and green independently (43 tests).
ruview-hal (ADR-317): one abstraction mapping any modality (CSI/802.11bf/BLE/
UWB/mmWave/acoustic/camera/lidar/IMU/custom) to a canonical ontology Observation.
SensorHal trait + two SYNTHETIC/L0 reference adapters; malformed input yields a
degraded UNKNOWN observation, never a panic; synthetic can never alias measured.
8 tests.
ruview-groundtruth (ADR-300): reference sensors as a formal VALIDATION plane
(never an estimator input, enforced by the type boundary); modality-agnostic
ReferenceSeries, deterministic cross-correlation alignment, AgreementReport with
mandatory SessionScope, emitting per-context ruview-evidence records; Measured
requires reference + coverage + reproducer. 15 tests.
ruview-track (ADR-304): privacy-preserving persistent tracks (opaque person ids,
coarse non-reversible features, no civil-identity binding); ambiguous detections
stay tentative rather than misassigned; cross-zone hand-off. 8 tests.
ruview-fusion (ADR-308): multiple HalObservations -> one probabilistic WorldState,
uncertainty-aware (confidence-weighted, not naive averaging); irreconcilable
conflict or insufficient coverage yields UNKNOWN, not a confident average.
9+ tests incl. irreconcilable_conflict_yields_unknown.
Flips ADR-300/304/308/317 to implemented; registers the four crates as workspace
members. SYNTHETIC/L0 throughout; no hardware/MEASURED claims.
Co-Authored-By: claude-flow <ruv@ruv.net>
Claude-Session: https://claude.ai/code/session_015TcKegTS7QqhWPC2L2SzaS
Elevates decision policy (action authorization) into phase 1 (ADR-318): a
fail-closed gate that authorizes a governed action only when the capability
certificate class, freshness, uncertainty ceiling, evidence floor, and live
domain state (KNOWN, not DEGRADED/UNKNOWN) all satisfy the action class —
convenience vs security vs safety-critical. UNKNOWN denies high-assurance
actions; every decision is the terminal witness-chain stage.
Amends ADR-297 with: the epistemic-reliability pipeline as the product thesis;
four non-negotiable rules (UNKNOWN first-class; certificates bind
cryptographically; one canonical downstream semantics; benchmarks expose
worst-domain + CIs); the certificate-staleness guard (certificates conditional
on a continuously evaluated domain signature; VALID->DEGRADED->UNKNOWN
auto-degrade triggers recalibration); the three-primitive commercial framing
(Runtime / Certify / Trust-Fleet); and acceptance test B (drift invalidation
before a false inference reaches an actuator).
Co-Authored-By: claude-flow <ruv@ruv.net>
Claude-Session: https://claude.ai/code/session_015TcKegTS7QqhWPC2L2SzaS
Frames the calibration/evidence/trust/deployment layer as a 20-primitive
phased program with a dependency DAG and phase assignments. Phase 1 is the
certificate spine (spatial ontology, authenticated identity, witness chain,
calibration cert, OOD gating, evidence engine, capability certificate,
multi-domain benchmark scorecard) — the acceptance test decomposed and
buildable without new hardware. Phase 2/3 are integration and research-forward
primitives. Child ADRs 298-317 follow.
Co-Authored-By: claude-flow <ruv@ruv.net>
Claude-Session: https://claude.ai/code/session_015TcKegTS7QqhWPC2L2SzaS
Turns the review's code-implementable P0/P1 items into ADRs: source
provenance state machine (synthetic never presents as live), UDP data-plane
bind hardening (loopback default + allowlist, step one), multi-node semantic
correctness (per-node inference, node-keyed rate limiter, stale state), model
release sanity gates (block degenerate/mislabeled heads), and CSI data-incident
repo controls. Also fixes the stale .gitignore rule so the active recordings
directories and CSI globs are covered going forward.
Tree removal of existing recordings, history rewrite, and withdrawal of the
published presence head are gated on maintainer sign-off (outward-facing /
destructive) and intentionally not done here.
Co-Authored-By: claude-flow <ruv@ruv.net>
Claude-Session: https://claude.ai/code/session_015TcKegTS7QqhWPC2L2SzaS
Adds wifi-veil (pinned git rev, dependency-free, MIT/Apache-2.0) to the
workspace and gates it behind a new 'veil' feature in wifi-densepose-bfld.
The bfld::veil module exposes deterministic attacker-vs-protector shield
assessments (re-ID collapse, throughput ratio, energy-conservation audit)
with a mandatory SYNTHETIC/L0 evidence label. Advisory only: no RF
emission, no frame mutation, BFLD invariants I1-I3 untouched. Default
build unchanged; 6 new feature-gated tests pass.
Co-Authored-By: claude-flow <ruv@ruv.net>
Claude-Session: https://claude.ai/code/session_015TcKegTS7QqhWPC2L2SzaS
