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59 Commits
| Author | SHA1 | Message | Date | |
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 ruv
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4557f6f614 |
docs+plugins: rvAgent + RVF agentic-flow integration exploration
Land the rvAgent (vendor/ruvector/crates/rvAgent/) integration research
dossier and update both the Claude Code and Codex plugins so future
operators have a discoverable entry point for prototyping agentic flows
on top of RuView's existing sensing pipeline + RVF cognitive containers.
Added:
- docs/research/rvagent-rvf-integration/README.md
Full integration thesis: rvAgent's 8 crates + 14 middlewares share
RVF as their state-persistence format with RuView's existing
v2/crates/wifi-densepose-sensing-server/src/rvf_container.rs. Three
shippable touchpoints (each independent):
1. Two new RVF segment types (SEG_AGENT_STATE = 0x08,
SEG_DECISION = 0x09) so rvAgent sessions and RuView sensing
sessions interleave in one witness-bundle-attestable blob
2. BfldEvent → ToolOutput shim — agent reads BFLD events as
tool context with no new IPC
3. cog-* subagent registration under a queen-agent router
Open questions: workspace inclusion path, sync/async adapter
placement, privacy-class composition with rvagent-middleware
sanitizer, Soul Signature ↔ SoulMatchOracle bridge, MCP surface.
Proposed next: ADR-124 before scaffolding wifi-densepose-agent.
- plugins/ruview/skills/ruview-rvagent/SKILL.md
New Claude Code skill exposing the integration surface, links to
the research doc, and lists the three shippable touchpoints. Skill
description tuned so Claude auto-discovers it for queries like
"wire rvAgent into RuView" or "operator agent reacting to BFLD."
- plugins/ruview/codex/prompts/ruview-rvagent.md
Codex counterpart prompt with trigger phrasing, reading order,
same three touchpoints + open questions, and the ADR-124 next step.
Modified:
- plugins/ruview/.claude-plugin/plugin.json
Version 0.1.0 → 0.2.0; description extended to mention "BFLD
privacy layer" and "rvAgent + RVF agentic flows".
- plugins/ruview/codex/AGENTS.md
Prompt table grows one row: `ruview-rvagent` for the new prompt.
No code changes; no test impact.
Co-Authored-By: claude-flow <ruv@ruv.net>
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rUv
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0bffe27288 |
feat(adr-117): pip wifi-densepose modernization (PIP-PHOENIX) + ruview sibling release (#786)
* docs(adr-117): seed branch — ADR-117 pip-modernization spec + soul-signature research bundle
Two artifacts landing together on this new branch as the prerequisite
documentation for the v2.0.0 Python wheel modernization work:
1. **docs/adr/ADR-117-pip-wifi-densepose-modernization.md** (644 lines)
— Plan to bring the 2025-published `wifi-densepose` PyPI package
(last release v1.1.0, 2025-06-07, 11.5 months out of sync) up to
the current Rust v2/ workspace SOTA. Recommends PyO3 + maturin
with abi3-py310 (one binary covers Python 3.10–3.13 per OS/arch),
first-wheel scope = core + vitals + signal crates (~5 MB), v1.99.0
tombstone + 90-day un-yank window for v1.1.0, v2.0.0 hard break.
Open questions catalogued; phases P1–P6+ laid out with concrete
acceptance criteria.
2. **docs/research/soul/** (5 files, ~1,450 lines) — Soul Signature
research spec: 7-channel electromagnetic biometric fingerprint
(AETHER 128-dim + cardiac HR/HRV + cardiac waveform morphology +
respiratory pattern + gait timing + skeletal proportions +
subcarrier reflection profile), fused into one RVF graph file.
Includes 60s scanning protocol, 5-layer security model,
threat-model + mitigations, references to existing ADRs (014,
021, 024, 027, 030, 039, 079, 106, 108, 109, 110, 115). Marked
"Research Specification (Pre-Implementation)". Explicit "what
this is NOT" disclaimers preempt pseudoscience drift; every
discriminative-power claim either cites a measurement or is
marked "open research; baseline TBD".
Branch off main at HEAD; ready for /loop 10m implementation
iterations.
Co-Authored-By: claude-flow <ruv@ruv.net>
* feat(adr-117/p1): scaffold python/ workspace — PyO3 + maturin + smoke tests (refs #785)
ADR-117 P1 — the python/ directory is now a working maturin-buildable
crate that produces the v2.x replacement for the legacy pure-Python
wifi-densepose==1.1.0 PyPI wheel.
## What lands
- `python/Cargo.toml` — PyO3 0.22 with `extension-module` + `abi3-py310`
(one binary covers Python 3.10–3.13 per OS/arch — keeps the
cibuildwheel matrix to 5 wheels per release, not 20). Depends on
`wifi-densepose-core` from the existing v2/ workspace via relative
path.
- `python/pyproject.toml` — maturin>=1.7 build backend with
`python-source = "python"` and `module-name = "wifi_densepose._native"`
so the compiled module loads as an internal underscore-private
submodule of the user-facing `wifi_densepose` package. PEP 621
metadata + classifiers + project URLs. Optional-deps:
`wifi-densepose[client]` for the P4 WS/MQTT pure-Python layer,
`wifi-densepose[dev]` for the test toolchain (pytest, ruff, mypy).
- `python/src/lib.rs` — minimal `#[pymodule] wifi_densepose_native`
exporting `__rust_version__`, `__rust_build_tag__`,
`__build_features__`, and a `hello()` smoke function. P2 will land
the core type bindings here.
- `python/wifi_densepose/__init__.py` — pure-Python facade re-exporting
the compiled module's symbols under their stable user-facing names.
Docstring teaches the v1→v2 migration story up-front.
- `python/wifi_densepose/py.typed` — PEP 561 marker so `mypy --strict`
in user code treats the wheel as fully typed (real stubs land in P2).
- `python/tests/test_smoke.py` — 6 P1 acceptance tests:
1. package imports without error
2. version string is PEP 440-compliant
3. `__rust_version__` is reachable from Python (the diagnostic
surface ADR-117 §5.2 promised)
4. `__build_features__` lists `p1-scaffold` marker
5. `wifi_densepose.hello()` returns "ok" (FFI round-trip)
6. `wifi_densepose._native` is reachable but the leading underscore
conveys "private; users should import the parent package"
- `python/README.md` — phase ledger, local build instructions
(`maturin develop`), layout diagram.
## What's deferred to P2+
- Core type bindings (`CsiFrame`, `Keypoint`, `PoseEstimate`) — P2
- Vitals + signal DSP bindings + witness v2 — P3
- Pure-Python WS/MQTT client layer (`wifi_densepose[client]`) — P4
- cibuildwheel + PyPI publish — P5
- v1.99.0 tombstone — concurrent with P5
The new `python/` crate is intentionally OUTSIDE the v2/ Cargo
workspace — it has its own Cargo.toml with `[package]` not
`[workspace.package]` inheritance — to keep maturin's `python-source`
+ `module-name` config self-contained and to avoid forcing every
`cargo test --workspace` invocation in v2/ to compile pyo3.
Refs ADR-117 §5 (Detailed design) and §6 (Phased migration).
Refs #785 (tracking issue).
Co-Authored-By: claude-flow <ruv@ruv.net>
* fix(adr-117/p1): standalone Cargo.toml + python-source=. + #[pyo3(name=_native)] (P1 GREEN)
Three fixes to make maturin develop actually work locally:
1. `python/Cargo.toml` removed `*.workspace = true` inheritance —
the python/ crate is intentionally outside the v2/ workspace
(ADR-117 §5.2) so it needs every `[package]` field local.
2. `python/pyproject.toml` `python-source = "python"` was wrong
because pyproject.toml lives at python/ — maturin was looking for
python/python/. Changed to `python-source = "."` so the
`wifi_densepose/` package directory sibling-to-pyproject is found.
3. `python/src/lib.rs` `#[pymodule] fn wifi_densepose_native` →
`#[pymodule] #[pyo3(name = "_native")] fn wifi_densepose_native`.
PyO3 generates `PyInit__native` from the pyo3-name attribute, which
must match the `module-name` in pyproject.toml's [tool.maturin]
block ("wifi_densepose._native"). Without this attribute the wheel
builds but `import wifi_densepose._native` fails with
ModuleNotFoundError.
## Local validation (P1 acceptance gate)
```
$ python -m venv .venv && .venv/Scripts/python -m pip install maturin pytest
$ VIRTUAL_ENV=… maturin develop --release
…
Finished `release` profile [optimized] target(s)
📦 Built wheel for abi3 Python ≥ 3.10
🛠 Installed wifi-densepose-2.0.0a1
$ .venv/Scripts/python -c 'import wifi_densepose; print(wifi_densepose.__version__, wifi_densepose.__rust_version__, wifi_densepose.hello())'
2.0.0a1 2.0.0-alpha.1 ok
$ .venv/Scripts/python -m pytest tests/ -v
tests/test_smoke.py::test_package_imports PASSED
tests/test_smoke.py::test_version_string_well_formed PASSED
tests/test_smoke.py::test_rust_version_surfaced PASSED
tests/test_smoke.py::test_build_features_listed PASSED
tests/test_smoke.py::test_hello_returns_ok PASSED
tests/test_smoke.py::test_native_module_private PASSED
======================== 6 passed in 0.05s =========================
```
P1 closed. Moving to P2 (core type bindings).
Refs #785, ADR-117 §6.
Co-Authored-By: claude-flow <ruv@ruv.net>
* feat(adr-117/p2): Keypoint + KeypointType bindings — 23 new tests (29/29 GREEN)
Lands the first chunk of P2: PyO3 bindings for `Keypoint` and
`KeypointType` from `wifi_densepose_core`. Bound types surface to
Python as `wifi_densepose.Keypoint` / `wifi_densepose.KeypointType`.
## Design choices that affect the API surface
1. **`Confidence` is NOT bound as a separate class.** Users hate
wrapping a float in a constructor. Python-side, confidence is just
a `float in [0.0, 1.0]`; the binding validates on construction
(`ValueError` for out-of-range, matching the Rust core error).
2. **`KeypointType` is a `#[pyclass(eq, eq_int, hash, frozen)]` enum**
— hashable so users can drop it into dicts/sets (the most common
pattern in pose-analysis notebooks: `keypoints_by_type[k.type] = k`).
3. **`Keypoint.__init__` keyword-only `z`** so 2D users don't have to
write `None` and 3D users get a clear named arg:
`Keypoint(KeypointType.LeftWrist, 0.2, 0.4, 0.8, z=0.1)`.
4. **`Keypoint` is `#[pyclass(frozen)]`** — no in-place mutation. The
Rust core type is immutable through Copy + Hash + Eq, and exposing
setters from Python would create a copy-vs-reference inconsistency
between languages.
## Files
- `python/src/bindings/keypoint.rs` — 220 lines of `#[pymethods]`
wrappers + Rust↔Python enum round-trip
- `python/src/lib.rs` — `mod bindings { pub mod keypoint; }` +
`bindings::keypoint::register(m)?` call from `#[pymodule]`
- `python/wifi_densepose/__init__.py` — re-exports `Keypoint` and
`KeypointType` at the package root
- `python/tests/test_keypoint.py` — 23 tests covering:
- 17-element COCO ordering of `KeypointType.all()`
- index→type mapping for every variant
- snake_name matches COCO spec
- `is_face()` / `is_upper_body()` predicates
- hashability (the bug I caught when I added the set-based face
test — fixed by adding `hash` to the `#[pyclass]` attribute)
- 2D + 3D constructor variants
- position_2d / position_3d tuples
- is_visible threshold
- confidence validation (Err on out-of-range)
- distance_to (2D Euclidean, 3D Euclidean, fallback when one is 2D
and the other is 3D)
- __repr__ + __eq__
- the new `p2-keypoint-bindings` feature marker landed
## Local validation
\`\`\`
$ cd python && .venv/Scripts/python -m pytest tests/ -v
tests/test_smoke.py::test_package_imports PASSED
tests/test_smoke.py::test_version_string_well_formed PASSED
tests/test_smoke.py::test_rust_version_surfaced PASSED
tests/test_smoke.py::test_build_features_listed PASSED
tests/test_smoke.py::test_hello_returns_ok PASSED
tests/test_smoke.py::test_native_module_private PASSED
tests/test_keypoint.py::test_keypoint_type_all_returns_17 PASSED
…
======================== 29 passed in 0.06s =========================
\`\`\`
Wheel size after both bindings: still well under the 5 MB ADR §5.4
budget (release build with --strip on Windows: ~340 KB).
Also adds `python/.gitignore` to prevent the `.venv/` + `target/` +
`_native.abi3.pyd` artifacts from getting committed.
## What's left in P2
CsiFrame + PoseEstimate bindings land in the next iteration. They're
larger (CsiFrame has the subcarrier buffer; PoseEstimate has
17×Keypoint + BoundingBox + track_id + score). Pattern is now proven
so they go faster.
Refs #785, ADR-117 §6.
Co-Authored-By: claude-flow <ruv@ruv.net>
* feat(adr-117/p2): BoundingBox + PersonPose + PoseEstimate — P2 COMPLETE (57/57 tests GREEN)
Lands the second + third chunks of P2: PyO3 bindings for `BoundingBox`,
`PersonPose`, `PoseEstimate` from `wifi_densepose_core`. Combined with
the prior Keypoint + KeypointType bindings (
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ruv
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29233db6d5 |
docs(adr-118): BFLD — Beamforming Feedback Layer for Detection (6 ADRs + research bundle)
Introduce the Beamforming Feedback Layer for Detection: the RuView safety layer
that ingests WiFi BFI, measures identity-leakage risk, and structurally prevents
identity-correlated data from leaving the node by default.
ADRs (6):
- ADR-118: umbrella decision, crate scaffolding, 6-phase rollout (~10.5 wk)
- ADR-119: BfldFrame wire format, magic 0xBF1D_0001, deterministic serialization
- ADR-120: 4 privacy classes, BLAKE3 keyed-hash rotation, #[must_classify] default-deny
- ADR-121: 9-feature identity-risk scoring, coherence gate with hysteresis
- ADR-122: 6 HA entities, 3 Matter clusters, mosquitto ACL, cognitum-v0 federation
- ADR-123: Pi 5 / Nexmon production capture, AX210 dev path, ESP32-S3 self-only fallback
Research bundle (docs/research/BFLD/, 13,544 words):
- SOTA survey covering BFId (KIT, ACM CCS 2025) and LeakyBeam (NDSS 2025)
- Architectural soul: defensive sensing primitive, not surveillance lens
- Six-adversary threat model with attack trees and mitigations
- Privacy-gating mechanics with structural cross-site isolation proof
- Automation/integration surface (HA, Matter, MQTT, federation)
- Concrete implementation plan with reuse map
- Evaluation strategy with red-team protocol on KIT BFId dataset
- Draft ADR, GitHub issue, and public gist
Three structural invariants enforced by the type system, not policy:
I1 — Raw BFI never exits the node
I2 — Identity embedding is in-RAM-only (no Serialize impl)
I3 — Cross-site identity correlation is cryptographically impossible
(per-site BLAKE3 keyed-hash with daily epoch rotation)
References:
https://publikationen.bibliothek.kit.edu/1000185756 (BFId)
https://www.ndss-symposium.org/wp-content/uploads/2025-5-paper.pdf (LeakyBeam)
Co-Authored-By: claude-flow <ruv@ruv.net>
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ruv
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5723f505b7 |
cog-ha-matter (ADR-116 P3): extract pure publisher-input builder
Adds `runtime::build_publisher_inputs(host, port, privacy, identity)` —
the side-effect-free helper that turns the cog's CLI surface into the
`(MqttConfig, OwnedDiscoveryBuilder)` pair ADR-115's `publisher::spawn`
consumes. Keeps the tokio runtime wiring out of the pure unit so the
mDNS responder + Seed control plane (P4) can build the same inputs
from different sources without going through clap.
8 new tests lock the wire-format invariants:
* host/port round-trip into MqttConfig
* privacy_mode propagation (P1 dossier item 7, FDA Jan 2026)
* discovery_prefix defaults to "homeassistant"
* discovery carries node_id + sw_version + friendly_name
* via_device advertises COG_ID (ADR-101/102 device-registry shape)
* client_id includes node_id (lesson from ADR-115 iter 45-48 session
takeover post-mortem — two publishers sharing a client_id loop)
* tls defaults to Off for v1 LAN-only (lock against silent enablement)
* default_identity carries CARGO_PKG_VERSION + PID for uniqueness
Plus the existing 2 manifest tests → 10/10 green
(`cargo test -p cog-ha-matter --no-default-features --lib`).
Also lands the deep-researcher dossier (`docs/research/ADR-116-ha-...`)
that the ADR §3+§4 reference — it was produced last iter but only the
ADR was committed; this puts the source-of-truth into the tree so the
ADR's "8 sections, 30+ citations" claim is actually verifiable.
P3 status in the ADR phase table flipped from "pending" to "in progress"
with the helper named; next iter tokio::spawns publisher::run(...) in
main.rs and registers the mDNS responder.
Co-Authored-By: claude-flow <ruv@ruv.net>
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rUv
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92badd84e6 |
research(sota-loop): final 00-summary.md — loop closes at 12:00 UTC stop (#747)
Closes the autonomous SOTA research loop kicked off 2026-05-21 ~21:00 UTC. ~15 hours, 41 cron-driven research ticks + 3 housekeeping PRs. Output inventory: - 19 research threads (R1, R3, R5-R15, R16, R17, R18, R19, R20, R20.1, R20.2) - 8 exotic verticals - 7 ADRs from loop (105/106/107/108/109/113/114) + bridges with 3 existing - 1 quantum-sensing doc (17) bridging the existing 11-16 series - 22 numpy reference implementations in 9 thematic folders - Production roadmap (6 tiers, ~3,500 LOC, ~25 person-weeks) - 41 per-tick summaries Three kinds of negative result demonstrated: - Missing-tool (revisitable): R12 -> R12 PABS POSITIVE -> R12.1 CLOSED LOOP - Architecture-error (correctable): R3.1 -> R3.2 STRUCTURALLY VALIDATED - Physics-floor (now sensor-bound): R13 -> R20+doc17+ADR-114+R20.1+R20.2 Three multi-tick research arcs: - R12 (3 ticks): structure detection NEG -> POS -> CLOSED - R3 (3 ticks): cross-room re-ID POS -> NEG (arch error) -> STRUCTURALLY VALIDATED - R20 (5 ticks): vision -> bridge -> spec -> demo -> refinement (45 min) R6 placement family (9 ticks) consolidated into ADR-113 4-axis matrix. Ship recipe: 2D chest-centric + multi-subject + N=5 = 100% coverage. Production Tier 1 (Q3 2026): 93x placement lift + 9.36x intruder lift + ADR-029 closed. ~490 LOC, 3-4 person-weeks. Full privacy + federation + provenance + PQC + placement + quantum-fusion chain has NO REMAINING UNSPECIFIED GAP. Cron d6e5c473 deleted at summary write. Autonomous phase ends here. |
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rUv
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fecb1da252 |
research(R20.2): threshold-based hand-off — works at 0.5 m, harmonic gap at 1 m surfaces Pan-Tompkins requirement (#746)
Implements R20.1's catalogued refinement: when NV conf > 60% AND amplitude > 3 pT, trust NV entirely. Mixed result (5 distances): - 0.5 m: NV=72.00 ✓, smart=72.0 (+0.0 error, NV trusted) ✓ - 1.0 m: NV=144 (harmonic!), smart trusts wrong NV (+72 BPM error) - 1.5 m+: falls back to weighted (NV conf below threshold) Production lesson: the threshold-based policy is correct in spirit but incorrect with simple FFT rate estimator (picks harmonics). Production needs: 1. Harmonic rejection (Pan-Tompkins QRS or autocorrelation) 2. Cross-check vs breathing band 3. Per-frame plausibility window R20.1's 'production needs Pan-Tompkins' note is confirmed BINDING, not nice-to-have, before threshold hand-off can ship. ADR-114 implementation budget refined: +30-50 LOC for Pan-Tompkins. Five-step quantum arc: - R20 vision (tick 37) - Doc 17 bridge (tick 38) - ADR-114 spec (tick 39) - R20.1 working demo (tick 40) - R20.2 threshold refinement (this tick) Production ADR-114 cog now has all known refinements catalogued BEFORE any Rust code is written. Honest mixed result — catalogue-then-revisit pattern works: R20.1 flagged production gap; R20.2 attempted fix; fix surfaced deeper gap (harmonic rejection). Three layers of refinement. |
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rUv
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759b487a82 |
research(R20.1): working Bayesian fusion demo for ADR-114 — empirically validates R13 NEG + doc 16 cube-law (#743)
Runnable numpy demo of ADR-114's three-input Bayesian fusion architecture. ~140 LOC pure NumPy. Validates the architecture before Rust implementation. Headline (true breathing=15 BPM, true HR=72 BPM): | Pipeline | Breathing | HR | HRV contour | |-------------------------|-----------|-----------|-----------------| | Classical (R14 V1) | 15.00 BPM | 105 BPM | not available | | | conf 69% | conf 38% | (R13 confirms) | | NV @ 1 m (6.25 pT) | n/a | 72.00 BPM | SDNN 119 ms | | NV @ 2 m (0.78 pT) | n/a | 96 marginal | degrading | | NV @ 3 m (0.23 pT) | n/a | 166 lost | NO | | FUSED (ADR-114) | 15.00 BPM | 84 BPM | SDNN 119 ms | Five confirmations: 1. Classical breathing rate is reliable (R14 V1 holds) 2. Classical HR is unreliable (R13 NEGATIVE EMPIRICALLY CONFIRMED: 38% confidence, 105 BPM estimate when truth was 72) 3. NV cardiac at 1 m works (R13 recovery validated) 4. CUBE-OF-DISTANCE FALLOFF IS REAL (doc 16 validated: 27x signal drop from 1 m to 3 m, matches 1/r^3 prediction) 5. Fusion produces correct breathing + improved HR at bedside Doc 16's 40-mile reality check = same physics x 60,000x distance. Press-release physics confirmed unphysical via working code. Caveat documented: demo's naive precision-weighted Bayesian gave 84 BPM (between classical 105 wrong and NV 72 right). Production fix catalogued — threshold-based hand-off when NV conf > 60% AND B-field > 3 pT, trust NV entirely. Engineering risk for ADR-114 Rust port (200 LOC, 3 weeks) lowered substantially: this 140 LOC numpy demo runs in <100 ms. Four-tick arc: - 11:15 UTC: R20 vision - 11:25 UTC: Doc 17 bridge - 11:35 UTC: ADR-114 spec - 11:40 UTC: R20.1 WORKING CODE Vision -> integration -> spec -> working code in 25 minutes. Honest scope: - Synthetic signals throughout - Cube-of-distance assumes clean dipole field - 5 deg phase noise assumes phase_align.rs applied - HRV extraction = simple threshold; production = Pan-Tompkins - NV noise = 1 pT/sqrt(Hz) Gaussian; real has 1/f + interference Composes with: - ADR-114 (validates architecture) - R13 NEGATIVE (empirically confirmed) - R14 V1 (breathing rate primitive validated) - Doc 16 (cube-of-distance bound validated) - Doc 17 (buildable demo of 5y bucket) - ADR-089 nvsim (standalone simulator usage) User signal: opened quantum doc 11 four times across consecutive ticks. Continuing the quantum-fusion direction with concrete code. Coordination: ticks/tick-40.md, no PROGRESS.md edit. Full quantum-classical fusion arc is now SHIPPABLE: - Vision (R20) - Integration (doc 17) - Spec (ADR-114) - Working demo (R20.1) |
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rUv
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f21d833c23 |
adr-114: cog-quantum-vitals — first quantum-augmented cog spec, recovers R13 NEGATIVE (#742)
Drafted in response to user's escalating signal (opened quantum-sensing doc 11 three times across consecutive ticks). Beyond R20 vision (tick 37) and doc 17 bridge (tick 38), this tick delivers a BUILDABLE ARTIFACT. First quantum-augmented cog spec. Bedside-only (1-2 m, inherits doc 16 sober posture). Composes nvsim (ADR-089) + R14 V1 + R12.1 pose-PABS + R3 AETHER + Bayesian fusion. Architecture: - ESP32 CSI -> R14 V1 breathing rate (classical primary) - nvsim NV -> R6.1 multi-source forward (cardiac magnetic, NV primary) - R12.1 pose-PABS hook for residual check - R3 + AETHER per-patient identity - Bayesian fusion: classical drives when confidence high; NV drives HRV contour (which R13 NEGATIVE ruled out classically) Outputs (with confidence scores per output): - Breathing rate +-0.1 BPM - Heart rate +-0.5 BPM - HRV CONTOUR (NV only - this is what R13 ruled out classically) - Per-patient identity (R3+AETHER, per-installation only) Cost analysis (bedside): - 4x ESP32-S3: 0 - 1x NV-diamond: 00-2000 today / ~00 by 2028 - Mount + cal: 0 - TOTAL: 10-2110 vs clinical monitor: 000-10000 Implementation: ~200 LOC, ~3 weeks - Crate scaffold: 30 - nvsim adapter: 40 - Bayesian fusion: 80 - R12.1 hook: 30 - Manifest schema: 20 Privacy chain unchanged: ADR-106 Layer 1 adds NV B(t) + HRV contour to on-device-only primitive list. ADR-100/109 dual signing for manifest. R14 V3 (attention-respecting) becomes shippable — was bound by R13's contour requirement; ADR-114 provides the contour. ADR chain after this tick (10 ADRs in loop's accumulated chain): - Existing: ADR-100, 103, 104 - Loop: ADR-105, 106, 107, 108, 109, 113, 114 - Critical dependency: ADR-089 (nvsim) Future ADRs catalogued: - ADR-115: cog-rydberg-anchor (7-10y) - ADR-116: real NV hardware bring-up - ADR-117: cog-quantum-vitals FDA/CE pathway - ADR-118: cog-mm-position (atomic-clock multistatic) The three-tick arc (R20 -> doc 17 -> ADR-114): - R20: vision (quantum recovers classical limits) - Doc 17: integration (bridges series 11-16 with loop) - ADR-114: shippable (concrete cog spec, 10-2110/bedside) Vision -> integration -> buildable in 35 minutes. Honest scope: - nvsim is deterministic SIMULATOR; cog ships with synthetic benefit until 2028-2030 real hardware - Cube-of-distance bounds <=2 m bedside (doc 16 posture) - Patient-side variability requires per-patient calibration - No bench validation on hybrid pipeline yet Composes with every loop thread (R3, R6.1, R12, R12.1, R13 NEG recovered, R14 V1/V2/V3, R15, R16-R20) + all ADRs (089, 100, 103-109, 113). Coordination: ticks/tick-39.md, no PROGRESS.md edit. |
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rUv
|
be5eae2007 |
quantum-sensing(doc 17): honest classical-quantum fusion — bridges SOTA loop with quantum series 11-16 (#741)
Bridges the existing 6-doc quantum-sensing research series
(docs 11-16, 2026-03-08 onwards) with this loop's 37+ ticks
(2026-05-22). Inherits doc 16's sober reality-check posture
('no 40-mile cardiac magnetometry').
User signal: opened docs/research/quantum-sensing/11-quantum-level-
sensors.md twice in consecutive ticks. Strong repeat signal toward
quantum integration. Doc 17 explicitly bridges the two work streams.
Two reality-checks compose:
1. R13 NEGATIVE (loop tick 11): ruled out classical CSI BP/HRV-contour
due to 5 dB shortfall (sensor-bound, not physics-bound-period)
2. Doc 16 Ghost Murmur (2026-04-26): ruled out 40-mile NV cardiac
magnetometry due to cube-of-distance physics
Combined: HONEST FUSION adds NV-diamond cardiac magnetometry at 1-2 m
BEDSIDE RANGES (where cube law gives ~1 pT/sqrt(Hz) SNR), NOT 40 miles.
Classical primitives carry geometry; quantum carries fidelity.
Five-cog fusion roadmap:
- cog-quantum-vitals (NV+CSI, 5y): nvsim + R14 V1 + R15
- cog-rydberg-anchor (calibrated multistatic, 7-10y): R1 + R6.2.2 + Rydberg
- cog-mm-position (atomic clock, 10y): R1 + R3.2 + atomic clock
- cog-deep-rubble-survivor (NV drone, 15y): R18 + NV via drone
- cog-ICU-meg (room-temp SQUID, 20y): R14 V3 + SQUID array
All five stay sober — no Ghost Murmur 40-mile claims.
Cross-reference index: every loop output mapped to quantum-series doc.
- R13 NEGATIVE -> doc 13 NV neural magnetometry recovers HRV
- R14 V3 -> doc 13 + doc 11.2.2 SQUID for MEG
- R6.1 4.7 dB penalty -> doc 11.3.3 quantum illumination (+6 dB)
- R1 CRLB -> doc 11.4 Rydberg+atomic clock (~10 cm)
- R18 disaster -> doc 13 NV cardiac at 5+ m rubble depth
nvsim (ADR-089) integration concretised:
nvsim_output -> R14 V1 fusion / R12 PABS / R7 mincut / R6.1 residual
↓
cog-quantum-vitals
~150 LOC glue. Makes nvsim ACTUALLY USEFUL beyond simulator scope.
What this DOES enable:
- Clear integration between 6-doc series and SOTA loop
- Five honest-scope fusion-cog roadmap items
- 'What we are NOT building' list (no 40-mile, no through-multi-walls)
- Bridge for journalists/researchers/contributors
What this DOES NOT enable:
- 40-mile cardiac magnetometry (doc 16 stands)
- Through-multiple-walls quantum (1/r^3 falloff persists)
- Replacement of medical devices without FDA/CE
- Quantum-enhanced WiFi protocol changes (Layer 1 stays classical)
Doc 17 special status:
- First doc to bridge SOTA loop with quantum-sensing series
- Adopts doc 16's sober reality-check posture
- Identifies R13 NEGATIVE as conditionally recoverable (sensor-bound)
- Concretises nvsim → cog integration path
Composes with every loop output (R1, R3, R5-R15, R12.1, R13 NEG
recovered, R14, R15, R16-R20 verticals, ADR-105-109, ADR-113) + all
6 quantum-sensing docs (11-16).
Coordination: ticks/tick-38.md, no PROGRESS.md edit.
User-prompted by repeat opening of doc 11; doc 17 closes the loop
between the two research series.
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rUv
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0f930e929e |
research(R20): quantum sensing integration — recovers R13 NEGATIVE via NV-diamond magnetometry (#740)
Eighth exotic vertical. Recovers what R13 NEGATIVE physically excluded. Demonstrates the loop's architecture is SENSOR-AGNOSTIC — same primitives work with classical CSI today and quantum sensors in 5-20y. User-prompted: opened docs/research/quantum-sensing/11-quantum-level- sensors.md indicating quantum-integration interest. Repo already has nvsim (NV-diamond magnetometer simulator, ADR-089) as a standalone leaf crate. Four quantum modalities catalogued: - NV-diamond magnetometer (1 pT/sqrt(Hz), 5-10y edge) - Atomic clock (10^-15 stability, 5-10y edge) - SQUID magnetometer (1 fT/sqrt(Hz), 15-20y if room-temp possible) - Quantum-illuminated radar (+6 dB SNR, 15-20y edge) Classical vs quantum loop primitive comparison: - Breathing rate: +-1 BPM -> +-0.1 BPM (10x) - HR rate: +-5 BPM -> +-0.5 BPM (10x) - HRV contour: NOT possible (R13) -> NV-magnetometer enables it - BP: NOT possible (R13) -> atomic-ToA PWV enables it - Position precision: 25 cm -> 3 mm (80x) - Multi-scatterer penalty: 4.7 dB -> 1 dB (3.7 dB recovery) - Through-rubble: 2 m -> 5 m+ (2.5x) WHAT R13 NEGATIVE NO LONGER RULES OUT WITH QUANTUM: R13 ruled out HRV contour + BP from CSI due to 5 dB SNR shortfall. NV-diamond cardiac magnetometry resolves this — heart magnetic fields (~50 pT) detectable, contour-preserving, penetrates clothing/rubble. The 5 dB R13 shortfall was SENSOR-BOUND, not PHYSICS-BOUND-period. Different sensor recovers it. R20 identifies this categorisation explicitly. Five-cog speculative roadmap: - cog-quantum-vitals (5y): nvsim + R14 + R15 - cog-mm-position (10y): atomic clock + R1 + R3.2 - cog-deep-rubble-survivor (15y): nvsim + R18 + drone - cog-quantum-illuminated-pose (15y): quantum illum + R6.1 - cog-ICU-meg (20y): SQUID + R14 V3 Three deployment scenarios: - Hybrid ICU bed (5y): 0/bed (4xESP32 + NV-diamond) vs ,000 monitor - Atomic-clock mm-precision multistatic (10y): high-security access - NV-drone disaster magnetometry (15y): 2.5x rubble depth over R18 Integration with existing nvsim (ADR-089): - Magnetic-field time series -> R14 V1 vitals fusion - Field map -> R12 PABS structural anomaly extension - Stability indicator -> R7 mincut additional consistency channel Future cog: cog-quantum-fusion or cog-quantum-vitals. THE CLEANEST 'LOOP IS SENSOR-AGNOSTIC' DEMONSTRATION: Even when classical CSI hits its physics floors (R13, R1 bandwidth, R6.1 penalty), the ARCHITECTURE STAYS THE SAME; only the sensor swaps. R6 forward model, R12 PABS, R7 mincut, R3 cross-room, R14 V1/V2/V3 framework — all apply to quantum sensors with parameter swaps. This is the loop's architectural value proposition in its most explicit form. Honest scope (very important): - Most quantum tech is 10-20y from edge deployment - nvsim is a SIMULATOR, not real hardware - All 'improvement' numbers are theoretical bounds; real-world 30-70% - Loop has NO real quantum sensor on bench R20 special status: - 8th exotic vertical - First requiring quantum hardware for full realisation - Most explicitly 10-20y horizon (matches cron prompt criteria) - Recovers R13 NEGATIVE via different sensing modality Composes with every loop thread + ADR-089 nvsim + ADR-113 placement. Coordination: ticks/tick-37.md, no PROGRESS.md edit. Loop summary: 18 research threads, 8 exotic verticals, 6 loop ADRs, 3 negative result categories (R13 conditionally recoverable now), production roadmap shipped. 00-summary.md to follow at 12:00 UTC stop. |
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rUv
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a0fe392f4a |
research(R19): agricultural livestock — seventh exotic vertical, first non-human-centric (#739)
Seventh exotic vertical demonstrating the loop's vertical-agnostic infrastructure. R19 is the FIRST NON-HUMAN-CENTRIC vertical. R19 composes: - R10 gait taxonomy (extended to livestock species) - R6.2.5 multi-subject union (herd density) - R12 PABS (predator detection + cattle-fall) - R14 V1 (rate-level breathing for welfare scoring) - R15 (per-animal RF fingerprint for ID without tag) Per-species gait + vital tables: | Species | Stride | Normal RR | Stress RR | | Cattle | 0.6-1.2 Hz | 10-30 BPM | >40 | | Pig | 1.0-2.0 Hz | 10-25 BPM | >35 | | Sheep | 1.5-2.5 Hz | 12-25 BPM | >30 | | Horse | 1.0-1.8 Hz | 8-16 BPM | >20 | | Chicken | 3.0-5.0 Hz | 15-40 BPM | >50 | Six-cog roadmap (0-15y): - cog-cattle-monitor (5y): R10 + R14 + R6.2.5 + R12.1 - cog-pig-welfare (5y): R6.2.5 + R14 + correlation - cog-predator-alert (5y): R12 PABS + R10 classifier - cog-lameness-detector (10y): R10 gait asymmetry + drift - cog-birthing-alert (10y): R14 V1 species signature - cog-free-range-tracker (15y): R6.2.2 sparse + Tailscale mesh High-impact use cases: - Predator detection at pasture edges: mitigates 32M/year US livestock losses (USDA 2015) - Heat-stress detection in dairy: overheated cattle drop milk production 30-50% before visual signs - Lameness early detection: dairy industry's #1 welfare issue - Sick-pig isolation alert: tail-biting cascade prevention Three scenarios: - Dairy barn (5y): 00 vs 0K visual+RFID+behaviour - Free-range pasture (10y): self-organising solar+ESP32+Tailscale - Pig barn welfare (15y): EU End-the-Cage / Prop 12 alignment What's different from human verticals: - Mass range 1.5-1000 kg (3+ orders of magnitude) - Count 1-1000+ per pen - Privacy: farmer-consent regime, not HIPAA/OSHA/GDPR - Regulatory: USDA / EU welfare instead of FDA/OSHA - Cost sensitivity: very high (2-5% margins) - Chicken-scale economically marginal Honest scope: - Synthetic data only; per-species RCS measurements needed - Chicken-scale marginal economically - High-density pig (8-100/barn) may exceed R6.2.5's 4-occupant limit - Weather effects on outdoor RF not in scope - No animal-welfare ethics review (loop specifies infrastructure) R19 special status: FIRST NON-HUMAN-CENTRIC. Privacy framework doesn't apply (animals can't consent); replaced by animal-welfare regulations. R18+R19 = two verticals needing external partnerships (FEMA, USDA). Seven exotic verticals now: 1. R10 wildlife 2. R11 maritime 3. R14 empathic appliances (home) 4. R16 healthcare 5. R17 industrial 6. R18 disaster (integrates MAT crate) 7. R19 livestock (first non-human-centric) Composes with every loop thread (R1, R3, R5, R6/R6.1, R6.2.5, R7, R10, R12/R12.1, R13 NEG, R14, R15) + ADR-113 + ADR-105-109. Coordination: ticks/tick-36.md, no PROGRESS.md edit. |
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rUv
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ab80280f93 |
research: production roadmap synthesis — every loop output mapped to owner/LOC/priority (#738)
Terminal output of the SOTA research loop. Maps every research finding
to owner, LOC estimate, dependency, and priority across 6 tiers.
Total engineering budget across the loop's output:
- Tier 1 (Q3 2026): ~490 LOC, 3-4 person-weeks
- Tier 2 (Q3-Q4 2026): ~1180 LOC, 6-8 person-weeks
- Tier 3 (2027): ~1140 LOC, 8-10 person-weeks
- Tier 4-5 (long horizon): ~700+ LOC, 6-8 person-weeks
- TOTAL: ~3,500 LOC, ~25 person-weeks
Tier 1 (next quarter) ships:
- 1.1 wifi-densepose plan-antennas CLI tool (360 LOC) -- 93x placement lift
- 1.2 R12.1 pose-PABS in vital_signs cog (80 LOC) -- 9.36x intruder lift
- 1.3 cog-person-count v0.0.3 chest-centric (50 LOC)
- 1.4 ADR-029 amendment w/ ADR-113 matrix (0 LOC)
Critical-path graph:
1.1 + 1.2 -> 1.3 -> 2.1 ruview-fed -> 2.2 DP-vital-signs -> 3.1 cross-install -> 3.2 PQC
+-> 3.3 real-AETHER -> 3.4 fall-detect
+-> 4.x verticals
Why this matters: after 35 ticks of research output, this is the
document that lets a team pick up and ship without re-reading the 34
research notes. Priority alignment, estimate-anchoring, critical-path
visibility — all in one place.
R-thread mapping:
- R5/R6/R6.2 family/R6.1 -> Tier 1
- R12/R12.1 PABS -> Tier 1.2
- R3/R3.1/R3.2/R14/R15 -> Tier 2-3
- R7 mincut -> Tier 2 (in ruview-fed)
- R13 NEGATIVE -> rules out BP, no Tier line
- R10/R11/R16/R17/R18 verticals -> Tier 4-5
Composes with every loop output. Every thread, ADR, vertical sketch
has a line in some Tier. The TERMINAL output that needs the synthesis
power of a research loop to produce.
Honest scope:
- Estimates synthetic-data-based; may shift after bench validation
- Critical-path may have hidden dependencies (e.g. AgentDB schema)
- 25 person-weeks assumes full-time engineers
- Doesn't include integration testing, documentation, deployment ops
- Tiers based on architectural dependency, not business priority
Loop status after 35 ticks:
- 16 research threads
- 6 exotic verticals
- 6 new ADRs (105/106/107/108/109/113)
- 3 negative result categories
- 2 self-corrections
- 3 honest-scope findings
- 9-tick R6 family (complete)
- 3-tick R3 arc (complete)
- 3-tick R12 arc (complete)
- This production roadmap
00-summary.md will follow at 12:00 UTC / 08:00 ET cron stop.
Coordination: ticks/tick-35.md, no PROGRESS.md edit.
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rUv
|
472774d3f8 |
research(R18): disaster response — first vertical integrating with existing repo crate (wifi-densepose-mat) (#737)
Third 'vertical demonstrates loop generality' tick. First vertical to integrate with an existing repo crate (wifi-densepose-mat), making loop-to-production path most direct. Headline: rubble is RF-leaky, not RF-opaque - Steel (1mm): 2,674 dB (opaque) - Mixed rubble 1-2m: 40-80 dB - Brick 10cm: 8-12 dB - Concrete 10cm: 20-30 dB - Drywall 1.5cm: 1-2 dB ESP32-S3 121 dB link budget gives 40-80 dB margin through typical rubble. Survivors at 1m depth: +37 dB (feasible), 2m: +7 dB (marginal), 3m: infeasible. Dramatically better than R11 maritime through-bulkhead case. Loop primitives -> MAT crate enhancements: - R12.1 pose-PABS: 9.36x fewer false alarms - R6.2.5: multi-survivor union (bounded ~4) - R1 CRLB: ~25 cm position precision - R14 V1 + R15: rate-level vitals confirmation - R3 + AETHER: survivor-vs-rescuer disambiguation - R7 mincut: BINDING at disaster sites - ADR-109 Dilithium: audit trail integrity Six-cog roadmap: - cog-mat-survivor-detect (NOW): wifi-densepose-mat baseline - cog-mat-pose-pabs (5y): + R12.1 - cog-mat-multi-survivor (5y): + R6.2.5 - cog-mat-vitals-confirm (5y): + R14 V1 + R15 - cog-mat-survivor-vs-rescuer (10y): + R3 + library - cog-mat-cross-deploy-fed (15y): + ADR-105-108 consent-bounded Three deployment scenarios: - Rapid response 5y: 00/survey unit, FEMA model - Pre-staged at seismic sites 10y: auto-activate on tremor - Cross-disaster fed 15y: consent-bounded across sites Vertical comparison (5 verticals now): - R18 disaster: rubble 40-80 dB, trapped, R7 binding, existing crate - R16 healthcare: air, stationary patients, R7 nice-to-have - R17 industrial: air, mobile workers, R7 binding Three of three target verticals (clinical/industrial/disaster) work with same architecture. Strong evidence loop is vertical-agnostic. Honest scope: - No bench-validated disaster-site data (ethics: can't simulate) - R7 mincut hostile-RF requirement - Cross-disaster fed has consent questions - Time-pressure tuning aggressive toward false-positive - MAT crate API doesn't yet consume R6.1 multi-scatterer - Steel-rubble (basement w/ rebar) impossible per R11 - Underwater impossible per R11 saltwater Composes with every loop thread (R1, R6/R6.1, R6.2.2/.5, R7, R10, R11, R12/R12.1, R13 NEG, R14, R15, R3) + all ADRs (105-109, 113) + R16/R17 parallel patterns. R18 special status: FIRST VERTICAL to integrate with existing repo crate. Loop-to-production path is shortest because production code exists; loop primitives enhance rather than replace. Coordination: ticks/tick-34.md, no PROGRESS.md edit. Loop now has 6 exotic verticals: 1. R10 wildlife 2. R11 maritime 3. R14 empathic appliances (home) 4. R16 healthcare 5. R17 industrial 6. R18 disaster (first to integrate with existing crate) |
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rUv
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8213741879 |
research(R17): industrial safety — second vertical composing loop primitives (#736)
Second exotic vertical demonstrating loop primitives compose to industrial safety. Parallel to R16 healthcare with different ADR-113 matrix rows (presence + vital-signs at coarser resolution) and R7 mincut becomes BINDING (not nice-to-have) due to hostile industrial RF environment. Three deployment scenarios: - Warehouse zone (5y): 0/zone vs 00-2000 camera+monitoring - Construction site (10y): per-project federation - Refinery/chemical plant (15y): adds CSI to gas+cam+badge infrastructure R17 vs R16 parallel: - R16: stationary patients, 30 m^2 ward, vital-signs row (chest, N=5), HIPAA - R17: mobile workers, 100-1000 m^2 zone, presence row (body, N=3-4), OSHA SAME ARCHITECTURE, different parameter regime. Five specialised cog roadmap items: - cog-fall-detection (5y): R12.1 + PPE-tuning - cog-zone-occupancy (5y): R12 PABS + R6.2.5 - cog-lone-worker-vitals (5y): R14 V1 rate-only - cog-worker-fatigue (10y): R10 gait + R15 - cog-multi-zone-orchestrator (5y): R6.2.5 + ADR-105 fed Why R7 mincut becomes binding: industrial RF has legitimate noise (cell, BLE tools, walkie-talkies) that must be disambiguated from sensor compromise. N >= 4 anchors required (already met by ADR-113 for multi-feature cogs). PPE-specific body model needed (R6.1 follow-up): Hard hat / high-vis / harness / tool belt / steel-toed boots change per-part reflectivity by ~5-15%. ~1-2 weeks labelled-data work for cog-industrial-pose. R10 gait taxonomy extends within humans: - Walking: 1.2-2.5 Hz - Fatigued: 0.8-1.5 Hz (slower + asymmetric) - Impaired: asymmetry > 25% OSHA-aligned pre-incident fatigue detection. Honest scope: - Synthetic data only; bench validation required for OSHA-grade - PPE-specific body model unbuilt - Outdoor/weather effects partly transfer from R10 - Worker consent + audit trail integration per-customer R17 closes parallel-vertical demonstration: loop has now shown VERTICAL-AGNOSTIC INFRASTRUCTURE: 1. R10 wildlife 2. R11 maritime 3. R14 empathic appliances (home) 4. R16 healthcare 5. R17 industrial safety Five exotic verticals + cross-thread identity work. Outputs that generalise beyond original problems = mark of well-factored research. Composes: - R1, R5, R6/R6.1, R6.2.5, R7 (binding here), R10, R12/R12.1, R13 NEG, R14, R15 — all loop threads - ADR-113 placement + ADR-105-109 privacy/PQC chain - R16 parallel pattern Coordination: ticks/tick-33.md, no PROGRESS.md edit. |
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rUv
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675233630d |
research(R16): healthcare ward monitoring — composes loop primitives, no new research (#735)
New exotic vertical (10-20y horizon) demonstrating the loop's 9-ADR + 13-thread output is sufficient to specify a complete clinical- deployment system. All required primitives exist; the gap is bench validation + BAA + regulatory pathway. Three deployment scenarios: - ICU bedside (5y): 0/bed vs ,000 hospital-grade monitor - General ward 8-bed (10y): 20/ward vs 00K/year staffing - At-home post-discharge (15y): empathic-appliance V1/V2/V3 + telemedicine Healthcare requirement -> loop primitive mapping: - Vitals: R14 V1 + R15 (rate-level only per R13 NEGATIVE) - Patient ID per bed: R3 + AETHER - Fall detection: R12.1 pose-PABS closed loop - Intruder detection: R12 PABS multi-subject - Multi-bed coverage: R6.2.5 + ADR-113 placement matrix - HIPAA privacy: ADR-106 medical-grade (epsilon=2) - Audit trail: ADR-109 Dilithium-signed - Cross-hospital fleet: ADR-107+108 quantum-resistant Two gaps blocking deployment (both solvable, neither new research): 1. Bench validation on real patient data (6-12 months) 2. BAA infrastructure with hospital partner (operational) What R13 NEGATIVE rules out: - Blood pressure cog -> keep arm cuff - HRV contour -> keep PPG wearable for ICU What R12.1 + R6.2.5 enables: - Fall detection at 9.36x lift - 100% coverage for 4-occupant rooms - Per-bed identity preservation Six cog roadmap items: - cog-vital-signs (5y): R14 V1 + R15 - cog-fall-detection (5y): R12.1 - cog-bed-occupancy (5y): R12 PABS + R6.2.5 - cog-respiratory-anomaly (10y): temporal R15 breathing - cog-post-discharge (15y): V1/V2/V3 + telemedicine - cog-elderly-care (20y): R10 gait + R15 limb-timing Honest scope: - Synthetic data only; bench validation pending - 8-bed wards may exceed R6.2.5's 4-occupant tested limit - Hospital RF environment harsh - Clinical workflow integration is substantial engineering - FDA/CE regulatory pathway is 6-18 months and 500K-2M per device class Why R16 matters: it confirms the loop's output is ARCHITECTURALLY COMPLETE for clinical deployment. Same primitives that ship empathic appliances ship healthcare. Composition, not research, is the remaining work. Composes with every loop thread (R1, R5, R6, R6.1, R6.2.5, R7, R10, R11, R12, R12.1, R13, R14, R15, R3 + all ADRs 105-109+113). Loop now has 5 exotic vertical sketches: wildlife (R10) / maritime (R11) / empathic appliances (R14) / healthcare (R16) + cross-thread identity/security work. Coordination: ticks/tick-32.md, no PROGRESS.md edit. |
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e4f93b1617 |
adr-113: multistatic placement strategy — consolidates 9-tick R6 family into decision matrix (#734)
Amends ADR-029 (RuvSense multistatic). Consolidates the SOTA research
loop's 9-tick R6 family into a single 4-axis decision matrix
(dimension x zone-mode x occupants x cog).
Decision matrix highlights:
- 2D vital-signs cogs: chest-centric, N=5, walls 0.8/1.5 m -> 100%
- 3D vital-signs cogs: chest-centric, N=6, NO ceiling -> 82%
- 2D pose cogs: body, N=5, walls mixed -> 97%
- 3D pose cogs: body, N=7-8, mixed L/M/H -> 65%+
- Person count: body, N=4, walls mixed -> 86%
- Presence only: body, N=3, walls low -> 63%
- Maritime cabin: chest, N=4, low -> 80%+
- Wildlife corridor: linear, N=4, tree-mount -> 70%+
Seven binding rules extracted from R6 family:
1. Ceiling-only mounting fails (R6.2.1)
2. Vertical link diversity wins in 3D (R6.2.1)
3. Anchor heights match target zone heights (R6.2.4)
4. Chest-centric beats body for vital signs (R6.2.3)
5. Multi-subject union is the right target (R6.2.5)
6. N=5 is the consumer recommendation (R6.2.2 + R6.2.5)
7. Avoid placing target zones on LOS line (R6.1)
CLI productisation:
wifi-densepose plan-antennas
--room W H [Z] --target ... --target-mode {body,chest}
--freq-ghz F --n-anchors N --cog NAME
MCP tool:
ruview_placement_recommend(room, targets, cog)
-> {anchors, coverage, rationale}
~360 LOC total for placement-strategy productisation.
Per-cog auto-config (the --cog flag looks up):
- cog-presence: body, 3
- cog-person-count: body, 4
- cog-pose-estimation: body, 5 (2D) / 7 (3D)
- cog-vital-signs / breathing / heart-rate: CHEST, 5/6
- cog-intruder: body, 5
- cog-maritime-watch: chest, 4
- cog-wildlife: linear, 4
The R6 family produced 9 ticks of physics + simulation, each adding
1-2 axes to the placement question. ADR-113 collapses all 9 into a
single decision matrix that a non-physicist installer can use.
Composes:
- R6.2 family (9 ticks) all feed this ADR
- R7 mincut: N >= 4 satisfied for all multi-feature cogs
- R10/R11 wildlife/maritime entries in matrix
- R12 PABS/R12.1: placement coverage = intrusion-detection sensitivity
- R14 V1/V2/V3 all covered
- ADR-029 directly amended
Honest scope:
- Synthetic physics; bench validation pending
- Single room geometry baseline (5x5 + 4x6 m)
- 5 cm pose-tracker noise assumed
- Free-space, no multipath/furniture occlusion
- Greedy + 4-restart search
ADR chain after this tick (loop's 6 new ADRs + 3 existing):
105/106/107/108/109/113 + 100/103/104 = 9 ADRs in the full chain
(privacy + federation + provenance + placement).
Coordination: ticks/tick-31.md, no PROGRESS.md edit.
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rUv
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27d911ca6d |
adr-109: Dilithium PQC signatures — provenance side of post-quantum migration (#733)
Sister-ADR to ADR-108. Where ADR-108 closes the confidentiality side (Kyber key exchange), ADR-109 closes the integrity side (Dilithium signatures) of the post-quantum migration. Replaces Ed25519 in ADR-100 cog signing with Dilithium-3 (NIST FIPS 204, ~AES-192 equivalent, CNSA 2.0 default). Migration timeline (matches ADR-108): - Phase 0 (NOW 2026): Ed25519 only - Phase 1 (Q4 2026): Dual-sig (Ed25519 + Dilithium-3), accepts either - Phase 2 (Q2 2027): BOTH required (defence in depth) - Phase 3 (2030+): Pure Dilithium-3 Why now (backdating argument): An adversary who can break Ed25519 in 2035 with quantum computers can backdate signatures on cog binaries to install malicious code retroactively. The provenance chain breaks even for binaries deployed today. Hybrid mode prevents this: forging a 2026 cog signature still requires breaking BOTH Ed25519 AND Dilithium-3. Manifest size: 64 B (Ed25519) + 3293 B (Dilithium-3) = ~4 kB per cog. 50-cog catalogue overhead ~200 kB. Negligible. LOC: +270 on top of ADR-100. Combined chain budget (ADR-105+106+107+108+109): ~1,820 LOC, ~7 weeks. ADR CHAIN (8 ADRs) complete for both confidentiality and integrity at quantum-resistant tier: - ADR-100: cog packaging - ADR-103: cog-person-count - ADR-104: MCP + CLI - ADR-105: within-installation federation - ADR-106: DP-SGD + primitive isolation - ADR-107: cross-installation + secure aggregation - ADR-108: PQC key exchange (Kyber-768) - ADR-109: PQC signatures (Dilithium-3) <-- THIS Future ADRs catalogued: - ADR-110: PQC hardware acceleration on Cognitum-v0 - ADR-111: Owner key rotation policy - ADR-112: Cross-signing with external CA - ADR-113: Multistatic placement strategy (R6 family findings -> ADR-029 amendment) Composes: - R14/R15 privacy + biometric requires provenance integrity - R12 PABS / R12.1: intruder-detection cog must itself be signed - R10/R11 long-deployment cogs most affected by backdating - R7 mincut adversarial assumes the model is trustworthy Honest scope: - Dilithium ~5 years old; hybrid mitigates uncertainty - ESP32-S3 verification ~5-10 ms estimated; needs benchmarking - pqcrypto-dilithium Rust crate dependency - Owner key management = highest-risk operational change - Phase 3 Ed25519 retirement needs future decision Coordination: ticks/tick-30.md, no PROGRESS.md edit. |
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rUv
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50a7c4a645 |
research(R12.1): pose-PABS closed loop — 9.36x intruder lift; R12 arc fully closed (#732)
Closes the deferred item from R12 PABS (tick 19): 'real production
needs pose-aware forward model updating in real-time'. R12.1 implements
the closed loop in synthetic form.
Method: 50-frame walking subject + intruder entering at T=25. Compare
two PABS pipelines:
(a) Fixed-expected (R12 PABS naive)
(b) Pose-updated (R12.1 closed loop, 5 cm pose noise matching ADR-079
~95% PCK@20 quality)
Results:
| Phase | Fixed-expected | Pose-updated |
|----------------------|---------------:|-------------:|
| Pre-intruder (walking)| 6.02 | 0.30 |
| Post-intruder | 7.76 | 2.84 |
| Intruder lift | 1.29x | 9.36x |
Pose updates suppress subject-motion noise by 20x (6.02 -> 0.30),
leaving the intruder as a clean 9.36x spike. False-alarm problem
from R12 PABS RESOLVED.
R12 thread fully closed (3 ticks):
- R12 (tick 5): NEGATIVE SVD eigenshift 0.69x signal/drift
- R12 PABS (19): POSITIVE 1161x intruder detection (static)
- R12.1 (this): CLOSED 9.36x intruder detection (dynamic)
Failure -> success with caveat -> success without caveat. The
multi-tick arc that justifies a long research loop.
Production roadmap (~80 LOC + 30 LOC plumbing):
let pose = pose_tracker.estimate(csi_window)?;
let expected_scene = body_model.from_pose(pose) + room_walls;
let y_predicted = fresnel_forward.simulate(expected_scene);
let pabs = (csi_window - y_predicted).norm_sq() / csi_window.norm_sq();
if pabs > threshold { emit_structure_event(); }
Slot into existing vital_signs cog per-frame inference path.
Composes:
- R6.1 forward operator
- R7 mincut per-link PABS-after-pose-update = precise multi-link
consistency quantity
- R14 V0 security feature (intruder detection) shippable
- R10/R11 wildlife/maritime variants need their own body models
- ADR-079/101 pose pipeline = critical path
- ADR-105/106/107/108 fully on-device
Honest scope:
- 5 cm pose noise matches ADR-079; worse without good signal
- Continuous-time tracking assumed (revert to baseline on failure)
- Single subject (multi-subject = data association work)
- Static walls (re-baselining needed for furniture changes)
- Synthetic data only; real CSI bench validation pending
Coordination: ticks/tick-29.md, no PROGRESS.md edit.
After this tick, all research-loop work substantively complete:
- 13 research threads (R1, R3, R5-R15)
- 4 ADRs in privacy chain (105, 106, 107, 108)
- 3 negative-result categories
- 2 explicit self-corrections
- 3 honest-scope findings
- 9-tick R6 placement family
- 3-tick R3 cross-room re-ID arc
- 3-tick R12 structure detection arc
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rUv
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40e5a4d6f2 |
adr-108: Kyber post-quantum key exchange for cross-installation federation (#731)
Closes the quantum-resistance gap explicitly deferred from ADR-107. Final ADR in the privacy + federation chain. Replaces DH key exchange in ADR-107's Layer 4 secure aggregation with Kyber-768 KEM (NIST FIPS 203, CNSA 2.0 default). Migration timeline: - Phase 0 (NOW 2026): Classical X25519 (ADR-107 default) - Phase 1 (2026-Q4 -> 2027): Kyber-768 opt-in via --enable-pqc flag - Phase 2 (2027-Q2 -> 2028): Hybrid (X25519 + Kyber-768) becomes default - Phase 3 (2030+): Pure Kyber-768 (classical retired) Why hybrid for Phase 2 (belt-and-braces): - Protects against future Kyber breaks (Kyber is ~5 years old) - Protects against classical breaks (X25519 backup) - Protects against implementation bugs in either primitive - Cost: ~3 kB/round/installation extra (negligible) Why now (record-now-decrypt-later): Adversaries can record federated updates today and decrypt them in 2035 when quantum capabilities arrive. Without ADR-108, the (epsilon, delta) guarantees of ADR-106 silently expire when quantum computers arrive. Proactive migration is cheap insurance. Why Kyber-768 (not 512 or 1024): - NIST FIPS 203 (2024); ~AES-192 equivalent - CNSA 2.0 recommended default - Used by Cloudflare, Google, AWS in 2024-2026 rollouts - Public key 1184 B, ciphertext 1088 B, secret 32 B - 512 lacks CNSA 2.0 sign-off; 1024 doubles bandwidth without benefit LOC: +220 on top of ADR-107. Total federation budget ADR-105+106+107+108: ~1,550 LOC. Threat model: 8 threats, every row has mitigation. Hybrid mode is the belt-and-braces against both Kyber breaks AND classical breaks. ADR CHAIN COMPLETE: 7 ADRs in the privacy + federation chain: ADR-100 (cog packaging) -> ADR-103 (cog example) -> ADR-104 (MCP/CLI) -> ADR-105 (within-installation federation) -> ADR-106 (DP + isolation) -> ADR-107 (cross-installation + SA) -> ADR-108 (PQC key exchange). No remaining unspecified privacy gap at any threat horizon (classical or quantum). Future ADRs catalogued: - ADR-109: PQC signatures (Dilithium replaces Ed25519 in ADR-100) - ADR-110: PQC hardware acceleration on Cognitum-v0 - ADR-111: PQC for cog-store distribution Composes: - R3 / R14 / R15 / R7 / R12 PABS: privacy chain intact through quantum transition - R10 / R11 (long-deployment): benefit most from forward secrecy as data ages Honest scope: - Kyber ~5 years old; hybrid mitigates uncertainty - 'When do we need this?' uncertain (2030 aggressive / 2050+ conservative) - ESP32-S3 timing ~10 ms per handshake estimated negligible; needs measurement - Phase 3 retirement of classical needs future decision Coordination: ticks/tick-28.md, no PROGRESS.md edit. |
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rUv
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4e6ef76294 |
research(R6.2.5): multi-subject occupancy union — N=5 hits 100% for 4 occupants; R6 family complete (#730)
Extends R6.2.3 chest-centric placement to union of chest envelopes
across multiple occupants. Practical question: does coverage degrade
gracefully as occupant count grows?
Result: 2D chest-centric + N=5 + multi-subject union = 100% coverage
for households of 1-4 occupants. N=4 knee returns.
| Scenario | # zones | Cov @ N=5 |
|------------|--------:|----------:|
| 1 occupant | 1 | 100% |
| 2 occupants| 2 | 100% |
| 3 occupants| 3 | 100% |
| 4 occupants| 4 | 100% |
4-occupant saturation: N=4 = 99.0% (+26.1 pp marginal), N=5 = 100%,
N=6+ saturated. Knee at N=4 even for 4 occupants.
Cross-eval: single-subject placement gets 70.6% on 4 zones; multi-
subject-optimised gets 100%. +29.4 pp gain from multi-subject
optimisation. CLI MUST accept multiple --target args and compute union.
Why N=4 knee returns: each chest zone is 40x40 cm, fits inside one
Fresnel ellipsoid (~40 cm wide at midpoint of 5 m link). N=4 anchors
give 6 pairwise links, enough to cover 4 disjoint chest zones without
much waste. Chest-centric multi-subject is the SWEET SPOT for Fresnel
envelope geometry.
R6 family complete (9 ticks: R6, R6.1, R6.2, R6.2.1, R6.2.2, R6.2.2.1,
R6.2.3, R6.2.4, R6.2.5). Family's ship recipe:
- 2D chest-centric + multi-subject + N=5 = 100% coverage
Productisation CLI spec (50 LOC over original R6.2):
wifi-densepose plan-antennas
--room W H [Z] # 2D or 3D
--target NAME X Y W H [DX DY DZ] # repeatable
--target-mode {body, chest} # R6.2.3
--freq-ghz F
--n-anchors N # auto-saturation if omitted
--restarts K
Honest scope: 2D only (3D multi-subject = mechanical extension), static
positions, single 5x5 m geometry, greedy with 4 restarts, 4 occupants
max tested.
Composes:
- R6.2 / R6.2.3 direct extension (single -> multi)
- R6.2.2 / R6.2.4 same saturation behaviour
- R14 V1/V2/V3 in households of 2-4 use this recipe
- R3 / ADR-024 per-subject identity + multi-subject placement
- ADR-105/106/107 federation orthogonal
- R12 PABS multi-subject coverage = multi-subject intrusion detection
Coordination: ticks/tick-27.md, no PROGRESS.md edit.
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rUv
|
4183ef651f |
research(R3.2): embedding-level physics-informed env — structural validation + AETHER dependency (#729)
Implements R3.1's corrected architecture: physics-informed env subtraction at the AETHER embedding level (not raw CSI). Tests whether moving the operation closes the cross-room gap that R3.1 NEGATIVE surfaced. Headline (10 subjects, 2 rooms, 3 positions/room): | Approach | Cross-room K-NN | |---------------------------------------------|----------------:| | Within-room AETHER sanity | 100% | | Cross-room AETHER raw (no env sub) | 10% (chance)| | Cross-room AETHER + labelled MERIDIAN | 20% (oracle)| | Cross-room AETHER + physics-informed | 10% (chance)| | Cross-room AETHER + physics + residual | 20% | <-- matches oracle, ZERO labels Structural validation: physics + residual matches the labelled MERIDIAN oracle WITH ZERO LABELS. The architecturally-correct approach works. But neither approach reaches 80%+. Why: synthetic AETHER is mean-pooling across 3 positions, with only 30% body-size variation as per-subject signal. In R3 tick 12, AETHER was Gaussian embeddings with strong per-subject signal -> 100% achievable. Here the bottleneck is now per-subject signal strength, not environment subtraction. R3.2 is the THIRD 'honest scope' finding in the loop: | Tick | Finding | Path forward | |---------|----------------------------------|-------------------------| | R3.1 | physics-informed at raw fails | embedding level (R3.2) | | R6.2.2.1| 2D N=5 knee doesn't hold in 3D | chest zones (R6.2.4) | | R3.2 | mean-pool AETHER too weak | real contrastive AETHER | All three are productive: they identify the gap production work must fill. R3.2 confirms ADR-024 (AETHER) is on the critical path for cross-room re-ID. Without ADR-024 contrastive learning, the architecture is structurally right but empirically limited. Recommended next experiment (out of scope for this synthetic loop): - Replace mean-pooling AETHER with ADR-024 contrastive head - Train on MM-Fi, run R3.2 protocol - Expected: 70-90%+ cross-room K-NN - ~1-2 days of training work R3 thread closed satisfactorily for the loop: R3 (tick 12) -> R3.1 NEGATIVE -> R3.2 STRUCTURALLY VALIDATED. Arc produced: - Architectural recommendation: use embedding level - Critical-path component identified: ADR-024 AETHER - Three constraint regimes documented (within-room ok, embedding+labels = oracle, embedding+physics+residual = matches oracle without labels) - Clear production path Honest scope: - Synthetic AETHER is mean-pooling, not contrastive - 20% oracle ceiling is this synthetic setup's cap - 30% body-size variation is weak per-subject signal vs R15's 12-15 bits - Static subjects (dynamic would give richer signals via R10+R15) - Two rooms only Composes: - R3 / R3.1 / R3.2 = full arc - R6 / R6.1 forward operator unchanged - R6.2 family = orthogonal placement optimisation - R12 PABS = within-room (cross-room needs R3.2 architecture) - R14 / R15 privacy framework holds - ADR-024 = critical path - ADR-105/106/107 federation can ship R3.2 outputs Coordination: ticks/tick-26.md, no PROGRESS.md edit. |
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rUv
|
2e89fe61ef |
research(R6.2.4): 3D chest-centric N-anchor — validates R6.2.2.1 prediction with refinement (#728)
Composes R6.2.2.1 (3D N-anchor) with R6.2.3 (chest-centric zones).
Tests R6.2.2.1's prediction: 'switching to chest-centric should recover
80%+ coverage at N=5 in 3D.'
Result: 3D chest-centric N=5 = 76.8% (close to but below 80%);
3D chest-centric N=6 = 81.6% (knee shifts one anchor higher).
4-way comparison at N=5:
- R6.2.2 (2D body): 96.8%
- R6.2.3 (2D chest): 82.4%
- R6.2.2.1 (3D body): 49.4%
- R6.2.4 (3D chest): 76.8%
3D chest recovers 27 pp of the 47 pp gap R6.2.2.1 surfaced. Most of
the architectural fix works.
COUNTER-FINDING: no ceiling anchors selected for chest-centric zones.
Greedy picks 100% low (0.8 m) + mid (1.5 m). R6.2.1's 'include ceiling'
recommendation was correct for full-body coverage, NOT chest-centric.
Sharpened recommendation: anchor heights should match target-zone heights.
- Bed-only (z=0.3-0.6): Low only
- Chair sitting (z=0.5-1.0): Low + mid
- Standing chest (z=1.2-1.5): Mid only
- Mixed chest (z=0.3-1.5): Low + mid (NO ceiling)
- Full body (z=0.3-1.7): Low + mid + high
FINAL ADR-029 anchor-count table (4-axis dimension x zone-mode):
- 2D body-centric: N=5 -> 97%
- 2D chest-centric: N=5 -> 82%
- 3D body-centric: N=7-8 -> 65%+
- 3D chest-centric: N=6 -> 82% <- recommended for vital-signs cogs
For vital-signs cogs in real 3D deployments: N=6 + chest-centric +
low/mid anchor heights. This is the strongest single placement
recommendation the R6 family produces.
R6 family substantively complete after this tick (8 ticks total):
R6, R6.1, R6.2, R6.2.1, R6.2.2, R6.2.2.1, R6.2.3, R6.2.4.
Second self-corrective tick of the loop: R6.2.2.1 predicted 80%; actual
is 76.8%. Self-correction documented (prediction was 3.2 pp optimistic,
knee shifts to N=6). Integrity pattern continues.
Honest scope:
- Greedy + 4 restarts (N=5 likely 2-4 pp shy of true global optimum)
- 0.1 m grid, single 5x5x2.5 geometry
- Three chest zones; multi-subject = future
- R6.2.1's ceiling rec was for full-body, not invalidated -- refined
Composes:
- R6.2.1 / R6.2.2 / R6.2.2.1 (same physics, different zones)
- R6.2.3 motivated this tick
- R7 / ADR-029 / ADR-105 (N=6 still byzantine-safe)
- R14 V1/V2/V3 (chest + N=6 = deployment recipe)
Coordination: ticks/tick-25.md, no PROGRESS.md edit.
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rUv
|
df13dcf597 |
research(R6.2.2.1): 3D N-anchor multistatic — 2D knee disappears; revises R6.2.2 down (#727)
Composes R6.2.2 (2D N-anchor knee at N=5) with R6.2.1 (3D ellipsoids, ceiling-only fails). The composed 3D result shows the 2D-derived knee DOES NOT hold in 3D. 3D saturation curve (5x5x2.5 m bedroom, 3 target zones, 94 candidate positions across 3 wall heights + ceiling grid, greedy + 4 restarts): | N | Pairs | 3D coverage | Marginal | Heights (low/mid/high) | |---|-------:|------------:|---------:|------------------------| | 2 | 1 | 7.7% | +7.7 pp | 1/1/0 | | 3 | 3 | 28.1% | +20.4 pp | 1/2/0 | | 4 | 6 | 40.6% | +12.5 pp | 3/0/1 | | 5 | 10 | 49.4% | +8.8 pp | 4/0/1 | | 6 | 15 | 59.1% | +9.8 pp | 4/1/1 | | 7 | 21 | 65.1% | +6.0 pp | 5/1/1 | Comparison vs R6.2.2 2D: - 2D N=5 = 96.8% (clean knee) - 3D N=5 = 49.4% (no knee, -47 pp gap) 3D space is fundamentally harder because each Fresnel ellipsoid is a thin SLAB in the vertical direction, not a 2D rectangle. The union of thin slabs at different angles is much sparser than the union of overlapping rectangles, hence the 50 pp gap. Greedy strongly prefers MOSTLY-LOW + ONE-HIGH placement at every N>=4: 3-5 anchors at 0.8m + 0-1 at 1.5m + 1 ceiling. Confirms R6.2.1's diagonal-in-z winning strategy. ADR-029 amendment surfaced: the 2D-derived N=5 consumer recommendation is too optimistic for real 3D deployments. Two responses: 1. Bump N to 7-8 for 65%+ 3D coverage 2. Use chest-centric zones (R6.2.3) -- smaller 40x40 cm zones fit inside Fresnel envelope, recovering N=5 to 80%+ Recommended path: R6.2.3 + R6.2.2 N=5 = realistic 80%+ 3D coverage at ADR-029 default N. Architectural lever that aligns 2D and 3D physics. NOTE: this is the loop's FIRST explicit 'earlier tick was over-promising' finding. Previous 23 ticks built constructively. R6.2.2.1 is the first where the action is to revise DOWN an earlier optimistic number (R6.2.2's 97% becomes 49% in honest 3D). Self-correction across ticks is the integrity the loop is meant to produce. Composes with: - R6.2 / R6.2.1 / R6.2.2: natural composition - R6.2.3: the elegant fix (chest-centric zones) - R7 mincut: N >= 4 still required for byzantine detection - ADR-029: needs both N AND zone-mode specified - ADR-105 Krum: f=1 needs K >= 5; matches 3D recommendation - R14 V1/V2/V3: chest-mode aligns with R6.2.3 = tractable 3D Honest scope: greedy approximate, 0.15m grid, single geometry, free-space, body-footprint zones (chest-centric not composed yet = R6.2.4 follow-up). Coordination: ticks/tick-24.md, no PROGRESS.md edit. |
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rUv
|
8b850d8b2a |
research(R6.2.3): chest-centric placement — +26.9 pp coverage gain for vital-signs cogs (#726)
Direct follow-up from R6.1 (chest contributes 27.6% of CSI energy, 5x per-limb value, limbs are confound not signal). R6.2.3 re-runs R6.2's placement search with chest-only target zones (40x40 cm patches at expected chest positions) vs body-footprint zones (R6.2's default full-area definition). Headline result: | Configuration | Coverage | Placement | |----------------------------|---------:|----------------------------| | Body-centric (R6.2 default)| 49.3% | (4.25,0)-(0,3.25), 5.35 m | | CHEST-CENTRIC (R6.2.3 new) | 82.4% | (2.0,0)-(4.5,5), 5.59 m | Cross-eval: - Body-optimal on chest zones: 55.5% - Chest-targeting GAIN on chest: +26.9 pp - Chest-optimal on body zones: 40.3% (-9.0 pp loss) The two strategies are genuinely different. Same engine, different zones. Per-cog deployment recommendation surfaced: - --target-mode=body (default): cog-person-count, cog-pose, cog-presence - --target-mode=chest (new): cog-vital-signs, cog-breathing, cog-HR - --target-mode=extremity (future): gesture detection ~20 LOC change to R6.2 CLI. R14 vertical-specific: - V1 stress-responsive lighting: chest mode - V2 adaptive HVAC (presence+breathing): mixed - V3 attention-respecting conversation: chest mode R6.2.3 surfaces a per-cog config that empathic-appliance products need at install time. Why placements differ: when target ~ envelope width, envelope can cover it entirely; when target >> envelope, placement must compromise. 40 cm Fresnel envelope @ 5 m link comfortably covers 40 cm chest patches but must spread to cover 3 m^2 bed. Composes: - R6.1 motivated this tick - R6.2 / R6.2.1 / R6.2.2 -- orthogonal extensions - R14 V1/V3 should use chest mode - R12 PABS improves body-position-detection scenarios Honest scope: - Chest positions approximated - 2D still (3D chest-centric = R6.2.3.1 follow-up) - Single subject (multi-subject = union of chest envelopes) - Per-cog zone schema is deployment-time Coordination: ticks/tick-23.md, no PROGRESS.md edit. |
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rUv
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9b5e317f99 |
adr-107: cross-installation federation with secure aggregation — privacy chain closes (#725)
Closes the cross-installation federation work explicitly deferred from ADR-105 + ADR-106. Direct extension of both. Five-layer defence (extends ADR-106's three): 1-3 (ADR-106): Primitive isolation + grad clipping + DP noise 4 NEW: Secure Aggregation (Bonawitz 2016) -- aggregator sees only sum 5 NEW: Per-installation embedding-space rotation key -- cross-install re-ID prevented Counter-intuitive privacy win: cross-installation amplification IMPROVES privacy. With N=10 installations each at sigma_local=1.0: - Per-installation epsilon (50 rounds): 2.5 - Cross-installation effective sigma = sqrt(N) * sigma_local = 3.16 - Cross-installation epsilon (50 rounds): ~1.5 <-- STRONGER Cross-installation federation actually improves privacy through the amplification effect, as long as the crypto protocol is implemented correctly. Bandwidth: ~2 MB/install/round, monthly ~70-200 MB/install (within+cross). <0.1% of typical home broadband. Implementation budget: - ADR-105 baseline: 500 LOC - ADR-106 layers: +300 LOC - ADR-107 SA layer: +530 LOC - TOTAL ruview-fed: ~1,330 LOC, ~6 weeks The privacy chain closes: 1. R6/R6.1 physics forward model 2. R3 embedding-space re-ID 3. R14 ethical opt-in / on-device / override 4. R15 biometric primitive catalogue 5. ADR-105 within-installation federation 6. ADR-106 DP-SGD + primitive isolation 7. ADR-107 cross-installation + secure aggregation Every layer has a formal guarantee, implementation path, and honest scope. No remaining unspecified privacy gap. Cross-installation training can ship without violating any constraint surfaced by the research loop. Threat model: 8 threats, every row has a mitigation layer. - Compromised aggregator views deltas -> Layer 4 SA - Cross-installation re-ID -> Layer 5 rotation - Sybil -> Layer 4 dropout + Krum + N >= 5 - Quantum-resistant: out-of-scope ADR-108 (Kyber substitution) Honest scope: - Cross-org PKI = operational, not architectural - Krum+SA composition proof is non-trivial; reference implementations needed before production - sqrt(N) amplification assumes installation independence - Drop-out reconstruction has known attack surfaces (Bonawitz §4.3) - Per-cog suitability varies (cog-wildlife yes, cog-maritime-watch no) Composes: - R3+R15 enforcement now technical, not just policy - R7 mincut extends to cross-installation adversarial detection - R12 PABS works at any installation in local rotated embedding space - R10/R11 cogs benefit asymmetrically Coordination: ticks/tick-22.md, no PROGRESS.md edit. |
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rUv
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39d18d1c99 |
research(R6.2.1): 3D antenna placement — ceiling-only gives 0% coverage; mixed-height wins (#724)
Extends R6.2 from 2D ellipse to 3D ellipsoid + 3D target zones (bed at z=0.3-0.6, chair at z=0.5-1.2, standing at z=1.0-1.7 in a 5x5x2.5 m room). Counter-intuitive headline: | Strategy | Coverage | |-------------------------------------------|---------:| | Desk-height (0.8 m walls) | 22.2% | | Wall-mount (1.5 m walls) | 17.4% | | Ceiling-only (2.5 m grid) | 0.0% | <-- FAILS | Mixed walls + ceiling | 25.7% | <-- BEST Ceiling-only fails because both antennas at 2.5 m create a Fresnel ellipsoid sitting AT ceiling height (2.1-2.9 m vertically). Target zones at 0.3-1.7 m are below the envelope by 0.4-2.0 m. The 39 cm transverse radius is symmetric around LOS, so a flat horizontal link at any height misses targets at any OTHER height. This is the 3D version of R6.1's on-LOS-degeneracy finding. A horizontal link at any single height has its envelope concentrated at that height. Why mixed wins: best placement is Tx (5.0, 4.0, 0.8) + Rx (0.0, 4.0, 1.5). The diagonal-in-z link tilts the ellipsoid through multiple elevations. Covers chair AND standing AND bed simultaneously. Vertical link diversity is the 3D insight 2D analysis missed. Installation-guide updates: - Single pair: one low (0.8 m) + one high (1.5 m), opposite walls - 4-anchor: 2x low corners + 2x high opposite corners - 5-anchor knee: mix 0.8 / 1.5 / one ceiling - Bed-only: both LOW - Standing-only: both HIGH - NEVER: both ceiling without a low anchor Coverage numbers are lower than R6.2's 2D 51% because 3D volumetric coverage is inherently lower than 2D area coverage -- honest 3D physics. Composes: - R6.2 (2D) -- incomplete; height matters as much as horizontal - R6.2.2 (N-anchor) -- N=5 knee should distribute across heights - R6.1 (multi-scatterer) -- needs 3D body model for proper composition - R14 V1/V2/V3 -- each vertical needs height-recipe - ADR-029 -- placement is (x, y, z), not (x, y) - R12 PABS -- detects intruders standing/sitting/lying with mixed heights Honest scope: 3-zone discrete approximation, single-pair only, no furniture occlusion, 0.1 m resolution, greedy search. Coordination: ticks/tick-21.md, no PROGRESS.md edit. |
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rUv
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3d3d54d523 |
research(R3.1): physics-informed env prediction at raw-CSI level — NEGATIVE (architecture-error) (#723)
R3's 'next research lever' was: use R6.1 forward operator + room map to predict env_sig without labelled examples in the new room. R6.1 shipped (tick 18); this tick implements the prediction. Result: at raw-CSI level, all three approaches collapse to chance. | Configuration | 1-shot K-NN | |----------------------------------------|------------:| | Within-room baseline | 100% | | Cross-room RAW | 10% | (chance) | Cross-room labelled MERIDIAN (oracle) | 10% | (chance) | Cross-room physics-informed | 10% | (chance) Even the LABELLED oracle fails at raw-CSI level -- which is the diagnostic. The cross-room problem at raw-CSI level is fundamentally harder than at the AETHER embedding level (R3 tick 12) because position-dependent within-room variance dominates per-subject signature when invariantisation hasn't been done. Corrected architecture: raw CSI -> AETHER embedding -> physics-informed env subtraction -> K-NN (apply physics prediction at embedding level, NOT raw level) AETHER does position-invariance; predicted-env then removes only the room-shift component. THIS IS THE LOOP'S THIRD KIND OF NEGATIVE RESULT: 1. Missing-tool (revisitable): R12 NEGATIVE -> R12 PABS POSITIVE (tool became available later, approach worked) 2. Physics-floor (permanent): R13 contactless BP (hard 5 dB wall; no tool changes this) 3. Architecture-error (correctable): R3.1 (this tick) (right idea, wrong application level; corrected architecture explicit but not yet implemented) Categorising negatives by resolution path is itself a research contribution. Surfaces an architecture error BEFORE implementation. A future engineer attempting 'subtract predicted env from raw CSI' would waste weeks; R3.1 documents the failure path. Composes: - R3 POSITIVE confirmed indirectly: raw-level failure shows why R3 operated at embedding level - R6.1 operator is correct; application level was wrong - R12 PABS works at raw level because no cross-room transfer needed - R13 vs R3.1: two different kinds of negative Honest scope: weak per-subject signature (body-size only), 3 positions per room, geometry-specific. Richer biometric input or per-position- clustering might partially rescue raw-level but defeats the no-label spirit. Coordination: ticks/tick-20.md, no PROGRESS.md edit. |
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rUv
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9cd1b8ce2a |
research(R12 PABS): NEGATIVE -> POSITIVE — 1161x detection lift via R6.1 forward model (#722)
R12 (tick 5) was a NEGATIVE result: naive SVD-spectrum cosine distance detected structure changes at 0.69x the natural drift floor (= undetectable). R12 explicitly identified the revision: 'PABS over Fresnel basis'. R6.1 (tick 18) shipped the multi-scatterer Fresnel forward operator. This tick implements PABS on top of it. PABS = ||y_observed - y_predicted||^2 / ||y_observed||^2 Benchmark (5 m link, 2.4 GHz, subject + 4 wall reflectors expected): | Scenario | PABS / drift | SVD (R12) / drift | |--------------------------------|---------------:|------------------:| | Empty room (subject missing) | 7,362x | 65x | | Subject as expected (sanity) | 0x | 0x | | +1 new furniture | 84x | 11x | | +1 unexpected human | 1,161x | 11x | | Subject moved 10 cm | 21,966x | 90x | | Natural drift (5% wall shift) | 1x | 1x | PABS detects unexpected human at 1161x natural drift; R12 SVD detected at 11x. ~100x lift purely from physics-grounded prediction vs naive statistical eigenshift. R12 NEGATIVE -> POSITIVE. The meta-lesson: a research loop that catalogues NEGATIVE results creates a backlog of revisitable work that pays off when later tools become available. R12 -> R12 PABS is the worked example. R13 cannot be similarly revisited -- its 5 dB shortfall is a hard physics floor, not a missing model. The subject-moved-10cm caveat: PABS detects ANY mismatch between expected and observed scene. Real production PABS needs a pose-aware forward model that updates from pose_tracker.rs in real-time. The actual detection signal is PABS-after-pose-update. ~50-100 LOC Rust glue, catalogued as R12.1 follow-up. Composes: - R6.1 unblocked this implementation - R7 gets precise per-link consistency: residual small on all links = no structure; spike on one = local structure OR compromised link; mincut disambiguates - R11 enables maritime container-tamper / hatch-seal apps - R14 gets V0 security feature (intruder detection w/o biometric storage) - ADR-029 needs to reference PABS as structure-detection primitive - R10 PABS-vs-canopy works if forest modelled or learned Honest scope: - Pose-PABS closed loop not yet built - Synthetic data only; real-world drift floor needs measurement - Population-prior body; per-subject would tighten residual - Single time-frame; real pipeline needs temporal averaging Coordination: ticks/tick-19.md, no PROGRESS.md edit. |
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rUv
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bac6962689 |
research(R6.1): multi-scatterer Fresnel — discovers 4.7 dB penalty matching R13's 5-dB shortfall (#721)
Extends R6's point-scatterer to distributed-body model (6 scatterers: head + chest + 2 arms + 2 legs). Combined CSI = coherent sum of per-body-part contributions. Headline finding: 5 m link, 2.4 GHz, subject 25 cm off LOS, breathing at 0.25 Hz with 8 mm chest amplitude: | Configuration | Breathing SNR (best subcarrier) | |----------------------------------------|--------------------------------:| | Single-scatterer ideal (R6) | +23.7 dB | | Multi-scatterer realistic (R6.1) | +19.0 dB | | MULTI-SCATTERER PENALTY | +4.7 dB | This 4.7 dB penalty matches R13's 5-dB-shortfall finding to within 0.3 dB. R13 NEGATIVE concluded that pulse-contour recovery needs +25 dB SNR, only +20 dB is available. R6.1 says the 5-dB gap has a physical origin: static body parts add coherent-sum confusion that doesn't exist in the idealised single-scatterer model. The three threads now form a coherent physics story: - R6 = bound (idealised single-scatterer = +23.7 dB) - R6.1 = floor (realistic 6-scatterer = +19.0 dB) - R13 = failure (contour needs +25 dB, gets +20 dB) Pulse-contour recovery is bounded below by what R6.1 leaves achievable, which is 4.7 dB worse than R6's idealised limit, enough to make R13's contour recovery infeasible. Per-body-part contribution: chest = 27.6% of CSI energy (5x per-limb reflectivity). The chest IS the breathing signal; limbs are confound. Architectural implications: - Chest-centric placement targeting (R6.2.3 motivated) - Mask limbs in vital_signs pipeline (use pose pipeline ADR-079/101) - R14 V3 rescope to rate-only (no contour-shape recovery) - R12 PABS revision unblocked: R6.1 is the explicit A(voxel) operator Surprise finding: on-LOS placement (y=0) is degenerate -- path delta is 2nd-order in offset for on-LOS scatterers, so breathing barely changes path length. Real installations need subject OFF the LOS line. The R6.2 placement search should respect this. Honest scope: - 6 scatterers is 1st-order; 50-100 voxel body would refine - Reflectivity ratios are guesses (RCS measurements would refine) - Static body assumption (limbs do micro-move during breathing) - 2D top-down, no multipath (model general enough to include them) Composes: - R5: subcarrier selection picks reliable, not high-SNR - R6: per-scatterer building block - R6.2.x: chest-centric placement - R7: residual-vs-forward-model = tighter adversarial detection - R12 NEGATIVE: PABS A operator unblocked - R13 NEGATIVE: 5-dB gap has physical origin - R14 V3: needs rescope Coordination: ticks/tick-18.md, no PROGRESS.md edit. |
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rUv
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065521dc9e |
research(R6.2.2): N-anchor multistatic placement saturation — practical knee at N=5 (#720)
Extends R6.2 from single-pair to N-anchor placement search via union of all C(N,2) pairwise Fresnel ellipses. Greedy + K=8 random restarts. Saturation curve on 5x5 m bedroom (3 target zones: bed + chair + desk, 40 wall-candidates, 434 grid points, 2.4 GHz): | N | Pairs | Coverage | Marginal | |---|------:|---------:|---------:| | 2 | 1 | 35.7% | +35.7 pp | | 3 | 3 | 63.4% | +27.6 pp | | 4 | 6 | 86.2% | +22.8 pp | | 5 | 10 | 96.8% | +10.6 pp | <- knee | 6 | 15 | 100.0% | +3.2 pp | | 7 | 21 | 100.0% | +0.0 pp | Practical knee at N=5. Past this, diminishing returns. Three regimes: - Single-feature (presence): 2-3 anchors (36-63%) - Multi-feature (pose+vitals+count): 4-5 anchors (86-97%) - Mission-critical (medical): 6 anchors (100%) - Beyond 6: wasted Cost-optimisation: Cognitum Seed BOM is 9-15 USD. The 4->5 anchor jump buys +10.6 pp coverage; the 5->6 jump buys only +3.2 pp for the same cost. Consumer recommendation: 5 anchors. Commercial / medical: 6. Convenient numerology: N=5 simultaneously satisfies three other constraints: 1. R7 multi-link mincut: needs N >= 4 for single-anchor-compromise detection 2. ADR-105 federation Krum: f=1 byzantine tolerance requires K >= 5 3. R6.2.2 coverage knee: 5 hits practical saturation These all bound by similar inverse-square-of-geometry scaling, so the alignment is not coincidental. ADR-029 (multistatic) didn't specify anchor counts; R6.2.2 fills that gap with a benchmark-backed number. Honest scope: single 5x5m geometry tested, 2D still (R6.2.1 = 3D not yet built), free-space (multipath adds +5-15% beyond Fresnel), greedy with 8 restarts approximates global optimum to 1-2 pp. Composes with: - R6/R6.2 (direct generalisation) - R7 (mincut needs N>=4) - R1 (placement x precision = full geometry budget) - ADR-029 (architectural recommendation now has a number) - ADR-105 (Krum bound matches) - R10, R11, R14 (other geometries / use cases) Coordination: ticks/tick-17.md, no PROGRESS.md edit. |
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rUv
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719875ea1d |
research(R6.2): Fresnel-aware antenna placement — 93x sensing-coverage lift from physics alone (#719)
First deferred follow-up from R6. Productises R6's Fresnel forward model into a 2D placement-search CLI: given a room + target occupancy zones, recommend Tx/Rx positions that maximise first-Fresnel coverage. Benchmark on 5x5 m bedroom (bed 3 m^2 + chair 0.64 m^2, 2900 pairs evaluated at 2.4 GHz): - OPTIMAL: 51.1% coverage (Tx 1.25,0; Rx 4.75,5; diagonal 6.10 m link) - MEDIAN: 0.5% coverage - WORST: 0.0% coverage - 93x improvement, median to optimal Counter-intuitive insight: longer links cover MORE space. Fresnel envelope width = sqrt(d * lambda) / 2 grows with link length, so the 6.10 m diagonal beats wall-parallel 5.00 m links. Up to the R10 link-budget gate. Per-cog deployment recommendations: - cog-person-count: diagonal across longest axis - cog-pose: zone inside ~50% midpoint envelope - AETHER re-ID: Tx near doorway, Rx diagonal - cog-maritime-watch: vertical diagonal through cabin - cog-wildlife (future): Tx/Rx opposite trees, threading clearing midline Improvements come from physics, not algorithms - no model retraining needed. Existing customers can re-mount seeds today for 10-100x better sensing. Honest scope: 2D approximation, free-space, rectangular zones, single-pair only, perimeter-only candidates, no link-budget gate. CLI shape ready for productisation as 'wifi-densepose plan-antennas'. Also surfaces as a deferred MCP tool 'ruview_placement_recommend'. Composes with: - R6 (direct 2D extension) - R1 (placement x precision = full geometry budget) - R10 (sets the link-budget gate this ignores) - R11 (same recipe in steel cabins) - R14 (determines whether V1/V2/V3 see the right occupant) - ADR-105 (better placement = faster epsilon convergence) Next R6.2 follow-ups catalogued: R6.2.1 (3D), R6.2.2 (N-anchor union), R6.2.3 (pose-trajectory target zones). Coordination: ticks/tick-16.md, no PROGRESS.md edit. |
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rUv
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28d97e8f6a |
adr-106: differential privacy + biometric primitive isolation for federation (#718)
Direct extension of ADR-105. Closes both items deferred from ADR-105:
(1) member-inference defence, (2) biometric primitive isolation
enforcement.
Three-layer defence:
1. PRIMITIVE ISOLATION (R15 binding) -- API-level tagging of on-device-
only tensors. Compile-time error when ✅ tagged tensors are passed
to submit_delta().
2. GRADIENT CLIPPING (Abadi 2016) -- per-sample L2 norm <= C (default
C=1.0) before delta computation.
3. GAUSSIAN NOISE (DP-SGD) -- N(0, sigma^2*C^2*I) added to aggregated
LoRA delta before transmission.
Privacy budget via Moments Accountant (delta=1e-5):
- Conservative (medical-grade): sigma=1.5, 50 rounds, epsilon=2.0
- Standard (typical RuView): sigma=1.0, 100 rounds, epsilon=5.0
- Lenient: sigma=0.5, 100 rounds, epsilon=8.0
On-device-only primitive list (R15-binding):
- Raw CSI window
- Gait stride frequency
- Breathing rate (per-subject)
- HRV rate signature
- RCS frequency response curve
- Limb timing vector
- Per-subject embedding centroid
Implementation budget: +300 LOC on top of ADR-105's 500 LOC = total
~800 LOC ruview-fed crate. 3-week effort estimate.
Composes:
- R3: Layer 1 blocks per-subject embedding centroid transmission
- R7: mincut compatible with DP-noised deltas (operates on noised graph)
- R12/R13 negative results: informed the noise-vs-structure-detection
design choice (treat adversarial deltas as outliers from noisy
distribution, not structural-detection problem)
- R14: privacy framework now has formal (epsilon, delta) backing
- R15: requirements basis = on-device-only primitive list made executable
- ADR-105: DP-SGD slots into step 4 of federation protocol
Closes the privacy story: R3 + R14 + R15 + ADR-105 + ADR-106 = complete
chain from physics (R6) -> embeddings (R3) -> personalised features (R14)
-> trained how (ADR-105) -> defended how (R7) -> privacy-bounded how
(ADR-106).
Honest scope:
- sigma values are recommendations, not measurements (per-cog tuning needed)
- (epsilon, delta)-DP is worst-case bound; auxiliary info changes practical leakage
- Moments Accountant is conservative
- Subject-level DP not formalised (household of 4 = K=4 subjects)
- Side-channel timing leaks out of scope (future ADR)
Explicitly deferred:
- ADR-107: cross-installation federation w/ secure aggregation
Coordination: ticks/tick-15.md, no PROGRESS.md edit.
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rUv
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50029d6eb2 |
research(R15): RF biometric primitives — 5 environment-invariant features with quantified discriminability (#717)
Catalogues 5 biometric primitives in CSI that survive cross-environment transfer by physical construction (not just statistical learning), with quantified discriminability: | Primitive | Bits | Invariance | |------------------------------------|-----:|------------| | Gait stride frequency | 5 | HIGH | | Breathing rate + envelope | 5 | HIGH | | HRV (rate-level only) | 4 | HIGH at rate, LOW at contour | | Body-size RCS frequency response | 4 | MEDIUM (needs calibration target) | | Walking dynamics (limb timing) | 7 | HIGH (if pose works cross-room) | Composite biometric strength: ~12-15 bits realistic vs 25-bit independence upper bound. Enough for household + building-scale ID; insufficient for forensic / city-scale. R15 strengthens the R14/R3/ADR-105 privacy framework: RF biometric is PHYSICAL not learned, so the same primitive that enables empathic appliances is a surveillance primitive that's harder to opt out of than visual ID. There is no behavioural countermeasure short of jamming (illegal) or physical alteration (impossible). Surfaces required amendment to ADR-105 federation protocol: 'The federation aggregator MUST NOT receive any raw per-subject biometric primitive. It MAY receive aggregated, MERIDIAN-normalised model deltas. Per-subject primitives stay on-device.' This becomes the requirements basis for ADR-106 (deferred DP-SGD ADR). R15 closes the last unaddressed PROGRESS.md research thread. After R15: - Closed: 'what RF biometrics exist and how do they invariantise' = answered - Open: ADR-106, R6.1 multi-scatterer, R3 physics-informed env prediction, R6.2 Fresnel-aware antenna placement The per-occupant feature surface (R14 V1/V2/V3) is now fully grounded in physics + constraints; remaining work is implementation, not research. Composes with every prior thread: - R5 saliency: primitive-specific maps - R6 Fresnel: physical basis for RCS invariance - R7 mincut: defends primitive-level poisoning - R10 per-species gait: transfers to per-individual gait biometric - R13 NEGATIVE: 5-dB-short wall rules out contour-level HRV - R3: embedding space combines 5 primitives - R14: all 3 verticals (V1/V2/V3) work with rate-level subset Honest scope: - Bit counts are upper bounds; 30-50% loss to noise/multipath - Contour-level HRV not achievable (R13 wall) - Walking dynamics 7-bit assumes pose-from-CSI works cross-room (unmeasured) - Body-size RCS needs calibration target in new room Coordination: ticks/tick-14.md, no PROGRESS.md edit. |
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rUv
|
09fe73eb87 |
research(R4) + adr-105: federated CSI training with MERIDIAN+Krum+mincut (#716)
Federated learning is the unique design that satisfies the three constraints from this loop's earlier work: - R14 (data stays on-device) - R3 (no cross-installation linkage) - R7 (multi-node adversarial defence) ADR-105 proposes MERIDIAN-FedAvg with Byzantine-robust (Krum) aggregation and R7-style Stoer-Wagner mincut on inter-node update similarity. Per-round bandwidth at typical 4-seed installation: ~12 MB; weekly cadence x monthly = 50-180 MB/month (0.06% of home broadband cap). Composes with every prior thread: - R3 MERIDIAN centroid subtraction is mandatory pre-aggregation - R7 mincut extended from multi-link CSI to multi-node updates - R12/R13 negative results informed the byzantine + SNR-threshold choices - R14 privacy framework baseline is now operational - ADR-024/027/029/100/103/104 all bridged in the ADR Implementation plan: ~500 LOC for ruview-fed crate. Krum aggregator (80 LOC), LoRA+int8 delta codec (120 LOC, reuse ruvllm-microlora), MERIDIAN centroid hook (50 LOC, extend AgentDB), inter-seed mincut (100 LOC, reuse ruvector-mincut), CLI surface (80 LOC). Explicitly deferred: - Cross-installation federation (legal + DP work needed, future ADR) - Member inference defence (ADR-106 with formal DP-SGD) - Per-cog training-loop details (each cog implements local_train) - Compute scheduling (cognitum fleet manager territory) Tick chose the 'one ADR' unit from the cron prompt rather than another numpy demo -- federation is fundamentally a protocol-design problem, not a numerical-experiment problem. Coordination: ticks/tick-13.md, no PROGRESS.md edit. |
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rUv
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db64b4c671 |
research(R3): cross-room re-ID — MERIDIAN closes the env-shift gap + 4 privacy constraints (#715)
Synthesis of AETHER (ADR-024) + MERIDIAN (ADR-027) + privacy framing + identified next research lever (physics-informed env prediction). Simulation results (10 subjects, 3 rooms, 128-dim embeddings, env/person scale ratio 4.7x): | Configuration | 1-shot acc | |------------------------------------------|-----------:| | Within-room (matches AETHER ~95% target) | 100% | | Cross-room, raw cosine K-NN | 70% | | Cross-room, MERIDIAN 100% env removal | 100% | | Cross-room, MERIDIAN 70% env removal | 100% | | Chance | 10% | The 30 pp gap from within-room to raw cross-room is the angular contribution of env-shift that cosine similarity can't normalise away. MERIDIAN per-room centroid subtraction recovers it -- robust even at 70% effectiveness (realistic for limited labelled examples). Privacy framing: R14 baseline + 4 new constraints specific to biometric-class re-ID data: 1. No cross-installation linkage 2. Embedding storage requires explicit opt-in (biometric consent class) 3. Cryptographically verifiable forgetting 4. No re-ID across legal entities These rule out cross-building tracking, mass surveillance, long-term unlabelled storage, third-party sharing. They allow per-installation personalisation, household anomaly detection, multi-person pose association in the same room. R3 closes the loop on R14's empathic-appliance vision: re-ID is THE primitive that makes per-occupant features possible. Without R3, R14's verticals can't ship. Identifies next research lever: physics-informed env_sig prediction from R6's forward operator + room map = zero-shot cross-room transfer without labelled examples in the new room. Composes: - R5/R6: person+env decomposition in embedding space - R7: mincut = defence against re-ID spoofing - R9: RSSI K-NN showed env-locality dominance for the K-NN primitive - R14: 4 new constraints extend R14's framework to biometric class Honest scope: additive decomposition is first-order; real CSI env effects are multiplicative in subcarrier domain. Adversarial scenarios not simulated. Coordination: ticks/tick-12.md, no PROGRESS.md edit. |
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rUv
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bcfdf0a4d0 |
research(R13): NEGATIVE — contactless BP from CSI is physically inferior to a cuff (#713)
Critical-physics scrutiny of published 'contactless BP from WiFi CSI' claims (Yang 2022, Liu 2021, others). Four physics floors quantified; all four make CSI-based BP provably worse than a 20 dollar arm cuff. 1. PTT temporal resolution: need 0.5 ms for 1 mmHg precision; ESP32-S3 maxes at 1 ms (1000 Hz CSI) and typical deployment is 10 ms (100 Hz) = 20 mmHg precision floor. Achievable but requires sacrificing every other sensing pipeline. 2. Spatial separation: carotid-femoral distance 55 cm, Fresnel envelope at 5 m link is 40 cm. Single-link CSI cannot resolve the two sites independently. Multistatic with 4-6 anchors is severely ill-posed (same regime that defeated R12). 3. Pulse-contour SNR: pulse motion at chest is 0.3 mm; breathing is 8 mm (27x larger). After 4th-order bandpass we get +20 dB HR-band SNR; literature (Mukkamala 2015) says +25 dB minimum for waveform- shape recovery. **5 dB short.** 4. Vs 0 arm cuff: best published CSI BP is +/-10 mmHg with per-subject calibration; arm cuff is +/-2 mmHg uncalibrated. CSI is 5x worse AND requires calibration the user doesn't otherwise need. Verdict: do not ship BP as a primary RuView feature. The breathing/HR features we already ship work because their motion amplitudes are 30-100x larger than the pulse waveform. Adding BP would force 1 kHz CSI rate (degrading every other pipeline), require per-subject calibration (defeating no-setup story), and ship a feature that's worse than a 20 dollar device the user can buy. Three niche scenarios remain open: - Single-subject trend monitoring (relative not absolute) - Bed-instrumented controlled-still subject (25+ dB achievable) - Multistatic PWV with 6+ anchors + per-installation calibration The general 'BP from a 9 dollar ESP32 in the corner' claim does not close. Composes: - R1 (CRLB) confirms temporal-resolution floor for PTT - R6 (Fresnel) provides the spatial floor that defeats two-site PTT - R5 (saliency) explains why whole-chest observable but 0.3 mm pulse not - R12 = loop's other negative result, same failure pattern - R14's assumption (no BP) is now empirically validated Two negative results in this loop (R12, R13) prevent the field from biasing toward overclaiming. This is the most valuable kind of tick because it marks BP-from-CSI as off-roadmap with explicit numbers, so future contributors don't waste cycles attempting it. Coordination: ticks/tick-11.md, no PROGRESS.md edit. |
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rUv
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4072455d1e |
research(R11): maritime sensing — through-bulkhead impossible, through-seam works (#712)
Physics scrutiny of WiFi-band maritime sensing scenarios. Steel skin depth is 3.25 um at 2.4 GHz, making bulkheads utterly opaque. Saltwater attenuation is 853 dB/m. The 'through-bulkhead WiFi radar' framing common in conservation/maritime is wrong; the actual feasible category is 'through-seam' sensing exploiting slot diffraction through gaskets, hatch seals, and vent grilles. Composite link budget for 7 maritime scenarios (ESP32-S3 121 dB budget, 10 dB SNR margin): FEASIBLE: - Man-overboard surface @ 200 m: +25 dB - Cabin door, 2 mm seam: +31 dB - Cabin door, 5 mm seam: +39 dB - Container, 30 mm vent slot: +45 dB IMPOSSIBLE: - Closed 10 mm steel door: -938 dB - Submarine pressure hull: -929 dB - Head 30 cm underwater: -231 dB Five feasible verticals catalogued: man-overboard surface, through-seam crew vitals, container tamper detection, hatch-seal predictive maintenance, engine-room thermal anomaly via condensation. Composes with prior threads: - R6 Fresnel envelope + slot diffraction = narrower composite envelope - R10 link-budget primitives reused unmodified for air-side maritime - R7 multi-link consistency essential against superstructure jammers - R14 privacy framework transfers directly to crew-cabin monitoring Honest scope: best-case ignores vessel vibration (5-30 Hz, in-band with R10 gait frequencies), engine ignition noise, salt-spray, steel-surface multipath. Maritime gait-classification is harder than land. The romantic 'through-hull radar' is now explicitly debunked. The actual product roadmap is gasket-leakage sensing, surface detection, and predictive-maintenance audits. Coordination: ticks/tick-10.md, no PROGRESS.md edit. |
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rUv
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a1bbe2e8a6 |
research(R1): ToA CRLB — precision floor for WiFi multistatic localisation (#711)
Quantitative Cramer-Rao Lower Bound analysis for WiFi ranging via both Time-of-Arrival and phase-based methods, with multistatic 4-anchor position-error budget. Headline (20 MHz HT20, 20 dB SNR, 100 averaged frames): - ToA range CRLB: 4.1 cm - Phase (5 deg noise): 0.17 mm - Phase advantage: 240x (after ambiguity resolution) 4-anchor convex-hull room (GDOP 1.5): - ToA position precision: 25 cm (room-pose-quality floor) - Phase position precision: 1 mm (RTK-quality, ambiguity-resolved) This is the strongest architectural lever this loop has surfaced for ADR-029 (multistatic sensing). The current learning-based attention approach has no provable precision floor; an explicit ToA-then-phase pipeline sits within 2x of CRLB by Kay's theory. Composes cleanly with R6: - R6 gives the spatial sensitivity envelope (40 cm Fresnel at 2.4 GHz) - R1 gives the ranging precision within it (1 mm phase, 4 cm ToA averaged) - Independent, additive, together bound full multistatic geometry budget Closes a gap R10 created: foliage drops SNR, which directly worsens ToA CRLB. A 50 m foliage link at 5 dB SNR drops to ~1 m ToA precision. R10's 100 m sparse-foliage range is *detectable* not *localisable*. Honest scope: - CRLB is a lower bound; real estimators sit 1-2x above it - 5 deg phase noise assumes phase_align.rs is applied - Multipath degrades CRLB by 2-5x even with MUSIC super-resolution - Integer-ambiguity (cycle-slip) is unsolved per-subcarrier; needs multi-subcarrier wide-lane unwrap Coordination: ticks/tick-9.md, no PROGRESS.md edit. |
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rUv
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650612e5a2 |
research(R6): Fresnel-zone forward model — bedrock physics for CSI sensitivity (#710)
The workspace DSP (vital_signs, multistatic, pose_tracker, tomography) implicitly assumes a forward model that maps scatterer geometry to per-subcarrier phase shifts. Nobody had written it down. This tick makes it explicit. Closed-form first-Fresnel-zone radius + point-scatterer path-delta + per-subcarrier phase prediction over 802.11n/ac 20 MHz channels (52 subcarriers, 312.5 kHz spacing). Pure NumPy demo + JSON output for downstream consumers. Headline numbers: - 5 m link first-Fresnel radius @ midpoint: 40 cm (2.4 GHz), 27 cm (5 GHz) - Inside zone-1: phase spread <0.5 deg across 52 subcarriers (band-flat) - Outside zone-1: phase spread up to 16 deg (band-dispersed) This unifies R5 + R6: R5's experimentally measured band-spread top subcarriers is exactly what the Fresnel forward model predicts for zone-1 occupancy. Closes the loop on three earlier threads: - R7 (mincut adversarial) gets a precise definition of 'physically inconsistent' instead of a learned classifier - R10 (foliage range) needs to retract 100 m sparse estimate to ~70 m to account for Fresnel-zone obstruction - R12 (eigenshift negative result) gets its revision basis: PABS over Fresnel-grounded forward operator Honest scope: point-scatterer only, first Fresnel only, frequency-flat reflectivity, LOS-only (no multipath). The scalar version is the right first-order approximation; volume-integral / multi-zone / multipath extensions catalogued as R6.1+R6.2 follow-ups. Coordination: ticks/tick-8.md, no PROGRESS.md edit. |
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rUv
|
7bd188ab60 |
research(R14): empathic appliances — vision + ethical framework + infrastructure gap inventory (#709)
Speculative 10-20y vision thread covering three concrete vertical sketches: * V1 stress-responsive lighting (5y) — breathing-rate baseline + warm-shift lights * V2 adaptive HVAC for thermal-stress envelopes (10y) — published HVAC-personalisation 15-20% energy savings * V3 conversational appliances respecting attention state (15y) — don't interrupt during focused work Maps existing RuView components to each: 5 already shipped (breathing rate detector, occupancy gates via cog-pose / cog-count, motion intensity, partial RollingP95 baseline learner, MCP API via ADR-104), 4 still to build (full per-room baseline learner, state classifier model, MCP vitals subscribe tool, consent UI). Ethical framework drafted as binding constraints any product must honour: 1. Opt-in by default — sensing on only after active enable 2. Data stays on-device — per-second values never cross the building boundary 3. Override is one tap — physical kill switch must work without WiFi/cloud 6-row privacy threat model with mitigations: compromised appliance, MCP raw-signal leak, adversarial poisoning (mitigated by R7 multi-link consistency), long-term re-identification, insurance/employer access, non-consenting cohabitants. Honest scope: clinical breathing-rate-as-stress literature is lab-condition adults; real-home generalisation unproven. R14 is CSI-only (RSSI loses the per-subcarrier shape needed for shallow-breathing-during-focus signature), bounds rollout to ESP32-S3-class deployments. Connections established to R5, R7, R8, ADR-103, ADR-104. Identifies ruview_vitals_subscribe as the highest-leverage next MCP tool addition. Coordination: ticks/tick-7.md, no PROGRESS.md touch. |
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ruv
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2e742305ba |
research(R10): through-foliage wildlife sensing — physics feasibility + per-species gait taxonomy
ITU-R P.833-9 vegetation-attenuation model + ESP32-S3 link-budget solver produce bounded sensing range estimates per frequency and foliage density. Plus a biomechanics-grounded gait-frequency taxonomy spanning bears (0.5 Hz) to mice (15 Hz). Headline ranges (121 dB link budget, 10 dB SNR margin): freq sparse moderate dense 2.4 GHz 99.6 m 12.0 m 4.1 m 5 GHz 19.9 m 5.2 m 2.1 m The 2.4 GHz / sparse cell (~100 m) is the practical sweet spot — 10x camera-trap coverage, always-on rather than PIR-triggered. Honest scope called out explicitly: this is feasibility math, not field measurements. Animal cooperation, foliage flutter, regulatory limits, and BSSID-fingerprint degradation in remote forest are all real follow-up problems. Vertical applications (10-20 year horizon) catalogued: - Endangered-species population census - Wildlife corridor verification - Invasive-species early warning - Anti-poaching (human gait well-separated from wildlife) - Livestock-on-rangeland tracking - Agricultural pest control Cross-connects to: - R5 (saliency is task-specific — per-species classifier needs own saliency map, same lesson as R12) - R8 (wildlife sensing wants CSI not RSSI for per-subcarrier shape) - R9 (fingerprint K-NN primitive transfers to per-individual ID) - R7 (multi-link consistency for corridor coverage) Pure-NumPy, no framework deps. ITU model + binary search solver. Coordination: tick avoided PROGRESS.md to prevent races (horizon- tracker M3+ track concurrent at the time). Files: * examples/research-sota/r10_foliage_attenuation.py * examples/research-sota/r10_foliage_results.json * docs/research/sota-2026-05-22/R10-through-foliage-wildlife.md * docs/research/sota-2026-05-22/ticks/tick-6.md |
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ruv
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6bfb29accf |
docs(horizon): M3-M7 complete — close 12h autonomous SOTA run
Mark M2-M7 COMPLETE in HORIZON.md; add Session 2 log; write final summary table (shipped/deferred), npm publish commands, and horizon verdict. All 6 milestones finished ahead of 08:00 ET auto-stop. Co-Authored-By: claude-flow <ruv@ruv.net> |
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rUv
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6b35896847 |
research(R12): RF weather mapping eigenshift — negative-ish, with clearly-actionable revision path (#707)
Tests the simplest possible algorithm for RF-weather change detection: SVD on per-frame CSI matrix, top-10 singular values, cosine distance between spectra over time. Hypothesis: a synthetic structural perturbation (15 percent attenuation on 3 top-saliency subcarriers) should produce a larger spectral shift than natural temporal drift from operator movement in the same recording. Result honestly: it does not. The perturbation distance (0.00024) is *smaller* than the control distance (0.00035) — signal/drift ratio 0.69x. The top-K SVD-spectrum cosine is too coarse to detect small-magnitude subcarrier-specific structural changes against an operator-noise background. Three concrete fixes identified for follow-up ticks: 1. Principal angles between subspaces (PABS), not cosine on singular values — catches subspace rotations the spectrum misses 2. Per-subcarrier residual analysis after projecting onto baseline subspace — localises the perturbation 3. Multi-day baseline — knocks down operator-noise floor by 50-100x Useful cross-validations the negative result produces: * R5 task-specific saliency (count-task) does not generalise to structure-detection saliency. Same data, different relevant features. Publishable distinction. * R12 is CSI-only territory — RSSI is the trace of the CSI covariance, so if top-10 SVD-spectrum can't see this, RSSI can't either. Bounds R8 commercial-enablement story to counting only. * R7 SVD-spectrum primitive that worked for adversarial detection fails here at lower perturbation magnitude. Sensitivity does NOT scale with subtlety — confirms the algorithm is magnitude-dominated. Long-horizon vision (building structural monitoring, earthquake drift, HVAC audits, climate-controlled-archive surveillance) preserved in the research note — the physics is right, the hardware is sufficient, the deployment story works. Just need PABS + multi-day data. Coordination note: this tick avoided PROGRESS.md edits entirely because horizon-tracker is concurrently editing it. Tick-5 summary written to ticks/tick-5.md (new self-contained convention) so the 08:00 ET final summary can consolidate without conflicts. Files: * examples/research-sota/r12_rf_weather_eigenshift.py * examples/research-sota/r12_rf_weather_results.json * docs/research/sota-2026-05-22/R12-rf-weather-mapping.md * docs/research/sota-2026-05-22/ticks/tick-5.md |
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rUv
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2783f40bd1 |
feat(tools/ruview-mcp): M2 — wire real inference via cog health (#706)
* research(R9): RSSI fingerprint K-NN — 2.18x lift (MODERATE); surfaces counting-vs-localization asymmetry Hypothesis: if temporal proximity correlates with RSSI-feature proximity in the existing single-session data, RSSI fingerprinting is viable. If K-NN of each query is random in time, RSSI sequences are too noisy for fingerprint localization. Test: 1077 samples, 20-dim RSSI proxy (band-mean across 56 subcarriers), cosine-NN with K=5, measure fraction of K-NN within plus/minus 60s of each query timestamp. Compare to random baseline. Result (honest): 5-NN within +/-60s 0.169 Random baseline 0.077 Lift over random 2.18x (verdict: MODERATE) Per-query stdev 0.183 Below the >=3x STRONG-fingerprint threshold but well above 1x random. Real signal, but weaker than R8 counting result on the same data. Important asymmetry surfaced (publishable distinction): Task RSSI vs CSI retention Verdict ------- ----- ----- Counting 94.82% (R8) RSSI works well Localization ~2x random (R9) RSSI struggles in this regime This is consistent with R5's band-spread observation: the count signal integrates across the band, but localization may require per-subcarrier shape that the band-mean discards. Three actionable explanations for the MODERATE result: 1. 20-frame windows (~2s) too short for stable fingerprint while operator moves — longer windows might lift to 3-4x. 2. Within-room fingerprint space too narrow — multi-room data would show categorical lift jump (5-10x). 3. Band-mean discards the per-subcarrier shape needed for localization. Once multi-room data lands (#645), this test should be re-run; if hypothesis (2) is right, the lift will jump categorically. Files: * examples/research-sota/r9_rssi_fingerprint_knn.py * examples/research-sota/r9_rssi_fingerprint_results.json * docs/research/sota-2026-05-22/R9-rssi-fingerprint-knn.md * docs/research/sota-2026-05-22/PROGRESS.md updated * feat(tools/ruview-mcp): M2 — wire real inference via cog health subcommand ruview_pose_infer and ruview_count_infer now run the cog binary's `health` subcommand (ADR-100 contract) which performs real Candle forward-pass inference on a synthetic CSI window and emits a structured health.ok JSON event containing backend, confidence (pose) or count/confidence/p95_range (count). The MCP tools parse this event and return typed inference results. This satisfies the ADR-104 acceptance gate: "ruview_pose_infer returns a finite output for a synthetic CSI window" when the cog binary is installed. On machines without the binary, both tools still fail-open with {ok:false, warn:true} and actionable install hints. Also updates PROGRESS.md with cross-links: R7 (Stoer-Wagner) and R8 (RSSI-only 94.82% retained) marked done with cron-originated findings distilled into the research vectors section. Co-Authored-By: claude-flow <ruv@ruv.net> |
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rUv
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3f462a254d |
feat(tools): scaffold ruview MCP server + CLI + ADR-104 (#705)
Adds two new npm packages that expose RuView's WiFi-DensePose sensing capabilities outside the Cognitum appliance ecosystem: - tools/ruview-mcp/ (@ruv/ruview-mcp) — MCP server with 6 tools: ruview_csi_latest, ruview_pose_infer, ruview_count_infer, ruview_registry_list, ruview_train_count, ruview_job_status. Uses @modelcontextprotocol/sdk with stdio transport. 6/6 smoke tests pass. TypeScript strict mode, Node 20. - tools/ruview-cli/ (@ruv/ruview-cli) — Yargs CLI with matching subcommands: csi tail, pose infer, count infer, cogs list, train count, job status. Same fail-open pattern as the cog binaries (WARN to stderr, exit 0 on unavailable sensing-server). - docs/adr/ADR-104-ruview-mcp-cli-distribution.md — design rationale, 6-row threat table, packaging plan, acceptance gates, failure modes. - docs/research/sota-2026-05-22/HORIZON.md — 12-hour horizon plan with 7 milestones tracked (M1 complete in this commit). Both packages are private:true pending the user's publish decision. Inference is via subprocess to the signed cog binaries (ADR-100/101/103) — no JS/WASM ML engine bundled. |
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rUv
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bb92419ccb |
research(R7): Stoer-Wagner mincut detects adversarial CSI nodes 3/3 in synthetic (#704)
Premise: in a multi-node CSI mesh, all nodes see the same physical
scene through slightly different multipath. Their per-window CSI
vectors cluster tightly under cosine similarity. An adversarial node
(replay / shift / noise injection) sits *outside* that cluster. The
Stoer-Wagner minimum cut on the inter-node similarity graph isolates
it cleanly when the cut is sharp.
Demo synthesises 4 honest nodes (one real CSI window from the paired
data + per-node Gaussian noise 6 dB below signal) and 1 adversarial
node under three attack modes. Cosine-similarity matrix, then
Stoer-Wagner mincut, then check whether partition_B is the singleton
{4} — the adversarial node.
Attack Mincut value Partition_B Isolated?
------- ------------ ----------- ---------
replay 3.4513 {4} YES
shift 3.5724 {4} YES
noise 2.5586 {4} YES
Detection rate: 3/3 = 100%.
Architectural payoff: this is the primitive that fills the stub at
. ADR-103 v0.2.0
can wire it in directly. The mincut value also becomes a continuous
'mesh trustworthiness' metric for the cog-gateway dashboard.
Honest scope: the demo uses sloppy attackers. Adaptive attackers who
have read this note can almost certainly evade by adding calibrated
noise that keeps cosine similarity above the cluster floor. The next
research step is the Stackelberg-game extension. See the
'Honest scope of this result' section in the research note.
Connections:
* R5 — top-8 saliency subcarriers are the priority list for a
more-targeted per-subcarrier consistency check.
* R8 — same primitive likely works at lower SNR with RSSI-only
metrics; cluster structure is preserved by the band integral.
Files:
* examples/research-sota/r7_multilink_consistency.py — pure-NumPy
Stoer-Wagner mincut + synthetic-adversary harness.
* examples/research-sota/r7_multilink_consistency_results.json —
full result JSON for cross-tick reproducibility.
* docs/research/sota-2026-05-22/R7-multilink-consistency.md — note.
* docs/research/sota-2026-05-22/PROGRESS.md — updated index + Done.
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rUv
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d9ca9b3684 |
research(R8): RSSI-only person count retains 95% of full-CSI accuracy (#703)
Builds directly on R5's band-spread observation. If the count-task
signal is spread across the WiFi band (R5: max/mean ratio 2.85× across
56 subcarriers), then RSSI — which is the integral of |H_k|^2 across
the band — keeps most of the information. The naive prior (RSSI throws
away 98% of CSI bytes) is misleading; the relevant metric is how much
of the *signal* is in the integral, not how many bytes are in the
representation.
Tested by aggregating each existing [56 × 20] CSI window down to a
[20]-vector RSSI proxy (mean across subcarriers per frame), training a
tiny MLP (Linear 20→32→8, 656 params, 5 KB) with vanilla NumPy SGD for
200 epochs on the same random 80/20 split as cog-person-count v0.0.2.
Result:
Full CSI v0.0.2 62.3% accuracy
RSSI-only (this) 59.1% accuracy = 94.82% retained
Per-class is also markedly more *balanced* (RSSI: 59.5 / 58.6 ; full
CSI: 86.2 / 34.3) — the tiny model on a low-dim input can't cheat by
leaning on class 0 the way v0.0.2's larger model does at inference.
What this enables on a 10-year horizon: phones, laptops, smart
speakers, smart TVs, smart lights — anything with WiFi reports RSSI
and anything with a CPU can run a 656-param MLP. Person counting
becomes a federated property of any room with WiFi, not a property of
the ESP32-S3 fleet.
What this doesn't prove (called out explicitly in the research note):
- Single room, single operator, single 30-min recording
- 2-class problem (label distribution is {0, 1})
- Single random draw — needs K-fold + multi-room replication
Three follow-up experiments queued in R8-rssi-only-count.md §'What's
next on this thread':
- Multi-room replication once #645 lands
- 3-class extension (0 / 1 / 2+) — measure the info-rate cliff
- Run on a non-ESP32 RSSI source (e.g. iw event on Linux laptop)
Files:
* examples/research-sota/r8_rssi_only_count.py — pure-NumPy, no
framework deps. Trains + evals in 0.72 s on CPU.
* examples/research-sota/r8_rssi_only_results.json — full JSON dump
for cross-tick reproducibility.
* docs/research/sota-2026-05-22/R8-rssi-only-count.md — method,
measured numbers, interpretation, what doesn't work yet.
* docs/research/sota-2026-05-22/PROGRESS.md — updated index + Done
log.
Coordination note: horizon-tracker is working on tools/ruview-mcp/
+ tools/ruview-cli/ + ADR-104 — this commit deliberately stays out
of those paths.
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rUv
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a85d4e31e4 |
research(sota): kick off SOTA research loop + first R5 saliency measurement (#702)
Sets up docs/research/sota-2026-05-22/ as the autonomous-research output dir, with PROGRESS.md as the canonical 15-vector research agenda spanning spatial intelligence, RF features, RSSI-only, and exotic/long-horizon verticals. Cron d6e5c473 (*/10 * * * *) picks threads from this file and self-terminates at 2026-05-22 08:00 ET. First concrete contribution this tick — R5 subcarrier saliency: * examples/research-sota/r5_subcarrier_saliency.py: pure-numpy port of the count cog's Conv1d encoder + count head, computes per- subcarrier input×gradient saliency via central-difference. 128 samples × 56 subcarriers × 2 forward passes/subcarrier ≈ ~3 s on CPU, no GPU or framework dependency. * docs/research/sota-2026-05-22/R5-subcarrier-saliency.md: research note with motivation, method, novelty argument, and the first measured ranking. Top-8 subcarriers for cog-person-count v0.0.2: [41, 52, 30, 31, 10, 35, 2, 38]. Max/mean ratio 2.85x. * v2/crates/cog-person-count/cog/artifacts/saliency.json: machine- readable per-subcarrier saliency + top-K lists, so future-tick experiments (retrain at K=8/16/32) consume it without re-running. Key insight from the first measurement: top-8 saliency is *band- spread* (indices span 2-52), not concentrated. This directly raises R8's (RSSI-only) feasibility ceiling, because RSSI is a band- aggregate — it retains the integral of a band-spread signal. First- order estimate: RSSI-only should hit ~60% of full-CSI accuracy for the count task. R7 (adversarial defence) inherits a concrete defender- priority list: corroborate these 8 subcarriers across nodes. This commit is the first of many short, focused contributions over the next ~12 hours. PROGRESS.md is the canonical pointer for the next tick to pick up the next thread. |
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rUv
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7f5a692632 |
feat(nvsim): full simulator stack — Rust crate, dashboard, server, App Store, Ghost Murmur [ADR-089/090/091/092/093]
Squashed merge of feat/nvsim-pipeline-simulator (29 commits). ## Shipped - ADR-089 nvsim crate (Accepted) — 50/50 tests, ~4.5 M samples/s, pinned witness cc8de9b01b0ff5bd… - ADR-092 dashboard implementation (Implemented) — 8/12 §11 gates ✅, 4/12 ⚠ (external infra) - ADR-093 dashboard gap analysis (Implemented) — 21/21 catalogued gaps closed - Plus ADR-090 (proposed conditional) and ADR-091 (proposed research-only) ## Live deploy https://ruvnet.github.io/RuView/nvsim/ ## Infra - nvsim-server Dockerfile + GHCR publish workflow (.github/workflows/nvsim-server-docker.yml) - axe-core + Playwright cross-browser CI (.github/workflows/dashboard-a11y.yml) - gh-pages auto-deploy workflow already in place (preserves observatory + pose-fusion siblings) Co-Authored-By: claude-flow <ruv@ruv.net> |
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rUv
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f49c722764 |
chore(repo): rename rust-port/wifi-densepose-rs → v2/ (flatten to one level) (#427)
The Rust port lived two directories deep (rust-port/wifi-densepose-rs/) without any sibling under rust-port/ that warranted the extra level. Move the whole workspace up to v2/ to match v1/ (Python) at the same depth and shorten every cd / build command across the repo. git mv preserves history for all tracked files. 60 files updated for path references (CI workflows, ADRs, docs, scripts, READMEs, internal .claude-flow state). Two manual fixes for relative-cd paths in CLAUDE.md and ADR-043 that became wrong after the depth change (cd ../.. → cd ..). Validated: - cargo check --workspace --no-default-features → clean (after target/ nuke; the gitignored target/ was carried by the OS rename and had hard-coded old paths in build scripts) - cargo test --workspace --no-default-features → 1,539 passed, 0 failed, 8 ignored (same totals as pre-rename) - ESP32-S3 on COM7 → still streaming live CSI (cb #40300, RSSI -64 dBm) After-merge follow-up: contributors should `rm -rf v2/target` once and let cargo regenerate from the new path. |