frank/ruvnet--RuView
1
0
Fork 0
mirror of https://github.com/ruvnet/RuView synced 2026-07-17 16:33:18 +00:00
Code Issues Packages Projects Releases Wiki Activity
620 Commits 545 Branches 235 Tags
4e6ef76294e1a72d19b0761c6f66893e19727d86
Commit Graph

40 Commits

Author SHA1 Message Date
rUv 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.
 2026-05-22 05:37:29 -04:00
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.
 2026-05-22 05:24:53 -04:00
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.
 2026-05-22 05:12:48 -04:00
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.
 2026-05-22 04:58:10 -04:00
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.
 2026-05-22 04:43:34 -04:00
rUv 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.
 2026-05-22 04:27:48 -04:00
rUv 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.
 2026-05-22 04:17:47 -04:00
rUv 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.
 2026-05-22 04:04:38 -04:00
rUv 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.
 2026-05-22 03:49:41 -04:00
rUv 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.
 2026-05-22 03:36:42 -04:00
rUv 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.
 2026-05-22 03:17:14 -04:00
rUv 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.
 2026-05-22 03:04:17 -04:00
rUv 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.
 2026-05-22 02:48:16 -04:00
rUv 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.
 2026-05-22 02:38:10 -04:00
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.
 2026-05-22 02:24:42 -04:00
rUv 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.
 2026-05-22 02:13:10 -04:00
rUv 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.
 2026-05-22 02:00:35 -04:00
rUv 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.
 2026-05-22 01:53:51 -04:00
rUv 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.
 2026-05-22 01:38:35 -04:00
rUv 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.
 2026-05-22 01:31:09 -04:00
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.
 2026-05-22 01:18:01 -04:00
ruv 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
 2026-05-22 00:59:11 -04:00
ruv 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>
 2026-05-22 00:06:40 -04:00
rUv 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
 2026-05-21 23:52:49 -04:00
rUv 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>
 2026-05-21 23:43:32 -04:00
rUv 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.
 2026-05-21 23:33:18 -04:00
rUv 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.
 2026-05-21 23:28:46 -04:00
rUv 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.
 2026-05-21 23:18:09 -04:00
rUv 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.
 2026-05-21 23:05:55 -04:00
rUv 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>
 2026-04-27 12:41:01 -04:00
rUv 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.
 2026-04-25 21:28:13 -04:00
ruv 2a58fe478b docs(research): three-tier Rust node design + 2026-Q2 SOTA survey + decision tree 
Three exploratory research documents under docs/research/:

- architecture/three-tier-rust-node.md (3,382 words) — exploration of a
  dual-ESP32-S3 + Pi Zero 2W node architecture with BQ24074 power-path,
  ESP-WIFI-MESH + LoRa fallback + QUIC backhaul, and an esp-hal/Embassy
  vs esp-idf-svc Rust toolchain split. Status: Exploratory — not adopted.

- sota/2026-Q2-rf-sensing-and-edge-rust.md (3,757 words) — twelve-section
  state-of-the-art survey covering WiFi CSI through-wall pose, IEEE 802.11bf
  (ratified 2025-09-26), edge ML on ESP32-class hardware, embedded Rust
  ecosystem maturity (esp-hal 1.x, esp-radio rename, embassy-executor
  ISR-safety on esp-idf-svc), LoRa for sensor mesh fallback, QUIC for IoT
  backhaul, solar power-path management beyond BQ24074, mesh routing
  alternatives, and Pi Zero 2W secure-boot reality.

- architecture/decision-tree.md (1,461 words) — Mermaid decision tree
  mapping each load-bearing decision in the three-tier proposal to its
  dependencies, evidence-for-yes/no, and prospective ADR slot.

No production code, firmware, or ADRs touched. Research-only.

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-04-25 20:41:14 -04:00
ruv a4bd2308b7 feat: ADR-069 ESP32 CSI → Cognitum Seed RVF pipeline (v0.5.4-esp32) 
Hardware-validated pipeline connecting ESP32-S3 CSI sensing to Cognitum
Seed (Pi Zero 2 W) edge intelligence appliance via 8-dim feature vectors.

Firmware:
- New 48-byte feature vector packet (magic 0xC5110003) at 1 Hz with
  normalized presence, motion, breathing, heart rate, phase variance,
  person count, fall detection, and RSSI
- Compressed frame magic reassigned 0xC5110003 → 0xC5110005
- Guard against uninitialized s_top_k read when count=0

Bridge (scripts/seed_csi_bridge.py):
- UDP→HTTPS ingest with bearer token, hash-based vector IDs
- --validate (kNN), --stats, --compact, --allowed-sources modes
- NaN/inf rejection, retry logic, SEED_TOKEN env var support

Validated on live hardware:
- 941 vectors ingested, 100% kNN exact match
- Witness chain SHA-256 verified (1,325 entries)
- 1,463 Rust tests passed, Python proof VERDICT: PASS

Research: 26 docs covering Arena Physica, Maxwell's equations in WiFi
sensing, SOTA survey 2025-2026, GOAP implementation plan

Security: removed hardcoded credentials, added NVS patterns to
.gitignore, source IP filtering, NaN validation

Co-Authored-By: claude-flow <ruv@ruv.net>
 2026-04-02 19:32:18 -04:00
rUv 341d9e05a8 ruv-neural: publish 11 crates to crates.io — full implementation, no stubs 
* Add temporal graph evolution & RuVector integration research

GOAP Agent 8 output: 1,528-line SOTA research document covering temporal
graph models (TGN, JODIE, DyRep), RuVector graph memory design, mincut
trajectory tracking with Kalman filtering, event detection pipelines,
compressed temporal storage, cross-room transition graphs, and a 5-phase
integration roadmap.

Part of RF Topological Sensing research swarm (10 agents).

https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv

* Add transformer architectures for graph sensing research

GOAP Agent 4 output: 896-line SOTA document covering Graph Transformers
(Graphormer, SAN, GPS, TokenGT), Temporal Graph Transformers (TGN, TGAT,
DyRep), ViT for RF spectrograms, transformer-based mincut prediction,
positional encoding for RF graphs, foundation models for RF sensing, and
efficient edge deployment with INT8 quantization.

Part of RF Topological Sensing research swarm (10 agents).

https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv

* Add attention mechanisms for RF sensing research

GOAP Agent 3 output: 1,110-line document covering GAT for RF graphs,
self-attention for CSI sequences, cross-attention multi-link fusion,
attention-weighted differentiable mincut, spatial node attention,
antenna-level subcarrier attention, and efficient attention variants
(linear, sparse, LSH, S4/Mamba). 8 ASCII architecture diagrams.

Part of RF Topological Sensing research swarm (10 agents).

https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv

* Add sublinear mincut algorithms research

GOAP Agent 5 output: 698-line document covering classical mincut complexity,
sublinear approximation (sampling, sparsifiers), dynamic mincut with lazy
recomputation hybrid, streaming sketch algorithms, Benczur-Karger
sparsification, local partitioning (PageRank-guided cuts), randomized
methods reliability analysis, and Rust implementation with const-generic
RfGraph, zero-alloc Stoer-Wagner, SIMD batch updates.

Part of RF Topological Sensing research swarm (10 agents).

https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv

* Add CSI edge weight computation research

GOAP Agent 2 output: ~700-line document covering CSI feature extraction,
coherence metrics (cross-correlation, mutual information, phasor coherence),
multipath stability scoring (MUSIC, ESPRIT, ISTA), temporal windowing
(EMA, Welford, Kalman), noise robustness (phase noise, AGC, clock drift),
edge weight normalization, and implementation architecture showing 32KB
memory for 120 edges within ESP32-S3 capability.

Part of RF Topological Sensing research swarm (10 agents).

https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv

* Add contrastive learning for RF coherence research

GOAP Agent 7 output: 1,226-line document covering SimCLR/MoCo/BYOL for CSI,
AETHER-Topo dual-head extension, coherence boundary detection with multi-scale
analysis, delta-driven updates (2-12x efficiency), self-supervised pre-training
protocol, triplet networks for 5-state edge classification, and MERIDIAN
cross-environment transfer with EWC continual learning.

Part of RF Topological Sensing research swarm (12 agents).

https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv

* Add resolution and spatial granularity analysis research

GOAP Agent 9 output: 1,383-line document covering Fresnel zone analysis,
node density vs resolution (16-node/5m room → 30-60cm), Cramer-Rao lower
bounds with Fisher Information Matrix, graph cut resolution theory,
multi-frequency enhancement (6cm coherent dual-band limit), RF tomography
comparison, experimental validation protocols, and resolution scaling laws
(8.8cm theoretical limit).

Part of RF Topological Sensing research swarm (12 agents).

https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv

* Add RF graph theory and minimum cut foundations research

GOAP Agent 1 output: Graph-theoretic foundations covering max-flow/min-cut
for RF (Ford-Fulkerson, Stoer-Wagner, Karger), RF as dynamic graph with
CSI coherence weights, topological change detection via Fiedler vector and
Cheeger inequality, dynamic graph algorithms, comparison to classical RF
sensing, formal mathematical framework, and 9 open research questions.

Part of RF Topological Sensing research swarm (12 agents).

https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv

* Add ESP32 mesh hardware constraints research

GOAP Agent 6 output: ESP32 CSI capabilities (52/114 subcarriers), 16-node
mesh topology with 120 edges, TDM synchronized sensing (3ms slots),
computational budget (Stoer-Wagner uses 0.07% of one core), channel hopping,
power analysis (0.44W/node), dual-core firmware architecture, and edge vs
server computing with 100x data reduction on-device.

Part of RF Topological Sensing research swarm (12 agents).

https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv

* Add system architecture and prototype design research

GOAP Agent 10 output: End-to-end architecture with pipeline diagrams,
existing crate integration mapping, new rf_topology module design (DDD
aggregate roots), 100ms latency budget breakdown, 3-phase prototype plan
(4-node POC → 16-node room → 72-node multi-room), benchmark design with
8 metrics, ADR-044 draft, and Rust trait definitions (EdgeWeightComputer,
TopologyGraph, MinCutSolver, BoundaryInterpolator).

Part of RF Topological Sensing research swarm (12 agents).

https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv

* Add quantum sensing and quantum biomedical research documents

Agent 11: Quantum-level sensors (729 lines) — NV centers, SQUIDs, Rydberg
atoms, quantum illumination, quantum graph theory (walks, spectral, QAOA),
hybrid classical-quantum architecture, quantum ML (VQC, kernels, reservoir
computing), NISQ applications (D-Wave, VQE), hardware roadmap.

Agent 12: Quantum biomedical sensing (827 lines) — whole body biomagnetic
mapping, neural field imaging without electrodes, circulation sensing,
cellular EM signaling, non-contact diagnostics, coherence-based diagnostics
(disease as coherence breakdown), neural interfaces, multimodal observatory,
room-scale ambient health monitoring, graph-based biomedical analysis.

Part of RF Topological Sensing research swarm (12 agents).

https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv

* Add research index synthesizing all 12 documents (14,322 lines)

Master index for RF Topological Sensing research compendium covering:
graph theory foundations, CSI edge weights, attention mechanisms,
transformers, sublinear algorithms, ESP32 hardware, contrastive learning,
temporal graphs, resolution analysis, system architecture, quantum sensors,
and quantum biomedical sensing. Includes key findings, proposed ADRs
(044, 045), and 5-phase implementation roadmap.

https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv

* Add SOTA neural decoding landscape and 10 application domains research

- Doc 21: Comprehensive SOTA map (2023-2026) of brain sensors, decoders,
  and visualization systems with RuVector/mincut positioning analysis
- Doc 22: Ten application domains for brain state observatory including
  disease detection, BCI, cognitive monitoring, mental health diagnostics,
  neurofeedback, dream reconstruction, cognitive research, HCI, wearables,
  and brain network digital twins with strategic roadmap

https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv

* Add NV diamond neural magnetometry research document (13/22)

Comprehensive 600+ line document covering NV center physics, neural
magnetic field sources, sensor architecture, SQUID comparison, signal
processing pipeline, RuVector integration, and development roadmap.

https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv

* Add ruv-neural workspace Cargo.toml with 12 crate definitions

Workspace structure for the rUv Neural brain topology analysis system.
12 mix-and-match crates with shared dependencies including RuVector
integration, petgraph, rustfft, and WASM/ESP32 support.

https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv

* Add ruv-neural crate ecosystem — 12 mix-and-match crates (WIP)

Initial implementation of the rUv Neural brain topology analysis system:
- ruv-neural-core: Core types, traits, errors, RVF format (compiles)
- ruv-neural-sensor: NV diamond, OPM, EEG sensor interfaces (in progress)
- ruv-neural-signal: DSP, filtering, spectral, connectivity (in progress)
- ruv-neural-graph: Brain connectivity graph construction (in progress)
- ruv-neural-mincut: Dynamic minimum cut topology analysis (in progress)
- ruv-neural-embed: RuVector graph embeddings (in progress)
- ruv-neural-memory: Persistent neural state memory + HNSW (compiles)
- ruv-neural-decoder: Cognitive state classification + BCI (in progress)
- ruv-neural-esp32: ESP32 edge sensor integration (compiles)
- ruv-neural-wasm: WebAssembly browser bindings (in progress)
- ruv-neural-viz: Visualization + ASCII rendering (in progress)
- ruv-neural-cli: CLI tool (in progress)

Agents still writing remaining modules. Next: fix compilation, tests, push.

https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv

* Fix ruv-neural crate compilation: all 12 crates build and 1200+ tests pass

- Fix node2vec.rs type inference error (Vec<_> → Vec<Vec<f64>>)
- Fix artifact.rs with full filter-based detection implementations
- Fix signal crate ConnectivityMetric re-export and trait method names
- Fix embed crate EmbeddingGenerator trait implementations
- Complete spectral, topology, and node2vec embedders with tests
- Complete preprocessing pipeline with sequential stage processing
- All workspace crates compile cleanly, 0 test failures

https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv

* Add ruv-neural-cli README

https://claude.ai/code/session_01DGUAowNScGVp88bK2eiuRv

* fix: convert desktop icons from RGB to RGBA for Tauri build

Tauri's generate_context!() macro requires RGBA PNG icons. All 5 icon
files (32x32.png, 128x128.png, 128x128@2x.png, icon.icns, icon.ico)
were RGB-only, causing a proc macro panic on Linux builds.

Fixes #200

Co-Authored-By: claude-flow <ruv@ruv.net>

* Add Subcarrier Manifold and Vitals Oracle modules for 3D visualizations

- Implemented Subcarrier Manifold to visualize amplitude data as a 3D surface with height and age attributes.
- Created Vitals Oracle to represent vital signs using toroidal rings and particle trails, incorporating breathing and heart rate dynamics.
- Both modules utilize Three.js for rendering and include custom shaders for visual effects.

* feat: complete ruv-neural implementation — physics models, security, witness verification

Replace all stubs/mocks with production physics-based signal models:
- NV Diamond: ODMR Lorentzian dip, 1/f pink noise (Voss-McCartney), brain oscillations
- OPM: SERF-mode, 50/60Hz powerline harmonics, full cross-talk compensation
  via Gaussian elimination with partial pivoting
- EEG: 5 frequency bands, eye blink artifacts (Fp1/Fp2), muscle artifacts,
  impedance-based thermal noise floor
- ESP32 ADC: ring-buffer reader with calibration signal generator, i16 clamp

Security hardening (SEC-001 through SEC-005):
- RVF bounded allocation (16MB metadata, 256MB payload)
- sample_rate validation (>0, finite)
- Signal NaN/Inf rejection
- ADC resolution_bits overflow clamp
- HNSW HashSet visited tracking + bounds checks

Performance optimizations (PERF-001 through PERF-005):
- 67x fewer FFTs via pre-computed analytic signals
- VecDeque O(1) eviction in memory store
- Thread-local FFT planner caching
- BrainGraph::validate() for edge/weight integrity
- Eigenvalue convergence early termination

Ed25519 witness verification system:
- 41 capability attestations across all 12 crates
- SHA-256 digest + Ed25519 signature
- CLI commands: `witness --output` and `witness --verify`

README: ethics warning, hardware parts list (AliExpress), assembly instructions

Co-Authored-By: claude-flow <ruv@ruv.net>

* docs: add crates.io badges and install instructions to ruv-neural README

Add version badges linking to each published crate on crates.io,
cargo add instructions, and crate search link in the Crate Map table.

Co-Authored-By: claude-flow <ruv@ruv.net>

---------

Co-authored-by: Claude <noreply@anthropic.com>
 2026-03-09 10:52:24 -04:00
ruv d4dc5cb0bc Merge remote-tracking branch 'origin/claude/use-cases-implementation-plan-tT4s9' into ruvsense-full-implementation 2026-03-01 21:21:24 -05:00
Claude 374b0fdcef docs: add RuView (ADR-028) sensing-first RF mode for multistatic fidelity 
Introduce Project RuView — RuVector Viewpoint-Integrated Enhancement — a
sensing-first RF mode that improves WiFi DensePose fidelity through
cross-viewpoint embedding fusion on commodity ESP32 hardware.

Research document (docs/research/ruview-multistatic-fidelity-sota-2026.md):
- SOTA analysis of three fidelity levers: bandwidth, carrier frequency, viewpoints
- Multistatic array theory with virtual aperture and TDM sensing protocol
- ESP32 multistatic path ($84 BOM) and Cognitum v1 + RF front end path
- IEEE 802.11bf alignment and forward-compatibility mapping
- RuVector pipeline: all 5 crates mapped to cross-viewpoint operations
- Three-metric acceptance suite: joint error (PCK/OKS), multi-person
  separation (MOTA), vital sign sensitivity with Bronze/Silver/Gold tiers

ADR-028 (docs/adr/ADR-028-ruview-sensing-first-rf-mode.md):
- DDD bounded context: ViewpointFusion with MultistaticArray aggregate,
  ViewpointEmbedding entity, GeometricDiversityIndex value object
- Cross-viewpoint attention fusion via ruvector-attention with geometric bias
- TDM sensing protocol: 6 nodes, 119 Hz aggregate, 20 Hz per viewpoint
- Coherence-gated environment updates for multi-day stability
- File-level implementation plan across 4 phases (8 new source files)
- ADR interaction map: ADR-012, 014, 016/017, 021, 024, 027

https://claude.ai/code/session_01JBad1xig7AbGdbNiYJALZc
 2026-03-02 02:07:31 +00:00
Claude c707b636bd docs: add RuvSense persistent field model, exotic tiers, and appliance categories 
Expands the RuvSense architecture from pose estimation to spatial
intelligence platform with persistent electromagnetic world model.

Research (Part II added):
- 7 exotic capability tiers: field normal modes, RF tomography,
  intention lead signals, longitudinal biomechanics drift,
  cross-room continuity, invisible interaction layer, adversarial detection
- Signals-not-diagnoses framework with 3 monitoring levels
- 5 appliance product categories: Invisible Guardian, Spatial Digital Twin,
  Collective Behavior Engine, RF Interaction Surface, Pre-Incident Drift Monitor
- Regulatory classification (consumer wellness → clinical decision support)
- Extended acceptance tests: 7-day autonomous, 30-day appliance validation

ADR-030 (new):
- Persistent field model architecture with room eigenstructure
- Longitudinal drift detection via Welford statistics + HNSW memory
- All 5 ruvector crates mapped across 7 exotic tiers
- GOAP implementation priority: field modes → drift → tomography → intent
- Invisible Guardian recommended as first hardware SKU vertical

DDD model (extended):
- 3 new bounded contexts: Field Model, Longitudinal Monitoring, Spatial Identity
- Full aggregate roots, value objects, domain events for each context
- Extended context map showing all 6 bounded contexts
- Repository interfaces for field baselines, personal baselines, transitions
- Invariants enforcing signals-not-diagnoses boundary

https://claude.ai/code/session_01QTX772SDsGVSPnaphoNgNY
 2026-03-02 01:59:21 +00:00
Claude 25b005a0d6 docs: add RuvSense sensing-first RF mode architecture 
Research, ADR, and DDD specification for multistatic WiFi DensePose
with coherence-gated tracking and complete ruvector integration.

- docs/research/ruvsense-multistatic-fidelity-architecture.md:
  SOTA research covering bandwidth/frequency/viewpoint fidelity levers,
  ESP32 multistatic mesh design, coherence gating, AETHER embedding
  integration, and full ruvector crate mapping

- docs/adr/ADR-029-ruvsense-multistatic-sensing-mode.md:
  Architecture decision for sensing-first RF mode on existing ESP32
  silicon. GOAP integration plan (9 actions, 4 phases, 36 cost units).
  TDMA schedule for 20 Hz update rate from 4-node mesh.
  IEEE 802.11bf forward-compatible design.

- docs/ddd/ruvsense-domain-model.md:
  Domain-Driven Design with 3 bounded contexts (Multistatic Sensing,
  Coherence, Pose Tracking), aggregate roots, domain events, context
  map, anti-corruption layers, and repository interfaces.

Acceptance test: 2 people, 20 Hz, 10 min stable tracks, zero ID swaps,
<30mm torso keypoint jitter.

https://claude.ai/code/session_01QTX772SDsGVSPnaphoNgNY
 2026-03-02 00:17:30 +00:00
Claude 91a3bdd88a docs: Add remote vital sign sensing modalities research (RF, radar, quantum) 
Covers Wi-Fi CSI, mmWave/UWB radar, through-wall RF, rPPG, quantum radar,
and quantum biomedical instrumentation with cross-modality relevance to
WiFi-DensePose ADR-014 algorithms.

https://claude.ai/code/session_01Ki7pvEZtJDvqJkmyn6B714
 2026-02-28 14:40:00 +00:00
Claude cc82362c36 docs: Add SOTA research on WiFi sensing + RuVector with 20-year projection 
Comprehensive research document covering:
- WiFi CSI pose estimation SOTA (CVPR 2024, ECCV 2024, IEEE IoT 2025)
- ESP32 sensing benchmarks (88-97% accuracy, 18.5m through-wall)
- HNSW vector search for signal fingerprinting
- ONNX Runtime WASM for edge inference
- NIST post-quantum cryptography (ML-DSA, SLH-DSA) for sensor mesh
- WiFi 7/8 evolution (320MHz channels, 3984 CSI tones, 16x16 MIMO)
- 20-year projection through 2046 with cited sources

https://claude.ai/code/session_01Ki7pvEZtJDvqJkmyn6B714
 2026-02-28 09:12:02 +00:00