rUv
db64b4c671
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.
3.3 KiB
Tick 12 — 2026-05-22 06:08 UTC
Thread: R3 (cross-room re-ID) Verdict: Cross-room re-ID is technically feasible (MERIDIAN closes the env-shift gap) and ethically constrained (4 additional privacy constraints beyond R14 baseline).
What shipped
examples/research-sota/r3_crossroom_reid.py— pure-numpy simulation of person + environment + noise decomposition with 4 K-NN configurations.examples/research-sota/r3_reid_results.json— machine-readable predictions.docs/research/sota-2026-05-22/R3-crossroom-reid.md— synthesis of AETHER (ADR-024) + MERIDIAN (ADR-027) + privacy framing + physics-informed extension path.
Headline numbers
| Configuration | 1-shot accuracy |
|---|---|
| Within-room (matches AETHER ~95%) | 100% |
| Cross-room, raw cosine K-NN | 70% |
| Cross-room, MERIDIAN 100% env removal | 100% |
| Cross-room, MERIDIAN 70% env removal (realistic) | 100% |
| Chance | 10% |
The 30 pp gap from within-room to raw cross-room is exactly the angular contribution of the env-shift that cosine similarity can't normalise away. MERIDIAN-style per-room centroid subtraction recovers it — even at 70% effectiveness (realistic for limited labelled examples).
Privacy constraints surfaced
R14 baseline (opt-in default, on-device data, one-tap override) + 4 new constraints specific to re-ID:
- No cross-installation linkage (each install = isolated embedding space)
- Embedding storage requires explicit opt-in (biometric-class consent)
- Cryptographically verifiable forgetting (not just unlabelled storage)
- No re-ID across legal entities (hard-walled inter-org boundaries)
These rule out: cross-building tracking, mass surveillance, long-term unlabelled storage, third-party data sharing. They allow: per-installation personalisation, household anomaly detection, multi-person pose association in the same room.
Why R3 matters as a synthesis
R3 closes the loop on the empathic-appliance vision from R14: re-ID is the primitive that makes per-occupant features possible (V1 stress-responsive lighting needs to know it's "this person", not "any person"). Without R3, R14's verticals can't ship; with R3 + its privacy constraints, they can.
It also identifies the next research lever: physics-informed env_sig prediction from R6's forward operator + a room map → zero-shot transfer without labelled examples in the new room.
Composes cleanly
- R5/R6: person + env decomposition lives in the embedding space; physics-informed env prediction is the unbuilt sophistication.
- R7: mincut multi-link consistency = defence against re-ID spoofing.
- R9: RSSI K-NN showed env-locality dominance for the K-NN primitive; CSI is harder but the same decomposition works.
- R14: the four R3 privacy constraints extend R14's framework to biometric-class data.
Honest scope landed
- Additive decomposition is a first-order model; real CSI env effects are multiplicative in subcarrier domain
- The 70% raw-cosine K-NN number depends on env / person scale ratio (here ~4.7×)
- Adversarial scenarios not simulated; R7 mincut would weigh in
Coordination
ticks/tick-12.md. No PROGRESS.md edit. Branch research/sota-r3-crossroom-reid.
Remaining threads
R4 (federated learning), R15 (RF biometric across rooms — now partly subsumed by R3).
~5.8h to cron stop. 12 threads landed (2 negative results, 1 synthesis).