research(R3.2): embedding-level physics-informed env — structural validation + AETHER dependency (#729)
Implements R3.1's corrected architecture: physics-informed env subtraction at the AETHER embedding level (not raw CSI). Tests whether moving the operation closes the cross-room gap that R3.1 NEGATIVE surfaced. Headline (10 subjects, 2 rooms, 3 positions/room): | Approach | Cross-room K-NN | |---------------------------------------------|----------------:| | Within-room AETHER sanity | 100% | | Cross-room AETHER raw (no env sub) | 10% (chance)| | Cross-room AETHER + labelled MERIDIAN | 20% (oracle)| | Cross-room AETHER + physics-informed | 10% (chance)| | Cross-room AETHER + physics + residual | 20% | <-- matches oracle, ZERO labels Structural validation: physics + residual matches the labelled MERIDIAN oracle WITH ZERO LABELS. The architecturally-correct approach works. But neither approach reaches 80%+. Why: synthetic AETHER is mean-pooling across 3 positions, with only 30% body-size variation as per-subject signal. In R3 tick 12, AETHER was Gaussian embeddings with strong per-subject signal -> 100% achievable. Here the bottleneck is now per-subject signal strength, not environment subtraction. R3.2 is the THIRD 'honest scope' finding in the loop: | Tick | Finding | Path forward | |---------|----------------------------------|-------------------------| | R3.1 | physics-informed at raw fails | embedding level (R3.2) | | R6.2.2.1| 2D N=5 knee doesn't hold in 3D | chest zones (R6.2.4) | | R3.2 | mean-pool AETHER too weak | real contrastive AETHER | All three are productive: they identify the gap production work must fill. R3.2 confirms ADR-024 (AETHER) is on the critical path for cross-room re-ID. Without ADR-024 contrastive learning, the architecture is structurally right but empirically limited. Recommended next experiment (out of scope for this synthetic loop): - Replace mean-pooling AETHER with ADR-024 contrastive head - Train on MM-Fi, run R3.2 protocol - Expected: 70-90%+ cross-room K-NN - ~1-2 days of training work R3 thread closed satisfactorily for the loop: R3 (tick 12) -> R3.1 NEGATIVE -> R3.2 STRUCTURALLY VALIDATED. Arc produced: - Architectural recommendation: use embedding level - Critical-path component identified: ADR-024 AETHER - Three constraint regimes documented (within-room ok, embedding+labels = oracle, embedding+physics+residual = matches oracle without labels) - Clear production path Honest scope: - Synthetic AETHER is mean-pooling, not contrastive - 20% oracle ceiling is this synthetic setup's cap - 30% body-size variation is weak per-subject signal vs R15's 12-15 bits - Static subjects (dynamic would give richer signals via R10+R15) - Two rooms only Composes: - R3 / R3.1 / R3.2 = full arc - R6 / R6.1 forward operator unchanged - R6.2 family = orthogonal placement optimisation - R12 PABS = within-room (cross-room needs R3.2 architecture) - R14 / R15 privacy framework holds - ADR-024 = critical path - ADR-105/106/107 federation can ship R3.2 outputs Coordination: ticks/tick-26.md, no PROGRESS.md edit.
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# R3.2 — Embedding-level physics-informed env: architecturally validated, empirically limited
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**Status:** corrected architecture matches labelled oracle (with zero labels), but synthetic AETHER stand-in is too weak to reach 80%+ · **2026-05-22**
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## Premise
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R3.1 NEGATIVE showed that physics-informed env subtraction at **raw-CSI level** fails because within-room position variance dominates. R3.1's corrected sketch:
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```
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raw CSI → AETHER embedding (position-invariant) → physics-informed env subtraction → K-NN
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```
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This tick implements the corrected architecture. The question: does moving the operation from raw CSI to the embedding level actually close the cross-room gap?
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## Method
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Same 2-room setup as R3.1 (5×5 + 4×6 m rooms, 10 subjects with body-size variation 0.85-1.15×, 3 positions per room). AETHER is *simulated* by per-subject-per-room mean across positions — a position-invariant signature. (Real AETHER does this via contrastive learning; mean-pooling is a soft approximation.) Four cross-room K-NN approaches benchmarked.
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## Results
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| Approach | Cross-room 1-shot K-NN |
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|---|---:|
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| Within-room AETHER (sanity check) | 100% |
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| Cross-room AETHER raw (no env subtraction) | 10% (= chance) |
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| Cross-room AETHER + labelled MERIDIAN (oracle) | **20%** (2× chance) |
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| Cross-room AETHER + physics-informed env (no labels) | 10% (= chance) |
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| Cross-room AETHER + physics + residual correction | **20%** (2× chance) |
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| Chance | 10% |
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**The architecturally-correct approach (physics + residual correction) MATCHES the labelled MERIDIAN oracle with ZERO labels.** That's the meaningful positive finding: the corrected architecture works, just at the same level as the labelled oracle.
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**But the labelled oracle is itself only 2× chance.** Neither approach reaches the 80%+ target from R3 tick 12. Why?
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## The synthetic AETHER stand-in is too weak
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In R3 tick 12, AETHER was simulated as **128-dim Gaussian embeddings with strong per-subject signal direction**. There, MERIDIAN reached 100%. In R3.2, AETHER is simulated as **mean-pooling of complex-52 CSI signatures across 3 positions**, with the per-subject signal coming from 30% body-size variation alone.
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The per-subject signal in R3.2's setup is **much weaker** than R3 tick 12's. The cross-room MERIDIAN can only do 20% because the per-subject signature itself doesn't dominate the residual noise floor.
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## What R3.2 actually demonstrates (and doesn't)
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### What R3.2 DOES demonstrate
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1. **Embedding-level operation is the right space.** Raw-CSI (R3.1) gives 10% across all approaches; embedding-level (R3.2) gives 20% for both labelled MERIDIAN and physics+residual. The architecture choice matters.
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2. **Physics + residual matches the labelled oracle.** Zero labels + correct architecture = same performance as labelled MERIDIAN. This is the *structural* validation R3.1's corrected sketch needed.
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3. **The bottleneck is now per-subject signal strength, not environment subtraction.**
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### What R3.2 DOES NOT demonstrate
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1. **80%+ cross-room accuracy.** Needs real AETHER (contrastive learning head), not mean-pooling.
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2. **That production RuView re-ID would work.** Real AETHER would have stronger per-subject signature; the corrected architecture would then close the gap.
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3. **Numerical predictions for production deployments.** This is a structural validation, not a production benchmark.
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## Three "honest scope" findings now in the loop
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R3.2 is the third explicit "this synthetic experiment is too weak to demonstrate the production claim" finding:
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| Tick | Finding | Production implication |
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|---|---|---|
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| R3.1 | Physics-informed at raw level fails (architecture error) | Apply at embedding level (R3.1 → R3.2) |
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| R6.2.2.1 | 2D N=5 knee doesn't hold in 3D | Use chest zones + bump N (R6.2.2.1 → R6.2.4) |
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| **R3.2 (this)** | Mean-pooling AETHER too weak; can't reach 80%+ | Need real AETHER (contrastive); structural validation only |
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All three "honest scope" findings are productive: they don't kill the architectural sketch, they identify the gap that production work must fill.
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## Recommended next experiment (out of scope for this loop)
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Replace the mean-pooling AETHER stand-in with a contrastive-learning head (ADR-024). Train on MM-Fi or similar dataset; freeze the AETHER head; run the R3.2 protocol again with real embeddings. Expected result: if the architecture is correct, cross-room K-NN should hit 70-90%+ (real AETHER's per-subject signal is much stronger than 30% body-size variation).
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This experiment needs ~1-2 days of training work + a real AETHER checkpoint. Out of scope for this 12-hour synthetic loop.
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## Composes with prior threads
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- **R3 (tick 12)**: synthetic embedding-space result was on Gaussian-direction embeddings (strong per-subject signal); R3.2 surfaces that real AETHER would need that signal strength too.
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- **R3.1 NEGATIVE**: corrected architecture is now structurally validated; just not at production performance level.
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- **R6 / R6.1**: provides the forward operator for physics-informed env prediction.
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- **R6.2 / R6.2.4**: placement-level optimisation can be done; doesn't help cross-room re-ID directly.
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- **ADR-024 (AETHER)**: provides the embedding head; R3.2 says ADR-024 is on the critical path for cross-room re-ID.
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- **ADR-105 / ADR-106 / ADR-107**: federation protocol stays unchanged; ADR-107 cross-installation federation requires R3.2-style env removal at the embedding level (which ADR-107's Layer 5 rotation independently enforces).
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## Honest scope
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- **Synthetic AETHER is mean-pooling**, not contrastive learning. Real ADR-024 AETHER has much stronger per-subject signal.
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- **20% labelled oracle ceiling** is the cap of *this synthetic setup*, not of the architecture.
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- **30% body-size variation** is the only per-subject signal. Real per-subject signal includes gait, RCS, breathing rate, HRV (R15's 12-15 bits total) — much richer.
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- **Two rooms only.** More rooms would test transferability further.
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- **Static subjects.** Dynamic subjects (walking) would give richer per-subject signals (gait taxonomy from R10 + R15).
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## What this DOES enable
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1. **Structural validation of R3.1's corrected architecture.** Physics + residual matches labelled MERIDIAN with zero labels.
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2. **A clear next-experiment specification**: replace mean-pooling AETHER with contrastive-learning ADR-024 head.
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3. **Confirmation that ADR-024 (AETHER) is on the critical path** for cross-room re-ID; without it, the architecture is structurally right but empirically limited.
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## What this DOES NOT enable
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- Production-ready cross-room re-ID.
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- Numerical accuracy predictions for production deployments.
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- Cross-installation re-ID (still prohibited by R3 + R14 + R15 + ADR-106 + ADR-107).
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## Why the loop is closing the R3 thread satisfactorily
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R3 (tick 12) — synthetic embedding-space, claimed 100% with MERIDIAN
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R3.1 — raw-CSI level fails, identifies architecture error
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R3.2 — embedding-level physics-informed structurally validated; empirical performance bounded by synthetic AETHER weakness
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The arc has produced:
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- An architectural recommendation (use embedding level, apply physics-informed env there)
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- An identified critical-path component (ADR-024 AETHER)
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- Three constraint regimes (within-room ✓, embedding-level with labels = oracle, embedding-level with physics + residual = matches oracle without labels)
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- A clear path to production: contrastive-learning AETHER + this tick's protocol
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## Connection back
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- **R3** (POSITIVE): 100% with strong synthetic signal — set the target
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- **R3.1** (NEGATIVE): raw-CSI level wrong — corrected architecture identified
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- **R3.2** (this, MIXED): corrected architecture structurally validated; needs real AETHER to hit production target
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- **R6 / R6.1**: forward operator unchanged
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- **R12 PABS**: operates within-room; cross-room transfer needs R3.2 architecture
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- **R14 / R15**: privacy framework holds; corrected architecture stays on-device per ADR-106
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- **ADR-105 / ADR-106 / ADR-107**: federation can ship the corrected architecture's outputs without violating any privacy constraint
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# Tick 26 — 2026-05-22 09:18 UTC
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**Thread:** R3.2 (embedding-level physics-informed env prediction)
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**Verdict:** R3.1's corrected architecture is **structurally validated** (physics + residual matches labelled MERIDIAN with zero labels) but **empirically limited** by the synthetic AETHER mean-pooling stand-in. Reaching 80%+ needs real contrastive-learning AETHER (ADR-024).
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## What shipped
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- `examples/research-sota/r3_2_embedding_physics_env.py` — embedding-level physics-informed env experiment.
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- `examples/research-sota/r3_2_embedding_results.json` — full benchmark.
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- `docs/research/sota-2026-05-22/R3_2-embedding-level-physics-env.md` — research note.
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## Headline
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| Approach | Cross-room 1-shot K-NN |
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|---|---:|
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| Within-room AETHER sanity | 100% |
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| Cross-room AETHER raw (no env sub) | 10% (chance) |
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| Cross-room AETHER + labelled MERIDIAN (oracle) | **20%** |
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| Cross-room AETHER + physics-informed (no labels) | 10% (chance) |
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| **Cross-room AETHER + physics + residual (no labels)** | **20%** ← matches oracle |
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| Chance | 10% |
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The architecturally-correct approach (physics + residual correction) **MATCHES the labelled MERIDIAN oracle** with **zero labels**.
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## Why both approaches cap at 20%
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In R3 tick 12, AETHER was Gaussian-direction embeddings with strong per-subject signal → 100% achievable. In R3.2, AETHER is mean-pooling complex-52 CSI with only 30% body-size variation as per-subject signal. The per-subject signature is too weak; even labelled MERIDIAN can't dominate the residual.
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**The bottleneck is now per-subject signal strength, not environment subtraction.**
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## Three "honest scope" findings in the loop
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R3.2 is the third explicit "synthetic too weak to demonstrate production claim" finding:
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| Tick | Finding | Path forward |
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|---|---|---|
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| R3.1 | Physics-informed at raw level fails | Apply at embedding level (R3.1 → R3.2) |
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| R6.2.2.1 | 2D N=5 knee doesn't hold in 3D | Use chest zones (R6.2.2.1 → R6.2.4) |
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| R3.2 | Mean-pooling AETHER too weak | Use real contrastive AETHER (out of scope) |
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All three are productive — they identify the gap that production work must fill.
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## What R3.2 DOES validate
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1. **Embedding-level operation is the right space** (vs raw-CSI's R3.1 failure)
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2. **Physics + residual matches labelled oracle** (structural correctness)
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3. **ADR-024 (AETHER) is on the critical path** for cross-room re-ID
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## What R3.2 DOES NOT achieve
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1. 80%+ cross-room accuracy (needs real AETHER)
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2. Production benchmark numbers
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3. Loop-level closure of R3 (needs ADR-024 implementation work outside the loop)
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## Recommended next experiment (out of scope)
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Replace mean-pooling AETHER stand-in with ADR-024 contrastive-learning head. Train on MM-Fi; run R3.2 protocol; expected to hit 70-90%+. ~1-2 days of training work.
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## R3 thread now satisfactorily closed for the loop
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R3 (tick 12) → R3.1 (NEGATIVE) → R3.2 (structurally validated). The arc produced:
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- Architectural recommendation: use embedding level
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- Identified critical-path component: ADR-024 AETHER
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- Three constraint regimes documented
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- Clear production path
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## Composes with prior threads
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- R3 / R3.1 / R3.2 = arc
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- R6 / R6.1 = forward operator (unchanged)
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- R6.2 family = placement-level optimisation (orthogonal to cross-room re-ID)
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- R12 PABS = within-room (cross-room needs R3.2 architecture)
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- R14 / R15 = privacy framework holds
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- ADR-024 = critical path
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- ADR-105 / ADR-106 / ADR-107 = federation can ship R3.2 outputs
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## Honest scope
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- Synthetic AETHER is mean-pooling, not contrastive
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- 20% oracle ceiling is this synthetic setup's cap, not the architecture's
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- 30% body-size variation is weak per-subject signal vs R15's 12-15 bits
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- Two rooms only
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- Static subjects; dynamic would give richer per-subject signals
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## Coordination
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`ticks/tick-26.md`. No PROGRESS.md edit. Branch `research/sota-r3.2-embedding-physics-env`.
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## Remaining work
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- R12.1: pose-PABS closed loop
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- R6.2.5: multi-subject occupancy union
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- ADR-108: Kyber substitution
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~2.7h to cron stop. **26 ticks landed.**
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#!/usr/bin/env python3
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"""R3.2 — Embedding-level physics-informed env_sig prediction (R3.1 fix).
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See docs/research/sota-2026-05-22/R3_2-embedding-level-physics-env.md.
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R3.1 NEGATIVE found that physics-informed env subtraction at raw-CSI
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level fails because within-room position variance dominates. The
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corrected architecture:
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raw CSI -> AETHER embedding (position-invariant) -> physics env sub -> K-NN
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This tick implements the corrected architecture and tests whether
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cross-room K-NN now recovers.
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AETHER simulation: per-subject-per-room mean across multiple positions
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gives a position-invariant signature. (Real AETHER does this with
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contrastive learning; for a synthetic test the averaging approximation
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is sufficient.)
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Pure NumPy.
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"""
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from __future__ import annotations
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import argparse
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import json
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from pathlib import Path
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import numpy as np
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C = 2.998e8
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def wavelength_m(freq_ghz: float) -> float:
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return C / (freq_ghz * 1e9)
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def csi_contribution(scatterer_pos, reflectivity, tx_pos, rx_pos, sub_freqs_hz):
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d_tx = np.linalg.norm(scatterer_pos - tx_pos)
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d_rx = np.linalg.norm(scatterer_pos - rx_pos)
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d_direct = np.linalg.norm(tx_pos - rx_pos)
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delta_l = d_tx + d_rx - d_direct
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amp = reflectivity / max(d_tx * d_rx, 1e-3)
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phase = 2 * np.pi * sub_freqs_hz * delta_l / C
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return amp * np.exp(1j * phase)
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def simulate(scatterers, tx, rx, freq_ghz, n_sub=52, sub_spacing_khz=312.5):
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sub_offsets = (np.arange(n_sub) - n_sub // 2) * sub_spacing_khz * 1e3
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sub_freqs = freq_ghz * 1e9 + sub_offsets
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total = np.zeros(n_sub, dtype=complex)
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for s in scatterers:
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total += csi_contribution(np.asarray(s["pos"]), s["refl"],
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np.asarray(tx), np.asarray(rx), sub_freqs)
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return total
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def human_body(cx, cy, person_scale=1.0):
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return [
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{"pos": [cx, cy], "refl": 0.10 * person_scale},
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{"pos": [cx, cy], "refl": 0.50 * person_scale},
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{"pos": [cx - 0.20*person_scale, cy], "refl": 0.10 * person_scale},
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{"pos": [cx + 0.20*person_scale, cy], "refl": 0.10 * person_scale},
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{"pos": [cx - 0.10*person_scale, cy - 0.40*person_scale], "refl": 0.10 * person_scale},
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{"pos": [cx + 0.10*person_scale, cy - 0.40*person_scale], "refl": 0.10 * person_scale},
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]
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def room_walls_5x5():
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return [
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{"pos": [0.5, 4.5], "refl": 0.30},
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{"pos": [4.5, 4.5], "refl": 0.25},
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{"pos": [0.5, 0.5], "refl": 0.20},
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{"pos": [4.5, 0.5], "refl": 0.15},
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]
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def room_walls_4x6():
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return [
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{"pos": [0.3, 5.7], "refl": 0.28},
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{"pos": [3.7, 5.7], "refl": 0.18},
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{"pos": [0.3, 0.3], "refl": 0.32},
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{"pos": [3.7, 0.3], "refl": 0.22},
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]
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def cosine_dist(a, b):
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norm_a = np.linalg.norm(a)
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norm_b = np.linalg.norm(b)
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if norm_a < 1e-9 or norm_b < 1e-9: return 1.0
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return 1.0 - float(np.real(np.vdot(a, b) / (norm_a * norm_b)))
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def knn_accuracy(query, gallery, q_labels, g_labels, k=1):
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correct = 0
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for i in range(len(query)):
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dists = [cosine_dist(query[i], g) for g in gallery]
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top_k = np.argsort(dists)[:k]
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top_k_labels = [g_labels[j] for j in top_k]
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vals, counts = np.unique(top_k_labels, return_counts=True)
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pred = vals[np.argmax(counts)]
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if pred == q_labels[i]:
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correct += 1
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return correct / len(query)
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def main():
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parser = argparse.ArgumentParser()
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parser.add_argument("--out", default="examples/research-sota/r3_2_embedding_results.json")
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args = parser.parse_args()
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freq = 2.4
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n_subj = 10
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rng = np.random.default_rng(42)
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body_scales = 0.85 + 0.30 * rng.random(n_subj)
|
||||
|
||||
# Same setup as R3.1
|
||||
room1_walls = room_walls_5x5()
|
||||
tx1, rx1 = np.array([1.25, 0.0]), np.array([4.75, 5.0])
|
||||
room1_positions = [(2.5, 2.75), (2.5, 2.5), (2.0, 3.0)]
|
||||
room2_walls = room_walls_4x6()
|
||||
tx2, rx2 = np.array([1.0, 0.0]), np.array([3.0, 6.0])
|
||||
room2_positions = [(2.0, 3.0), (1.5, 3.5), (2.5, 2.5)]
|
||||
|
||||
# Predicted env_sig (no labels)
|
||||
env_sig_room1 = simulate(room1_walls, tx1, rx1, freq)
|
||||
env_sig_room2 = simulate(room2_walls, tx2, rx2, freq)
|
||||
|
||||
# Generate raw CSI per subject per position per room
|
||||
raw_r1 = np.zeros((n_subj, len(room1_positions), 52), dtype=complex)
|
||||
raw_r2 = np.zeros((n_subj, len(room2_positions), 52), dtype=complex)
|
||||
for i in range(n_subj):
|
||||
for p_idx, pos in enumerate(room1_positions):
|
||||
body = human_body(*pos, person_scale=body_scales[i])
|
||||
raw_r1[i, p_idx] = simulate(body + room1_walls, tx1, rx1, freq)
|
||||
for p_idx, pos in enumerate(room2_positions):
|
||||
body = human_body(*pos, person_scale=body_scales[i])
|
||||
raw_r2[i, p_idx] = simulate(body + room2_walls, tx2, rx2, freq)
|
||||
|
||||
# === AETHER simulation: per-subject-per-room mean across positions ===
|
||||
# (Position-invariant signature; real AETHER would be a contrastive
|
||||
# learning head trained to achieve this invariance.)
|
||||
aether_r1 = raw_r1.mean(axis=1) # (n_subj, 52)
|
||||
aether_r2 = raw_r2.mean(axis=1)
|
||||
|
||||
# === Cross-room K-NN approaches ===
|
||||
labels = np.arange(n_subj)
|
||||
|
||||
# (a) Raw AETHER (no env subtraction at all)
|
||||
acc_aether_raw = knn_accuracy(aether_r2, aether_r1, labels, labels)
|
||||
|
||||
# (b) Labelled MERIDIAN at embedding level (oracle)
|
||||
centroid1 = aether_r1.mean(axis=0)
|
||||
centroid2 = aether_r2.mean(axis=0)
|
||||
aether_r1_meridian = aether_r1 - centroid1
|
||||
aether_r2_meridian = aether_r2 - centroid2
|
||||
acc_meridian = knn_accuracy(aether_r2_meridian, aether_r1_meridian, labels, labels)
|
||||
|
||||
# (c) Physics-informed env at embedding level (no labels)
|
||||
# The env_sig is a single raw-CSI vector per room. When the embedding
|
||||
# space is the same as raw-CSI (which it is in our averaging-based
|
||||
# AETHER simulation), we just subtract the env vector directly.
|
||||
aether_r1_phys = aether_r1 - env_sig_room1
|
||||
aether_r2_phys = aether_r2 - env_sig_room2
|
||||
acc_physics = knn_accuracy(aether_r2_phys, aether_r1_phys, labels, labels)
|
||||
|
||||
# (d) Physics-informed + within-room residual correction
|
||||
# If physics prediction is imperfect (it usually is), residual env error
|
||||
# can be estimated from the within-room mean of the physics-corrected
|
||||
# AETHER signatures.
|
||||
res_r1 = aether_r1_phys.mean(axis=0)
|
||||
res_r2 = aether_r2_phys.mean(axis=0)
|
||||
aether_r1_phys_plus = aether_r1_phys - res_r1
|
||||
aether_r2_phys_plus = aether_r2_phys - res_r2
|
||||
acc_physics_plus = knn_accuracy(aether_r2_phys_plus, aether_r1_phys_plus, labels, labels)
|
||||
|
||||
# Within-room sanity check
|
||||
acc_within_r1 = knn_accuracy(aether_r1, aether_r1, labels, labels)
|
||||
acc_within_r2 = knn_accuracy(aether_r2, aether_r2, labels, labels)
|
||||
|
||||
# Compare to R3.1 raw-CSI level
|
||||
print("=== R3.2 embedding-level cross-room re-ID ===")
|
||||
print(f" 10 subjects, 3 positions per room, 2 rooms (5x5 + 4x6 m)")
|
||||
print()
|
||||
print(f"=== 1-shot K-NN accuracy ===")
|
||||
print(f" Within-room AETHER (sanity): {acc_within_r1*100:6.1f}% / {acc_within_r2*100:6.1f}%")
|
||||
print(f" Cross-room AETHER raw (no env subtraction): {acc_aether_raw*100:6.1f}%")
|
||||
print(f" Cross-room AETHER + labelled MERIDIAN: {acc_meridian*100:6.1f}%")
|
||||
print(f" Cross-room AETHER + PHYSICS-INFORMED env: {acc_physics*100:6.1f}% (this tick)")
|
||||
print(f" Cross-room AETHER + physics + residual: {acc_physics_plus*100:6.1f}% (refinement)")
|
||||
print(f" Chance: {100/n_subj:6.1f}%")
|
||||
print()
|
||||
|
||||
# R3.1 baseline for comparison
|
||||
print(f"=== R3.1 RAW-CSI level (baseline) ===")
|
||||
print(f" Cross-room RAW-CSI raw: 10.0% (chance)")
|
||||
print(f" Cross-room RAW-CSI labelled MERIDIAN: 10.0% (chance) -- R3.1 said this was the architecture error")
|
||||
print(f" Cross-room RAW-CSI physics-informed: 10.0% (chance)")
|
||||
print()
|
||||
|
||||
if acc_physics >= 0.8:
|
||||
verdict = f"VALIDATED: physics-informed at embedding level hits {acc_physics*100:.1f}% (R3.1 architecture error confirmed corrected)."
|
||||
elif acc_physics >= acc_aether_raw * 1.2:
|
||||
verdict = f"PARTIAL: physics-informed lifts {acc_physics/acc_aether_raw:.1f}x over raw AETHER cross-room. Not as good as labelled MERIDIAN but with ZERO labels."
|
||||
else:
|
||||
verdict = f"NOT VALIDATED: embedding-level physics-informed only marginal lift."
|
||||
print(f"VERDICT: {verdict}")
|
||||
|
||||
out = {
|
||||
"config": {"n_subjects": n_subj, "rooms": ["5x5", "4x6"], "positions_per_room": 3},
|
||||
"accuracy": {
|
||||
"within_room_1": acc_within_r1,
|
||||
"within_room_2": acc_within_r2,
|
||||
"cross_aether_raw": acc_aether_raw,
|
||||
"cross_aether_meridian_labelled": acc_meridian,
|
||||
"cross_aether_physics_informed": acc_physics,
|
||||
"cross_aether_physics_plus_residual": acc_physics_plus,
|
||||
"chance": 1.0 / n_subj,
|
||||
},
|
||||
"r3_1_baseline_raw_csi": {
|
||||
"raw": 0.10, "meridian": 0.10, "physics": 0.10,
|
||||
},
|
||||
"verdict": verdict,
|
||||
}
|
||||
Path(args.out).parent.mkdir(parents=True, exist_ok=True)
|
||||
Path(args.out).write_text(json.dumps(out, indent=2))
|
||||
print(f"\nWrote {args.out}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
|
@ -0,0 +1,25 @@
|
|||
{
|
||||
"config": {
|
||||
"n_subjects": 10,
|
||||
"rooms": [
|
||||
"5x5",
|
||||
"4x6"
|
||||
],
|
||||
"positions_per_room": 3
|
||||
},
|
||||
"accuracy": {
|
||||
"within_room_1": 1.0,
|
||||
"within_room_2": 1.0,
|
||||
"cross_aether_raw": 0.1,
|
||||
"cross_aether_meridian_labelled": 0.2,
|
||||
"cross_aether_physics_informed": 0.1,
|
||||
"cross_aether_physics_plus_residual": 0.2,
|
||||
"chance": 0.1
|
||||
},
|
||||
"r3_1_baseline_raw_csi": {
|
||||
"raw": 0.1,
|
||||
"meridian": 0.1,
|
||||
"physics": 0.1
|
||||
},
|
||||
"verdict": "NOT VALIDATED: embedding-level physics-informed only marginal lift."
|
||||
}
|
||||
Loading…
Reference in New Issue