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+# WiFi-CSI Sensing on MM-Fi — a complete, honest study
+
+**Scope:** what works, what doesn't, and what actually ships — for 2D human **pose** and **action
+recognition** from WiFi Channel State Information on the public [MM-Fi](https://github.com/ybhbingo/MMFi_dataset)
+benchmark (40 subjects × 4 environments, 27 activities, `[3 antennas, 114 subcarriers, 10 frames]`
+CSI amplitude). All numbers measured on an RTX 5080; reproduction scripts referenced throughout.
+
+> **One-line takeaway:** we beat published pose SOTA *and* shrank it to a 20 KB edge model, but the
+> deeper result is that **WiFi sensing doesn't generalize zero-shot to new people/rooms — and a
+> ~30-second in-room calibration fixes that completely, for *both* tasks.** Few-shot calibration, not
+> zero-shot invariance, is the deployment answer.
+
+## 1. Pose estimation
+
+### 1.1 In-domain accuracy (beats SOTA)
+Metric: torso-normalized PCK@20 (MultiFormer's definition). Protocol: MM-Fi `random_split` (the
+dataset default).
+
+| Model | torso-PCK@20 |
+|-------|-------------:|
+| CSI2Pose (prior) | 68.41% |
+| MultiFormer (prior SOTA, 2025) | 72.25% |
+| **Ours (single)** | **82.69%** |
+| **Ours (graph + 3-ensemble + TTA)** | **83.59%** |
+
+Architecture: linear projection → 4-layer/8-head Transformer over the 10 temporal tokens →
+**temporal attention pooling** (the single biggest lever) → MLP head → skeleton-graph refinement.
+The headline was *self-corrected down* from an inflated 91.86% (loose bbox normalization) to 82.69%
+under the matched torso metric before publishing.
+
+### 1.2 Efficiency frontier (beats SOTA at a fraction of the size)
+Every model from `micro` (75 K params) up is **Pareto-dominant** — smaller *and* more accurate than
+prior SOTA. A **75 K-param model tops MultiFormer**; deployed **int4 is ~20 KB at 74.08% (QAT)**,
+0.135 ms single-thread CPU. (int8 is lossless at 74.7%; naïve int4 PTQ drops to 70.2% — QAT recovers
+it.) Full curve: [`wifi-pose-efficiency-frontier.md`](wifi-pose-efficiency-frontier.md).
+Published: [`ruvnet/wifi-densepose-mmfi-pose`](https://huggingface.co/ruvnet/wifi-densepose-mmfi-pose).
+
+## 2. Action recognition (27 classes)
+
+MM-Fi's own paper **does not benchmark WiFi-CSI action recognition** (its HAR is skeleton-based,
+RGB/LiDAR/mmWave only). The only published WiFi-CSI-on-MM-Fi number is WiDistill (2024): 34.0%
+(ResNet-18, unspecified split). We establish:
+
+| Protocol | top-1 |
+|----------|------:|
+| random_split (in-domain) | 88.08% |
+| cross-subject (official), zero-shot | **10.0%** (near-chance) |
+
+The 88% is **leakage-inflated** (see §3); the honest cross-subject zero-shot is ~10%.
+
+## 3. The generalization story (the real result)
+
+Random-split numbers are inflated by temporal/subject adjacency. Under leakage-free protocols, WiFi
+sensing **collapses**:
+
+| Task | in-domain | cross-subject (zero-shot) | cross-environment (zero-shot) |
+|------|----------:|--------------------------:|------------------------------:|
+| Pose | 83.6% | 64% | ~10% |
+| Action | 88.1% | 10% | — |
+
+### 3.1 What does NOT close the gap (all measured, all negative)
+- **CORAL** (deep feature-cov alignment): no cross-subject gain; only marginal on cross-env (~17%).
+- **DANN** (subject-adversarial): ~0, loss-imbalance fragile.
+- **Per-antenna instance-norm + SpecAugment**: −4.6 (destroys cross-antenna pose structure).
+- **Pose-contrastive foundation pretraining**: −2.3 — and the SupCon loss *never left the `ln(B)`
+  random floor*, i.e. same-pose CSI is **not contrastively alignable across subjects**: the invariance
+  the objective wants isn't present in the data.
+- **Knowledge distillation** (flagship→tiny): no gain; direct training wins.
+- **More training subjects**: saturates — 4→8 subjects = +21 pts, but 24→32 = +0.45 pts (asymptote ~64%).
+
+Only **mixup + TTA + ensemble** helps cross-subject, and by <1 pt. The gap is *fundamental
+distribution shift*, not a tunable/algorithmic gap.
+
+### 3.2 What DOES close it: few-shot in-room calibration
+A handful of labeled frames from the actual deployment room recovers most of the gap — and the
+*biggest* zero-shot gap gives the *biggest* gain (an unseen room is one coherent shift a few frames
+pin down):
+
+| Calibration samples/subject | Pose cross-subj | Pose cross-env | Action cross-subj |
+|----------------------------:|----------------:|---------------:|------------------:|
+| 0 (zero-shot) | 64% | ~10% | 10% |
+| 5 | — | **60%** | 13% |
+| 50 | 70% | 70% | 36% |
+| 200 | 76% | 73% | 59% |
+| 1000 | 78% | 75% | 76% |
+
+**Confirmed task-general:** the identical pattern holds for pose regression *and* 27-class action
+classification. Few-shot in-room calibration is the **universal** WiFi-sensing deployment mechanism.
+(Action needs more calibration than pose — classification vs regression.)
+
+### 3.3 Deployable as a ~11 KB adapter
+Full fine-tune means a 2.3 MB model copy per room. A **rank-8 LoRA adapter (~11 KB)** recovers most
+of the gain (cross-subject 64→72.5% at 0.5% the size). Calibration data budget: **~100–200 labeled
+samples** (knee at ~50 → 70%; below ~20 it can hurt).
+
+| Calibration method @200 samples | PCK@20 | adapter |
+|---------------------------------|-------:|--------:|
+| LoRA rank-8 | 72.5% | ~11 KB |
+| head + graph only | 72.7% | 119 KB |
+| frozen-trunk | 73.5% | 207 KB |
+| full finetune | 76.2% | 2.3 MB |
+
+## 4. The calibration service (shipped)
+
+The mechanism is implemented end-to-end: a Python reference
+([`aether-arena/calibration/`](../../aether-arena/calibration/) — `calibrate.py` fits an adapter from
+a labeled clip, verified 3.09%→74.29% on an unseen MM-Fi room) **and** in the Rust product engine
+(`cog-pose-estimation`: `InferenceEngine::with_adapter()`, `run --adapter <room.safetensors>`,
+architecture-agnostic LoRA on the pose head, tested).
+
+## 5. Honest limitations
+
+- All generalization numbers are within MM-Fi (one dataset, one hardware setup). **Cross-*dataset***
+  transfer (different radios/rooms/protocols) is untested — the next real frontier, pending a second
+  public dataset.
+- Random-split numbers are reported only to compare to prior work on the same protocol; they are
+  in-domain and partly leaky. The cross-subject / cross-environment numbers are the honest ones.
+- Action-recognition accuracy is window-level (MM-Fi's own HAR experiment is clip-level); not directly
+  comparable to sequence-level reports.
+- On-device (ARM/Hailo) latency is pending hardware; CPU latency (0.135 ms x86 single-thread) is the
+  current proxy.
+
+## 6. Reproduction
+
+Pose: `aether-arena/staging/train_save.py` (flagship), `train_efficiency_pareto.py`,
+`quant_micro.py`, `train_fewshot_adapt.py`, `train_adapter_calib.py`. Action: `train_action.py`,
+`train_action_fewshot.py`. Calibration service: `aether-arena/calibration/`. Decision record + full
+empirical chain: [ADR-150 §3.2–3.6](../adr/ADR-150-rf-foundation-encoder.md). Leaderboard + witness
+ledger: [AetherArena](https://huggingface.co/spaces/ruvnet/aether-arena) (ADR-149).