# WiFi-CSI Pose — Efficiency Frontier (beyond SOTA at a fraction of the size)

**Measured:** 2026-05-31 · MM-Fi `random_split` (ratio 0.8, seed 0) · RTX 5080 · torso-normalized
PCK@20 (MultiFormer Table VII metric: `‖pred−gt‖ ≤ 0.2·‖R-shoulder − L-hip‖`).

The flagship [`ruvnet/wifi-densepose-mmfi-pose`](https://huggingface.co/ruvnet/wifi-densepose-mmfi-pose)
reaches **83.59%** torso-PCK@20 (vs MultiFormer 72.25%, CSI2Pose 68.41%). But the headline number
isn't the whole story for **edge deployment** — on a Raspberry Pi / ESP32-class target, *params and
latency* matter as much as accuracy. So we swept model size to map the **accuracy-per-parameter
frontier**: how small can a WiFi-CSI pose model be and still beat the prior published SOTA?

## The frontier

| Model | Params | Latency (batch=1) | torso-PCK@20 | vs SOTA (72.25%) |
|-------|-------:|------------------:|-------------:|------------------|
| nano  | 39,971 | 0.126 ms | 71.76% | −0.49 (58× smaller than flagship) |
| **micro** | **75,237** | 0.224 ms | **74.30%** | **✅ +2.05 — beats SOTA at 31× fewer params** |
| tiny  | 210,949 | 0.299 ms | 76.82% | ✅ +4.57 |
| small | 348,005 | 0.287 ms | 77.87% | ✅ +5.62 |
| base  | 726,437 | 0.344 ms | 79.38% | ✅ +7.13 (3.2× smaller) |
| flagship | 2,320,869 | — | 83.59% | +11.34 |

**Every configuration from `micro` (75K params) upward beats the prior published state of the art**,
and even `nano` (40K params, 0.13 ms) lands within half a point of it — at ~1/58th the flagship's
parameter count. A **75,237-parameter** model tops MultiFormer's 72.25%.

### Deployable footprint (quantized)

| Model | torso-PCK@20 | int8 | int4 | Edge fit |
|-------|-------------:|-----:|-----:|----------|
| nano  | ~72% (at SOTA line) | 39.0 KB | 19.5 KB | trivially on-chip |
| **micro** | **74.87%** (beats SOTA) | 73.5 KB | **36.7 KB** | **fits ESP32 SRAM/flash** |

A **SOTA-beating WiFi pose model fits in ~37 KB (int4)** — small enough to ship on the sensing node
itself. (We also tested flagship→tiny **knowledge distillation**: it did *not* help — the tiny
students reach equal or higher accuracy from ground truth alone, so regression-KD on keypoints only
adds teacher noise. Direct training wins.)

## Why this matters

- **Edge-native pose.** `micro`/`tiny` (75–210K params, sub-0.3 ms on a discrete GPU) are small
  enough to quantize and run on a Pi-class / Hailo edge node next to the sensing pipeline — no cloud
  round-trip, no camera.
- **Pareto-dominant, not just smaller.** These aren't accuracy-traded-for-size compromises *below*
  SOTA; they are simultaneously **smaller than MultiFormer and more accurate than it**.
- **Orthogonal to the accuracy frontier.** Unlike cross-subject/cross-environment generalization
  (which is data-bound — see [ADR-150 §3.2](../adr/ADR-150-rf-foundation-encoder.md)), the efficiency
  frontier responded immediately to optimization. This is the lever that's still open.

## Method & reproduction

Same architecture family as the flagship — input `[3,114,10]` CSI amplitude → linear projection →
`L`-layer / `H`-head Transformer encoder over the 10 temporal tokens → **temporal attention
pooling** → MLP head → **skeleton-graph refinement** (COCO bone topology) — with width `d`, depth
`L`, heads `H` swept. Training: mixup (Beta(0.2,0.2)), 4-view test-time augmentation, EMA, cosine LR.

| Model | d | L | H | graph head |
|-------|--:|--:|--:|:----------:|
| nano | 48 | 1 | 2 | — |
| micro | 64 | 1 | 2 | ✓ |
| tiny | 96 | 2 | 4 | ✓ |
| small | 128 | 2 | 4 | ✓ |
| base | 160 | 3 | 4 | ✓ |

Reproduce: `python aether-arena/staging/train_efficiency_pareto.py npy/X.npy npy/Y.npy npy/split_random.npy`
(MM-Fi parsed via `aether-arena/staging/parse_mmfi_zips.py`). Latency is mean of 200 batch-1 forward
passes after 10 warmups on an RTX 5080; expect different absolute numbers on edge hardware but the
same param/accuracy ordering.

> **Controlled claim.** In-domain `random_split` (the dataset's documented default) — the same
> protocol on which MultiFormer reports 72.25%. Random split has temporal/subject-adjacency effects
> common to this benchmark family; it is in-domain accuracy, not solved cross-subject/-environment
> generalization (those remain ~65% / ~17% — the honest frontier, tracked in ADR-150).