docs(research): WiFlow-STD audit writeup (published as public gist + upstream issue)
Gist: https://gist.github.com/ruvnet/47d4369c0bd251ed233bbc450d50f6e6 Upstream report: DY2434/WiFlow...issues/3 Co-Authored-By: claude-flow <ruv@ruv.net>
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# We audited a state-of-the-art WiFi pose model. Here's what broke, what reproduced, and the 30× smaller model that nearly matches it.
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*RuView team, June 2026. All numbers measured; full scripts and forensics in the
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[RuView repo](https://github.com/ruvnet/RuView/tree/main/benchmarks/wiflow-std).*
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## The setup
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WiFi sensing is having a moment: a 2026 preprint ("WiFlow", arXiv 2602.08661)
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claims **97.25% pose-estimation accuracy (PCK@20) from WiFi signals alone**,
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with a tiny 2.23M-parameter model — and unlike most papers, it ships
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everything: code, trained weights, and a 360,000-sample dataset.
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We build WiFi sensing systems, so before adopting any external number we run
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it through a simple rule: **a claim is "CLAIMED" until we reproduce it, then
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it's "MEASURED."** Here's what happened when we tried.
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## Day 1: nothing works
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- **The code doesn't run.** The package imports a class that doesn't exist.
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(One-line fix.)
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- **The released model scores 0.08%, not 97.25%.** The shipped checkpoint was
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trained under a different data normalization than the shipped dataset —
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it's a real trained model, just not *this* pipeline's model. Even letting it
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cheat with a fitted per-keypoint correction only reaches 72%.
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- **The dataset is corrupted.** Its last 13 files contain garbage values up to
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3.4×10³⁸ (float32's maximum). Subtle consequence: the training loop uses
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fp16 mixed precision with no guards, so the first corrupted batch overflows
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and **permanently poisons the model's BatchNorm statistics**. Training from
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the public download produces NaN from epoch 1, every time.
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- The training script also crashes before its own test phase ever runs
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(calls an undefined function), and ignores its `--data_dir` flag.
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At this point a less patient reader concludes "fraud." That would be wrong.
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## Day 1, later: actually, the science is real
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We repaired the artifacts — fixed the import, zeroed the 9,072 corrupted
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windows, retrained from scratch with the authors' own code and
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hyperparameters on one GPU (~50 minutes):
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| Metric | Published | Our retrain |
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|---|---|---|
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| PCK@20 | 97.25% | **96.1–96.6%** |
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| PCK@50 | 99.48% | 99.0–99.1% |
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| Params | 2.23M | 2,225,042 (exact) |
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**The claims reproduce.** What didn't survive contact was the *packaging*:
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wrong checkpoint, corrupted upload, broken glue code. This distinction —
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**artifact rot vs. bad science** — is the single most useful thing a
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reproduction can establish, and you can't establish it without actually
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running the thing.
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(We filed all six defects upstream with fixes:
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[issue #3](https://github.com/DY2434/WiFlow-WiFi-Pose-Estimation-with-Spatio-Temporal-Decoupling/issues/3).
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And to be clear: the authors released more than 90% of papers do. That's the
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only reason this audit was possible.)
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## Day 2: the model is also 2.6× too big
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Once we could train, we asked: does the architecture need 2.23M parameters?
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| Variant | Params | Accuracy (PCK@20) | Size on disk |
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|---|---|---|---|
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| Original | 2,225,042 | 96.61% | 8.97 MB |
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| **Half** | **843,834** | **96.62%** ✨ | — |
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| Quarter | 338,600 | 96.05% | — |
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| **Tiny** | **56,290** | **94.11%** | **295 KB** |
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The half-width model **matches the original exactly** (and converges faster).
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The tiny one — 1/39th the parameters — gives up 2.5 points and runs at
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**0.66 ms per inference on a laptop CPU** (~1,500 poses/second) as a 295 KB
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ONNX file. For edge devices, that's the interesting end of the curve.
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Quantization footnote: the paper's "~2.2 MB int8" estimate is reachable
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(we measured 2.44–2.53 MB) but only via conv-capable toolchains — PyTorch's
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one-line dynamic quantization converts *literally nothing* on this model
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(it has no Linear layers), a trap worth knowing about.
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## What we took away
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1. **Run the artifact, not the README.** Every number in a paper is one
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`git clone` away from being either confirmed or understood. Both outcomes
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are valuable; only one is publishable by the original authors.
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2. **fp16 + unvalidated data = silent model death.** Mixed-precision training
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with no NaN/inf guards doesn't fail loudly — it corrupts BatchNorm buffers
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and ships a broken model with a green progress bar. Validate inputs, or
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train in fp32, or guard the autocast.
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3. **Evidence-grade your own claims too.** Mid-audit, the same forensics
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tooling caught one of *our own* published accuracy numbers resting on a
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degenerate evaluation (a constant-output model scored with a flawed
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metric). We retracted it the same day. The rule has to cut both ways or
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it's marketing, not measurement.
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4. **Over-parameterization hides in SOTA tables.** Nobody publishes the
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half-size ablation that matches their headline model. Run it yourself;
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it's an hour of GPU time and sometimes it *is* the result.
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*Reproduction scripts, corruption masks, the efficiency-sweep configs, and a
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numerically parity-proven Rust port (max divergence 1.2e-7) are all in
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[`benchmarks/wiflow-std/`](https://github.com/ruvnet/RuView/tree/main/benchmarks/wiflow-std).*
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