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docs(#882): complete the "100% presence" retraction across all docs (#916)

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The v1 "100% presence accuracy" headline was already retracted in the
README / user-guide intro / proof-of-capabilities — but 6 secondary
spots still flatly claimed "100% accuracy, never false alarms", which
made proof-of-capabilities.md's "replaced everywhere" assertion untrue.

Completed the retraction in-place with the honest label-free metric
(82.3% held-out temporal-triplet; v1 was a single-class recording where
a constant "yes" scores ~99.98%):

- docs/readme-details.md — 2 benchmark tables + the pre-trained-model row
- docs/user-guide.md — capability table, model-file comment, applications list
- CHANGELOG.md — annotated the historical entry in-place (kept as public
  record per built-in-public ethos, not rewritten)

Verified: no remaining flat "100% presence/accuracy" claim lacks a
retraction marker; proof-of-capabilities.md "replaced everywhere" is now
accurate.
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rUv
2026-06-02 18:50:39 +02:00
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parent 88b835dd89
commit 91b0e625bd
3 changed files with 7 additions and 7 deletions
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docs/user-guide.md
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@@ -1119,7 +1119,7 @@ What it ships (and what it does not):
| Capability | Status |
|------------|--------|
| Presence detection (occupied / empty) | ✅ Trained head — 100% accuracy on validation |
| Presence detection (occupied / empty) | ✅ Trained head — v2 encoder reports 82.3% held-out temporal-triplet acc (v1's "100% on validation" was a single-class recording — retracted, [#882](https://github.com/ruvnet/RuView/issues/882)) |
| 128-dim CSI embeddings (re-ID, similarity, downstream training) | ✅ Trained encoder |
| Single-person breathing / heart-rate | ⚠️ Server still uses heuristic DSP — model does not replace this yet |
| 17-keypoint full-body pose | 🔬 No keypoint weights shipped yet — pose pipeline runs but without a learned head |
@@ -1824,7 +1824,7 @@ huggingface-cli download ruvnet/wifi-densepose-pretrained --local-dir models/pre
# model.safetensors — 48 KB contrastive encoder
# model-q4.bin — 8 KB quantized (recommended)
# model-q2.bin — 4 KB ultra-compact (ESP32 edge)
# presence-head.json — presence detection head (100% accuracy)
# presence-head.json — presence detection head (v2 encoder: 82.3% held-out triplet acc)
# node-1.json — LoRA adapter for room 1
# node-2.json — LoRA adapter for room 2
```
@@ -1833,7 +1833,7 @@ huggingface-cli download ruvnet/wifi-densepose-pretrained --local-dir models/pre
The pre-trained encoder converts 8-dim CSI feature vectors into 128-dim embeddings. These embeddings power all 17 sensing applications:
- **Presence detection** — 100% accuracy, never misses, never false alarms
- **Presence detection** — v2 encoder: 82.3% held-out temporal-triplet accuracy (v1's "100%" was a single-class recording — retracted, [#882](https://github.com/ruvnet/RuView/issues/882))
- **Environment fingerprinting** — kNN search finds "states like this one"
- **Anomaly detection** — embeddings that don't match known clusters = anomaly
- **Activity classification** — different activities cluster in embedding space