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

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.
This commit is contained in:
rUv
2026-06-02 18:50:39 +02:00
committed by GitHub
parent 88b835dd89
commit 91b0e625bd
3 changed files with 7 additions and 7 deletions
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@@ -430,7 +430,7 @@ Model release (no new firmware binary). Firmware remains at v0.6.0-esp32.
- Security fix merged via PR #310.
### Performance
- Presence detection: 100% accuracy on 60,630 overnight samples.
- Presence detection: 100% accuracy on 60,630 overnight samples. *(Retracted — that recording was single-class (one sleeping person, 6,062/6,063 frames "present"), so a constant "yes" scores ~99.98%. Superseded by the honest 82.3% held-out temporal-triplet metric; see [#882](https://github.com/ruvnet/RuView/issues/882). Kept here as the in-place public record.)*
- Inference: 0.008 ms per sample, 164K embeddings/sec.
- Contrastive self-supervised training: 51.6% improvement over baseline.