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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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@@ -430,7 +430,7 @@ Model release (no new firmware binary). Firmware remains at v0.6.0-esp32.
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- Security fix merged via PR #310.
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### Performance
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- Presence detection: 100% accuracy on 60,630 overnight samples.
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- 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 Superseded by the honest 82.3% held-out temporal-triplet metric; see [#882](https://github.com/ruvnet/RuView/issues/882).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.)*
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- Inference: 0.008 ms per sample, 164K embeddings/sec.
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- Contrastive self-supervised training: 51.6% improvement over baseline.
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