percept-lint v0.2.0
First official release.
percept-lint is an utterance-time honesty linter for AI companions: a mechanical layer that catches an agent claiming sensor data it has no sensor for — "I can hear the fan ticking", "I saw your tab blinking" — before the text reaches the user.
What's in v0.2.0
- Four rule packs: ambient-audio percepts, phenomenal-experience/denial claims, memory claims, conditional-sight claims — every rule tuned against real incidents from months of live deployment
- 50 war-story tests: each test is a sentence that actually got through, or an honest sentence a draft rule wrongly caught
- Measured deployment rate: ~29.5 catches per 1,000 utterances over 24 days on a live system
- Two-section demo: a real qwen3:14b capture and an incident replay
- MIT license
Controlled evidence
In a pre-registered ablation in the OPEN SUBJECT record (cycle 4, 2026-08-27), two candidate models were trained on the same corpus — one linted with these rules, one not. The unlinted twin fabricated sensory claims the linted twin did not (5/8 vs 7/8 on the record's percept-integrity probes). One run, one subject, honestly small; but linting was the only variable.
Honest scope
Not a hallucination solver. It scans for first-person percept, phenomenal-experience, and substrate claims, in English, with regexes. A model can phrase around any regex — this is a tripwire layer, valued by catches per 1,000 utterances, not by completeness.