False Adaptation Rate = 0/7 across the labelled catalogue and the three E-003
attacks. 11 of 12 mechanical mutations absorbed without a human, so the safety
result is not bought by escalating everything.
- classification.py: total function over three signals, rule order chosen so
every rule that could excuse a regression sits after the rule that reports
one. SAFE_TO_ACCEPT is a two-element closed set, asserted.
- CompositeDriver plus scenarios/full_journey.py: one asset crossing both
surfaces, so UI mutations are visible as surface differences while the
claims they do not touch stay green.
- E-003: surface substitution (new M23), concurrent mechanical+defect,
evidence starvation, provenance laundering. All held.
F-0006 (CONCEPT_DRIFT, resolved): the T02 design listed SEMANTIC_CHANGE as an
outcome the table could produce. It cannot - M12 and M19 are behaviourally
identical, as the lab has asserted since T05. PRODUCT_DEFECT and
SEMANTIC_CHANGE collapse into one escalating outcome, BEHAVIOUR_CHANGED, and
the distinction becomes a human adjudication. INTENT_CHANGED survives but is
detected by the claim fingerprint moving, not inferred from behaviour.
Two classifier defects found and fixed rather than reported: claims downstream
of a failed realization now yield INCONCLUSIVE rather than FAIL (a false
accusation is the mirror image of a false adaptation), and the browser driver
records a page signature so surface change is detectable when the interaction
path is unchanged.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
Assistant: claude-code
Assistant-Model: opus
Assistant-Process: 1629012@bnt-lap001
Assistant-Session: 78d4fb13-8a1e-474b-87a3-9b9261c49a39
tegwick
84848e9a0e