A stable agentic realization becomes deterministic code. All four exit
criteria met; 163 tests pass.
- crystallization.py: trajectory capture, stability assessment requiring the
same path across several runs, CrystallizedDriver, pytest codegen
- crystallized/test_grant_access.py: generated, runs with no model, carries
its lineage in the docstring
- descendant preserves the ancestor's oracle set, agrees with it across five
lab versions, and still catches a seeded defect
- reversibility shown both ways via new M24 (grant endpoint renamed): the
frozen descendant fails loudly rather than searching, and the agentic
ancestor recovers from the same mutation
F-0007 (open): the 54% cost reduction must not be quoted in support of the
thesis. The T07 runtime is token-free, so the measured saving is one page
fetch, one parse and a two-candidate scoring pass. The saving the concept
actually claims - tokens, latency, retry variance - is unmeasured. Together
with F-0005 this makes a bounded live-model experiment the highest-value next
investment.
Assertions in the generated test are imported rather than restated, so it is
not fully standalone. Deliberate: paraphrased claims would be a second
unverified statement of intent.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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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.
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Two decisions taken with the operator: stdlib HTML driver instead of
Playwright (F-0004), and a deterministic discovery runtime instead of a live
model. Both sit behind interfaces so the alternatives drop in later.
- html.py: stdlib DOM parse and query
- agentic.py: DiscoveryRuntime (agentic arm, ignores data-td by construction)
and RecordedSelectorRuntime (control arm, uses the strongest identifier the
page offers)
- browser.py: per-actor sessions over real HTTP, constructed per call so no
actor inherits another's connection state
- cost/nondeterminism metrics recorded from the first run
F-0005 (CONCEPT_DRIFT): the H-001 result is a narrowing. Where test ids are
preserved, discovery 9/9 and recorded selectors 9/9 - the semantic action buys
nothing. Where they are dropped, discovery 2/3 and recorded 0/3. The concept
model presents semantic actions as generally superior; the evidence says
conditionally superior.
M21 and M22 added mid-task: the deciding side of the axis was N=1. M22 (field
names renamed) defeats the heuristic and is the first concrete evidence that a
live model would add capability, not just cost.
Co-Authored-By: Claude Opus 5 <noreply@anthropic.com>
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H-001..H-005 with explicit falsification conditions, E-001..E-003, the
Concept-Implementation Fitness Map, findings log and ID convention. Plain
files, no tooling.
- fitness map corrects Improvement Loop section 13 levels downward: nothing
exceeds C1 without implementation
- H-005 (Energy) dormant by decision - events captured, no scoring written
- H-001 gets a genuine control arm so the semantic-action thesis is not
trivially true
- E-003 added: deliberately attacks the safety boundary rather than only
sampling benign cases
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