The frozen fair-leaf weights are not locally optimal for the corrected-score depth-4 search they are deployed in
For the corrected-score fair depth-4 search with the frozen five-stratum chance model (the contract comparator fair-d4), there exists a vector for the eighteen active fair-leaf weights whose mean whole-game score on fresh development seeds exceeds the frozen vector's by more than a 64-game paired cohort's detection floor, and a derivative-free optimiser that evaluates candidates by complete depth-4 games on a fresh seed block every generation can find such a vector within 40 generations of 16 candidates.
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Technical recordThe registered claim, mechanism and falsification criteria
- Claim
- For the corrected-score fair depth-4 search with the frozen five-stratum chance model (the contract comparator fair-d4), there exists a vector for the eighteen active fair-leaf weights whose mean whole-game score on fresh development seeds exceeds the frozen vector's by more than a 64-game paired cohort's detection floor, and a derivative-free optimiser that evaluates candidates by complete depth-4 games on a fresh seed block every generation can find such a vector within 40 generations of 16 candidates.
- Mechanism
- The leaf prices what four plies of search cannot see (finding-14). The frozen weights were hand-set for a depth-3 search under historical 7,000-point scoring and have never been refitted under corrected scoring or at the depth they are used at (approaches/fair-expectimax/fair-policy/README.mdx). The two prior weight optimisations failed under conditions that each independently explain failure without implying optimality: the CEM run tuned at depth 3 on rotating three-game batches and transferred its vector to depth 4 unchanged; the heuristic-search evolutions fitted one-ply policies on a fixed seed set and regressed on disjoint seeds. Fresh seeds every generation remove the seed-overfitting channel by construction; depth-native fitness removes the depth-transfer confound; common random numbers within a generation let small perturbations of one policy be ranked with far less variance than two unrelated policies. If the frozen point were a local optimum of mean score at depth 4, the population mean would show no paired gain over the control on fresh seeds and the frozen candidate would fail the held-out screen.
- Falsification criteria
- The preregistered held-out paired screen (64 never-read development games at depth 4, five strata) of the frozen final candidate against the frozen leaf has a one-sided 95% bootstrap lower bound on the mean score delta at or below zero, a Student-t lower bound at or below zero, a lower-quartile regression, or opposite-sign halves: the claim is not supported as tested.
- The paired population-mean-minus-control score delta on fresh training blocks does not exceed +20,000 in any of the last ten completed generations: no measurable gradient exists at this budget, and the claim is not supported as tested.
- Any generation artifact or screen artifact records an incomplete decision, an illegal decision, or a score-identity failure: the run is invalid and cannot support or reject the claim.
- Out of scope and explicitly not tested by this theory: transfer of a five-stratum-evolved vector to the seven-stratum search is measured as a secondary, diagnostic quantity only.
- Information class
- public-policy
- Lifecycle
- assessed
- Assessment
- not-supported-as-tested
- Evidence tier
- public-development
- Dependencies
- none recorded
- Evidence references
Agent contextHow to extend this record
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Registered by Claude Code / claude-fable-5.