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","port_sha256":"b90dca166f70f254b811b88d11ee6d75018f6df50a20cf74765ad02d7693604f","notes":["Local measurements; no leaderboard submission or ranking claim. Each real arm ran alone against 30 bps VanillaStrategy via run_batch(..., configs, 8). Config draws match the pinned MatchRunner defaults.","CG weights and feature scaling fixed throughout. Flat weights order: W1 rows (8x19), b1 (8), W2 rows (2x8), b2 (2). Weights already quantized to 1e-6; constants frozen to WAD integers. Integer and count settings unchanged.","Gate B compares independent replica paths on the specified seeds to the supplied +2.00 real reference; no new real CG-vs-T calibration was run.","CMA_stds=.05 applies to all 33 log constants. CG neural CMA_stds are unused because the head is fixed. Positivity follows log coordinates; decay, alpha and tail slopes capped at one, as specified by CA.","The first segment evaluated two ES internal-coordinate means incorrectly. Neither was selected. Corrected phenotype conversion from generation 30 onward; the two invalid monitor rows are omitted. Training fitness and generation-best evaluations were unaffected.","Only monitor scores selected the frozen point. Holdout 40000000..40008191 evaluated once after freezing; over_IB is over_T minus supplied 1.7690. Holdout se is paired candidate-minus-CG; vs_T includes its separate uncertainty.","Real arb/retail split reconstructed with each arm actual initialization fee and checked against engine arb volume. Total edge is engine output.","Embedded code and port decode as base64 then zlib. Frozen port accepts W=[1]+feature_multipliers+feature_offsets+flat weights (217); PT contains frozen constants. Offline binary dependencies are not bundled; evaluation used the requested SETUP runtime."]}