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","port_sha256":"743399f929cf229ee1a1b1fbc31b70d53bc37f18db6d6a4e32358e6f03a20329","reproduction_kit_zb64":"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proposal at every 10th generation rescored on monitor. From segment 5, also score the CMA mean at those checkpoints. Retain historical highest monitor score, starting from unchanged HA score zero.","constant_bounds":"For decay/alpha/flow blend/stale fast weight: clip to [0.001,0.9999]. Others: [max(1e-6,0.2*initial), max(0.01,3*initial)]. ANCHOR_N rounds to integer; other named constants use real-unit scales. Audit L*_N* entries fixed. Stale slow weight = 1-fast weight. Head clipped [-12,12] and rounded 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clone https://github.com/benedictbrady/amm-challenge test/scratch/amm-challenge","git checkout 6b6921f8d463314aa7c96234e1be25a25245f704","sh scratch/setup.sh (verbatim; once)","All subsequent checkout commands prefix . scratch/env.sh &&","python scratch/build_port.py; repair SIGMA_TOX_COEF term to tox_linear; clear dependent replica Numba cache","python scratch/speed.py","python scratch/diff.py 50","python scratch/gate_b.py","timeout 470 python scratch/train.py 360 (checkpointed segments, final shorter segment)","python scratch/freeze.py","python scratch/diff_c.py 50","python scratch/validate_replica.py heldout","python scratch/validate_replica.py fresh","timeout 470 python scratch/real.py ARM START N (N<=64; arms separately vs Vanilla, workers=8)"],"notes":["All result measurements are local unless explicitly labeled supplied target. No leaderboard placement is claimed. Search target was 45 minutes; actual checkpointed search duration is reported in segments, with final checks reserved to fit the item time limit.","Initial port repair changed SIGMA_TOX_COEF interaction from raw toxicity EMA to anchored linear toxicity. Final A/B were rerun after deleting dependent replica Numba caches.","Two preliminary training processes overlapped and were stopped; their checkpoint state was discarded. The reported search restarts exactly at HA after that reset. Only post-reset search curves and generations are reported.","Retail/arb edge decomposition reconstructs pre-arbitrage reserves from constant product, prior spot and prior fees, as in the cited harness. First T probe retained no traces; its edge is included but paired decomposition has n=511 for comparisons with T. No confirmation seed/arm was rerun.","Compilation/runtime libraries were installed only in test/scratch for measurement. Delivered source and reproduction scripts are embedded; runtime installation files are not included in this bounded JSON deliverable.","No final heldout or real seed was used to choose or update weights. Candidate freezes solely on monitor.","The real engine uses the MatchRunner draw order with each fresh seed as config RNG seed, defaults 10000 steps, initial reserves 100/10000, price 100, mu=0, dt=1, size sigma=1.2, buy probability=0.5; ranges size 19..21, arrival .6..1, sigma .000882..001008.","Frozen learned parameters equal HA. Regenerated Solidity rewrites float-port constants as integer literals, so its source differs from HA. The tiny measured real delta versus HA is a numeric regeneration effect, not a learned gain."],"missing_checks":["T seed 7108192 retail/arb decomposition unavailable; paired decomposition versus T uses 511 seeds."],"failed_checks":[],"search_outcome":{"improved_over_HA":false,"best_monitor_gain_vs_HA":0.0,"checkpointed_minutes":42.35810992481808},"reproduction_kit_files":["setup.sh","replica.py","train.py","freeze.py","diff.py","diff_c.py","gate_b.py","speed.py","real.py","validate_replica.py"]}