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these replica seeds A4 minus A0 is +1.183373 edge, while supplying the true first-trade flag (A1) adds +0.006648; the residual gain in this experiment is therefore mostly beyond flag certainty. The specified callback-time rival intervention A2 is -7.512657, and A3 is -8.657714 versus A0 (-1.145058 beyond A2), so this implemented rival-bound intervention recovers no positive fraction of the true-price gain. This is not evidence that all rival-derived bounds are useless or that exact post-order tracking has negative value: the port invokes our callback before the rival executes its leg, while LEAN's split identity estimates the rival after that order; forcing the current pre-leg spot and synced=True changes that decision-state meaning. The timing audit measures this mismatch directly. A read-only boundary audit also finds 1,623 dominant point components rejected just one binary64 step outside the band on the eight check seeds, so A3 needs numerically stable boundary handling before it can support a ceiling claim. A3 is also an approximate Gaussian/mixture conditioning algorithm, not an optimization over every bound-aware policy. The remaining information would need better price inference between tell pins, and an oracle aligned to the rival's eventual post-order state to isolate the value of exact tracking; neither inference nor that counterfactual was selected or implemented here. Replica levels are not board scores.","limitations":["Literal callback-time rival truth is earlier than the post-order state implicit in LEAN's router estimate. Consequently A2/A3/A5 do not measure a post-order rival-information ceiling; report their signed results, not an asserted upper bound.","A3 includes A2; no separate bound-only arm was scored. Incremental A3-A2 is conditional on A2's altered rival input.","Strict A3 truncation has one-ulp boundary sensitivity: the debug audit finds 1623 dominant point rejections within 8.881784197001252e-16 log units. Arms were not changed or rescored after the block began; treat A3 as a raw diagnostic with this defect, not a validated information ceiling.","A3 uses Taylor's Gaussian truncation approximation and finite-mixture moment matching, including an explicit all-zero-mass boundary fallback; it is not an exact Bayesian posterior.","A4/A5 retain LEAN's inferred phase and qm tables; they are true-price interventions, not a re-solve of the perfect-information control problem.","No contract produced: these are hidden-truth ORACLE diagnostics. F and engine gates do not apply."],"reproduction":{"credit":"Taylor (gtmcknight): replica, Gaussian filter, solved policy, qm tables; github.com/gtmcknight/amm-sim @4812a6382311624e1506aca131ca35c378f930d9, research/solved (README read first). LEAN overlay credit also to qt1, TC-A #770, o1-a #541, o1-b #1497, tca-path, buy-grid.","format":"base64(tar.xz); standard-library files, no symlinks; standalone measurement runner and all six reference/blob dependencies 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","instructions":"Decode data with base64.b64decode, verify sha256, extract into an empty scratch directory. Run python3 -I oracles.py block START END, beginning at 64000 and resuming at the next seed, END exclusive, ending at 64512. Keep each command under 8 minutes (16-seed chunks conservative). The first 128 seeds include split accumulators. Original source modules and blob are embedded verbatim; no network or third-party package is needed. check mode needs baseline.json, obtainable from checks.unmodified_baselines_before_instrumentation. Debug mode runs 64000 64008. Hardware/libm differences can affect last-bit edges; P source is identified separately."}}