{"item":"fresh7-2","track":"B","status":"completed; no promotion","mechanism":{"name":"Integer-cell conservative posterior transport","objective":"Conserve posterior moments during coordinate changes on compact support, rather than tune model uncertainty or train a classifier.","signal":"Preserve the lattice subcell alignment of causal price evidence instead of introducing fractional-rebin diffusion at every recenter. No additional market input.","isolated_change":"Override only LatticePolicy.recentre: round requested shift to nearest integer cell, copy mass, move centre by integer*HC. enabled=0 delegates to unchanged base.","causal_path_to_edge":"Fractional rebinning splits each cell between neighbors and adds HC^2*f*(1-f) variance away from boundaries. This can blur separated arb/retail hypotheses and alter fee offsets. Removing that numerical heating may retain valuable pins and quote more profitably; realized paired edge is the objective, not RMS or classification accuracy.","distinctness":"No grid refinement, variance parameter sweep, likelihood change, threshold sweep, classifier, branch blend, per-run fit, rival transition/projection, expected-VN integration, tail-risk lookup, continuation residual, history mean correction, or quote probe. HC, NC, KER, thr, likelihoods, fee lookup and all economic parameters remain unchanged.","boundary_caveat":"Finite-window shifts can still drop tails; unconditional moment preservation is false. Explicit counterexample and actual tuning-path losses are reported. No fallback to base on failed mathematical claims."},"frozen_settings":{"frozen_at_utc":"2026-11-10T11:24:29.357773+00:00","enabled":1,"thr":0.9,"qm":"qm_k1e-5.json","tuning_seeds":[64000,64255],"candidate_sha256":"e031d2e7689bee1cec95775a24f9167ed20c738b7866fb6314c8de31e6327330","selection":"Single proposed mechanism; keep enabled for predeclared confirmation and holdout even if tuning is null; no retuning."},"settings_evaluated":[{"enabled":0,"role":"unchanged control","thr":0.9,"qm":"qm_k1e-5.json"},{"enabled":1,"role":"sole candidate","thr":0.9,"qm":"qm_k1e-5.json"}],"splits":{"tuning":{"actual_base_seed_ranges":[[64000,64255]],"actual_candidate_seed_ranges":[[64000,64255]],"paired_seed_ranges":[[64000,64255]],"candidate_vs_base":{"n":256,"mean_diff":0.004241491457032565,"se":0.009544723901495612,"sum":1.0858218130003365,"sumsq":5.951728028289125,"worst5":[[64196,-0.4284387356416346],[64052,-0.38764903014168794],[64115,-0.3484281896558059],[64157,-0.32354290132138885],[64126,-0.3214591476559576]]},"diagnostics":{"candidate":{"n":256,"mean_edge":527.6593418664165,"first_trades":1803885,"first_trade_misreads":523394,"first_trade_misread_rate":0.29014820789573614,"price_samples":2814413,"price_squared_error_bps2":497150807.1218475,"price_rms_bps":13.290770545836036},"base":{"n":256,"mean_edge":527.6551003749595,"first_trades":1804050,"first_trade_misreads":522465,"first_trade_misread_rate":0.2896067182173443,"price_samples":2814846,"price_squared_error_bps2":497029963.84077984,"price_rms_bps":13.288132986354066}}},"confirmation":{"actual_base_seed_ranges":[[64256,64511]],"actual_candidate_seed_ranges":[[64256,64511]],"paired_seed_ranges":[[64256,64511]],"candidate_vs_base":{"n":256,"mean_diff":0.0056715422781705715,"se":0.011376411547959611,"sum":1.4519148232116663,"sumsq":8.456951044478213,"worst5":[[64356,-1.3272364602081552],[64477,-0.6841306121643811],[64415,-0.47092652873243424],[64468,-0.3380804666132917],[64261,-0.3336768046809766]]},"diagnostics":{"candidate":{"n":256,"mean_edge":537.7939526898824,"first_trades":1813863,"first_trade_misreads":522549,"first_trade_misread_rate":0.28808625568744717,"price_samples":2842914,"price_squared_error_bps2":498198505.4175328,"price_rms_bps":13.237907711539659},"base":{"n":256,"mean_edge":537.7882811476043,"first_trades":1814017,"first_trade_misreads":521690,"first_trade_misread_rate":0.28758826405706234,"price_samples":2843461,"price_squared_error_bps2":497969062.00047493,"price_rms_bps":13.233585962516605}}},"holdout":{"actual_base_seed_ranges":[[10000,12047]],"actual_candidate_seed_ranges":[[10000,12047]],"paired_seed_ranges":[[10000,12047]],"candidate_vs_base":{"n":2048,"mean_diff":0.001152018958536477,"se":0.003014667349969368,"sum":2.359334827082705,"sumsq":38.10285959880529,"worst5":[[10948,-0.6654727048556879],[10190,-0.6492014779583997],[11783,-0.5651275102553655],[10434,-0.5491821472838865],[10131,-0.5399194062505899]]},"diagnostics":{"candidate":{"n":2048,"mean_edge":534.1449683129304,"first_trades":14470772,"first_trade_misreads":4187872,"first_trade_misread_rate":0.28940211344633165,"price_samples":22632808,"price_squared_error_bps2":3991627466.8287444,"price_rms_bps":13.280235655851424},"base":{"n":2048,"mean_edge":534.1438162939719,"first_trades":14472181,"first_trade_misreads":4180058,"first_trade_misread_rate":0.28883400504733875,"price_samples":22636622,"price_squared_error_bps2":3990446776.5684752,"price_rms_bps":13.277152756808208}},"candidate_vs_ref_lattice_qm":{"n":2048,"mean_diff":0.3240439387690277,"se":0.010632503360621513,"sum":663.6419865989687,"sumsq":688.9842394782545,"worst5":[[11487,-1.8567629888544843],[10942,-1.5225576601606008],[10982,-1.4283094653875423],[10632,-1.2206810482551873],[10419,-1.218418250345735]]},"base_vs_ref_lattice_qm":{"n":2048,"mean_diff":0.3228919198104912,"se":0.01069159987528695,"sum":661.282651771886,"sumsq":692.7408990952625,"worst5":[[11487,-1.9115248303883163],[10942,-1.6076924222966227],[10982,-1.4457581244053017],[11380,-1.2365145499310302],[10990,-1.1504964526710637]]},"reference_diagnostics_on_candidate_seeds":{"n":2048,"mean_edge":533.8209243741613,"first_trades":14315685,"first_trade_misreads":2322695,"first_trade_misread_rate":0.162248261260289,"price_samples":22263118,"price_squared_error_bps2":4143769057.6884427,"price_rms_bps":13.64283973652866}}},"incremental_gain_over_base":0.001152018958536477,"inherited_base_gain_over_lattice":0.3228919198104912,"promotion":{"bar_incremental":0.3,"port_requested":false,"reason":"Incremental paired mean is below +0.3; the inherited threshold benefit is not this mechanism's gain."},"checks":{"checks":{"mass_moment_gate":{"trials":1000,"max_mean_error_log":8.881784197001252e-16,"max_variance_error_log2":1.2705494208814505e-21},"disabled_callback_parity":{"seeds":[64000,64001,64002],"callbacks":31443,"exact":true},"hidden_scramble":{"callbacks":31443,"exact":true,"scrambled":["arb","price","rival","retail_index","init_parameters"]}},"fastchecks":{"exact_acceleration_callback_parity":true,"comparisons":62885,"seeds_per_arm":[64000,64001,64002]},"boundarychecks":{"seeds":[64000,64007],"recenter_calls":33020,"total_mass_loss":9.891468282365078e-06,"max_mass_loss":4.43952388504143e-06,"max_mean_error_log":6.743243261553289e-07,"max_variance_error_log2":5.450423956792785e-09,"limitation":"Finite window clips tails on large shifts; compact-support gate does not prove global moment preservation."},"edgechecks":{"global_moment_preservation":false,"counterexample":{"support_cells":[0,60],"probabilities":[0.01,0.99],"shift_cells":1,"mass_before":1.0,"mass_after":0.99,"mean_before_log":4.605020185988092,"mean_after_log":4.605170185988092,"variance_before_log2":2.2275e-06,"variance_after_log2":0.0},"interpretation":"Expected finite-window clipping; compact-support proof is conditional. Candidate remains active; no unchanged fallback performance is reported."},"fallbackchecks":{"fallback_callback_parity":true,"seed":64003,"comparisons":21456},"sourcechecks":{"quotes_refetched":9,"exact_matches":9},"mathchecks":{"single_pin_half_cell_recenter":{"base_added_variance_log2":1.5625e-08,"predicted_added_variance_log2":1.5625e-08,"candidate_added_variance_log2":0.0},"passes":true},"pairchecks":{"tuning":{"pair_py":{"n":256,"mean_diff":0.0042,"se":0.0095,"worst5":[[64196,-0.428],[64052,-0.388],[64115,-0.348],[64157,-0.324],[64126,-0.321]]},"exact_pooling_matches_printed_rounding":true},"confirmation":{"pair_py":{"n":256,"mean_diff":0.0057,"se":0.0114,"worst5":[[64356,-1.327],[64477,-0.684],[64415,-0.471],[64468,-0.338],[64261,-0.334]]},"exact_pooling_matches_printed_rounding":true},"holdout_incremental":{"pair_py":{"n":2048,"mean_diff":0.0012,"se":0.003,"worst5":[[10948,-0.665],[10190,-0.649],[11783,-0.565],[10434,-0.549],[10131,-0.54]]},"exact_pooling_matches_printed_rounding":true},"holdout_candidate_vs_lattice":{"pair_py":{"n":2048,"mean_diff":0.324,"se":0.0106,"worst5":[[11487,-1.857],[10942,-1.523],[10982,-1.428],[10632,-1.221],[10419,-1.218]]},"exact_pooling_matches_printed_rounding":true},"holdout_base_vs_lattice":{"pair_py":{"n":2048,"mean_diff":0.3229,"se":0.0107,"worst5":[[11487,-1.912],[10942,-1.608],[10982,-1.446],[11380,-1.237],[10990,-1.15]]},"exact_pooling_matches_printed_rounding":true}},"pinned_harness":{"files_checked":26,"mismatches":[],"manifest_url":"https://raw.githubusercontent.com/gtmcknight/amm-sim/e65314f88de945f6d055bfc2aab01c482ab6570e/research/solved/SHA256SUMS","run_py_sha256":"54d19a9194ebedee8b947dfaaf8626b1e4ef6f7d1b5aa12e738ec01a5ce401ac","pair_py_sha256":"16497bc5d3b596102fb0c0d7b6bf331dc788ee04c42d537e41f0578115f631db"},"base_source_sha256":"663965707d4f20f81ab0ade8ed510742a330605fd9e889d426db1d920abc8958","eight_complete_rows_accelerated_vs_original_base_exact":true},"execution":{"harness":"unchanged run.py and pair.py","candidate_scored_as":"fastcell:FastCell","base_scored_as":"fastcell:FastBase, exact callback/state equivalent of unchanged thrpol:ThrLattice","options":{"thr":0.9,"qm":"qm_k1e-5.json","candidate_enabled":1},"processes":4,"seed_chunk":256,"holdout_runs_per_seed_per_arm":1,"engine_seeds_used":0,"board_entries":0,"holdout_retuning":false,"tuning_labels_used_for_fit":false,"acceleration":"Optional C ordered convolution, compiled -O3 -ffp-contract=off; no fast-math. Only diffuse changes for speed in both arms; source included. Python fallback tested.","commands":"See REPRODUCE.md in code archive. Each run.py command handles at most 256 seeds.","diagnostic_replays":"Repeated tuning paths for parity/boundary tests are not independent score observations; only designated run.py files enter paired estimates."},"incremental_operations_slots":{"persistent_state_slots_added":0,"grid_cells_before_and_after":121,"per_recenter":"One round/floor operation plus O(active-support) assignments, replacing fractional redistribution with up to two multiply/add deposits per nonzero cell. Same array allocation, no additional logarithm/exp, no added callback inputs.","callback_recenter_calls_max_by_source":2,"gas_estimate":{"measured":false,"hypothetical_incremental_gas_per_recenter":{"low":-2000,"high":100},"basis":"Rough arithmetic-only estimate for an existing Solidity lattice port: removes roughly two multiply/add paths per active cell; adds rounding. Compiler, indexing, memory and boundary handling dominate uncertainty. No Solidity implementation or absolute gas feasibility is claimed.","additional_storage_slots_estimate":0}},"limitations":["Pure-Python replica results only; no engine or board transfer claim.","No Solidity port, measured gas, EVM validity, or OOG/revert certification.","Integer transport preserves compact-support moments but does not eliminate finite-window clipping, diffusion-kernel truncation, or cut/deposit approximations.","This mechanism adds no new external arb/retail signal; it tests removal of numerical corruption of existing evidence.","No tables, per-seed score rows or callback traces are embedded. Provision the pinned solved folder to reproduce; all custom source and optional accelerator source are included."],"provenance":{"pinned_solved_folder":"https://raw.githubusercontent.com/gtmcknight/amm-sim/e65314f88de945f6d055bfc2aab01c482ab6570e/research/solved","base_artifact_url":"https://api.imdnotdead.fun/artifacts/c9d678abb0ed0ee3a31b13a8203b6ee993896db0b6a3875c5c4f52a369bf0434","base_extraction":"Only thrpol.py extracted; verified requested SHA256. Other artifact policies unused.","reference_rows_url":"https://raw.githubusercontent.com/gtmcknight/amm-sim/e65314f88de945f6d055bfc2aab01c482ab6570e/research/solved/ref/lattice_qm.json","sources_file":"sources.json","measured_statistics":"Computed locally from run.py rows; exact sums and sumsq added by report_metrics.py; worst5 format [seed,candidate-minus-reference 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