{"item":"mass8-1","status":"complete","track":"B; no engine or board entry","outcome":"Survivor accounting repaired and exact finite tests pass. Confirmation repaired-minus-original +0.0049330957518542995, SE 0.007749818807814528, n=256: no clear scoring gain. No holdout retuning.","mechanism":{"repair":"Only RivalT.step_kernel: restore incoming survivor mass after intermediate compress(cur,2Q). Final compress(tot,Q) alone conditions the order-count mixture. compress itself and all other policy math unchanged.","equation":"cur_n = M_n * compress(one_order(cur_(n-1)),2Q), where M_n is sum of unnormalized one-order survivor weights; mix pn[n] * cur_n before final conditioning.","why":"Otherwise n>=2 terms omit earlier no-own-fill probabilities. With S=3,NMAX=2 and singleton input the first expansion has <=6 nodes, so intermediate compression preserves positions and only mass restoration matters.","source":"https://api.imdnotdead.fun/artifacts/4bff6228dad5f9d86ce37985cfd0f4653753ebfb690756c547cd9ac88efb1f0b"},"frozen_settings":{"policy":"rivalt:RivalT","qm":"qm_k1e-5.json","pol":"pol_mid.json","thr":0.9,"joint":1,"K":1,"rt":2,"Q":3,"S":3,"NMAX":2,"lamr":0.8,"settings_tried":1,"frozen_before":"All confirmation and holdout callbacks; no holdout retuning","rivalt_py_sha256":"c37ca0fb51f650488b66a876eeb5d1f882eca665240c7332c7b80e39009ff49e","original_rivalt_py_sha256":"ebb01cff8629b894289234b9c650b8ff3ecd0eb1165fba281a51900ce1970de4"},"seed_ledger":{"tuning_expected":[65000,65255],"confirmation_expected":[65256,65511],"holdout_expected":[10000,12047],"pilot":"65000..65007, diagnostic only; excluded as duplicate rows from paired scores","callback_replay":"65000,65001; recorded tuning callbacks replayed for equivalence, no candidate selection","holdout_scored_once_per_arm":true,"engine_seeds":[],"partial_policy":"Missing ranges below are missing, never imputed. Coordination-only scratch files contain no score rows and are excluded."},"tuning":{"arms":{"repaired":{"n":256,"seed_ranges":[[65000,65255]],"mean_edge":534.1647556409038,"first_trades":1821573,"misread":510546,"misread_rate":0.28027754034562435,"callbacks":2856648,"price_rms_bps":13.046133330413356},"fresh_thr09":{"n":256,"seed_ranges":[[65000,65255]],"mean_edge":534.0040006684245,"first_trades":1812930,"misread":523301,"misread_rate":0.28864931354216655,"callbacks":2840030,"price_rms_bps":13.303493409878591},"original":{"n":256,"seed_ranges":[[65000,65255]],"mean_edge":534.1636348274998,"first_trades":1822363,"misread":510243,"misread_rate":0.2799897715219196,"callbacks":2858366,"price_rms_bps":13.038159789518202}},"repaired_minus_thr09":{"n":256,"seed_ranges":[[65000,65255]],"mean_diff":0.1607549724792543,"se":0.03183035847434959,"sum":41.1532729546891,"sumsq":72.75544318239578,"worst5":[[65019,-1.2680057956397377],[65020,-0.9972117997915007],[65211,-0.9669288840848367],[65110,-0.8707068343871924],[65018,-0.8691869696189656]],"unchanged_pair_py":{"n":256,"mean_diff":0.1608,"se":0.0318,"worst5":[[65019,-1.268],[65020,-0.997],[65211,-0.967],[65110,-0.871],[65018,-0.869]]}},"missing_ranges":{"repaired":[],"fresh_thr09":[],"original":[]},"repaired_minus_original":{"n":256,"seed_ranges":[[65000,65255]],"mean_diff":0.0011208134038998008,"se":0.007669032369854183,"sum":0.286928231398349,"sumsq":3.8397032659465054,"worst5":[[65198,-0.37336911669285655],[65186,-0.36550529125213416],[65240,-0.35397116604798384],[65227,-0.32976612371794545],[65013,-0.25884105905652177]],"unchanged_pair_py":{"n":256,"mean_diff":0.0011,"se":0.0077,"worst5":[[65198,-0.373],[65186,-0.366],[65240,-0.354],[65227,-0.33],[65013,-0.259]]}}},"confirmation":{"arms":{"repaired":{"n":256,"seed_ranges":[[65256,65511]],"mean_edge":532.5824476179512,"first_trades":1817999,"misread":509009,"misread_rate":0.27998310230093637,"callbacks":2844696,"price_rms_bps":12.988131871554886},"fresh_thr09":{"n":256,"seed_ranges":[[65256,65511]],"mean_edge":532.4233303516058,"first_trades":1807623,"misread":521367,"misread_rate":0.2884268456420393,"callbacks":2826325,"price_rms_bps":13.244719793612889},"original":{"n":256,"seed_ranges":[[65256,65511]],"mean_edge":532.5775145221993,"first_trades":1818865,"misread":508847,"misread_rate":0.2797607299057379,"callbacks":2846412,"price_rms_bps":12.979167704928988}},"repaired_minus_thr09":{"n":256,"seed_ranges":[[65256,65511]],"mean_diff":0.1591172663452991,"se":0.035705242142898136,"sum":40.73402018439657,"sumsq":89.70462851900496,"worst5":[[65335,-1.671224522000216],[65275,-1.5525968261343337],[65366,-1.3829518197749167],[65424,-1.2294105873868375],[65309,-1.160064998234759]],"unchanged_pair_py":{"n":256,"mean_diff":0.1591,"se":0.0357,"worst5":[[65335,-1.671],[65275,-1.553],[65366,-1.383],[65424,-1.229],[65309,-1.16]]}},"missing_ranges":{"repaired":[],"fresh_thr09":[],"original":[]},"repaired_minus_original":{"n":256,"seed_ranges":[[65256,65511]],"mean_diff":0.0049330957518542995,"se":0.007749818807814528,"sum":1.2628725124747007,"sumsq":3.9269265356686565,"worst5":[[65417,-0.4424318603158781],[65274,-0.3104557060652269],[65362,-0.28842788874806047],[65470,-0.27872268135399736],[65311,-0.2711067951707946]],"unchanged_pair_py":{"n":256,"mean_diff":0.0049,"se":0.0077,"worst5":[[65417,-0.442],[65274,-0.31],[65362,-0.288],[65470,-0.279],[65311,-0.271]]}}},"holdout":{"arms":{"repaired":{"n":2048,"seed_ranges":[[10000,12047]],"mean_edge":534.3589860455492,"first_trades":14550965,"misread":4082850,"misread_rate":0.2805896378693784,"callbacks":22783845,"price_rms_bps":13.017707370696224},"fresh_thr09":{"n":2048,"seed_ranges":[[10000,12047]],"mean_edge":534.1438162939719,"first_trades":14472181,"misread":4180058,"misread_rate":0.28883400504733875,"callbacks":22636622,"price_rms_bps":13.277152756808208},"ref_lattice_on_paired_seeds":{"n":2048,"seed_ranges":[[10000,12047]],"mean_edge":533.8209243741613,"first_trades":14315685,"misread":2322695,"misread_rate":0.162248261260289,"callbacks":22263118,"price_rms_bps":13.64283973652866}},"repaired_minus_thr09":{"n":2048,"seed_ranges":[[10000,12047]],"mean_diff":0.21516975157733545,"se":0.011930726786329782,"sum":440.667651230383,"sumsq":691.5534656542686,"worst5":[[11696,-2.5538426343099445],[11303,-2.274179741829471],[10387,-1.9363411281032086],[10300,-1.835666948852463],[10962,-1.7266193193642039]],"unchanged_pair_py":{"n":2048,"mean_diff":0.2152,"se":0.0119,"worst5":[[11696,-2.554],[11303,-2.274],[10387,-1.936],[10300,-1.836],[10962,-1.727]]}},"missing_ranges":{"repaired":[],"fresh_thr09":[]},"repaired_minus_ref_lattice":{"n":2048,"seed_ranges":[[10000,12047]],"mean_diff":0.5380616713878267,"se":0.014746584075033061,"sum":1101.950303002269,"sumsq":1504.572513995067,"worst5":[[10942,-2.967455020922216],[11303,-2.768903642453779],[11696,-2.2336101116684404],[10287,-1.875869870591032],[11601,-1.7903744997912554]],"unchanged_pair_py":{"n":2048,"mean_diff":0.5381,"se":0.0147,"worst5":[[10942,-2.967],[11303,-2.769],[11696,-2.234],[10287,-1.876],[11601,-1.79]]}},"thr09_minus_ref_lattice":{"n":2048,"seed_ranges":[[10000,12047]],"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]],"unchanged_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]]}}},"gain_decomposition":{"incremental_vs_threshold":"Use holdout.repaired_minus_thr09; it excludes the inherited threshold gain. Total vs lattice is holdout.repaired_minus_ref_lattice.","historical_inherited_threshold_gain":0.3228919198104912,"historical_original_RivalT_minus_thr09":0.20841243644167456,"source":"https://api.imdnotdead.fun/artifacts/4bff6228dad5f9d86ce37985cfd0f4653753ebfb690756c547cd9ac88efb1f0b","historical_not_new_measurements":true,"repaired_minus_original_holdout":null,"repaired_minus_original_holdout_se":null,"reason":"Original holdout per-seed rows are not supplied in the source artifact. Historical aggregate subtraction is not a paired estimate; paired repaired-minus-original results are computed only on fresh tuning/confirmation rows."},"checks":{"accounting":{"counterexample":{"correct_raw":[0.5,0.15,0.05],"original_raw":[0.5,0.15,0.1],"repaired_conditioned":[[100.0,0.7142857142857143],[110.0,0.2142857142857143],[120.0,0.07142857142857144]],"original_conditioned":[[100.0,0.6666666666666666],[110.0,0.19999999999999998],[120.0,0.13333333333333333]]},"finite_enumeration":{"nmax":[0,1,2,3,4],"max_abs_errors":[0.0,5.551115123125783e-17,5.551115123125783e-17,6.938893903907228e-18,2.0816681711721685e-17],"compression":"Q=100 avoids position approximation"},"empty_zero_transition":{"empty":true,"all_orders_rejected":true,"zt1":true,"lam0":true,"zero_weight_no_invented_mass":true,"positive_input_scale_invariance":true}},"callbacks":{"65000":{"callbacks":12269,"disabled_change_exact_original":true,"joint0_exact_thr09":true,"zt1_exact_jointK1":true,"hidden_scramble_exact":true,"replay_exact":true,"max_replay_fee_error":0.0},"65001":{"callbacks":9366,"disabled_change_exact_original":true,"joint0_exact_thr09":true,"zt1_exact_jointK1":true,"hidden_scramble_exact":true,"replay_exact":true,"max_replay_fee_error":0.0}},"policy_blindness":"Unchanged run.py Blind forwards only six afterSwap values; ignored run parameters and hidden fields scrambled behind Blind; fixed input callback replay exact.","unchanged_harness":"Fetched 26 reference files, all SHA256SUMS matched; run.py and pair.py unchanged. summarize_mass.py checks paired n and rounded mean against pair.py and adds unrounded sum/sumsq.","reference_harness_sha256":{"run.py":"54d19a9194ebedee8b947dfaaf8626b1e4ef6f7d1b5aa12e738ec01a5ce401ac","pair.py":"16497bc5d3b596102fb0c0d7b6bf331dc788ee04c42d537e41f0578115f631db"},"custom_source_provenance":"Original archive from source URL; tar extracted as data. Only planned step_kernel patch accepted by build_result.py; jointpol.py/thrpol.py checked byte-identical."},"approximation_errors":{"state_source":"first 12 new-step callbacks on each of repaired tuning seeds 65000,65001","states":24,"N_per_state_per_MC_arm":20000,"router":"Independent replica-formula clamped router, not rival_only; discrete GH sampling and exact finite enumeration use the same law. Continuous MC samples lognormal sizes. Truncated arm rejects N>2 with zero contribution, leaving unnormalized Poisson mass.","separation":{"accounting_original_minus_repaired":{"mass":{"min":0.0,"max":0.04053945765146494,"max_abs":0.04053945765146494},"mean_bps":{"min":-0.3189390647673207,"max":1.506425103752207,"max_abs":1.506425103752207},"sd_bps":{"min":0.0,"max":1.352196209799482,"max_abs":1.352196209799482}},"compression_repaired_minus_exact":{"mass":{"min":0.0,"max":0.0,"max_abs":0.0},"mean_bps":{"min":0.0,"max":1.7763568394002505e-11,"max_abs":1.7763568394002505e-11},"sd_bps":{"min":-1.319537843639642,"max":2.220446049250313e-16,"max_abs":1.319537843639642}},"quadrature_exact_minus_continuous_mc":{"mass":{"min":-0.04246726279914764,"max":0.05098590000857062,"max_abs":0.05098590000857062},"mean_bps":{"min":-1.7418014895653755,"max":2.251279116052274,"max_abs":2.251279116052274},"sd_bps":{"min":-2.2256809719665176,"max":2.954711448679766,"max_abs":2.954711448679766}},"count_truncation_mc":{"mass":{"min":-0.012299999999999978,"max":0.012899999999999967,"max_abs":0.012899999999999967},"mean_bps":{"min":-0.051604835880070254,"max":0.10137610516380846,"max_abs":0.10137610516380846},"sd_bps":{"min":-0.1869716307580358,"max":0.03575626525422837,"max_abs":0.1869716307580358}},"discrete_mc_validation":{"mass":{"min":-0.006621035882778459,"max":0.006428964117221547,"max_abs":0.006621035882778459},"mean_bps":{"min":-0.11515298050213119,"max":0.001308124577548142,"max_abs":0.11515298050213119},"sd_bps":{"min":-0.021423247867293327,"max":0.08645430605840332,"max_abs":0.08645430605840332}}},"mc_uncertainty":{"max_mass_se":0.0035334047142946984,"max_mean_se_bps":0.08556863523945361},"count_tail_probability":0.04742259607149024,"notes":["Accounting and size quadrature remain separate: fixing accounting is not evidence the continuous kernel is exact.","Final Q=3 log-mean compression reduces variance. Discrete exact comparison has zero mass error on all 24 singleton tuning states.","MC deltas contain sampling error; separate-trial tail differences are estimates, not exact truncation bounds.","Tiny <=1e-4 own router allocations are absorbed wholly by rival_only, an unchanged approximation.","No changes to GH constants, arrival factors, NMAX, size law, rt logic, or hidden rival collapse."]},"cost":{"incremental_persistent_slots":0,"incremental_transient":"One scalar mass and a replacement list of at most 2Q entries per order; no new persistent policy state.","incremental_operations_per_kernel_bound":"At Q=3,S=3,NMAX=2 with <=Q input nodes: two extra survivor sums over <=18 and <=36 nodes (<=54 additions), then <=6+6 weight rescalings (<=12 multiplications). Router calls, sorting and quadrature counts unchanged. No last-iteration optimization was introduced after freeze.","gas_estimate":{"label":"UNMEASURED arithmetic-only estimate, not port gas or validation","simple_opcode_lower_component":222,"assumption":"54 ADD at 3 gas + 12 MUL at 5 gas; excludes fixed-point scaling, loops, memory, compiler, storage and original kernel. Actual repair overhead unknown; no EVM executed."},"total_policy_gas":null,"engine_validation":null},"limitations":["No engine, Solidity, gas measurement or board entry.","Historical original holdout advantage is source evidence only; no invented SE for repaired-minus-original.","Approximation diagnostics cover 24 tuning states, not all reachable states.","No claim that repairing accounting improves policy score; report observed paired results, including negative gains.","Large tables and per-seed traces excluded from code archive; pinned reference fetch supplies tables. Small unchanged reference modules are bundled so mass_checks.py runs offline.","Any incomplete score ranges are explicit; nothing was fabricated."],"code_zb64":"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custom source/generators and required small unchanged reference modules; tables excluded"}