{"item":"values7-2","track":"B","status":"completed_negative_incremental_result","mechanism":{"name":"Passive VN expected-value shortlist under actual posterior","description":"On ambiguous first callbacks only, evaluate the same nine clamped actions against five price nodes, average held-action passive VN before choosing one action. Compare actual lattice posterior with moment-matched Gaussian using identical kernel/action generation. Base threshold, phase and rival-state rules are unchanged. No hidden inputs enter the policy.","objective":"E[V_N(w-e, conditional_rival, a-e, b+e, base_phase)] + inherited moment-only kappa incentive","rival_projection":"Same synced absolute-spot or clipped-band mode as original qmdp; scalar tracked rival comes from unchanged ThrLattice. No price-dependent arb/retail branch phase model.","approximation":"Five equal-mass conditional-mean bins of the lattice posterior; Gaussian control uses five normal quantile-bin means. Nine-action local shortlist at baseline and +/-5 bps per side. This is not a continuous/global exact maximizer.","posterior_scope":"Actual base lattice posterior, not true latent price posterior. Phase and rival uncertainty are frozen to base rules."},"frozen_settings":{"policy":"shortpolicy:FastShort","step_lattice_units":1.0,"threshold":0.9,"ambiguous_pi_open_interval":[0.05,0.95],"quadrature_nodes":5,"shortlist_max":9,"kappa":1e-05,"qm":"qm_k1e-5.json","candidate_settings":1,"modes":["posterior","gaussian"],"rival_phase_rules":"unchanged ThrLattice; same synced/c-band conditional projection as qmdp","source_sha256":{"shortpolicy.py":"26b4dea2203836e0236062e776cd9ccbc44be4cd728ee664b9f3fb453441beb6","actionvalues.py":"ec0059746402861696b660998a22f24c4e49d5320faa39208b75f1d0c3ac0d57","thrpol.py":"663965707d4f20f81ab0ade8ed510742a330605fd9e889d426db1d920abc8958","fastdiffuse.py":"6d03d8cfcbd669fdebdabd72257a52948e4042ee179e1899518f3a36ce4751e6"},"freeze_utc":"2026-11-10T11:29:40Z","tuning_range":[64000,64255],"confirmation_range":[64256,64511],"holdout_range":[10000,12047],"selection":"No retuning: one preregistered setting, tested regardless of tuning sign"},"tuning":{"paired":{"posterior_over_base":{"n":256,"mean_diff":-0.10239594427828735,"se":0.021569553973734137,"sum":-26.213361735241563,"sumsq":33.055378522682574,"worst5":[[64145,-1.0481492475381629],[64172,-0.959311971710008],[64234,-0.947216643198999],[64028,-0.9118779647008637],[64159,-0.8958480196747018]]},"gaussian_kernel_over_base":{"n":256,"mean_diff":-0.006666245994716213,"se":0.008266157337820768,"sum":-1.7065589746473506,"sumsq":4.471916775611428,"worst5":[[64148,-0.3878150573438006],[64244,-0.3658517292307124],[64098,-0.3145745713331962],[64182,-0.3066313771417981],[64026,-0.30082768306209573]]},"posterior_over_gaussian":{"n":256,"mean_diff":-0.09572969828357114,"se":0.020093015195483403,"sum":-24.506802760594212,"sumsq":28.701474903852784,"worst5":[[64234,-1.0583081487775985],[64145,-0.9824558493587574],[64172,-0.905837505530485],[64219,-0.8494950340276546],[64006,-0.8283541680941653]]}},"diagnostics":{"posterior":{"n":256,"first_trades":1810998,"misread_count":527869,"first_trade_misread":0.29147961510725023,"callbacks":2830765,"callback_price_rms_bps":13.084513561435351,"mean_edge":527.5527044306813},"gaussian":{"n":256,"first_trades":1804544,"misread_count":522612,"first_trade_misread":0.28960889842530857,"callbacks":2815705,"callback_price_rms_bps":13.274012077587258,"mean_edge":527.6484341289648},"base":{"n":256,"first_trades":1804050,"misread_count":522465,"first_trade_misread":0.2896067182173443,"callbacks":2814846,"callback_price_rms_bps":13.288132986354066,"mean_edge":527.6551003749595}},"actual_seed_ranges":{"posterior":[[64000,64255]],"gaussian":[[64000,64255]],"base":[[64000,64255]]}},"confirmation":{"paired":{"posterior_over_base":{"n":256,"mean_diff":-0.09542545886249587,"se":0.021039583093734052,"sum":-24.428917468798943,"sumsq":31.22825028414555,"worst5":[[64294,-0.906857729428566],[64402,-0.8970252127469394],[64415,-0.8641368033707977],[64280,-0.8297476370344157],[64502,-0.8141662994485159]]},"gaussian_kernel_over_base":{"n":256,"mean_diff":-0.013811769754884962,"se":0.008640185865780633,"sum":-3.53581305725055,"sumsq":4.92217138983603,"worst5":[[64427,-0.46398552015352834],[64394,-0.335241196919128],[64352,-0.334697521679459],[64366,-0.3104068410295895],[64505,-0.2755725408056833]]},"posterior_over_gaussian":{"n":256,"mean_diff":-0.08161368910761091,"se":0.020557364547773946,"sum":-20.893104411548393,"sumsq":29.292833209093736,"worst5":[[64294,-0.9198201425343768],[64402,-0.8370420503336504],[64502,-0.8343091963614597],[64280,-0.7919725134134978],[64415,-0.7826878102483761]]}},"diagnostics":{"posterior":{"n":256,"first_trades":1820851,"misread_count":526754,"first_trade_misread":0.2892900078040433,"callbacks":2859026,"callback_price_rms_bps":13.036390431169457,"mean_edge":537.6928556887417},"gaussian":{"n":256,"first_trades":1814330,"misread_count":521930,"first_trade_misread":0.2876709308670418,"callbacks":2844150,"callback_price_rms_bps":13.226971655278264,"mean_edge":537.7744693778494},"base":{"n":256,"first_trades":1814017,"misread_count":521690,"first_trade_misread":0.28758826405706234,"callbacks":2843461,"callback_price_rms_bps":13.233585962516605,"mean_edge":537.7882811476043}},"actual_seed_ranges":{"posterior":[[64256,64511]],"gaussian":[[64256,64511]],"base":[[64256,64511]]}},"holdout":{"paired":{"posterior_over_base":{"n":2048,"mean_diff":-0.08763557032498584,"se":0.007795216919643147,"sum":-179.477648025571,"sumsq":270.47276739688203,"worst5":[[11061,-1.315666968740402],[10212,-1.285143780934277],[10822,-1.252063499107635],[11256,-1.2404395288534715],[10962,-1.2330622638423847]]},"gaussian_kernel_over_base":{"n":2048,"mean_diff":-0.009244917469779512,"se":0.0032505193914442415,"sum":-18.93359097810844,"sumsq":44.46989785927353,"worst5":[[10446,-0.952348236165335],[10434,-0.8128219722486847],[11163,-0.727837847658634],[10190,-0.5607418049671651],[10131,-0.5526526973998784]]},"posterior_over_gaussian":{"n":2048,"mean_diff":-0.07839065285520633,"se":0.007672686008845775,"sum":-160.54405704746256,"sumsq":259.3837277869809,"worst5":[[10822,-1.5431645198904107],[11091,-1.282444833421323],[11061,-1.1989866213788218],[11256,-1.1710536068211468],[10212,-1.1692643939928757]]},"posterior_over_lattice_reference":{"n":2048,"mean_diff":0.23525634948550536,"se":0.012417070280384744,"sum":481.805003746315,"sumsq":759.7249525424764,"worst5":[[10578,-1.9011529507821479],[10632,-1.6231060264199186],[10982,-1.5370742743957635],[10336,-1.4540332660417334],[11541,-1.4163040412776127]]},"gaussian_over_lattice_reference":{"n":2048,"mean_diff":0.3136470023407117,"se":0.010772844754774124,"sum":642.3490607937775,"sumsq":687.9997070365387,"worst5":[[11487,-1.86114949094042],[10942,-1.6808972637131774],[10982,-1.6159411654493852],[10336,-1.2828280136162675],[10885,-1.1747203469212764]]},"base_over_lattice_reference":{"n":2048,"mean_diff":0.3228919198104912,"se":0.01069159987528695,"sum":661.282651771886,"sumsq":692.7408990952625,"worst5":[[11487,-1.9115248303883163],[10942,-1.6076924222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original qmdp lookup interpolates refined offsets. The new kernel interpolates action values before refinement. These operations need not commute. A nonzero error is not excused as a tie."},"grid_pass":true,"max_value_error":0.0,"max_grid_fee_error_bps":0.0,"non_tie_argmax_mismatches":0,"genuine_ties":0,"all_endpoint_gate_pass":true,"offgrid_gate":"Independent same-action evaluation in check_short.py; policy interpolation equality is diagnostic only.","grid_cases":682,"phases":[0,1,2],"kappas":[0,1e-05]},"independent_kernel_and_callbacks":{"independent_offgrid":{"actions":720,"max_value_error":1.6653345369377348e-16,"pass":true},"callback_checks":{"counts":{"disabled":20742,"active":41603},"hidden_scrambled_callbacks":62345,"full_state_exact":true,"max_fee_error_bps":0.0,"seeds":[64000,64001],"both_modes":true},"pi_endpoints":[{"pi":0.0,"phase":0,"exact_base_lookup":true},{"pi":0.0,"phase":1,"exact_base_lookup":true},{"pi":0.0,"phase":2,"exact_base_lookup":true},{"pi":1.0,"phase":0,"exact_base_lookup":true},{"pi":1.0,"phase":1,"exact_base_lookup":true},{"pi":1.0,"phase":2,"exact_base_lookup":true}],"quadrature_tuning":{"n_callbacks":200,"max_value_error":0.0005182781768944822,"mean_value_error":0.0001105074917175493,"max_regret":1.879038034737629e-05,"mean_regret":4.605727877652656e-07,"argmax_changes":8,"max_fee_drift_bps":4.986519763133801,"gaussian_5_vs_101":{"n":200,"max_value_error":0.001839441035921631,"max_regret":1.0856700250964912e-05,"argmax_changes":9,"max_fee_drift_bps":2.237901498646222,"reference":"101-bin Gaussian conditional means; same actions, kernel and moment-only incentive"}}},"causal_base_state":{"seeds":[64000,64003],"callbacks":42396,"all_base_filter_phase_rival_state_exact":true,"hard_posterior_endpoint_callbacks_pi0_pi1":[2443,232],"phase_counts":[7575,23275,11546],"candidate_vs_base_lookup_fee_drift":{"fee_observations":84792,"rms_bps":0.9323489028398507,"max_bps":4.9987261425066265},"note":"Replay shadows candidate fees; base receives no candidate hypotheses."},"exact_diffusion":{"seeds":[63000,63019],"n":20,"callbacks":210961,"active_acceleration":true,"max_fee_difference_bps":0,"full_belief_and_state_bit_exact":true,"four_arms":["ThrLattice","ValuePolicy(alpha=0)","FastBase","FastValue(alpha=0)"],"fastmath":false,"scope":"diffuse only; original thrpol.py, run.py and pair.py unmodified"},"pure_python_active_callback_parity":{"pure":{"callbacks":10758,"active":4512,"sha256":"6489d0a2c6d4873911e4139d695d991564d520248c2e29c647299e2ef207b08a","edge":435.95872806641324,"accelerated":false},"numerical":{"callbacks":10758,"active":4512,"sha256":"6489d0a2c6d4873911e4139d695d991564d520248c2e29c647299e2ef207b08a","edge":435.95872806641324,"accelerated":true},"exact_digest_match":true,"seed":64002},"standard_library_solver_backend":{"grid":{"DP_NW":2,"DP_NA":3,"DP_AMINL":-1},"sweeps":2,"VN_values":4995,"max_VN_error":0.0,"QMDP_values":735,"max_QMDP_error":1.3877787807814457e-17,"full_grid_fallback_runtime_measured":false},"unpacked_source_replay":{"seed":64002,"callbacks":10758,"sha256":"6489d0a2c6d4873911e4139d695d991564d520248c2e29c647299e2ef207b08a","matches_working_copy":true},"reference_gates":{"matched_published_digits":true,"seeds":[10000,10002],"usage":"Calibration only; these calls did not use candidate or tune it.","stdout":"seed   perfect-info      gaussian+plain    gaussian+qm       lattice+qm\n10000  448.261189  443.455598  443.522608  443.663520\n10001  576.779199  571.463409  571.623551  571.977293\n10002  531.886497  525.168028  525.865859  526.411631\n"},"source_pin":{"thrpol_sha256":"663965707d4f20f81ab0ade8ed510742a330605fd9e889d426db1d920abc8958","run_py_unchanged":true,"pair_py_unchanged":true},"value_regeneration":{"DP_STATE":true,"bounded_invocation_sweeps":[10,50,50],"total_sweeps":110,"gain_per_run":536.8206098114669,"convergence_diff_last_sweep":1.88e-07,"tol":2e-07,"DP_SAVEVN":1,"float_precision":64,"grid":{"own_rows":321,"phase0_columns":13,"phase1_2_columns":49,"actions_per_side":43,"AMINL":-12,"step_bps":5},"VN_file_bytes":527054136,"table_arrays_in_archive":false},"original_pair_py":{"comparisons_executed":12,"stdout_sha256":"1132500d5f50cae028da9a906dfdc9448a253a8698b4b948c1bef885b2384654","full_precision_sum_sumsq":"summarize.py; same rows and sample SE formula as pair.py"}},"attribution":{"incremental_gain_over_base":-0.08763557032498584,"posterior_shape_gain":-0.07839065285520633,"gaussian_kernel_change_over_base":-0.009244917469779512,"inherited_threshold_gain_over_lattice":0.3228919198104912,"paired_statistics":"holdout.paired","interpretation":"Posterior-minus-Gaussian isolates shape. Gaussian-minus-base includes interpolation, shortlist, quadrature and refinement effects together; no pure interpolation-only causal estimate. Base-minus-lattice is inherited threshold gain."},"cost":{"incremental_operations_per_changed_callback":{"shortlist_actions_max":9,"price_nodes_max":5,"four_axis_corner_reads_max":720,"corner_weight_multiplications_approx":2880,"corner_value_additions_approx":720,"inherited_incentive_table_reads_max":72,"posterior_scan_cells":121,"quantile_sort_complexity":"O(121 log 121), can remove sort because cells are ordered","transcendentals_additional":"2 log1p and 2 exp; Gaussian control also 8 inverse CDF calls (4 distinct arguments). Other counts approximate."},"incremental_required_persistent_slots_for_quote_mechanism":0,"note_slots":"Mechanism needs no extra persistent slots; pi/newstep can be local. Python keeps a redundant causal audit snapshot. Inherited lattice/table storage is not contract-feasible.","gas_estimate":{"measured":false,"label":"Illustrative arithmetic/table-access scenario, not a gas benchmark or gas guarantee","incremental_gas_range":[120000,2000000],"assumptions":"720 reads: 72k warm SLOAD gas, up to 1.512m cold, plus math/indexing. Excludes base gas and table deployment/compression. Not an EVM feasibility claim."}},"limitations":["No +0.3 incremental gain; no port recommended.","Five-node quadrature and local shortlist are approximations with measured errors.","Off-grid 6.909 bps fee drift is a diagnostic, not a commutation-equality gate.","Full-grid stdlib solver runtime unmeasured; reduced-grid parity does not prove full-grid runtime or convergence.","No engine, EVM, measured gas, board entry, or independent behavioral certification. Replica scores are not board scores."],"promotion":{"port_eligible":false,"reason":"Incremental holdout mean is below +0.3; posterior arm also loses to its matched Gaussian control.","board_entry":false},"code_zb64":"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","code_sha256_uncompressed_tar":"b84e5ec1a283f194608504554935330a4c01f9d158c43ef003b05110c1a80dae","code_tar_bytes":133120,"source_packaging":{"transformation":"Remove comments/docstrings and normalize formatting; executable AST checked identical with docstrings removed. Frozen core policy files, original run.py and pair.py are byte-exact.","transformed_supporting_modules":["valuepolicy.py","regenerate.py","plainbackend.py","offline.py","build_qm.py","check_short.py","check_causal.py","check_endpoints.py","plain_short.py","check_backend.py","check_diffusion.py","summarize.py","replica2.py","evalpol.py","real.py","latt.py","par.py","dp.py","qmdp.py","gates.py"]},"code_encoding":"base64(zlib(USTAR)); source only; optional NumPy/Numba not required by policy/solver fallback","reproducibility":{"instructions":"REPRODUCE.md in source tar","tables_or_traces_shipped":false,"optional_libraries":"Experiment used numpy 2.5.4 and numba 0.68.1, installed only in disposable scratch. No installed package is a required dependency of the delivered fallback.","full_grid_stdlib_regeneration_time":"unmeasured","reference_sources":"sources.json; all reference Python needed for offline regeneration is included"}}