{"provenance":{"published_holdout":"https://raw.githubusercontent.com/gtmcknight/amm-sim/66604b3426d8e97a73e2c39940b9b4dece51cc73/research/tell/tell_qm.json","algorithms":"Pinned tellref.py, tellpol.py, real.py, latt.py and run.py; exact URLs and verified quotes in sources.json.","measured_numbers":"Local stdlib replica experiments from bundled drivers. Per-seed edge differences are losslessly compressed below; tuning and diagnostic summaries are harness outputs.","gas_and_state":"Analytic illustrative estimates only; no Solidity measurements."},"hypothesis":"Keeping the non-Gaussian between-pin posterior on a <=0.25 bps lattice improves the corrected TellPolicy while leaving tables, rival update, RHO and evidence fixed. This is a numerical lattice experiment, not a proof bounding every conceivable filter.","frozen_parameters":{"spacing_log":2.5e-05,"spacing_bps":0.25,"sigma":0.000945,"kernel_sigmas":5.0,"fractional_bits":32,"slot_bits":64,"trim_mass_per_cell":1e-14,"soft":1,"posterior":"full mixture without .995 hard collapse","RHO":0.26,"qm":"qm_k1e-5.json","table_sha256":"5ff53e42ca87390e5f65d1caa3f7a480e67a8212fb0670274dfd562b05ba1afd","gamma_guard":"increment fee WAD by 128 while gamma equals binary64 1-0.003","initial_belief":"point mass ln(100)"},"parameters_sha256":"053e2259a7577c7898e427ae9199d5e61048c1a63cde0be2a9ef9032676b25c0","code_sha256":"01be92b448fb108a8870145837b7735c6d28417fc3f736eae12556ef4032815e","freeze":{"candidate":"exactfast:Policy","holdout_started":false,"sha256":{"baseline.py":"cff824228e2d245b020875c5dcbc15b885b76b2979cc4686d2e9b7f707ecc934","evalpol.py":"27fa051b1dfc3a5da824596c59ed5140823049ab05fd18185a4ece1995d65df1","exactfast.py":"01be92b448fb108a8870145837b7735c6d28417fc3f736eae12556ef4032815e","holdout.py":"2b5de36d3f9ef6200dbe0ab806440ab5e1034603bff6016ace355c0c6d055912","observed.py":"8083fb431616ddce9eb8e495d26933d5fc5eefa3d304d0f5e548e6d86820312c","par.py":"d81d358cf2f54303d120da34c01022e62428c2e9d6b1577f8c30099b2bfd1483","pol_mid.json":"efbb5ee102db4d312f64565d9bf7232b6becbe02b624164f744a402bbbf2983b","qm_k1e-5.json":"5ff53e42ca87390e5f65d1caa3f7a480e67a8212fb0670274dfd562b05ba1afd","real.py":"7fc9f4bdf9a9af094ec2eb0e9a8e8eec8b343c5226bf0ebaa30c798785cb7c58","replica2.py":"6e37b4e61e1ffa5ffb1e43912ddb8659105989aa986eb48fcd1d7608f8dca685","run.py":"54d19a9194ebedee8b947dfaaf8626b1e4ef6f7d1b5aa12e738ec01a5ce401ac","tellpol.py":"6e62a0e070d9778537e746438f03ea7a4b44cb1d9652ae13702b801997db5f6d","tellref.py":"72679df55e2ba46a903075543654b685e7b2bf86dfbef3d5122dff6368d4a4f2"},"tuning_completed":256,"utc":"2026-11-10T05:09:46.615621+00:00"},"tuning":{"range":[61900,62155],"completed":256,"filter_only_vs_corrected_gaussian":{"n":256,"mean_diff":0.015221247732459586,"se":0.0121954538975032,"sum":3.896639419509654,"sumsq":9.768347085541128,"worst5":[{"seed":62069,"diff":-0.6726777996825604},{"seed":62135,"diff":-0.5460035381555599},{"seed":62132,"diff":-0.4840908217206561},{"seed":62008,"diff":-0.3421926391829402},{"seed":61965,"diff":-0.32643609419915265}]},"precision_audit_44bit_vs_corrected_gaussian":{"n":40,"mean_diff":0.037592091354974856,"se":0.021358515524163988,"sum":1.5036836541989942,"sumsq":0.7681770625153193},"precision_audit_44bit_code_sha256":"64611a87b7bf65cf6fcf03229e0e709743869fd0e19cfd46db56d1884e5e4ece","packing_pilot":{"n":64,"mean_diff":0.020383574560648654,"se":0.016821629898102482,"sum":1.3045487718815139,"sumsq":1.1675152483123103},"packing_pilot_code_sha256":"099558738b8057bca0125baeb84f685d618b93d0b71e6f6a15471bb5ddcd1141","precision_audit_note":"Only tuning data used. Initial 44-bit representation was replaced by 32-bit packing for runtime; no policy selected using holdout. Both retain the full Bayes mixture."},"holdout":{"requested_range":[10000,12047],"completed":2048,"remaining_seed_ranges":[],"paired":{"n":2048,"mean_diff":0.006427658790347374,"se":0.003602358357337455,"sum":13.163845202631421,"sumsq":54.48745901345395,"worst5":[{"seed":10843,"diff":-1.6479463359687543},{"seed":11949,"diff":-1.140314986730914},{"seed":11362,"diff":-0.7453020321448776},{"seed":11289,"diff":-0.7387008500342631},{"seed":11413,"diff":-0.6538864582756219}]},"label":"Total gain includes integer-observation, fee-WAD round-trip and gamma-guard differences from shipped tell_qm.json.","filter_only_vs_corrected_gaussian":{"n":2048,"mean_diff":0.010220543741529503,"se":0.0036832968439983378,"sum":20.931673582652422,"sumsq":57.088910441505035,"worst5":[{"seed":10843,"diff":-1.6961145312299095},{"seed":11949,"diff":-1.1403149866834497},{"seed":11362,"diff":-0.7346854562164253},{"seed":11289,"diff":-0.7150686054972653},{"seed":11417,"diff":-0.559433920262677}]},"corrected_gaussian_vs_shipped":{"n":2048,"mean_diff":-0.0037928849511821294,"se":0.0014755468292888783,"sum":-7.767828380021001,"sumsq":9.15700341562732,"worst5":[{"seed":10303,"diff":-1.16185567977152},{"seed":11413,"diff":-0.4524868158399613},{"seed":11714,"diff":-0.39264586341732866},{"seed":11477,"diff":-0.37150688838130463},{"seed":11796,"diff":-0.359345120105786}]}},"observations":{"candidate":{"first":14348232,"misread":3136,"rate":0.00021856351360920287,"price_rms_bps":7.127136523296155,"callbacks":22274685},"corrected_gaussian":{"first":14350187,"misread":4150,"rate":0.0002891948376700596,"price_rms_bps":7.158509354296801,"callbacks":22281157},"shipped":{"first":14350251,"misread":3420,"rate":0.0002383233575496345,"price_rms_bps":7.149446150401305,"callbacks":22281321}},"error_by_time_since_inferred_arb":{"clock":"Each policy own last first-trade arb read (posterior > 0.5), including soft mixtures; initial clock 0.","candidate":[{"steps":"0","callbacks":12901084,"bias_bps":0.0002238528262738707,"rms_bps":0.7340400583888175,"reported_sd_rms_bps":0.7410863115802226},{"steps":"1","callbacks":2121382,"bias_bps":0.012266738600125178,"rms_bps":6.992109479467222,"reported_sd_rms_bps":6.965033534341921},{"steps":"2","callbacks":1520748,"bias_bps":0.00574856367570339,"rms_bps":9.737580843224755,"reported_sd_rms_bps":9.697990385545749},{"steps":"3-4","callbacks":2096120,"bias_bps":-0.008095430682270668,"rms_bps":11.624415844705418,"reported_sd_rms_bps":11.596881081195864},{"steps":"5-8","callbacks":2161467,"bias_bps":0.02018827053120698,"rms_bps":12.688085408228114,"reported_sd_rms_bps":12.676763789152675},{"steps":"9-16","callbacks":1230125,"bias_bps":0.01424008971280455,"rms_bps":12.9080724436847,"reported_sd_rms_bps":12.882712865405733},{"steps":"17+","callbacks":243759,"bias_bps":-0.032368341311352695,"rms_bps":12.878787799684034,"reported_sd_rms_bps":12.876529219663995}],"corrected_gaussian":[{"steps":"0","callbacks":12908041,"bias_bps":-0.0008570735550824466,"rms_bps":0.8054630475993281,"reported_sd_rms_bps":0.6130765745693637},{"steps":"1","callbacks":2122689,"bias_bps":0.011965801162980008,"rms_bps":7.070992135616758,"reported_sd_rms_bps":6.9634745277279},{"steps":"2","callbacks":1522569,"bias_bps":0.007308981394956173,"rms_bps":9.783582098314122,"reported_sd_rms_bps":9.573643325734194},{"steps":"3-4","callbacks":2097223,"bias_bps":-0.007163918947865159,"rms_bps":11.65606616381482,"reported_sd_rms_bps":11.277102851719008},{"steps":"5-8","callbacks":2160609,"bias_bps":0.018053274867938587,"rms_bps":12.725867041986282,"reported_sd_rms_bps":12.16193424435659},{"steps":"9-16","callbacks":1227364,"bias_bps":0.01769936200019367,"rms_bps":12.951947699824387,"reported_sd_rms_bps":12.27629738031961},{"steps":"17+","callbacks":242662,"bias_bps":-0.006037482308654314,"rms_bps":12.966750681055649,"reported_sd_rms_bps":12.241206263032144}]},"common_pin_clock":{"definition":"Baseline literal point pin: first-trade arb read with variance <=1e-24, initial clock 0. Used only by harness. Both arms use the same per-seed baseline clock.","candidate_error":[{"steps":"0","callbacks":12768198,"bias_bps":0.0007090395841955113,"rms_bps":0.7943287401476757},{"steps":"1","callbacks":2122544,"bias_bps":0.011381139747015061,"rms_bps":6.959025628139697},{"steps":"2","callbacks":1520537,"bias_bps":0.0051196595049682846,"rms_bps":9.676624307396054},{"steps":"3-4","callbacks":2105764,"bias_bps":-0.008277017107746532,"rms_bps":11.54534987054833},{"steps":"5-8","callbacks":2203065,"bias_bps":0.018890483081341693,"rms_bps":12.547149440603418},{"steps":"9-16","callbacks":1286722,"bias_bps":0.014264947813255486,"rms_bps":12.713263539343357},{"steps":"17+","callbacks":267855,"bias_bps":-0.03580481045982524,"rms_bps":12.633834383726846}],"corrected_gaussian_error":[{"steps":"0","callbacks":12820705,"bias_bps":-0.0002954813996525234,"rms_bps":0.43088073851677},{"steps":"1","callbacks":2106138,"bias_bps":0.01303792917943273,"rms_bps":6.977479007594145},{"steps":"2","callbacks":1514076,"bias_bps":0.005521481269924566,"rms_bps":9.731315238429545},{"steps":"3-4","callbacks":2099183,"bias_bps":-0.010055715029011003,"rms_bps":11.617487003392272},{"steps":"5-8","callbacks":2195106,"bias_bps":0.015797695225908952,"rms_bps":12.67617685251805},{"steps":"9-16","callbacks":1280029,"bias_bps":0.01673103148254963,"rms_bps":12.900883401330981},{"steps":"17+","callbacks":265920,"bias_bps":0.010111355502067399,"rms_bps":12.92214238226419}],"gain_by_age":[{"steps":"0","n":2048,"mean_diff":-0.002031990035193089,"se":0.001840371664741149,"sum":-4.161515592075446,"sumsq":14.207492509477479},{"steps":"1","n":2048,"mean_diff":-0.017021981665186797,"se":0.0012352252960535727,"sum":-34.86101845030256,"sumsq":6.989870399151571},{"steps":"2","n":2048,"mean_diff":-0.0037810931404286246,"se":0.0009200753113579207,"sum":-7.743678751597823,"sumsq":3.5781860058524746},{"steps":"3-4","n":2048,"mean_diff":0.0009542423669720371,"se":0.0012490080346674959,"sum":1.954288367558732,"sumsq":6.541872558375018},{"steps":"5-8","n":2048,"mean_diff":0.01516456790796259,"se":0.0014495908307380874,"sum":31.057035075507386,"sumsq":9.280210966637368},{"steps":"9-16","n":2048,"mean_diff":0.011562066570873327,"se":0.0013467764686188998,"sum":23.679112337148574,"sumsq":7.877722149872637},{"steps":"17+","n":2048,"mean_diff":0.005374731736551803,"se":0.0009708333297847494,"sum":11.007450596458092,"sumsq":4.010436127396186}]},"blindness_checks":{"unmodified_run_py":true,"module_policy":"exactfast:Policy","needs_flag":false,"policy_inputs":"Only int(float(callback)*1e18) then float(WAD)/1e18 side, amounts, timestamp, reserves and own state. Initialization sees no run parameters.","held_fixed":["lookup tables","rival update equations","RHO=0.26","SIG2=.000945**2"],"truth":"Only diagnostics harness sees p, arb, rival and seed; none are passed to the policy. Common clock never enters a policy.","intentional_differences":["full lattice posterior replaces Gaussian moments and hard-collapse soft rule","all elapsed silent steps processed rather than Gaussian cap 12"],"holdout_selection":"One frozen candidate; each completed seed scored once after freeze. Checkpoint resume omits completed seeds.","poisoned_truth_test":{"baseline:Policy":{"steps":500,"outputs_identical_with_poisoned_truth":true,"edge":63.38218325671199},"exactfast:Policy":{"steps":500,"outputs_identical_with_poisoned_truth":true,"edge":63.388836740983486}}},"numerics":{"exactfast":{"max_abs_coefficient_error":6.105658535184022e-11,"l1_error":6.0839481818680125e-09,"transition_variance_ratio":0.9999860563856359,"mass":0.9999999976716936}},"moment_check":{"max_mean_error_bps":0.0018041175930960662,"max_variance_error_bps2":0.006279801625707731,"intervals":5},"checks":{"fetched":34,"hash_mismatches":0,"vectors":11856,"vector_mismatches":0,"arbitrage_vectors_read":[4661,4661],"retail_vectors_read_as_arbitrage":[9,7195],"source_quotes_reverified":13},"solidity":{"status":"not implemented or measured; not recommended as a direct port","state_bytes_unmeasured_estimate":16000,"packed_state_bytes_unmeasured_estimate":2256,"packed_state_basis":"500 cells x 4-byte probabilities plus ~256 bytes scalar state; kernel constants separate. Cell count is illustrative, not a measured peak.","state_basis":"~500 active probability cells at 32 bytes each plus bounds and moments; support is dynamic and has no hard maximum.","ops_unmeasured_estimate":"Naive exact convolution ~200 active cells x 379 kernel cells per elapsed step (~75,800 multiply-adds); C-backed bigint multiplication used here has no EVM analogue.","gas_unmeasured_estimate":"~10^6 arithmetic gas/step if ~15 gas/multiply-add, plus memory/storage; storage writes could dominate. No afterSwap gas claim."},"code_zb64":"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","code_decoded_sha256":"01be92b448fb108a8870145837b7735c6d28417fc3f736eae12556ef4032815e","bundle_zb64":"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","bundle_decoded_sha256":"ce748ecf7d86929926a580e25a4e733ff575b35cfb7427ea46c6d92e5c258b84","bundle_note":"JSON path:text map of new code and pinned external-input SHA256 manifest. Large unchanged published tables/replica inputs are referenced, not embedded. Python stdlib only; no installed packages.","per_seed_rows":{"format":"Two columns, each exact signed little-endian integers with common denominator 2**e. Column bytes are transposed: all row byte 0, then all row byte 1, etc. Divide decoded integer by 2**e. Columns concatenate; seed order is ascending expanded completed_seed_ranges.","columns":["candidate_edge-shipped_edge","corrected_gaussian_edge-shipped_edge"],"column_encoding":[{"denominator_power_of_two":44,"signed_integer_bytes":6},{"denominator_power_of_two":44,"signed_integer_bytes":6}],"completed_seed_ranges":[[10000,12047]],"rows_zb64":"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","decoded_sha256":"80820d326171ff0a0324454f9a2d49f372a38559d6d72a4ff4d7055d87b9edb9","decoded_bytes":24576,"precision":"Lossless exact rational representation of binary64 differences from full precision rows; no rounding before pairing."},"limitations":["Replica only; not real engine performance.","0.25 bps cell discretization, 5-sigma kernel cutoff and 32-bit mass quantization approximate continuous Bayes; not an analytic ceiling.","Pin-age partitions are diagnostic, not an intervention on one filter state.","The unchanged qm tables receive only lattice mean and sd; shape-aware fee optimization is outside this experiment."],"conclusion":{"filter_mean_gain":0.010220543741529503,"filter_se":0.0036832968439983378,"at_least_0_1_observed":false,"interpretation":"The full lattice mixture makes only a small change to the corrected Gaussian on these seeds; most residual true-price gap is not recovered by this belief-shape substitution.","scope":"Full requested holdout"},"where_gaussian_loses":{"mechanism":"Repeated no-arb truncations produce a non-Gaussian posterior; Gaussian rematching is underdispersed at longer pin ages. Lattice retains skew, tails and arb/retail mixture rather than collapsing at posterior .995.","evidence":"See own-clock predicted-SD vs observed-RMS rows and common-clock actual price errors/gain bins. These reflect the full belief substitution, not an isolated skewness or hard-threshold ablation.","cheapest_closed_form_correction":"Not required unless gain reaches 0.1. A truncation-count variance inflation is a plausible cheap follow-up, untested here; no claim that it retains most of a gain.","measured_summary":{"own_clock_17plus":{"gaussian_rms_bps":12.966750681055649,"gaussian_reported_sd_rms_bps":12.241206263032144,"lattice_rms_bps":12.878787799684034,"lattice_reported_sd_rms_bps":12.876529219663995},"common_clock_gain_steps_5plus":0.03210136621538772,"common_clock_gain_steps_0to2":-0.02283506484080851,"interpretation":"The lattice improves uncertainty calibration and later edge, but gives back edge near pins. These are policy-path partitions, not causal ablations."}},"recommendation":"Keep the Gaussian for the contract: filter-only gain 0.01022 is below 0.1; this experiment does not justify a lattice port. No closed-form correction claiming to retain most of a >=0.1 gain is warranted.","execution_note":"Checkpoint resume loaded the compact report encoder. Completed paired seed rows were reused; frozen decision code and parameters did not change. All requested seeds completed."}
