{"item":"rc-band","batch":"e09-rival-cut","credit":"Taylor (gtmcknight): replica, filter, solved policy, qm tables; github.com/gtmcknight/amm-sim @4812a6382311624e1506aca131ca35c378f930d9, research/solved, README first.","status":"complete; replica only","provenance":{"readme_url":"https://raw.githubusercontent.com/gtmcknight/amm-sim/4812a6382311624e1506aca131ca35c378f930d9/research/solved/README.md","LB4_url":"https://api.imd.fun/artifacts/93cc15561f172c49c8bce174143333ede904fdbfcf51276cbbeb514d4f5b614e","TCA_url":"https://api.imd.fun/artifacts/dc359635b5851de9f5b3ecf81dc2107d46acd5dbe67e0be234bcfbe0c8a488e7","measurements":"All new numeric results are local executions of the bundled frozen code; predecessor result claims are not substituted for measurements."},"sha256_checks":{"tca_manifest_sha256":"272c6b758f6e6ead17a5bc832a0bbab64520f976450c94dbf7b87d3b7ed1b2c1","tca":{"artifact_sha256":"dc359635b5851de9f5b3ecf81dc2107d46acd5dbe67e0be234bcfbe0c8a488e7","bundle_sha256":"546b440855726c3cd2231e4879e0002dda182dc609ee9c5155c04e4790ca6fdb"},"lb4_all_file_hashes":true,"lb4":{"artifact_sha256":"93cc15561f172c49c8bce174143333ede904fdbfcf51276cbbeb514d4f5b614e","bundle_sha256":"f368c459fec21abaa30e179ffbf03cfbe1c7b194f1ad8b07de3637892d06e9f1"},"tca_all_manifest_files_verified":32},"P":{"status":"pass","command":"python3 -I gates.py","commit":"4812a6382311624e1506aca131ca35c378f930d9","unmodified":true,"run_before_variant":true,"columns":["seed","perfect-info","gaussian+plain","gaussian+qm","lattice+qm"],"rows":[[10000,"448.261189","443.455598","443.522608","443.663520"],[10001,"576.779199","571.463409","571.623551","571.977293"],[10002,"531.886497","525.168028","525.865859","526.411631"]],"exact_readme_6dp":true,"files_sha256":{"README.md":"02d2b51f17adbb5411a2a67f7a37255143ba381a73ff10abba557d7d23c0cef2","gates.py":"603280c1f595ca163ffaf2ba1ebdc7945fdb08dabae67fc53c922d39e1cce604"},"full_file_hashes":"bundle:scratch/evidence/P.json"},"layout":{"order":["TCA source_bundle into empty directory","LB4 overlay","delete scratch/ref/tca_tell.py, scratch/TellCore.sol, scratch/test_tell.py, scratch/test_policy.py","python3 -I scratch/materialize.py"],"blob_sha256":"3ac7cb8811ecfb6a17f499aa4e9b70dd55f7fb6432b3aec3b904e11a3dece38e","references":"Unmodified LEAN TellPolicy(blob,4,.0001), QMRef, and o1-b port copied from verified overlay; all runs 10000 steps."},"benchmark_first":{"seed":64000,"steps":10000,"policy":"LEAN","edge":439.32557072475703,"table_initialization_seconds":1.7967057069763541,"run_seconds":0.4384237308986485,"cut":"No settings, seeds or steps cut. Eight worker processes on choose; sequential report arms and seeds. Commands kept below eight minutes; long scoring commands use timeout 450."},"variant":{"name":"RIVAL BAND","picked_widening_bps_per_step":40,"base":"LEAN TellPolicy(blob,4,.0001)","definition":"log rival spot at last LEAN cand replacement +/- (30+widening*steps_since_sync) bps; cut posterior components and renormalize after retail-classified callbacks, before fee lookup; inferred arb pins untouched; exact Taylor trunc function, numerical empty intersection projects current mean to band endpoint with zero variance.","component_update":"Apply inherited trunc to each of up to two components; reweight by surviving mass; recompute mixture m,v and persist components. Skip when callback is classified arb or no sync has been observed.","unchanged":["tell classifier and its budget/floor","qm tables","fee lookup rule","LEAN rival tracker and synced flag"],"sync_convention":"port isBuy means pool buys X: cand=own spot*(1-bid fee)/0.997; otherwise cand=own spot*0.997/(1-ask fee). Record only when LEAN replaces s2h with cand. This reverses the retail-side buy/sell wording of README.","extra_assumptions":["Log-space half-width exactly 30 bps plus linear widening, not -log(0.997).","Old anchor remains after inferred arb, but cut is skipped on that callback.","Repeat cut on every retail-classified callback, including multiple callbacks in one step."]},"choose":{"seeds":[64000,64255],"steps":10000,"settings_written_before_scoring":true,"table":[{"setting_bps_per_step":null,"label":"disabled-band exact control","n":256,"mean_diff":0.0,"se":0.0},{"setting_bps_per_step":0,"n":256,"mean_diff":-171.80953201878637,"se":3.010486006151483},{"setting_bps_per_step":2,"n":256,"mean_diff":-171.6680055764768,"se":3.009304582356011},{"setting_bps_per_step":5,"n":256,"mean_diff":-171.54132139267216,"se":3.0097248433514703},{"setting_bps_per_step":10,"n":256,"mean_diff":-171.45728745455924,"se":3.0095865249253593},{"setting_bps_per_step":20,"n":256,"mean_diff":-171.35266240828835,"se":3.0094803274145043},{"setting_bps_per_step":40,"n":256,"mean_diff":-171.19198406594134,"se":3.0076633752604462}],"selection_rule":"Highest mean paired edge difference against LEAN among six active settings; control is diagnostic only; ties choose earlier listed setting.","control":{"paired":{"n":256,"mean_diff":0.0,"se":0.0,"min":0.0,"worst5":[{"seed":64000,"diff":0.0},{"seed":64001,"diff":0.0},{"seed":64002,"diff":0.0},{"seed":64003,"diff":0.0},{"seed":64004,"diff":0.0}]},"exact_fee_stream_matches":256,"callbacks":2772898},"choose_jsonl_sha256":"53b23ea2ad0f281ffb0efb9897f099a776f7303ec64572e986b83a60beb8bc6a"},"frozen":{"credit":"Taylor (gtmcknight): replica, filter, solved policy, qm tables; github.com/gtmcknight/amm-sim @4812a6382311624e1506aca131ca35c378f930d9, research/solved, README first.","pick":40,"source_sha256":{"build_variant.py":"3daf90beed6e040bfbafd1fe61e0e489c3997a7eb7ff4fbce5c9d26814c3e104","experiment.py":"5fe11a684fbb83323c8e262111ae7eeefe6fd9a6bb3145d4f55f30aa450ab2c1","plan.json":"2a1871ebb0a5b90d8decf0e59f584331c0995bd9a5c31f9bf3b29ecdcc47def1","ref/band.py":"69a81d663680ade1c7efeaba93ea6c868eb78cfe5481dfc44b0f55182806149f","ref/blob.bin":"3ac7cb8811ecfb6a17f499aa4e9b70dd55f7fb6432b3aec3b904e11a3dece38e","ref/lean_tell.py":"b70054f8c0561779e40c4c8df3f41ab08da05b9aef591816ac6c9034beb40a95","ref/policy.py":"962135885c1ad1ef142ed4b364870869e61948982c165ca2426ee864c1f10b51","ref/port.py":"81ec1136146777e10056c3a1e1ad5282de5a1b53e90d425decefc288e5948aac","ref/qmref.py":"1dfc9b7a3c2237ff7a93dd9f0af54b9bdab16f2ce0a7eb00784cb4432598db92","ref/twocomp.py":"9d3c1833321493fb51bbde4e1b4dabc2f8978bd6d774ee2af8dc8aed13f0f0af"},"choose_sha256":"53b23ea2ad0f281ffb0efb9897f099a776f7303ec64572e986b83a60beb8bc6a","report_not_run":true,"frozen_unix":1794300433.4251056},"report":{"seeds":[10000,10511],"steps":10000,"run_once":true,"execution_order":["all pick seeds ascending","all LEAN seeds ascending","all qt1 seeds ascending"],"paired_against_LEAN":{"n":512,"mean_diff":-177.52708005575735,"se":2.169462836753009,"min":-501.73704010585266,"worst5":[{"seed":10060,"diff":-501.73704010585266},{"seed":10401,"diff":-424.4652691824763},{"seed":10146,"diff":-342.62940911578363},{"seed":10475,"diff":-337.8605987868236},{"seed":10450,"diff":-325.4194247119392}]},"paired_against_qt1":{"n":512,"mean_diff":-174.39974914752747,"se":2.1688577805814724,"min":-499.3982455349983,"worst5":[{"seed":10060,"diff":-499.3982455349983},{"seed":10401,"diff":-421.1400236264418},{"seed":10146,"diff":-342.37189458530344},{"seed":10475,"diff":-333.9822652844393},{"seed":10450,"diff":-321.6689138870785}]},"mean_edge":{"band":362.5903270102321,"lean":540.1174070659895,"qt1":536.9900761577596},"band_cuts":4553525,"numerical_empty_intersections":295438,"logs_sha256":{"band":"24c34738e70a88be59ecb6264e65e4f09852f8048b6e14f03c08a382ff23fc5e","lean":"83c541d532f501d895bd89f8e30fab0d012e875ab1d846a382522710b2aec852","qt1":"72a1da9c0ecd2061395ff55b1fb7e4c40e665ae6021a03f0a656bfe24eea0f7b"},"exact_per_seed_edges":"Bundled scratch/report_edges.bin; 512 records <ddd, pick/LEAN/qt1, ascending seed order"},"observables_proof":{"credit":"Taylor (gtmcknight): replica, filter, solved policy, qm tables; github.com/gtmcknight/amm-sim @4812a6382311624e1506aca131ca35c378f930d9, research/solved, README first.","status":"pass","rows":[{"seed":64000,"arm":"lean","callbacks":9512,"fee_digest":"d786e7ebc0cdad1f4ca2639c19efd468db52983852546599675dec64b521c1e1","edge":439.32557072475703},{"seed":64000,"arm":"band","callbacks":9466,"fee_digest":"d97cef9ee69ff34e8d1bbd3640fa04a5616f487af7256855bff6d20277c20402","edge":298.74803376284495},{"seed":64001,"arm":"lean","callbacks":10767,"fee_digest":"e1d4fc707e3eb30baf981a7c76dd03b79cac014a37f3d762211199369b6db8e2","edge":478.1955975009779},{"seed":64001,"arm":"band","callbacks":11010,"fee_digest":"ed00712ef0c7c4b69df88cb54161593c0fd5e5bb921bf2bf7f578490631bc063","edge":345.09317259304845},{"seed":64002,"arm":"lean","callbacks":10492,"fee_digest":"bf9a50b6ab36da276e255b266e81b468d9893534d17c6c4e12e5aec44fa57356","edge":438.8127666040435},{"seed":64002,"arm":"band","callbacks":10638,"fee_digest":"5fb488dd888584ab1f98c6c4aad0a1120c73bd96dc0bbce1bd8f7ac3ae0ef841","edge":321.00807947035344},{"seed":64003,"arm":"lean","callbacks":10562,"fee_digest":"8b71b78117dd95c528fdf356ada52d0cda34db136cb9ed9eecc733506228ca8e","edge":574.105476820612},{"seed":64003,"arm":"band","callbacks":10686,"fee_digest":"a90559975baf1e991483bc8f2b99affb10256a7b56a53e22268427717a980cc0","edge":360.15924648720716}],"hidden_inputs":["_ti[4] path","on_trade arb, p, s2, j","init sigma, rate, size"],"comparison":"exact Python float fee pair equality on every callback and exact belief equality; garbage raises on conversion/comparison/truth testing; public inputs identical","scope":"four seeds, full 10000 steps each, LEAN and frozen pick"},"explanatory_choose_audit":{"credit":"Taylor (gtmcknight): replica, filter, solved policy, qm tables; github.com/gtmcknight/amm-sim @4812a6382311624e1506aca131ca35c378f930d9, research/solved, README first.","scope":"Post-freeze explanatory audit on four choose seeds only; no policy changes; hidden truth used by external audit only.","rows":[{"seed":64000,"edge":298.74803376284495,"cuts":7349,"true_price_outside_band":3039,"cuts_on_sync_step":6621,"true_price_outside_on_sync_step":3033},{"seed":64001,"edge":345.09317259304845,"cuts":8725,"true_price_outside_band":3426,"cuts_on_sync_step":7891,"true_price_outside_on_sync_step":3418},{"seed":64002,"edge":321.00807947035344,"cuts":8398,"true_price_outside_band":3224,"cuts_on_sync_step":7654,"true_price_outside_on_sync_step":3222},{"seed":64003,"edge":360.15924648720716,"cuts":8541,"true_price_outside_band":3583,"cuts_on_sync_step":7801,"true_price_outside_on_sync_step":3579}]},"recommendation":"Retain LEAN: the disabled control dominates all six active choose settings. The frozen active pick is reported to quantify this negative experiment, not recommended for deployment.","belief_error":{"definition":"Post-callback abs(m-ln(true price))*10000; callback-weighted, by elapsed timestamp since most recent true own arb. Hidden truth only in external observer. Each policy has its own callback population; these are descriptive, not matched callback differences.","LEAN":{"callbacks":5579658,"overall_mae_bps":3.6526394986900748,"columns":["steps_since_true_own_pin","callbacks","mae_bps"],"by_steps_since_pin":[["before_first_pin",2109,9.718519534651962],[0,3239664,0.027921602036827933],[1,530340,5.394350502485791],[2,379426,7.735348789365081],[3,293702,9.088476219805965],[4,229374,9.849155999883301],[5,183432,10.27579157478525],[6,145528,10.461443691283051],[7,115719,10.638608732881455],[8,92500,10.692939119692076],[9,74189,10.679267941694931],[10,59134,10.66893545468821],[11,47234,10.673604624730373],[12,37812,10.62595908268815],[13,30112,10.605323623778638],[14,24220,10.697096246555432],[15,19234,10.597604597256367],[16,15284,10.596147395417125],[17,12321,10.53502464901911],[18,9754,10.722063701119868],[19,7649,10.704109891415168],[20,6056,10.286543576478762],[21,4873,10.778005076666902],[22,4000,10.70248531350232],[23,3035,10.468448210997343],[24,2553,10.38503755334592],[25,2040,10.788282256026436],[26,1637,10.50709038231252],[27,1297,10.469302635882654],[28,1101,10.743702449387575],[29,832,11.053235383941118],[30,672,10.647989725930277],[31,541,11.227394642942881],[32,431,11.289993177267343],[33,359,12.09276731236488],[34,263,11.166391245607079],[35,247,10.877241775880993],[36,221,10.611443915964001],[37,169,11.106813558836146],[38,111,10.072821144122958],[39,96,10.06995877904084],[40,78,8.645558713092093],[41,60,8.811511709504225],[42,42,11.767830606035156],[43,51,9.371881507914441],[44,33,10.972753840860443],[45,24,10.859995795270047],[46,24,11.922733208054405],[47,16,11.99893761310078],[48,16,10.722083572198839],[49,9,14.534450050336131],[50,5,22.89999331793524],[51,4,4.370685094134963],[52,7,15.943882364254845],[53,2,17.905998681215785],[54,1,16.819760739856804],[55,2,3.463167455106664],[56,2,3.8590168023588944],[57,4,13.400320540992716],[58,3,14.358547004732122],[59,2,12.120993107451739],[61,1,15.697063008035528],[65,1,0.9694478343114099]]},"pick":{"callbacks":5712521,"overall_mae_bps":22.186337260790136,"columns":["steps_since_true_own_pin","callbacks","mae_bps"],"by_steps_since_pin":[["before_first_pin",1946,17.632038567794222],[0,3078017,24.22469865451565],[1,655852,20.695363031556617],[2,465506,19.923142624144862],[3,349917,19.527889094107625],[4,266526,19.61330983057348],[5,203770,19.34248822144697],[6,156714,19.15146104822376],[7,121184,19.314171177749795],[8,93189,19.290879062056774],[9,72219,19.228709348455038],[10,56242,19.082780240873888],[11,43032,19.287373676652447],[12,33790,19.290724449856434],[13,25706,18.96384145592574],[14,20229,19.47544718510755],[15,15457,19.16829810143915],[16,12155,20.13478966516003],[17,9073,18.69561442422175],[18,7130,19.129007885283908],[19,5518,19.686887160541072],[20,4193,20.54737873412258],[21,3363,19.394984491412817],[22,2557,18.265069707609],[23,1957,19.064225508725933],[24,1553,19.153241234521882],[25,1221,18.117134701267112],[26,979,19.35604099870654],[27,717,19.339067911108692],[28,635,16.574929353563512],[29,451,16.95963096377441],[30,360,23.88512953495268],[31,264,16.77024435524879],[32,222,13.572552262545015],[33,209,20.658334058283387],[34,112,30.958399180084445],[35,130,16.981611285595083],[36,86,14.916415218506149],[37,79,16.307903966727956],[38,72,17.991497159220994],[39,58,16.55761604367993],[40,26,38.27997299154857],[41,22,14.433499258087757],[42,18,13.970549222224925],[43,16,14.666838298084684],[44,13,17.605087823886475],[45,8,6.965300287494758],[46,9,17.691760735551025],[47,6,11.762906712894742],[48,3,13.850926134457708],[49,2,6.17796070629506],[50,5,23.522564456515127],[51,1,21.762862459517862],[52,2,36.12635774713269]]}},"contract_cost":{"status":"Not implemented or tested: this item is replica only.","extra_persistent_state":["last actual LEAN sync log spot","last sync timestamp (unset sentinel can encode validity)"],"arithmetic":"On a sync: one log (reusable if cached). On eligible callback: timestamp difference, one multiply-add for width, two bounds; at most two Gaussian truncations (CDF/PDF, square root, divisions), weight normalization, mixture moments. Point components need interval tests; empty mass needs clamp and reset. Keep updated two-component state for future propagation. Counters in Python are diagnostics and need not exist in contract.","gas":"Not measured; no F gate or deployment claim."},"limitations":["Replica levels are not board scores. No engine seeds or contracts used.","Band is heuristic: retail can move the rival outside its arbitrage band, and LEAN syncs may be mistaken.","Repeated cuts reuse correlated information and may overconcentrate belief.","Only six prespecified linear-widening settings evaluated. No tuning or policy edits after report.","P uses mandatory seeds 10000..10002 before any change, independently of the later frozen report."],"bundle":{"format":"base64(tar.xz)","sha256":"c3814de71a0e954311feb92943a0807e712ea32e5f4cde73def9425e6d0bb8b4","bytes":35752,"manifest_sha256":"760a9acb565691346268d64fd668b14a5c7969555ddea3448ee959b1cf7e9fbf","offline":"Standard library only; all runtime reference code and qm blob included; no symlinks or external dependencies.","data":"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"},"conclusion":"Frozen RIVAL BAND (40 bps/step) vs LEAN: -177.527080056, SE 2.169462837; vs qt1: -174.399749148, SE 2.168857781. Replica only. This does not improve LEAN on the report block."}