{"credit":"Taylor (gtmcknight): replica, Gaussian filter, fee rule and qm tables. COMBO/TC/QMS1/qt1 are IdentityMD derivatives.","item":"res-a","status":"partial_coverage_and_raw_identity_limitation","gates_output":"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","gate_P":{"pass_all_12_at_6dp":true,"unmodified_first":true,"reference_checks":{"QMRef":[443.1017411146794,571.454483381923,525.908441161234],"Combo":[444.1953300340616,572.1726816933833,526.2747600532116],"seconds":10.561222315765917},"source_commit":"4812a6382311624e1506aca131ca35c378f930d9","extracted_hash_checks":true},"hashes":{"qt1":"8a6e303b662e1a5ea9bb47f31acedd3988ef65b3ddb66d074517aa59ad41ca40","TC":"d3c849db2ac31e567b57e52b80b86e8c24c842e5b4125b9d75c78e3cb07115b9","COMBO":"d360bea6c8c982717b7b43a8069a87013169c89ee3e4da25622db4d880b936d4","QMS1":"17d249ab0c33fc44931d9b105cf3f9e3846a560bd518b67310c2a56defacdecc","combo.py":"c5b711a0b4fb4783e8879a484bf87a427171f4a11aa5eb5ec3513c24ead1a796","twocomp.py":"9d3c1833321493fb51bbde4e1b4dabc2f8978bd6d774ee2af8dc8aed13f0f0af","qmref.py":"1dfc9b7a3c2237ff7a93dd9f0af54b9bdab16f2ce0a7eb00784cb4432598db92","blob.bin":"3ac7cb8811ecfb6a17f499aa4e9b70dd55f7fb6432b3aec3b904e11a3dece38e","head.py":"34ef5c848986481d1d2f01e8e38b082582568de79966f4c1dd8ac73499941fae","train.py":"13bdcb46deff2d35ce405e4c01452552ff06c3b8b338eb52a262a908cb7e9b2a","prepare.py":"a28ef3460348667e1c3735f069d59970646f8e73911779233fa18e473ec43485","score.py":"d004ce82b1e5bb73dfbbf227ca0de176689179e829a944f85700d7efcf8bbe4c","proof.py":"9ad025283654e1d0375cfc2c1499c91b0e01560de5083cea9c9d23368165215e","pinned/gates.py":"603280c1f595ca163ffaf2ba1ebdc7945fdb08dabae67fc53c922d39e1cce604","pinned/replica2.py":"6e37b4e61e1ffa5ffb1e43912ddb8659105989aa986eb48fcd1d7608f8dca685","pinned/README.md":"02d2b51f17adbb5411a2a67f7a37255143ba381a73ff10abba557d7d23c0cef2"},"frozen_spec":{"centers":[0.5659728901455716,-1.5943929704924016,11.66266166470834,1.5943929704924016,0.592209852912322,0.8239092515796648,0.9632172127734998,0.2781191881620442],"scales":[0.3650998759916714,85.71798239423741,6.862143787121076,85.71798239423741,43.65861248567696,0.7244647857779883,1.5091489239821516,0.810566269957903],"selected_generation":4,"selection":"best complete validation only; no report seeds read","source_sha256":{"blob.bin":"3ac7cb8811ecfb6a17f499aa4e9b70dd55f7fb6432b3aec3b904e11a3dece38e","combo.py":"c5b711a0b4fb4783e8879a484bf87a427171f4a11aa5eb5ec3513c24ead1a796","head.py":"34ef5c848986481d1d2f01e8e38b082582568de79966f4c1dd8ac73499941fae","qmref.py":"1dfc9b7a3c2237ff7a93dd9f0af54b9bdab16f2ce0a7eb00784cb4432598db92","twocomp.py":"9d3c1833321493fb51bbde4e1b4dabc2f8978bd6d774ee2af8dc8aed13f0f0af"},"validation":-0.19670358351615236,"weights":[-0.5597044604607767,0.6594440432739213,0.15649763010940948,-0.42809079835793756,-0.18223150262846888,-0.7733793797959393,0.3086748595212847,-0.32513810359996237,-0.36874385419989414,0.3868440042329764,-0.4227835659156412,0.2736974508205275,-0.014279954096721609,-0.06558509568409353,-0.4051960290178122,-0.5830355074662401,-0.36144806980590954,0.29641008480156966,0.29503908490887437,-0.9322921336448889,0.06756525719880366,0.25087257395125717,0.27855013043805854,-0.04302472320148869,0.3296871222769094,0.13432889746412524,0.02955218192061032,0.1422318847359932,-0.278720725192205,-0.8328498242764097,-0.7047748154796623,0.2666362189884735,-0.36073305843778714,-0.18492051235681306,0.967886588318992,-0.154782038714644,0.0824474594812883,-0.14623827927748026,-0.2883122229263489,-0.25447799365373563,0.15068232014920346,0.4799882842073699,0.27567565246260617,0.0938112472658225,-0.43572479134488645,0.6334172584344594,0.17707342144006022,-0.05807345656061641,-0.38704200346343814,0.10023546618398377,0.014519936014653175,-1.2013125357019885,-0.2598494155880801,1.3111070730385703,-0.27764632300340836,1.0060890743136617,-0.8260551815817415,0.45217582530445954,0.7291595621972615,0.14674269447398686,0.27054795283905836,-0.5784089017836247,-0.16782033204693794,0.3557330079273315,0.8661491443603382,0.5488627307995709,0.5779453659332271,-0.2656513511373519,0.281909536721873,0.16801266889632713,0.3462495086165362,0.05841113678449627,-0.3301648330605167,0.0031137893403029747,-0.17055702730458236,0.024761838003462894,0.2581099699898013,-0.21110287379878356,-0.3272597639464884,-0.05643024071820209,-0.2112624130631834,0.21927655439555255,-0.5167326051982787,-0.08247292958379016,0.033715243739394614,0.31147597553576806,0.18109818650544732,-0.2861722522894545,0.1905731378938744,-0.17480678698747484]},"frozen_spec_sha256":"bd8c061e921bc034fc82a7e6369020ca79122aef2e20c18e104aa2d49cdf4f65","choose":{"expected_n":1024,"complete":true,"completed_n":1024,"seed_range":[64000,65023],"completed_seed_range":[64000,65023],"missing_n":0,"C-COMBO":{"n":1024,"mean_diff":-0.1954013666754842,"se":0.015565760827464317,"worst5":[[64901,-2.6828682092178724],[64697,-2.398615723978196],[64003,-1.924438572944723],[64574,-1.79790919666425],[64884,-1.7630022789995792]],"wins":359,"sum_d":-200.0909994756958,"sum_d2":292.9124773577109},"C-qt1":{"n":1024,"mean_diff":0.4142032798540473,"se":0.020907616596659472,"worst5":[[64101,-2.7192645701012452],[64795,-1.8993125611741561],[64116,-1.7156174281033145],[64447,-1.7136733863268319],[64374,-1.674832858154332]],"wins":761,"sum_d":424.14415857054445,"sum_d2":633.5966645504797}},"report":{"expected_n":2048,"complete":false,"completed_n":384,"seed_range":[10000,12047],"completed_seed_range":[10000,10383],"missing_n":1664,"C-COMBO":{"n":384,"mean_diff":-0.1714748293651267,"se":0.02284149368926531,"worst5":[[10256,-1.6669731845923934],[10335,-1.5216697954060692],[10040,-1.4758629385601125],[10381,-1.4272381895560784],[10202,-1.3544458334306455]],"wins":146,"sum_d":-65.84633447620865,"sum_d2":88.02342739631362},"C-qt1":{"n":384,"mean_diff":0.430911833134154,"se":0.03579627325384242,"worst5":[[10146,-3.489038710061209],[10162,-2.0895369231858467],[10148,-1.4537413013456444],[10256,-1.3158911975057208],[10340,-1.3136671114272076]],"wins":286,"sum_d":165.47014392351514,"sum_d2":259.7571592089043}},"training_curve":[[1,-0.15656857717003536,null],[2,-0.3308945338268092,null],[3,-0.2819503105460841,null],[4,-0.17844101034489768,-0.19670358351615236]],"training_curve_definition":"[generation, best population mean paired train gain, CMA-mean validation gain or null]; validation only every fourth completed generation","generations":4,"training_wall_seconds":2100.0253858566284,"training_compute_command_wall_seconds":1827.5485393977724,"wall_seconds":2100.0253858566284,"parameter_count":90,"frozen_weights":[-0.5597044604607767,0.6594440432739213,0.15649763010940948,-0.42809079835793756,-0.18223150262846888,-0.7733793797959393,0.3086748595212847,-0.32513810359996237,-0.36874385419989414,0.3868440042329764,-0.4227835659156412,0.2736974508205275,-0.014279954096721609,-0.06558509568409353,-0.4051960290178122,-0.5830355074662401,-0.36144806980590954,0.29641008480156966,0.29503908490887437,-0.9322921336448889,0.06756525719880366,0.25087257395125717,0.27855013043805854,-0.04302472320148869,0.3296871222769094,0.13432889746412524,0.02955218192061032,0.1422318847359932,-0.278720725192205,-0.8328498242764097,-0.7047748154796623,0.2666362189884735,-0.36073305843778714,-0.18492051235681306,0.967886588318992,-0.154782038714644,0.0824474594812883,-0.14623827927748026,-0.2883122229263489,-0.25447799365373563,0.15068232014920346,0.4799882842073699,0.27567565246260617,0.0938112472658225,-0.43572479134488645,0.6334172584344594,0.17707342144006022,-0.05807345656061641,-0.38704200346343814,0.10023546618398377,0.014519936014653175,-1.2013125357019885,-0.2598494155880801,1.3111070730385703,-0.27764632300340836,1.0060890743136617,-0.8260551815817415,0.45217582530445954,0.7291595621972615,0.14674269447398686,0.27054795283905836,-0.5784089017836247,-0.16782033204693794,0.3557330079273315,0.8661491443603382,0.5488627307995709,0.5779453659332271,-0.2656513511373519,0.281909536721873,0.16801266889632713,0.3462495086165362,0.05841113678449627,-0.3301648330605167,0.0031137893403029747,-0.17055702730458236,0.024761838003462894,0.2581099699898013,-0.21110287379878356,-0.3272597639464884,-0.05643024071820209,-0.2112624130631834,0.21927655439555255,-0.5167326051982787,-0.08247292958379016,0.033715243739394614,0.31147597553576806,0.18109818650544732,-0.2861722522894545,0.1905731378938744,-0.17480678698747484],"optimizer":{"name":"diagonal/separable CMA-ES","sigma0":0.3,"popsize":8,"mu":4,"initial_output_weights_zero":true,"random_seed_rule":"78231 + zero-based generation; Gaussian hidden initialization seed 64000","train_seeds":[64000,64511],"validation_seeds":[64512,64767],"batch_size":64,"n_steps":10000,"rotating_minibatches":8,"checkpoint_each_completed_generation":true,"partial_generation_discarded":true},"features":{"order":["pi retained from latest first callback","belief mean minus own post-trade mid, log bps","belief sd, bps","w, log bps","R, log bps","phase 0/1/2","own Y notional / (20 exp(-0.72))","max(timestamp delta - 1, 0)"],"standardization":"(feature-center)/scale; sample population moments from all callbacks on 16 choose seeds with zero output head","scale_seeds":[64000,64015],"scale_callbacks":173138,"centers":[0.5659728901455716,-1.5943929704924016,11.66266166470834,1.5943929704924016,0.592209852912322,0.8239092515796648,0.9632172127734998,0.2781191881620442],"scales":[0.3650998759916714,85.71798239423741,6.862143787121076,85.71798239423741,43.65861248567696,0.7244647857779883,1.5091489239821516,0.810566269957903],"network":"8 inputs, 8 tanh hidden units including biases, 2 tanh outputs including biases; 3 bps times outputs added to fb/fa, clipped to [0,0.1]"},"zero_head_identity_check":{"effective_replica_fees_bit_identical":true,"stored_fees_bit_identical":true,"raw_callback_returns_bit_identical":false,"seeds":[64000,64003],"callbacks":41645,"raw_counterexample":{"tested_seed_set":[64000,64001,64002,64003],"timestamp":6530,"COMBO":[0.0,0.15072871208495497],"zero_head":[0.0,0.1]},"explanation":"COMBO returns unclipped raw fees but stores clipped fees; replica2 clamps every callback return. Raw bit identity is incompatible with the requested output clipping on these callbacks. Asserted equality after replica2.clampf and on carried fees."},"hidden_observable_test":{"bit_identical":true,"randomized":["arb","p","s2","j","init sigma","init lam","init size"],"frozen_sha256":"bd8c061e921bc034fc82a7e6369020ca79122aef2e20c18e104aa2d49cdf4f65","runs":[{"seed":64000,"callbacks":9719,"fees_sha256":"fcf7cb92b1cf1a3939c920df87eb2f1c302e287073c2f7585510ce010a14e839"},{"seed":64001,"callbacks":10967,"fees_sha256":"2e4879aac3eb09bcd4712e2718252e3d111a7e1d5255c96773636ed70f46e299"},{"seed":64002,"callbacks":10619,"fees_sha256":"a11e5356794fe9155c0c718cc5762a798d2ab45accbf14e4a607f50dac4a3ca5"},{"seed":64003,"callbacks":10701,"fees_sha256":"6d521e9bf7177a7c081c4d6c18b0248e220b2ea73413c40689ea5e20dd0bdf84"}],"callbacks":42006,"method":"Two identical frozen policies; hidden fields independently randomized; packed IEEE754-double bid/ask fees must match on every identical own callback. R.run defaults; choose seeds only."},"seconds_per_1000_steps":{"median":0.09796371781267225,"mean":0.11554554535511918,"measurement":"C policy wall seconds per run divided by 10; defaults 10000 steps; a two-worker scoring pool; concurrent training may contend; cold table decoding included"},"findings":["Measured choose C-COMBO mean paired gain: -0.1954013666754842 on 1024 seeds.","Measured report C-COMBO mean paired gain: -0.1714748293651267 on 384 seeds; coverage False."],"limitations":["No contract, engine run, or submission; replica levels do not convert to board scores.","Raw zero-head callback identity fails because COMBO returns fees above 10%; effective and stored fees pass on the four tested choose seeds.","No report seed is used for scaling, training, checkpoint selection, or tuning.","Uncovered seeds are missing measurements, not passes.","Search is limited to 1 complete validation checkpoints; no convergence claim.","Item one-hour limit prevented full requested scoring coverage after 35-minute training; no full-window performance claim."],"source_bundle":{"format":"base64(zlib(UTF-8 JSON {path:text}))","sha256":"5726669c8d7ade74605f21c35def005adb3d68b6ca5068f178e24a76c106eba4","file_sha256":{"head.py":"34ef5c848986481d1d2f01e8e38b082582568de79966f4c1dd8ac73499941fae","combo.py":"c5b711a0b4fb4783e8879a484bf87a427171f4a11aa5eb5ec3513c24ead1a796","twocomp.py":"9d3c1833321493fb51bbde4e1b4dabc2f8978bd6d774ee2af8dc8aed13f0f0af","qmref.py":"1dfc9b7a3c2237ff7a93dd9f0af54b9bdab16f2ce0a7eb00784cb4432598db92","prepare.py":"a28ef3460348667e1c3735f069d59970646f8e73911779233fa18e473ec43485","train.py":"13bdcb46deff2d35ce405e4c01452552ff06c3b8b338eb52a262a908cb7e9b2a","score.py":"d004ce82b1e5bb73dfbbf227ca0de176689179e829a944f85700d7efcf8bbe4c","proof.py":"9ad025283654e1d0375cfc2c1499c91b0e01560de5083cea9c9d23368165215e","finalize.py":"63372f982464dee4d426df2dc6861b27f8199b7945eb42b4fdb4fa0c0d644a33","verify_result.py":"89d0ec884d6e5381afbcbe478886f38feeb9123cedbf9ef2800a8b00dbbf2953","replica2.py":"d150eb809336c2fd64ab4397d11c2fa249983f82fc0db979c0d52fa4359136ed","blob.bin.base64":"1426ae8bd81219e92f606628ea5f18c223084b5d7ab300793a83866537cced73","frozen.json":"bd8c061e921bc034fc82a7e6369020ca79122aef2e20c18e104aa2d49cdf4f65","frozen.sha256":"9bf884cd517f5a0ab90bc303c8f804eb19f9f8dbebae9489f8899d8d1653d6d0","restore.py":"9b386fbe781a678c8f3ee6903fd9202b36cb898df583bf0fc334e286196f97d8"},"data":"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"},"regeneration_commands":["python3 -I -c 'import json,zlib,base64,pathlib,hashlib; b=json.loads(pathlib.Path(\"result.json\").read_text())[\"source_bundle\"]; raw=zlib.decompress(base64.b64decode(b[\"data\"])); assert hashlib.sha256(raw).hexdigest()==b[\"sha256\"]; files=json.loads(raw); assert all(hashlib.sha256(t.encode()).hexdigest()==b[\"file_sha256\"][n] for n,t in files.items()); p=pathlib.Path(\"scratch\"); p.mkdir(exist_ok=True); [(p/n).write_text(t) for n,t in files.items()]; (p/\"blob.bin\").write_bytes(base64.b64decode(files[\"blob.bin.base64\"]))'","python3 -I scratch/score.py choose 32 # repeat until all 1024 are complete","python3 -I scratch/score.py report 32 # repeat until all 2048 are complete","To retrain separately: python3 -I scratch/prepare.py; create scratch/training_epoch.txt containing current time.time(); repeatedly run python3 -I scratch/train.py; each call is <=128 run tasks; stop at 2100 wall seconds; python3 -I scratch/score.py freeze.","Gate P regeneration requires the pinned repository research/solved checkout and its unmodified data files; python3 -I gates.py."],"training_seed_coverage":{"eligible_seed_range":[64000,64511],"complete_generation_unique_seeds":256,"including_partial_unique_seeds":320,"not_visited_seeds":192,"completed_population_policy_runs":2361,"including_partial_seed_range":[64000,64319]},"completed_validation_checkpoints":1,"local_checks":{"bundle_sha256":"5726669c8d7ade74605f21c35def005adb3d68b6ca5068f178e24a76c106eba4","offline_replay_seed":64000,"offline_replay_edges_bit_identical":["C","COMBO","qt1"],"network_used":false,"external_imports":false}}