{"credit":"Taylor (gtmcknight): replica, Gaussian filter, solved policy and qm tables; IdentityMD TC and COMBO are immediate policy bases.","item":"filter-only","status":"completed replica experiment; frozen compact classifier; no contract","hashes":{"score_checkpoints":{"choose_64000_256.json":"ed107e5b0217d6b63e213fe6cc2348bc7d240f7f4a8998287c658a6c28f46560","choose_64256_256.json":"7c5736d0ca356437a2407eec303769a8b23914cb9f63bbcc931d7ad532e255cf","choose_64512_256.json":"f07a9b21d6bc785b8eca296a28d6ca94e81c522cdfcf5db7986f79a4c6f6bf5a","choose_64768_256.json":"72fc54708afbd6b3bd426e6c85288b69c3c217d1ae861858313ad035723c32e8","report_10000_256.json":"62f6e4ab9301713d1b950cd7164650d689929b3fe0de88858fe47e647937666c","report_10256_256.json":"0c6f9d2985e8f6092608fa6c0d07f299d360f4a61dc7c107e84450ac59dce407","report_10512_256.json":"edc5db1e268d6b0af3e5a4fe89c2e21b20d42243fa3bedac49042d7d1ebf58d2","report_10768_256.json":"c06b918afeea8b2c3a756bced1cbb43d83a4e3f5a1da6b65e61f11e6c226cd38","report_11024_256.json":"e2e308ad1a92520fbb7a03fef2ef09354af435cebdf678ea020fad886c71c511","report_11280_256.json":"887d58625e9b39dd3ed3eab562bc2ac82e9dfa99caa3a333eea775320fdb4649","report_11536_256.json":"83a21908a3141c3c8b70e10e636107f9eabff0858920fbf697129deb50bb32f4","report_11792_256.json":"d75b4b875179b91269e02bae7c486cf8584a169ade5812efaeaa127ca8575c5b"},"pinned_commit":"4812a6382311624e1506aca131ca35c378f930d9","git_content_verified":["gates.py","replica2.py","evalpol.py","real.py","latt.py","pol_mid.json","qm_k1e-5.json","dp.py","qmdp.py","calib_out_2048.json"],"downloads":[{"file":"gates.py","url":"https://raw.githubusercontent.com/gtmcknight/amm-sim/4812a6382311624e1506aca131ca35c378f930d9/research/solved/gates.py","bytes":792,"sha256":"603280c1f595ca163ffaf2ba1ebdc7945fdb08dabae67fc53c922d39e1cce604","expected":null,"verified":null},{"file":"replica2.py","url":"https://raw.githubusercontent.com/gtmcknight/amm-sim/4812a6382311624e1506aca131ca35c378f930d9/research/solved/replica2.py","bytes":7503,"sha256":"6e37b4e61e1ffa5ffb1e43912ddb8659105989aa986eb48fcd1d7608f8dca685","expected":null,"verified":null},{"file":"evalpol.py","url":"https://raw.githubusercontent.com/gtmcknight/amm-sim/4812a6382311624e1506aca131ca35c378f930d9/research/solved/evalpol.py","bytes":4610,"sha256":"6e67667bfed7798bdd3384652a6efc6ba0b1fce653740d0f9af2bcbcb8e6a27c","expected":null,"verified":null},{"file":"real.py","url":"https://raw.githubusercontent.com/gtmcknight/amm-sim/4812a6382311624e1506aca131ca35c378f930d9/research/solved/real.py","bytes":9586,"sha256":"1305b5333731c046c3e6736cc2578eff4635c88132a39c22f5780d78aef2c81e","expected":null,"verified":null},{"file":"latt.py","url":"https://raw.githubusercontent.com/gtmcknight/amm-sim/4812a6382311624e1506aca131ca35c378f930d9/research/solved/latt.py","bytes":8893,"sha256":"925c0fe996b19fd51fb79a85d5e4dba4bc2f5453d0a156cc1e480f65a6a00cc8","expected":null,"verified":null},{"file":"pol_mid.json","url":"https://raw.githubusercontent.com/gtmcknight/amm-sim/4812a6382311624e1506aca131ca35c378f930d9/research/solved/pol_mid.json","bytes":780061,"sha256":"efbb5ee102db4d312f64565d9bf7232b6becbe02b624164f744a402bbbf2983b","expected":null,"verified":null},{"file":"qm_k1e-5.json","url":"https://raw.githubusercontent.com/gtmcknight/amm-sim/4812a6382311624e1506aca131ca35c378f930d9/research/solved/qm_k1e-5.json","bytes":7892018,"sha256":"5ff53e42ca87390e5f65d1caa3f7a480e67a8212fb0670274dfd562b05ba1afd","expected":null,"verified":null},{"file":"dp.py","url":"https://raw.githubusercontent.com/gtmcknight/amm-sim/4812a6382311624e1506aca131ca35c378f930d9/research/solved/dp.py","bytes":15595,"sha256":"ca4e7c378d23c46c7def4716611135c471a831d15234b984fe30d58d9eec5734","expected":null,"verified":null},{"file":"qmdp.py","url":"https://raw.githubusercontent.com/gtmcknight/amm-sim/4812a6382311624e1506aca131ca35c378f930d9/research/solved/qmdp.py","bytes":8609,"sha256":"63b8c35048718c37eb0a5dae9e70849fa3af257c335e4a2e3a625c833f1dc1eb","expected":null,"verified":null},{"file":"calib_out_2048.json","url":"https://raw.githubusercontent.com/gtmcknight/amm-sim/4812a6382311624e1506aca131ca35c378f930d9/research/solved/calib_out_2048.json","bytes":2948144,"sha256":"fdf24c7aa2b604d770336a5783276da8ed4d50bca9dbcc02e98b4da58f608d26","expected":null,"verified":null},{"file":"qt1.artifact","url":"https://api.imd.fun/artifacts/8a6e303b662e1a5ea9bb47f31acedd3988ef65b3ddb66d074517aa59ad41ca40","bytes":62223,"sha256":"8a6e303b662e1a5ea9bb47f31acedd3988ef65b3ddb66d074517aa59ad41ca40","expected":"8a6e303b662e1a5ea9bb47f31acedd3988ef65b3ddb66d074517aa59ad41ca40","verified":true},{"file":"TC.artifact","url":"https://api.imd.fun/artifacts/d3c849db2ac31e567b57e52b80b86e8c24c842e5b4125b9d75c78e3cb07115b9","bytes":23156,"sha256":"d3c849db2ac31e567b57e52b80b86e8c24c842e5b4125b9d75c78e3cb07115b9","expected":"d3c849db2ac31e567b57e52b80b86e8c24c842e5b4125b9d75c78e3cb07115b9","verified":true},{"file":"COMBO.artifact","url":"https://api.imd.fun/artifacts/d360bea6c8c982717b7b43a8069a87013169c89ee3e4da25622db4d880b936d4","bytes":25626,"sha256":"d360bea6c8c982717b7b43a8069a87013169c89ee3e4da25622db4d880b936d4","expected":"d360bea6c8c982717b7b43a8069a87013169c89ee3e4da25622db4d880b936d4","verified":true},{"file":"QMS1.artifact","url":"https://api.imd.fun/artifacts/17d249ab0c33fc44931d9b105cf3f9e3846a560bd518b67310c2a56defacdecc","bytes":29486,"sha256":"17d249ab0c33fc44931d9b105cf3f9e3846a560bd518b67310c2a56defacdecc","expected":"17d249ab0c33fc44931d9b105cf3f9e3846a560bd518b67310c2a56defacdecc","verified":true}],"extracted_and_authored":{"qmref.py":"1dfc9b7a3c2237ff7a93dd9f0af54b9bdab16f2ce0a7eb00784cb4432598db92","twocomp.py":"9d3c1833321493fb51bbde4e1b4dabc2f8978bd6d774ee2af8dc8aed13f0f0af","combo.py":"c5b711a0b4fb4783e8879a484bf87a427171f4a11aa5eb5ec3513c24ead1a796","replica2.py":"6e37b4e61e1ffa5ffb1e43912ddb8659105989aa986eb48fcd1d7608f8dca685","driver.py":"afdf134296d9b531ea9e64a6bda8496dc125684cb397b74210da492abde23786","verify.py":"48ce77d39e90386f344cd807997cb92f4d3deaad78097c6ba86ec66007dac90c","scratch/model.json":"06273163575c22127fa51438f994e0f9cb085e75321ebcfc471f0fd1e563912a","scratch/freeze.json":"485913b68305867cc83185e78511a8c50053cfafd34290d8c93bdbbe3896cb66","blob.bin":"3ac7cb8811ecfb6a17f499aa4e9b70dd55f7fb6432b3aec3b904e11a3dece38e","Strategy.sol":"c68887cff61180e12ba2b1ceb333edf76a830f91f63b5d775a8806fb9ec08aca","README.md":"02d2b51f17adbb5411a2a67f7a37255143ba381a73ff10abba557d7d23c0cef2"},"published_extraction_hashes_match":true},"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","gates":{"P":"all 12 match README to 6dp; unmodified gates.py run before instrumentation","qt1_actual_edges":{"10000":443.1017411146671,"10001":571.4544833819275,"10002":525.9084411612362},"qt1_check":"443.101741 / 571.454483 / 525.908441 match","F":"not applicable; no contract","Q":"1024 choose and 2048 report seeds, unmodified replica2 default 10000 steps/configs, fresh policy per seed; paired COMBO and actual QMRef on identical seeds"},"findings":["The classifier changes only the first-own-trade pi, consistently before posterior, phase and branch quoting. Other COMBO logic is retained.","On sampled COMBO choose callbacks, teacher Brier and log loss improve modestly; distillation retains part of the probability improvement. Lower classification error does not establish an economic gain.","Most gain versus qt1 is already present in COMBO. Interpret compact-minus-COMBO as the incremental classifier effect.","Report seeds were not used to fit, calibrate, distill, select or retune. Three explicitly required reference-gate seeds overlap the report set, but were never used for selection.","Report incremental gain over COMBO is +0.008646586 (SE 0.006812895): no convincing incremental classifier gain. The +0.606014932 over qt1 is chiefly inherited from COMBO."],"seed_coverage":{"training":{"seed_start":73000,"seed_end":73511,"sampled_callbacks":112007,"bytes":19777393,"sha256_concatenated_files":"8a51f99fb328798d85d9250c67b2c48876b905518a4376ed6684c8f2d16a6aec","sampling":"random.Random(seed+400000).randrange(32)==0; first-own callbacks with dt>=1 only; files concatenated in ascending seed order"},"calibration_distillation":{"seed_start":64000,"seed_end":65023,"sampled_callbacks":224607,"bytes":39660494,"sha256_concatenated_files":"9134d28dc362d4cc3970d2e46574dcade498e041187016ac3175edde4c0435ab","sampling":"random.Random(seed+400000).randrange(32)==0; first-own callbacks with dt>=1 only; files concatenated in ascending seed order"},"choose":{"range":[64000,65023],"n":1024,"policies":["teacher","compact","COMBO","qt1"]},"report":{"range":[10000,12047],"n":2048,"policies":["compact","COMBO","qt1"]},"engine_seeds":[]},"frozen_spec":{"credit":"Taylor (gtmcknight): replica, filter and qm tables","utc":"2026-11-10T07:48:49Z","selected":"compact","reason":"Compact is the candidate for portable inference; the calibrated teacher is a diagnostic comparator. Only choose-seed results available at freeze. No report-dependent selection or tuning.","model_sha256":"06273163575c22127fa51438f994e0f9cb085e75321ebcfc471f0fd1e563912a","driver_sha256":"afdf134296d9b531ea9e64a6bda8496dc125684cb397b74210da492abde23786","int16_table_sha256":"878e53952cc14dec9934284b117b5266521630936488a7dbb807493f2ba014a8","features":{"prior_mean":"mu=sum(w*m); v=sum(w*(v_i+(m_i-mu)^2)); sd=sqrt(max(v,1e-18)); prior=TwoComp.propagate(dt,lo,hi)","in_order":["clip(log(max(pi,1e-8)/max(1-pi,1e-8)),-20,20)","log(max(la,1e-12))","log(max(lr,1e-12))","(mu-log(previous_own_spot))*10000","sd*10000","(lp-mu)/sd","max(abs(log(own_spot/previous_own_spot)),1e-9)/sd","float(is_buy)","log1p(amountY)","log1p(amountX*own_spot)"],"la_lr_lp":"Unmodified COMBO likelihoods and arb price reading; side is replica is_buy, true means trader sells X. No hidden inputs."},"teacher":{"type":"40 Newton logistic GBTs, depth<=3, logit(pi) offset","training_rows":60000,"shuffle_seed":8701,"bins_per_feature":16,"learning_rate":0.15,"L2":20,"leaf_clip":0.6,"hessian_floor":0.02,"min_leaf":100,"stop_node_below":300,"calibration_grid":{"a":[0.8,0.9,1,1.1,1.2],"b":[-0.2,-0.1,0,0.1,0.2]},"calibration_chosen":[1.0,0.0]},"compact":{"type":"512-cell logit correction table; 16x8x4","axes":{"logit_pi_edges":[-6,-4,-3,-2,-1.5,-1,-0.5,0,0.5,1,1.5,2,3,4,6],"abs_normalized_innovation_edges":[0.25,0.5,0.75,1,1.5,2,3],"prior_sd_bps_edges":[10,15,25]},"index":"(count(f0>edge)*8+count(abs(f5)>edge))*4+count(f4>edge); equality stays in lower bin; saturated tails","distillation":"choose COMBO callbacks only: u[k]=sum(clip(calibrated_teacher_logit-f0,-3,3))/(count[k]+50), missing bins zero","rounding":"int(round(4096*u)); Python round ties to even; signed int16 little-endian; sigmoid exponent clipped to [-35,35]","inference":"sigmoid(f0+table[index]/4096); replaces pi, including hard 0.005/0.995 decisions","weights_location":"embedded source bundle scratch/model.json.table; scale=4096","parameter_bytes":1024,"extra_persistent_live_state":0,"carried_state":"Unchanged COMBO: m,v; up to two (weight,mean,variance) components; sp,s2h,fb,fa,tlast,cnt,synced, and diagnostic new_path_calls","temporary_features":10,"classifier_core_operations":"25 threshold comparisons, 7 abs calls in supplied implementation, 1 table load, 2 index multiplies, integer sums/adds, 1 scale division, 1 logit add, 1 exp and sigmoid division; clipping comparisons extra","feature_overhead":"At most 2 prior components: moment sums, one sqrt, six log/log1p evaluations including logit; divisions and normalizations. These are actual driver features, some redundant for compact inference.","gas_estimate":{"incremental_classifier_only":[14000,28000],"assumptions":"Sizing guess: log/exp 1800..3000 each plus arithmetic/table access and feature overhead. Excludes inherited COMBO filter and spline lookups; not measured gas or a gas gate pass.","measured":false}}},"probability":{"sampled_COMBO_choose":{"baseline":{"n":224607,"brier":0.11201839353914492,"logloss":0.35541526897580017,"error":0.16054263669431496,"ece":0.007836924263023194},"teacher":{"n":224607,"brier":0.11149157607331196,"logloss":0.3509922154858289,"error":0.15999056129150027,"ece":0.003908549913650465},"compact":{"n":224607,"brier":0.11178026713134713,"logloss":0.3537433125057325,"error":0.1601374845841848,"ece":0.0035447830360470075}},"distillation_and_quantization":{"quantization_probability_max":2.9783327802457293e-05,"quantization_probability_mean":6.958834757079198e-06,"distillation_probability_mae":0.01022175853130433,"distillation_probability_max":0.37946005070821986,"distillation_teacher_agreement":0.989986954992498,"quantization_logit_max_bound":0.0001220703125},"quantization_probability_global_bound":3.0517578125e-05,"choose_closed_loop":{"base":{"n":7201465,"brier":0.11163522162206421,"logloss":0.35376069600307547,"error":0.16027586053671025,"ece":0.0073213070293182345,"calibration_bins":[{"n":1516829,"mean_pred":0.019156862148129627,"arb_fraction":0.02424465776959697},{"n":425892,"mean_pred":0.14927668911681508,"arb_fraction":0.14529739934067792},{"n":396748,"mean_pred":0.24936906341483092,"arb_fraction":0.2476912296974402},{"n":369601,"mean_pred":0.34956572487561266,"arb_fraction":0.34759646213078427},{"n":362865,"mean_pred":0.45005254882308804,"arb_fraction":0.4438234605156188},{"n":382873,"mean_pred":0.5509956506094187,"arb_fraction":0.5412134049671823},{"n":438732,"mean_pred":0.6510433605474653,"arb_fraction":0.6402336734042651},{"n":514319,"mean_pred":0.752204421027925,"arb_fraction":0.737141734993263},{"n":728834,"mean_pred":0.8530349700180093,"arb_fraction":0.8418322965174512},{"n":2064772,"mean_pred":0.9668018587851006,"arb_fraction":0.9594119835022947}],"feature_mean":[-0.4799108765990578,2.404138727138965,3.106701702765049,-0.007581660275396289,15.309580224082557,-0.0002759534421221056,1.1818354913936138,0.5001472617030007,1.861064594809555,1.8610690269762],"feature_sd":[5.148001413533721,6.2338556168164265,1.6258296638456657,92.50555655026949,4.498151763833842,3.0681035271421346,1.8172854081121452,0.4999999783139904,0.8334009847900479,0.8334310712369247]},"teacher":{"n":7205341,"brier":0.11062060624887288,"logloss":0.34667879804223833,"error":0.15915429956750138,"ece":0.005676374911847613,"calibration_bins":[{"n":1468703,"mean_pred":0.021930965825407014,"arb_fraction":0.019368109141194644},{"n":456227,"mean_pred":0.14747031867033808,"arb_fraction":0.134928445707948},{"n":408829,"mean_pred":0.25122796607198133,"arb_fraction":0.24376939992026006},{"n":399681,"mean_pred":0.3483833394759936,"arb_fraction":0.35119758007010593},{"n":390499,"mean_pred":0.45036188100338853,"arb_fraction":0.4600677594564903},{"n":424479,"mean_pred":0.550724827224296,"arb_fraction":0.5637004421891306},{"n":453955,"mean_pred":0.650132627234848,"arb_fraction":0.6668149926754855},{"n":526857,"mean_pred":0.7526468961292083,"arb_fraction":0.7589307914671344},{"n":755804,"mean_pred":0.8533153120549337,"arb_fraction":0.8573571984271054},{"n":1920307,"mean_pred":0.9636477497406001,"arb_fraction":0.9657325625538}],"feature_mean":[-0.444685041228959,2.4656332723777523,3.1230374395522142,0.004782938379860905,15.562744407976702,-0.0008825653363937816,1.1615462914834,0.5001617550092355,1.8609856277565524,1.8609894537678278],"feature_sd":[5.058078126574874,6.1343462948680765,1.613655532209732,92.1662642377457,4.470603799794329,3.012543536693102,1.7867869862868597,0.4999999738353163,0.833479359521662,0.8335094644984686]},"compact":{"n":7205743,"brier":0.11118932716378427,"logloss":0.3505094831999841,"error":0.1596965087430956,"ece":0.0038145586067820383,"calibration_bins":[{"n":1463414,"mean_pred":0.02093410433609999,"arb_fraction":0.020740542320901674},{"n":483888,"mean_pred":0.1486073481913333,"arb_fraction":0.1391065701153986},{"n":401394,"mean_pred":0.2539143272577547,"arb_fraction":0.24893745297637732},{"n":404983,"mean_pred":0.3523208052334236,"arb_fraction":0.3544543845050286},{"n":370938,"mean_pred":0.454238553315887,"arb_fraction":0.4594622281890774},{"n":410982,"mean_pred":0.5512377289740151,"arb_fraction":0.5595038225518393},{"n":447207,"mean_pred":0.648156844997096,"arb_fraction":0.6609109428072458},{"n":524852,"mean_pred":0.7507095079298022,"arb_fraction":0.7573830336933078},{"n":772975,"mean_pred":0.8531210591564744,"arb_fraction":0.8581571202173421},{"n":1925110,"mean_pred":0.9645747228273149,"arb_fraction":0.9638940112513051}],"feature_mean":[-0.45370946734506157,2.4547566015277926,3.1226398224072964,-0.004700358850396589,15.5482809289447,-9.062958878821441e-05,1.1629373342656306,0.500125663654671,1.8610420293828562,1.8610459765838638],"feature_sd":[5.071332224784761,6.149913448089736,1.6124495757754407,92.18129730593898,4.491494343691963,3.019279129656976,1.789095085453855,0.49999998420864566,0.8334064627176241,0.8334365376895028]}},"report_closed_loop":{"base":{"n":14382445,"brier":0.11214416004727067,"logloss":0.35491219510199695,"error":0.16118622390003925,"ece":0.00737940336435889,"calibration_bins":[{"n":3021449,"mean_pred":0.019226345267177217,"arb_fraction":0.024312507012363937},{"n":856025,"mean_pred":0.14932876992432173,"arb_fraction":0.14631698840571244},{"n":799260,"mean_pred":0.24923235775333258,"arb_fraction":0.2487826239271326},{"n":743696,"mean_pred":0.3494930845060111,"arb_fraction":0.3464681805468901},{"n":726925,"mean_pred":0.45006326107707884,"arb_fraction":0.44548199607937544},{"n":769834,"mean_pred":0.5509080963628908,"arb_fraction":0.5404048145444342},{"n":877078,"mean_pred":0.6509096925837028,"arb_fraction":0.6387447866666363},{"n":1027747,"mean_pred":0.7521761918104066,"arb_fraction":0.737207698003497},{"n":1454691,"mean_pred":0.8529699018885908,"arb_fraction":0.8415161707881605},{"n":4105740,"mean_pred":0.9667628749448566,"arb_fraction":0.9591033528669619}],"feature_mean":[-0.4809675779948532,2.408777684970483,3.1115471119721945,-0.0803338492925061,15.328374150259679,-0.000842586087848338,1.1784630606714552,0.5003019305827348,1.8593353637622632,1.8593458041206599],"feature_sd":[5.1381514663885905,6.222453793631047,1.6237578636381824,92.16828666321945,4.502694238871564,3.05803900564141,1.8194502034510123,0.4999999088379149,0.8328551013577469,0.8328890078446142]},"compact":{"n":14391544,"brier":0.1117012794516771,"logloss":0.3517589117305221,"error":0.1606263372435925,"ece":0.00365931445140467,"calibration_bins":[{"n":2914066,"mean_pred":0.020967690956039755,"arb_fraction":0.02070028612941505},{"n":973492,"mean_pred":0.14869571674142235,"arb_fraction":0.14051271094164103},{"n":808622,"mean_pred":0.2538746377521683,"arb_fraction":0.24962961680488535},{"n":813164,"mean_pred":0.3522577787675557,"arb_fraction":0.35446970106891107},{"n":744980,"mean_pred":0.45433377171939604,"arb_fraction":0.46015463502375903},{"n":826392,"mean_pred":0.55114760387986,"arb_fraction":0.5587917114396074},{"n":893073,"mean_pred":0.6481351294523473,"arb_fraction":0.6598923044364794},{"n":1048556,"mean_pred":0.7507622856868695,"arb_fraction":0.756918085443219},{"n":1542566,"mean_pred":0.853075632177264,"arb_fraction":0.85790300058474},{"n":3826633,"mean_pred":0.9645682978402217,"arb_fraction":0.9636191921200701}],"feature_mean":[-0.4546388887484496,2.4590163502783953,3.1271220944622153,-0.08202851962619055,15.567106263636555,-0.0009890346843403528,1.1597113601501605,0.5003110159688217,1.859351548575393,1.8593620367715495],"feature_sd":[5.061583278581567,6.139367730868368,1.6110789164775112,91.85034467481411,4.496532609967802,3.0098943610797475,1.7898692825931166,0.49999990326905774,0.8329460659983877,0.8329802527587595]}},"distribution_shift_check":"Metrics and feature moments are measured on each policy own closed-loop callbacks; compare base/teacher/compact in choose and base/compact in report. Different callback sets are endogenous; static choose calibration is in-sample for calibration/distillation, never a report score."},"policy":{"teacher":{"choose":{"base":{"n":1024,"mean_diff":0.021061266973759818,"se":0.011413471095466525,"worst5":[[64697,-1.1488448286959283],[64338,-1.0866167208569664],[64715,-1.0544763703539957],[64327,-0.9526515357287053],[64375,-0.9491795335396773]],"sum_d":21.566737381130054,"sum_d2":136.916016977789},"qt1":{"n":1024,"mean_diff":0.6307205621396073,"se":0.019682267324564375,"worst5":[[64101,-2.3863941814921645],[64327,-1.216370005322915],[64670,-0.9733048117045087],[64374,-0.9600818385445109],[64377,-0.8955727698314604]],"sum_d":645.8578556309578,"sum_d2":813.1687244012912}},"report":null},"compact":{"choose":{"base":{"n":1024,"mean_diff":0.012284015136659221,"se":0.009773070344332729,"worst5":[[64522,-1.2945503351231764],[64220,-1.0162111563942062],[64455,-0.9703322619923256],[64589,-0.9646029026084761],[64800,-0.9547398422763536]],"sum_d":12.578831499939042,"sum_d2":100.20925212070398},"qt1":{"n":1024,"mean_diff":0.6219433103025066,"se":0.019393390160699395,"worst5":[[64101,-2.491549821699323],[64522,-1.7493684658181792],[64374,-1.2539080345221691],[64670,-1.088304549470422],[64322,-1.0317852415391826]],"sum_d":636.8699497497668,"sum_d2":790.0850642323766}},"report":{"base":{"n":2048,"mean_diff":0.008646585922404054,"se":0.006812894649529338,"worst5":[[11519,-1.7813842502965826],[11696,-1.5565117283487098],[10546,-1.0307734743827837],[11817,-0.9395962804940723],[11802,-0.9304765690577597]],"sum_d":17.708207969083503,"sum_d2":194.73891437372873},"qt1":{"n":2048,"mean_diff":0.6060149324275661,"se":0.013571022099650994,"worst5":[[11062,-2.075096732657016],[11376,-1.6897721034385995],[11808,-1.6286040620822178],[10519,-1.5431363774375768],[11746,-1.5268692416898375]],"sum_d":1241.1185816116554,"sum_d2":1524.235251922108}}},"COMBO":{"choose":{"qt1":{"n":1024,"mean_diff":0.6096592951658472,"se":0.019397126030482575,"worst5":[[64101,-2.776983093286617],[64845,-1.1638684798871282],[64374,-1.0234338236598433],[64795,-0.9831714543472572],[64555,-0.9461893680735898]],"sum_d":624.2911182498275,"sum_d2":774.7447499613373}},"report":{"qt1":{"n":2048,"mean_diff":0.5973683465051627,"se":0.013384460443758883,"worst5":[[10146,-3.5524969294269795],[11062,-2.3348491406695757],[11808,-1.822178230031227],[10519,-1.7826914653976473],[11376,-1.7502956875647442]],"sum_d":1223.4103736425732,"sum_d2":1481.8432243134876}}}},"paired_definition":"For identical seeds, d=edge(candidate)-edge(reference). se=sample_sd(d)/sqrt(n); worst5 are (seed,diff) in ascending diff order; sum_d and sum_d2 are unrounded Python sums.","checks":{"hidden_input_invariance":{"base":{"callbacks":9389,"exact_fees_and_live_state":true,"max_fee_difference":0},"teacher":{"callbacks":9389,"exact_fees_and_live_state":true,"max_fee_difference":0},"compact":{"callbacks":9389,"exact_fees_and_live_state":true,"max_fee_difference":0}},"base_instrumentation_identity":{"seeds":[64000,64001],"exact_edge_match":true},"frozen_before_report_and_unchanged":true,"offline_bundle_smoke":{"seeds":[73001,73002,73003,73004],"default_steps":10000,"policies":["compact","COMBO","qt1"],"completed_without_downloads_or_installs":true}},"runtime":{"choose_scoring_seconds":140.97420779200002,"report_scoring_seconds":187.091202584,"training_seconds":15.6,"collection_seconds":15.727539167,"calibration_data_seconds":29.7362615,"largest_checkpoint_seconds":35.585131292,"command_limit_seconds":480,"note":"Training elapsed rounded from stdout; scoring is sum of checkpoint chunks. No individual command exceeded 8 minutes; all replica work completed within one hour."},"versions":{"python":"3.9.6","implementation":"CPython","dependencies":"Python standard library only; no installs; no engine or Solidity runtime"},"recipe":{"decode":"python3 -I - <<'PYDECODE'\nimport pathlib,json,zlib,base64\np=pathlib.Path(\"artifacts/goal/batches/e02-classifier/items/filter-only/result.json\")\nd=json.loads(p.read_text());r=pathlib.Path(\"test/scratch/filter-only-rerun\");r.mkdir(parents=True,exist_ok=True)\nfiles=json.loads(zlib.decompress(base64.b64decode(d[\"source_bundle\"][\"data\"])))\nfor name,value in files.items():\n q=r/name;q.parent.mkdir(parents=True,exist_ok=True)\n q.write_bytes(base64.b64decode(value[\"base64\"]) if isinstance(value,dict) else value.encode())\nPYDECODE","run_frozen":["cd test/scratch/filter-only-rerun","python3 -I verify.py","python3 -I driver.py choose 64000 256 8","for choose_start in 64256 64512 64768; do python3 -I driver.py choose \"$choose_start\" 256 8 || exit; done","python3 -I driver.py report 10000 256 8","for report_start in 10256 10512 10768 11024 11280 11536 11792; do python3 -I driver.py report \"$report_start\" 256 8 || exit; done"],"regenerate_training":["python3 -I driver.py collect 73000 512 8","python3 -I driver.py train","python3 -I driver.py caldata 64000 1024 8","python3 -I driver.py calibrate","Check model SHA256 equals frozen_spec.model_sha256; re-run choose and verify the hash again before report. Keep source and weights frozen."],"source_checks":"Fetch pinned upstream raw URLs listed in hashes.downloads, verify SHA256; git fetch the exact pinned commit and compare git show commit:research/solved/FILE bytes. Artifacts must hash to URL suffix; extract fenced source blocks with one final newline and decode QMS1 Data base64. Published extraction hashes are verified in hashes.extracted_and_authored.","gate_regeneration":"Original gates.py requires upstream pol_mid.json and qm_k1e-5.json (hashes/URLs supplied). These large gate-only tables are not bundled. The delivered model, replica, COMBO, qt1, training and scoring driver are fully bundled and work offline.","paired_summary":"After each phase, read scratch/choose_*_256.json or scratch/report_*_256.json, concatenate their rows in seed order, and call driver.paired(rows,\"compact\",\"base\") and driver.paired(rows,\"compact\",\"qt1\"). Explicitly add the decoded directory to sys.path under python3 -I."},"source_bundle":{"format":"base64(zlib(UTF-8 JSON map filename -> text; binary entries -> {base64:string}))","uncompressed_sha256":"b3342a0f309c0af07470225b0a34511ca26b6db11b0a49922447b913529c68c1","data":"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"},"limitations":["Replica levels are not board scores. No engine results, contract, deployed-bytecode measurement, gas measurement or Gate F are claimed.","Only one GBT configuration and one distilled table were tested; calibration grid and table are chosen on allowed choose callbacks. No promise that these observables reach oracle performance.","GBT fits a deterministic 60000-row subsample of 112007 sampled training callbacks. Calibration/distillation use sampled callbacks; evaluation uses every callback.","The 1024-byte table is model storage only; QMS1 adds 10167 bytes, and unmodified Python references decode larger cached grids. Gas estimate is speculative.","Exact regeneration is specified for CPython 3.9.6; floating-point last bits or training sum order may differ on other versions.","Teacher is not report-scored because only the frozen compact candidate was nominated. No teacher report result is invented."]}