{"item":"adaptive-linear-grid","batch":"e01-a-table-recovery","method":"QPL1: ask side only (B = point mirror of A), F = A - w per table (0c,1s,1c,2s,2c) as a tensor piecewise-linear surface on adaptively spaced integer knots on all three axes (sd level, row, column; phase/synced tables kept, sd axis kept with 2-4 level knots linear between). Knots: per axis greedy split of the interval with the largest visit-weighted interpolation error; knot counts allocated across tables/axes by error drop per byte with 6-knot lookahead under a 10,167-byte budget (= C0 QMS1 blob). Values: censored visit-weighted least squares (Gauss-Seidel, 8 sweeps; nodes with F=0 only need prediction <= 0), quantized per record (table, level knot, row knot) as int16 base + int8 differences along the column knots, unit 2^qe bps chosen per table by weighted error, error feedback on clipped steps. Node F = clamp0(trilinear of knots); decode writes A = w + F (6 decimals), B = mirror.","frozen":{"name":"u1","fit_args":"qe=9 (auto), power=1, floor=1e-3, sweeps=8, budget=10167","blob_bytes":10160,"blob_sha256":"830868f9b3e5ad162c4f2070e6faed0782c7b4cc308ab69044b2cf7306f68099","decoded_qm_sha256":"d5645b6dd4ed8f1e33cc712d56d9eb916a50db7d5014c285b0f388cd114f96f9","visits_sha256":"4d0077f13f3cbaac3725e7c5b24008a851e5e584ecd4aa4657b7cf762ec8ec5e","deterministic":"refit from the same visits gave the identical blob sha256","tables":{"0c":"levels [0,7], 24 row knots, 11 col knots, qe -2","1s":"levels [0,3,7], 43 rows, 30 cols, qe -1","1c":"levels [0,2,3,7], 28 rows, 13 cols, qe -1","2s":"levels [0,3,7], 36 rows, 26 cols, qe 0","2c":"levels [0,3,7], 17 rows, 10 cols, qe -1"},"records":448,"max_record_bytes":31},"holdout":{"seeds":"10000..12047","command":"python3 -I run.py tellpol:TellPolicy 10000 2048 h.json soft=1 qm=q_u1.json","scored_once":true,"paired_vs_tell_qm":{"n":2048,"mean_diff":-0.010338,"se":0.004251,"sum":-21.173086,"sumsq":75.977602,"worst5":[[10843,-1.464078],[11659,-1.003288],[11821,-0.95033],[11013,-0.785937],[11206,-0.703885]]},"paired_vs_C0_compressed_TellPolicy_same_machine":{"n":2048,"mean_diff":0.101708,"se":0.006165,"sum":208.297721,"sumsq":180.528534,"worst5":[[12032,-1.256225],[10357,-0.829887],[11613,-0.829853],[10567,-0.820859],[10973,-0.818213]]},"C0_compressed_vs_tell_qm":{"n":2048,"mean_diff":-0.112046,"se":0.006275,"sum":-229.470807,"sumsq":190.75906,"worst5":[[10843,-1.8952],[11025,-1.389354],[11113,-1.126072],[10460,-1.09048],[10322,-1.074865]]},"full_qm_this_machine_vs_tell_qm (libm control)":{"n":2048,"mean_diff":-0.002024,"se":0.001199,"sum":-4.145598,"sumsq":6.030799,"worst5":[[11413,-0.465065],[11714,-0.398294],[10852,-0.337024],[11528,-0.318679],[11770,-0.30056]]},"paired_vs_full_qm_same_machine":{"n":2048,"mean_diff":-0.008314,"se":0.004216,"sum":-17.027489,"sumsq":74.64639,"worst5":[[10843,-1.426137],[11659,-0.990834],[11821,-0.95033],[11013,-0.730259],[11206,-0.703885]]},"u1":{"first_trade_misread":0.000255,"price_rms_bps":7.15,"mean_edge":536.521869},"C0_compressed":{"first_trade_misread":0.000236,"price_rms_bps":7.134,"mean_edge":536.420161},"full_qm_local":{"first_trade_misread":0.000249,"price_rms_bps":7.153,"mean_edge":536.530183},"vs_HA":"run.py line: u1 +6.964 se 0.044, C0-compressed +6.863 se 0.044, full local +6.973 se 0.044"},"tuning":{"seeds":"60000..60255 via unmodified run.py tellpol:TellPolicy soft=1","u1_vs_full":{"n":256,"mean_diff":-0.019819,"se":0.0126,"sum":-5.073641,"sumsq":10.465216,"worst5":[[60182,-0.785146],[60173,-0.600609],[60138,-0.579431],[60238,-0.484584],[60153,-0.467685]]},"C0_vs_full":{"n":256,"mean_diff":-0.122917,"se":0.016396,"sum":-31.466792,"sumsq":21.417689,"worst5":[[60020,-1.142346],[60122,-0.867211],[60210,-0.82389],[60165,-0.778983],[60182,-0.761183]]},"improvement_u1_over_C0_compressed":{"n":256,"mean_diff":0.103098,"se":0.016409,"sum":26.393151,"sumsq":20.298823,"worst5":[[60034,-0.67646],[60070,-0.518592],[60015,-0.491801],[60177,-0.423897],[60127,-0.417325]]},"u1":{"first_trade_misread":0.00027,"price_rms_bps":7.156,"mean_edge":529.675353},"C0_compressed":{"first_trade_misread":0.000245,"price_rms_bps":7.137,"mean_edge":529.572255},"full_qm":{"first_trade_misread":0.000227,"price_rms_bps":7.152,"mean_edge":529.695172}},"rows":[{"name":"t1","args":"qe=-2 (old allocator, no lookahead)","bytes":10134,"tune_vs_full":-0.0191,"se":0.0147},{"name":"t2","args":"qe=-1","bytes":10134,"tune_vs_full":-0.0222,"se":0.0149},{"name":"t3","args":"qe=-2 power=0.5","bytes":10156,"tune_vs_full":-0.0248,"se":0.0129},{"name":"t4","args":"qe=-2 floor=0.01 (allocator starved 1s columns: 7)","bytes":10154,"tune_vs_full":-0.1645,"se":0.0194},{"name":"t5","args":"qe=-2 sweeps=20","bytes":10134,"tune_vs_full":-0.0216,"se":0.0145},{"name":"t6","args":"qe=-1 power=0.5","bytes":10156,"tune_vs_full":-0.011,"se":0.0126},{"name":"t7","args":"power=0.75 sweeps=20 (columns starved: 7)","bytes":10143,"tune_vs_full":-0.2392,"se":0.0202},{"name":"u1 (frozen)","args":"lookahead allocator, qe auto","bytes":10160,"tune_vs_full":-0.0198,"se":0.0126},{"name":"u2","args":"qe auto power=0.5","bytes":10144,"tune_vs_full":-0.0315,"se":0.0131},{"name":"u3","args":"qe auto power=0.75","bytes":10158,"tune_vs_full":-0.0299,"se":0.0122},{"name":"u4","args":"qe auto floor=0.01","bytes":10162,"tune_vs_full":-0.0229,"se":0.0119},{"name":"u5","args":"qe auto power=0.5 floor=1e-4","bytes":10150,"tune_vs_full":-0.0302,"se":0.0129},{"name":"b11","args":"qe auto budget=11500 (info: extra bytes buy nothing measurable)","bytes":11475,"tune_vs_full":-0.0307,"se":0.0119},{"name":"C0 QMS1 spline","args":"baseline","bytes":10167,"tune_vs_full":-0.1229,"se":0.0164}],"table_error_vs_full_qm_bps":{"u1":{"max_abs":61.833,"visit_weighted_rms_all":0.3494,"per_table_A":{"0c":[23.893,1.3008,0.2452],"1s":[61.833,1.9217,0.3496],"1c":[21.7,1.281,0.44],"2s":[48.917,1.9297,0.4387],"2c":[34.966,1.4895,1.0444]}},"C0":{"max_abs":204.792,"visit_weighted_rms_all":0.8342,"per_table_A":{"0c":[37.215,1.2894,0.6091],"1s":[204.792,5.6035,0.8721],"1c":[47.094,2.2495,1.3936],"2s":[160.895,4.5858,0.7963],"2c":[82.95,6.4426,2.3907]}},"columns":"[max, unweighted rms over all nodes, visit-weighted rms]; B identical by mirror (u1 mirror exact; full qm differs from its mirror in 1 cell of 1c by 0.71 bps per qm-spline)","visits":"TellPolicy+full qm lookups on 60000..60255, bid mirrored to ask coords"},"fee_diff_vs_full_TellPolicy_on_shared_tuning_callbacks_bps":{"u1":{"n_fees":5540020,"max":22.6175,"rms":0.2304},"C0":{"n_fees":5547524,"max":36.1476,"rms":0.6775},"how":"tellprobe.ShadowPol: drive with the compressed tables, at every lookup also evaluate the full qm tables on the same (w, w2, phase, synced, sd)","note":"table approximation error, not a port gate: a Solidity port must still be within 0.01 bps of TellPolicy run with q_u1.json (test_qpl.py: the 8-knot contract rule equals real.py lookup over the decoded file to 4.8e-7 bps on 20000 random points)"},"bytes":{"payload_blob":10160,"C0_QMS1_blob":10167,"C0_runtime_free_before_detector":3971,"contract_layout_estimate":"ESTIMATE, not compiled: 448 records each <= 31 bytes; one record per PUSH32 word would be ~14.8 KB (worse than C0 306 words), so pack contiguously (10,160 bytes, ~318 words) as a bytes constant read once per call, or straddle-read 2 words per record. Decoder needs no division and no Cox-de Boor (C0 computes 6 divisions per node basis); its bytes were not measured."},"gas_estimate":{"label":"ESTIMATE (operation counts, not measured)","per_side":"3 linear span searches (<=4 level, <=43 row, <=30 col knots; binary search ~6+5+2 steps), 4 records read, column prefix sums of int8 differences (<=29 adds each), 4 corner nodes x 2 levels each a bilinear of 4 knot values + clamp, then bilinear and sd blend: roughly 8-12k gas","per_call_both_sides":"roughly 16-24k vs C0 report of ~34-43k per surface (two surfaces)","implied_afterSwap_peak":"C0 208,775 measured on engine; replacing its two spline surfaces by this would plausibly lower it by ~45-60k (estimate), leaving room for the detector under 245,000"},"checks":["fetch.sh: 34 files, 0 mismatched","C0 Strategy.sol sha256 c68887cff61180e12ba2b1ceb333edf76a830f91f63b5d775a8806fb9ec08aca (42,990 bytes)","QMS1 blob 10,167 bytes sha256 3ac7cb8811ecfb6a17f499aa4e9b70dd55f7fb6432b3aec3b904e11a3dece38e","C0 decoded tables via its qmref.py reproduce gauss check values 443.101741 / 571.454483 / 525.908441 exactly","TELL gate edges 10000..10002 on this machine: 446.79826 574.32668 529.637987 (README: 446.752500 574.326680 529.609154; README says libm can move them)","test_qpl.py: pack/parse round trip exact, decode == q_u1.json, B == mirror(A) exactly, contract rule vs real.py lookup max 4.8e-7 bps","TellPolicy classifier/filter and qm_k1e-5 weight unchanged (tellpol.TellPolicy run with qm=q_u1.json)"],"limits":["No Solidity port, no gas/size measurement, no engine run in this item.","Tuning gains among robust settings (u1..u5) are within noise; u1 frozen as the default setting, not the best tuning score.","Knot sets are not symmetric; mirror is applied at lookup (bid reads the same records at (NW-1-pw, last-p2))."],"code_zb64":"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","code_sha256":"c9f7caf7cb094cb86d33a6fbc8b70dca65b6ceee9f3df16a70e49035fac16581","code_name":"qpl.py","bundle_zb64":"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