{"credit":"Taylor (gtmcknight), github.com/gtmcknight/amm-sim@4812a6382311624e1506aca131ca35c378f930d9 research/solved: the solved policy, replica2.py replica, the arb/retail tell (tellpol.py, tellref.py) and run.py harness; BASE stalerival.py (artifact f5a140f5..., sha256 176f8115...) subclasses Taylor's tellpol.TellPolicy.","item":"rival-oracle","batch":"e02-rival-ceiling","idea":"ORACLE DIAGNOSTIC: what perfect knowledge of the rival (30 bps pool) spot is worth over BASE (stalerival.py: exact integer arb test, posterior odds, 0.35 rival pull). Hidden truth is read on purpose; advance is false by construction.","setup":{"fetch":"R=https://raw.githubusercontent.com/gtmcknight/amm-sim/66604b3426d8e97a73e2c39940b9b4dece51cc73/research; curl -LsSf $R/tell/fetch.sh -o fetch.sh && sh fetch.sh $R scratch/amm   # 34 files, 0 mismatched","base":"curl -sSfL https://api.imd.fun/artifacts/f5a140f51df99423d6ede60b618dbc6eabc37a3050ac439c80dc3824191e9f59 -o artifact.json; python3 -I -c \"import json,zlib,base64;d=json.load(open('artifact.json'));open('scratch/amm/stalerival.py','wb').write(zlib.decompress(base64.b64decode(d['code_zb64'])))\"  # sha256 176f8115fb67d99340f1de1e6994042dfaa41f830c22e000595df3377cc6a2b3","base_run":"PROCS=8 python3 -I run.py stalerival:Policy <seed0> <n> out.json soft=1 qm=qm_k1e-5.json a0=0.35 ainf=0.35","arm_run":"PROCS=8 python3 -I oracle_run.py rivaloracle:Policy <seed0> <n> out.json soft=1 qm=qm_k1e-5.json a0=0.35 ainf=0.35 arm=<off|b|c|d> thr=1e-9   # oracle_run.py = run.py whose wrapper passes arb,p,s2,j as keyword args (harness_zb64)","pinned_sha256":{"run.py":"54d19a9194ebedee8b947dfaaf8626b1e4ef6f7d1b5aa12e738ec01a5ce401ac","real.py":"7fc9f4bdf9a9af094ec2eb0e9a8e8eec8b343c5226bf0ebaa30c798785cb7c58","tellpol.py":"6e62a0e070d9778537e746438f03ea7a4b44cb1d9652ae13702b801997db5f6d","tellref.py":"72679df55e2ba46a903075543654b685e7b2bf86dfbef3d5122dff6368d4a4f2","replica2.py":"6e37b4e61e1ffa5ffb1e43912ddb8659105989aa986eb48fcd1d7608f8dca685","qm_k1e-5.json":"5ff53e42ca87390e5f65d1caa3f7a480e67a8212fb0670274dfd562b05ba1afd","stalerival.py":"176f8115fb67d99340f1de1e6994042dfaa41f830c22e000595df3377cc6a2b3"},"reproduce_base":{"seeds":[64900,64907],"switched_off_rows_equal_base":true,"keys":["edge","retail","arb","rv","av"]},"timing":"8 seeds, one process: 5 s (BASE) / 5.6 s (arm off); 64-seed block x 5 policies with PROCS=8: 40 s"},"rule":{"state_kept":"BASE state only (RealPolicy m, v, s2h, synced, cnt, tlast, sp, fb, fa; TellPolicy px, py; stalerival xw, yw, bw, aw, router_t, router_age) plus the arm name and thr. No new state: the oracle value is read from the harness each callback.","per_callback":["1. s = yw/xw from the WAD-truncated reserves (as BASE); fb0, fa0 = fees in force; cand = s*(1-fb0)/G2 if the pool bought X else s*G2/(1-fa0) (BASE's router identity).","2. gap = |ln(cand / s2_true)|; interior = retail and gap <= thr (1e-9); corner = retail and gap > thr. (On an interior split the replica router equalizes marginal prices, so cand == s2_true to float rounding <= 1e-14; corners sit at >= 1e-8.)","3. arm d and corner: run BASE's on_trade with a0 = ainf = 0 (pull skipped; bound clip, binding test and router_t update unchanged), then restore a0, ainf. Otherwise run BASE's on_trade unchanged.","4. use_truth = (arm b) or (arm c/d and interior). If use_truth: s2h := s2_true; synced := True; phs = 0 if BASE read the trade as arb else (1 if cnt <= 1 else 2); fees = lookup((ln s - m)*1e4, (ln s2h - m)*1e4, phs, sqrt(v)*1e4) then BASE's fee rounding (WAD clamp [0,1e17], +128 wei off the Vanilla gamma collision).","5. arm off: step 4 never fires; rows equal BASE exactly."],"diagnostic":"after each callback: e = (ln s2h - ln s2_true)*1e4, bucketed by age = t - router_t (BASE's steps since the last binding bound; 12 = 12 or more) and by kind (arb callback / interior retail / corner retail); the same before the update; gap histogram by decade."},"frozen":{"params":{"thr":1e-09,"a0":0.35,"ainf":0.35,"soft":1,"qm":"qm_k1e-5.json","arms":["off","b","c","d"]},"code_sha256":"318c3c57e1f485255398386655c384933cdbe51c91af3b5dd62c0e7303b54f93","harness_sha256":"44e5a4db438fcdaf72f56e25871d41e9c8fdaaf4e2104ca45a148ebf805d87bb","settings_written_before_scoring":["thr=1e-9 interior test on |ln(cand/s2_true)|","arm b: truth every callback, synced","arm c: truth on interior retail only","arm d: c + no pull on known corners"],"frozen_after":"choose block 64000..64063 (threshold check), before any report seed was run"},"choose":{"seeds":[64000,64063],"purpose":"only setting is thr; the gap histogram shows an empty band between interior (<=1e-14) and corner (>=1e-8) gaps, so thr=1e-9 is kept; no arm was picked (none to pick)","table":{"off":{"mean_diff":0.0,"se":0.0,"n":64},"b":{"mean_diff":0.0984,"se":0.0434,"n":64},"c":{"mean_diff":0.0397,"se":0.0218,"n":64},"d":{"mean_diff":0.0253,"se":0.044,"n":64}},"gap_hist_decades_1e-19_to_1e0_retail_callbacks":[78209,0,0,323388,129,0,0,0,0,0,3,14,171,903,7810,31190,7686,17,0,0],"counts":{"callbacks":690591,"arb_callbacks":241071,"interior_retail":401726,"corner_retail":47794}},"report":{"seeds":[10000,12047],"n":2048,"blocks_of_64":32,"base_mean_edge":536.6208,"switched_off_rows_equal_base_on_report_seeds":true,"arms":[{"name":"b_true_spot_every_callback","minus_base":{"mean_diff":0.1503,"se":0.0067,"min":-2.1484,"max":1.1924,"worst5":[[11701,-2.1484],[11303,-1.5309],[10004,-1.0756],[10166,-0.9366],[10887,-0.8571]],"n":2048,"retail_part":0.2869,"arb_part":-0.1366,"block_means_64":[0.0963,0.1925,0.1443,0.1588,0.1581,0.192,0.1487,0.1348,0.101,0.1591,0.1279,0.1773,0.138,0.1323,0.1408,0.0895,0.1453,0.1948,0.1391,0.2901,0.1187,0.187,0.1144,0.1933,0.1791,0.1294,0.1192,0.0996,0.1439,0.175,0.1524,0.1383],"drop_rule_mean_plus_3se_lt_0":false},"residual_rival_rms_bps_after_update_by_kind":{"arb_cb":[7822271,0.0],"interior":[13047169,0.0],"corner":[1561113,0.0]},"advance_test":{"mean_diff_ge_0.02":true,"mean_minus_2se_gt_0":true,"reads_hidden_input":true}},{"name":"c_true_spot_interior_retail_only","minus_base":{"mean_diff":0.0434,"se":0.0039,"min":-2.2128,"max":0.6954,"worst5":[[11701,-2.2128],[11261,-1.6509],[10278,-1.1035],[11643,-0.8019],[10449,-0.7215]],"n":2048,"retail_part":0.0958,"arb_part":-0.0524,"block_means_64":[-0.0005,0.0737,0.057,0.0766,0.0378,0.0584,0.0305,0.0505,0.0189,0.0491,0.0173,0.0375,0.0461,0.0138,0.0463,0.0493,0.0526,0.0717,0.043,0.0494,0.0415,0.0364,0.0444,0.0916,0.0421,0.0051,0.0241,0.0104,0.0421,0.0708,0.0479,0.0539],"drop_rule_mean_plus_3se_lt_0":false},"residual_rival_rms_bps_after_update_by_kind":{"arb_cb":[7812743,3.057],"interior":[13030244,0.0],"corner":[1563003,4.23]},"advance_test":{"mean_diff_ge_0.02":true,"mean_minus_2se_gt_0":true,"reads_hidden_input":true}},{"name":"d_as_c_no_pull_on_known_corners","minus_base":{"mean_diff":0.0324,"se":0.0059,"min":-2.4024,"max":1.4908,"worst5":[[11701,-2.4024],[11856,-1.0112],[10897,-0.9257],[10345,-0.9148],[10030,-0.9021]],"n":2048,"retail_part":-0.0461,"arb_part":0.0785,"block_means_64":[-0.0585,0.0684,0.0162,0.0418,0.0159,0.0571,0.027,0.0198,0.0614,0.0175,-0.0009,0.0224,0.0733,-0.0034,0.0169,0.0321,0.0627,0.0603,-0.0174,0.1265,0.0527,0.0182,-0.0069,0.1052,0.0332,0.0514,0.0051,-0.0045,0.0744,0.0484,-0.0021,0.0205],"drop_rule_mean_plus_3se_lt_0":false},"residual_rival_rms_bps_after_update_by_kind":{"arb_cb":[7786335,3.364],"interior":[13016347,0.0],"corner":[1552315,3.325]},"advance_test":{"mean_diff_ge_0.02":true,"mean_minus_2se_gt_0":true,"reads_hidden_input":true}}]},"cost":{"portable":false,"note":"Arms b/c/d read the rival's true spot (and the true arb flag for the interior test), which no contract is handed; nothing here is a contract candidate. Cost of the mechanics alone, had a detector existed:","extra_storage_words":0,"worst_ops_per_callback":{"retail":"BASE + 1 division, 1 ln, 1 abs/compare (interior test) + one extra table lookup (bilinear x2 + sd interpolation) and fee rounding when the truth is used","arb":"BASE + (arm b only) one extra table lookup","silent_step":"none"}},"diagnostics":{"base_rival_estimate_rms_bps_by_steps_since_last_binding_bound":[[0,13133481,0.928],[1,4533002,1.694],[2,2028822,2.72],[3,1077281,3.727],[4,603416,4.669],[5,353446,5.573],[6,214689,6.374],[7,135631,7.204],[8,88314,7.887],[9,59052,8.54],[10,40538,9.146],[11,28572,9.678],[12,81562,10.793]],"base_rival_estimate_rms_bps_after_update_by_kind":{"arb_cb":[7798145,3.212],"interior":[13023744,0.928],"corner":[1555917,4.233]},"base_rival_estimate_rms_bps_before_update_by_kind":{"arb_cb":[7798145,49.75],"interior":[13023744,46.75],"corner":[1555917,4.108]},"callback_counts":{"callbacks":22377806,"arb_callbacks":7798145,"interior_retail":13023744,"corner_retail":1555917,"policy_misread_first_trades":4265},"gap_hist_decades_1e-19_to_1e0_retail_callbacks":[2546808,0,0,10473110,3819,0,0,0,2,5,82,576,5400,29692,252778,1015549,251294,546,0,0],"format":"by_age rows are [age, callbacks, rms_bps]; by_kind values are [callbacks, rms_bps]; age 12 means 12 or more","threshold_check":{"interior_gap_max_decade":"1e-15..1e-14 (float rounding of the router solution)","corner_gap_min_decade_bulk":"1e-9..1e-8","ambiguous":7,"note":"retail callbacks with 1e-14 <= gap < 1e-9 over the report seeds; these few (of 14579661 retail callbacks) are 1000x above float rounding, so they are most likely corner fills clamped by a hair that the frozen thr=1e-9 labels interior; for them s2_true and cand differ by under 0.01 bps, so the label cannot move the score"}},"state_location":"run state (fetched research files, BASE, module, harness, per-block outputs) was kept under scratch/ while running and moved to test/scratch/scratch/ at the end, since only the item path may be written; the setup commands above are relative to that directory","advance":false,"pick":"none (oracle diagnostic; rows_zb64 is arm b, the full-knowledge 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","rows_d_zb64":"eNpdnVuy5aCORCdUfQPxZiwdd/7TaFu5kuPd9XNKYRtjASmRktj/+79Rnn//yn/KLvO//1KMf/9T/hO1DuT6XI7WK2J7L/dolvsr77UCeby3z9YQ53u51LOR13O59WFxv+JZB/G8XYmgK/H2LHqhJxGvWHZHrG/TrZ6F3N6Hp/sd2bFa7+1vx3pbvjzfN7dhcb13j9p999uxcrovn+x242rNjs2w+HasND9bs2OPpiynykorqKz25/ZarME6UuFlorL69uzpN6+u0ligopodq773SL3uZyuvfJ4eIedg1rCCm7o2rLOWXau9WE6lzRl+Pvu2Inhf03D+tZ9qi9Pc/k45BiPYckA3d/ecaU+nEFNttaKmrr5t66Fn38Zs9K2/aitrWByp9MOb+8y2CxOrq2PWWs9+jdF9OfX2N7VG6q1MT4cRP5N+1JyXnkqjacQ6XznensUpvpxKq3dAh5QWw60tzYf7shzSPunrUN98dWbX6vB8mFqf2/N8vn2rxytqarLdvs6cbNtqmiNnU7E41fj000sL0A9rsnn0Z47m8KtWDmd4LqwczqvCVfNNi6ZW04Lys106cL9WjuaeaHS9oxl7+OpS0wz92rn8hptOhY27OneuTuPZTn21YkTammZRLEthy7Nyd82cKyduWJ1bYzkNd/vt2Rj+yp3zrB7P6a31uTu3n7dr7ekDYjBY6Oykznal6yd15ml1ugYanRzBxnDPTvYs2r195e3H4k74O35TduwZHXUs0ghEBQdCRuAZ0oqcC2BM3y3YGMvy27XGBI6SPRsPVCFP4bbbzsFsxVdzMIOVGGkCYjFPIk1ALeBRhCADAxGRtmk1nk0LEAMNPLP47VUwxR5Q0jdN+hFCjOm2lgwCX5wG4Fl1fvjtVp1Hk+ZRlHAUDI+0AOGWMQDVTdfsVx9+OOHCBjeE/89c8NOJ/yB2VIHF2n5Y/ep++MhYcLfwvzO/QvBfGPTHZqd+/KLWZEp8Vfhq3bbx72OVomWvmGwh3K97+UWbee7raS+nZ0QvWoNuO5G/rubL0tdpDHoHwxYd7bkke/VsTOivx3O358x/FoJbXwnenn49JxiwFP3t2bSdCID/qlfAD+5H4n6cafFV2KiL9wxhWAWWQrj/zA2UMHKCreqm0Vn17fQLJQj2H5XS+nxnfucioL+AvJhVC9D6BvWLe5qo30v15RxL91Og32r1zesHAWNqRXr2Juo/H8vVJXvUfHlhjzyYi8F0VxL4n7nvy4liA1iKxP12O7qya3169q/148TF0uz36An5H2inb7vIrOPkhbD/Wcv0dQtfj0XBWPHq2bkCxr0bx8yTfDOevHxn32bx+tnZtzBG7pND4CVwtAQCSxsnkSwMuIn8D0Ty8MHDOJbxfSymtTxGEKDf/mic7Fm7Q5bY/0AF33HSjNepu2spGI6BLNDw4q2J/dVu2uO35goB+ar8/9HAvpr+/zO+G1FdO8ty2stJzx7Vawltt3Zw22gt4b9NXIwadG3TmrYAjxItY8v50poW4FksEzH79mgAcX79rpoG4LlqUS7jPJZzGRyWZ8UC2KmvsgCbBfqoK9XgjlUMJk7Io0x9Jwj3bLZyGbhtKc3mtmIDWrjxdMva8uXDfoPHZQTaYn3X9nZtesFW7QEeK2Q5B7QNi3Jmhx/Onnk3UtMMPF6dr+ZwAp4V739hrWtagW1ns6b732pY1GAWf3WXdbKkFTBwc6tsgDcyVSbAC7tiAlrz3bj/V3419niIqBv3f7FtqliB5mk4hBsjLGfXivuWdiA8qwY983dgBhrAXtMMPF1FKZiB7tEZG7cEUWgLVldZgTvLZAc27madoAb6v1aAz5oyUNPLQWYgbmPZseHRkhl4nBe3Jo/RQz0PfjS3L3RmUZtzK9xW4FhuXy+vphUYnhhrANN8Vnr/j+vKmzECf5c1ze7iWolnHbta0/1/xpK799ux0cJX8c2ss22dIebsf7ASUVu5i5VpAp6h5aPT+w+781Xe/+McubFU2eMA8K6jDZO/68jXwMY/8Cww8ypP7/95s+/WYPbjy7kC7ApX2YAHEf2uJVYHhR+hWQ90Jv//nW8pN5FA9pkeT+2FjIXH+kw/IQo9b/L/H1/KT3e50gsxt0xRfffU5sE3L7zjjiyt7eV3awcAADWRQDb5TSQQLwrtfUGBlv7/Y0PodTD9/RXBvtwNm5pyY4uZ4Na0ML1Qn42EZCxfSxLIhNEDa+kNDMxFq9qa0HbuAB5MC0R2Jn5WQ2mFCP4fRwOFPfAfLxmBBqqsefeLwDImXWtFm3TenODfFh5RMwFUfHd6Z6dZ7Piovn3g49A1wX/1YCT8Wx8tMdaLoTUBmb+xs2MCUhvof6dQov+jTbrd27+P29BAf3N/rWe/Tlgnyf48Hiv6zR1AsQ1rif7NW5km9H9QnI8eRRsfFCrwf4aDp0X+WCNDq9JfmdRP2eGmhthRsKhpCzDgGtpIvDgeOhE/o7tp8RjWkIif55O5PHMkO85qE/bf2Snsf0yAZWnMvGGbg1WGKLho93YhmadJYv/zcfQ7twDNPl4T9PflxrQFeBYQjS1tm7wOE/ufSUNPoH6m59QSY2x1C/ynb16iA9Bv+v994/+1RP5ZgeMm5N8AaJP3/3j3fJSg335Q2zmSa/vhLvqYFyft87ivvjq/1q7J93/2aHwiyG80bpsphpVpifzPVsgi+xL3TN7/tIbk/T/zk89M779f4JH3byhv9v6tfXn/xbuBJugPO81N0F/sWfWE/mbnsxdRGczgLui/lqEL+gt+bi9MM2ZKF/1fl58WXlSkXJYVv7Un8/NsP9yPNONsiHqwlcOL6nL9q32Zjus/8MJ6mJOlI9D/YGoP2DJ3TPR/bN+dfmw7vpoL06RpD9hiv6vSN3+XsP/d7CBraTLaXfTPfXWCfzfF3qF/DD8d+h+70isMuxUu/sfuSxf6P344Tyf6PwsCnSb6l81S6xBA8CxdBFBnjneB/7MRdFvjZ7jEAJXhh+nYtJw6W7ftI3+Qjxb1f8C2Dvqb6++d0fRcEPxXK7yL+qy+W37scmMizKwxgf99sVgWD7Sw34DcE/qbzUwfRJj44kGvumXN/QqX0RP8mx3PPtLt6RjiLtr/6bWfxo09ltktdT+eVJ5Z056e//FYyPG/nFefxJj8bsH/34SeBHIA9D41lkBIT9c/xhWXvF43Lrs0fTUXpnGzC/0fl4aOCv0fU8KrE/3XcduJ/nMMX+3aRhQ/zW4Jm9iXWFkgvQv/Z/O78cluXw4uHa3j+3vwL//jy6LNKm5Dl/P/LCLeljag3S9NG/CGLhDNZSAmX3BftfFv/GyazOW1BfHvsYb4N6nST/0ZDxH/w+Nx4KVQkZj/Z5fEq2QAyil+GrrA0xQDgL3owv9ZoEGGqH/TPaNgzHHXhwxAeCcxxP/s6ttTZQdKbRQ4s7AMlXFvz/E0IA2ZgA2QjjQBldEaAV/AvfL867HIRg4lDYV/zaKPMDlL0yEKFPQadv7R0Qh5ZvdpxXFatyzY8OgO2B/v3UYlaA7uDkUAnonFp6QJeNYAfVUEmNEeVV2zwqtoKRz4Afkzwq/KxVnCDx/xbbSM928VNhNmlqt4RItCjQafMBquGe7WkPv/bFh5GeHf8GfK/+/Vj2sN2K0ZsgFWcZqAOnEiR8YAnp2QRdlMKxj6B2s/0gDEmhYHEVRe3MVK+bMUADBzN9IEzOb5nhGAB1C5miage7s5CAB4Psv53/7CgZdNNGEQAlhePGkCwq7ZGPYafZktJnA1htxZT+G0ANNO5ZgALRg+cgNwPfyhDUDzWLABMK03MgRQ7BiMKV7W61D+fzgqOiB/gtjFmJukBLd2tKNCJAjgkPZY8RPBGdoB3PES++O1pxDAHWpZgHK7RuwXYbEJckubkffD2pmgQPh/04KDHQA+5BD9bxZwbDkZXknaADjYPaB+wvoU+j9bIXqmLUDxLmeI/jftNNgBbLd+iMzhIw5ZgGfJ0PXjsAnNaQ9QTBmMtAHD9n+I/SnGK0zA8vAp+FuWL+8f3nwcuRr4yjMtwOMDB6IsQKerswBmqHHKAtgOTrYAhrMpEzADjJ8Fxbl1xaUhlOevBZgKALxhIsmkAFlSigH3Kvo7h8X2E5OchH/xp2YagDYsyTnbLOSZ+N+Xr+KasRmZUD/H36D8n31FwvjWQPr/j1tIa1X7X3Bwivt3SHtWORnHV+Hxih+WN7t9N3smhibR/3Hc+IpGGouVK/h/duI01hTK8UAK/stelhNlTe5P+f8dkJ2Av+Pnkw1AAa5mbgCabcdsbOfoSldSxrEoMgPXaib624OcAv+6wMUp939AN8yOKaebyvzx7nhC/R+LWyauuC0NpU3gHEp8sPph/o/nhfDfChLxX7xQYP69251ifx5Xw7djLpuvszNnNIZIFg8OeT+BRZyCf141MeReCQn+DqrOKSSjz9D+xsgp6ucuG6gfcyoT6B/V75XGHFSbJv5xmif+/x1ZMf8Gqinmf/zdnn6ZBWIl1r64n3HvnS/TaaJuyvl3ctI0738fJvqLEvD9/ZmbHROYNxP8u6M2M8H/btameP/lYQf8u+e6Qr93uoL93khPYf+xRoT9zXmMU+yPt/QT9sd0wZTzfzt6oD7R52FX7jko5DfST9gf791mIn9bRgAhf/PGekL+LFpb4v3tDC1Bf/X+eEH8M4EXeT+d3dkS9Nd+LGtjXpEgpo6fXt9kzwXv74mz0vl/1ql6ukQAXXdnEftteAYr0b/34svtm262QpQZA7bI/TE/t4KUke2XaQ04hWmZ+2cQFgbAiXELBmgzARYWwNZ9VWe00Ls0AdcZWzIB1fHHJRvQup+eeET0XluAZt54kQNa3Zkjl5W+iAKyW7OSAnp2Nr6qLfAplhuog9hFb/tqdszZvEv0f3O/0v3vZiyWGCAnhS15/062WDIAEwO8enyzdRcEkHP6VtcO2MPZSebFaC42ALP5ccXl7ruSzov7tHZz3uIubABXtQHwLJT/72DUEgXUJ67SEv3fuu/uJMP49sG+0fL8IclWBgCqM4gWFJBt5soNwLsyJBIBsMVdMgK9huVcArZOi/CvifY1lWfmxobioPR8ksiI47VIAgrPoqlsFi+gqfw3vxn/3/OXHCATFks2oHfPqgU75QmsHYCz1pZ2AM3xmrXYnPgyZhN7tmwHPCvX+ZnwW/MfK7F2fJPvFkGAg8FfigKYz17Ef/1qpwB5rjj9c/pdS0vPl5NsdGrj2hpODx9BAGzMkhmopohWmoF6XyUOyPkQSxyQ3ZqVVqC1ezOI4c9Q9NfM1cL/dyB6KQNoQ45vZQA1RnMrAsCbtlI/414UzVIsdvkhgSgDMNzwFFAtRLZMdGOn738zhjcGAB5kO/ufpgkAeMe6E/9v5thW7qeDuTu0x8QT22J/JgOzQwkG/giRP04R2GL/HYfbdv7Zn25hf3MSxs7Mn962L9fHh+nG1y3yvzD9djI/o1dfHdo00hFRP/bHN9SP4w67ymHE3dzC/YO/uBt5XODvJvffaUCb0G/c6yRy8TK4HzuvW4k/Y7p1heX87oT+xz9y2yDs9mVhv3WizJ/mKdWhZT3SAv/h7Nqd4N+cyrCV+N+w3Tvd/0cnfIa4H/P9u8Oyg8Ab/9/JCJvgb2N3uhX8dQb2xv9nC7QF/49rSVeGnFn4hT1slhA1+/1ZGfztjiZsvP/d3DYxQ88F8f9OYdpTTB5b0D1JFqHbyvp3aHNrA+Bo5WYHYLpyK+vfEcJNAuj2YhH2Px69G98/tNWeBKb9PKk/3VqB/XHEfMP+eEhW+wmj7KXxvE+PHxJyLzkZXve5CajD63wp982TXnuAbfzeyv0367sF/nZvN8k/TuTaWwn2Ri/yPz2cov+Pp/x2yNzyIiRjWV7ZFc83T3KL/ymuDNhEAJxSsxP9h+PzW/RPc0L+Fvw78WgrBGyG8/GxtFGymD07Tkzegv8Hhv1uksYlHieAVsvpmh3m5VH+p1PQjtgfB5aOUn8cJDzw/w7un4KjPRHhpXxVcVY++igCDPodpf43S/HdHRzYf3t8R/DvcpIj+Lc2D9Hf2/IErehG4n+xpTmQ/46Lnsz8fIAcMcmf7pKpo+KvcEeU+Bnhqz+5D4fU/2pRVS8A3YH72RYX6cmImvnhZ7UoK3P5EPn13XL7HTw5uP2GwZPg36Y/QuDfvIKPiP/q/cuB+znN9yvBxgptcha7rx6lNzFWZP870Hm6FuWxWNVTvlrkTzOWnd5/Qn5H7I8TDk4nyorZOpf+4cPx/f+uC/+dinFI/gGbziApFXtwlPnpgMeB/6/FTyuQwyI+aQCGQzUH/sd+7hnaL9EzZf+YjDuC/+MpC/tjl/ng+7vm7MgAdHynkwbgWfB0RK6/KZ8zHTGk21BAhb3BUdmXSbCjzM/hmTXhWRAXhonPIPxrX/Mk+Nd5Lzd8e9oG/AHVs2DZb+Pz3yd35VD4tXw159m8bSkwjQoS+vvwPDH3b5WI/7G1Pbt9A+JH0H+7scVLsVk6QL9Lic5mQ279Kfi7nHR3KPtaRoGjGBPvyrzP5u3Lkd9/welQYOtHuyLDKETA7yDPMe9vQBHyXzSG/XFTJ2entPdW22rgrqyAnKZ2qO7XHsbL8qaXq368GyJN3ok4vinnb6mg+Fxfhiq+MomyGoxQ3W9lbxrU/RK9COp+iTW9NU8JmoOOgfzdN2treW+muOr4MjO/0hO5/u52OBXVHVE8uvMqWJ+YKEHQT0TzTcQXkYACxfl0f6M4n8bW8c28VbDPD8snG341vv8OPw5btn1dFclcbeVbf/Umq74mq3l4SPkHzt+onyKJ9FSVX8VdUeXXHbxGCL/74fWp4Hlz3RRLsqjsAqpJQjW/cYLLneTKyquV9nmfduVX4bvk+h960snG9mf1+U1mfdNBFGPgOwT9BW/jzXzIbEEPVyL/hMILSn7hP97AOlMWUZ7ittgVTEIlifuuNIxb8HtloVj3w5Svuh9CMTdNve/2DE7Hvwx/sxJ/4FjD5b7VU1LAfzumtB+o/aDcdxI3eCl0Ck3cOBrz0+KxYQNDBb/1IoQKfrvnCSn/JDmEKn7bcWNQ/9VKIeyL3QiV/Na7yhP5+3FPF0nP7gl7cq6K+j/u55YDSz8E/PvQMAn/2+tS9b4v+4A8Pklx7+YnkwcGvYT5357eJH6Gl+kWYnh6O++Tq4n8naMVQuW+/iTxPXB7r6PyJc/D5b7kSb1WWYlRvpz96iSRvCZIU5CPhvDRJAkh/1vihOzofSCTJeLbZcKJdoSqfTtVJ0G5r6uV3mnz5b5D9b4D3uZVkSoRMCwq+O1e0yHaf5BbHEHWj58m7ZNXJfZvo4cKfvs4vndQ5O/LZGMAZKr4fdahG8spZsugil+n1QcVv7VYZ8J+isci5Pazwin43ZYo9uKDSfis0y2pPJqBDwV8T/i97N2OLyvfh3EO5fsQp3iL1r9IroJfR/JCBb8x3LRgf/sLRfiU2xTRG9wDSn7v95PrM+6ryCpudAzctyRKEaCm4DeWBy4JnwrvFdEVufezCo40X6TOPfwssbjj6yIVPTBdwwishxI+g4uk+tsQxjBvzUcJ9cOvIt677+1sKz1Xyfgk/SRc8eueUelVPNUV8J2x+FBc/mk1CPmrp6/yfWwaQ5SPjUpA+XQvUlE+1j9s/502sP23rU2yihsTs+gZKtwv1iEe//JUEPA7IByx2jcdOgLkj87M0lkP1ZJKfsNrWi6/TRYVv3a5YolV8YiI7vFwke55mq8Kx9wUyB9X7t84bNx63/u4ZpodBgp+61WxCJ+5GS4yfqbn7WFnyeUH++NGtCKo9rJvGYr6ToMo1V7DUyv9/qdHvPrMTwTwTcVJrSA4rooIqaJuuNqXTMWo8vrJ1AoV+w528qFi30ImUFDsO9v0w+NzskNQ7FtIEQ8V+5a4Tzt5tyEDGcybGlBkuAA1od8GsV7GpyNrK95YbKr1HSSehmp9i11Han0hGUK1vk4OjZrQ3+d9laa/WxbhA+8XVPoW64BQL/zEWz4pytGiduLsMCrZPtZQpdiFb7LTj0mqHPZgSSnF1nYrn7yLqD7qp1uuvwPdYKJ4lZP9raBE/06te9T268Kq0Ldw7ES40pfoS1TQn2h8qNTX/FtUgr3FIk6/RdAfnXSynd2UCpD9Gfj83SpTtj9rUHW+zSogzusVWcX1nGZRA2n1JtXThkfdkV7fnDaJkFnUocANi1s1vp3ioahDSRj+Iuq8vA5I9CEPKOp0JS36INcf6jNU5VvtXLjK9yzL4jqn7x4kHSECYnyGmJ5W/SolE3vUYXqI8gQ1vu9mEzn9V3vWdVG2xNNLGrM+k+npnNcULvL1DFpU7KHPRP1DJmxQ4lupig5KfKe/wiFeJOUrekNVd/0cAxNVEd5plNnqlmduuvttWwNbkUG6IcTvhCaibuXrelGQ59M938jxhHQNCny9468ieq5CyPC0g+UCXw+NmJ7hSXLUMbxTlfe27YvaHTWL5xsNDhX3VpMDKu6tJiaavH2CQ9GKmERGUaW94VWh0t6XTEOc351CE71v60xlrysY38IiTU30RWmvSQyV9g5IyWhBpgpET6PCq7sr4RMBfP+AMfF1bKQfd/LdQYblZzgo722evirvDX8ZCf7+skohCaDfiPL6atfeACUpv980BOW9pM9GS3//moAGz0MecTT5+0SIg+reYsln9dGNJgbKGuFkB0CK4t6wBDXQECHGeK3wvnqcldtJVnw04N6Y01TdtSAGmuB+e6CU2k+ebjQ4HtuxxrluLEhKe0u1urqrW9za+hYOv7OHwJpfTjYUXdPRDnGuHP++Ghs+EM9yg5mkNRH8ZkSo733GzvL8c7xU3eva6aC691hHonkAcYp7nw0HDeHr+6Mp77L5pbo3IPCj2dufvn/8+0twCqp731J7ZDliy5d14NwVqdRm4ju6y/b1VvfSU4p7Pank7A/zJY3zfQwuSvCsXkaJ+p2somjK7AlDYjPsW4Wc7OAlKYY//GrcfYpsgvreN7SKTLYiiKoK37gzXkSPNw9U+IZdiZapPbtaSXL2m+eNoP9PDYeCCOC66Xgf80DtgGVefML+GlfuUCx0XWxPMynQhP4cwxgt0X/YI2iHkiC/m+JjLqvAd9hfUIHvdUEp8A0v3i78X+BRL4pw3bvFDS9fnUr49LNsK29bePzh2320oT6bIt/GkQzRIz5JTuEa34070kX1e9J3G4Dp2/EwmmUFIe7TnDs3EcnZpavBIqBn9edcsOiie+yPdPCfiakK38t9dDL8fS92Cfzr1VFBROUqIuzPwWhBda/LzKI30u6m5UDmVUR5vUOlwJcEq3CBb3c/RfR726MC38KxCOECX2+HKfAt1q5tAM51V5Q3rKG0Ad1+Z5cNMJ51zva0djneofpZTTLPoj6/gSnV9wbJK0GB79+LtN2VwLk+xfNP4G9lc6oPWH6re8Ny/1QGRB8cheGmlBF+36SI4PQnkNyz8W1U3OvkquiUdg1/BC6/t0RdXM+pFuma1xmHO3AgQ3QO9sFJ6MC/uUvKez1JqO69FyHIJCXVM23+qO0t96rOAuabVNXV8eip7C1WEMiPIJxobhZtsYemrNfnyQRlvW/tgGQBvxnlvtlO+qpoHpyirsx+SDaV9I6LUJtIDZ+Q7v4Kr8Xtk/D4hgT9Vj0M5HP6C482udhkanrfEBfy75RPgsfnS4Zqep99Dt0Uv1PuzfIr7tXFKQuWcx9Sr3jI1lDHVNFbvYMe8vep9w0KeptnMgW9tKV63rfOCFH9YnW5nNfs9CCwy9GB7yk9sJCI599fMlCMcHkejYvgocb9PW1LiUYLsf37KxWJEf1raqjnZQINzvK8LS0p1+/V9gg7MRTV9biNquREXJRRCTjQlM5yWP5ggrr32f4NPI1qQth3z59YHrW85jJGJUiz/S4g1dpOZ/8xQRbZgwDQg4ROKiKCal4fwfDmDCiCj6RDmTwL7knOiBlum8evwkP0m85PvF6lvM1rbBDUdbRp2OGnMeXye582nMsP1g0d5WZvVLW8/b5JIV2AQ6W81c79gOAxRquW18e6xRC9PygufCvGRBlaJNPCgylf3ztu1fI63yqGj3MYvpwa6542KuUd91U58/X/qcoHD4TO8VnWPAFdT86pxCyPscgdAEhFvEHeZbiIl2MhgyLeftyUstGPryq92guIFP47A1Z8txCq4DXRO6D0u9tSFk+9z+qY8Ob5ohTOsKrWD3k4iOVivIbA/tLwA3KHONQA7GmKYO69V3U1Xvdi9K0OOfjbM54kHjNjFPA2iktjOJTrxatQbvHsOHBhnuQid8jOC+p3fShdUL/rE4BC9buFgy9j4OB7M+ICXipMY3B8m5f24Th6upacvmvWQ/W7zRzzTLwfnLUSUwf4m9NT9W7lGKmYhQwLX+U0mmYZupWlrurdx9/tiHbwA1lFnyzASQ2XafcZIFj4emUIeNyH+AzLOipnWsz0Ojts1PB6vGbgVNzGlGYB7FDDG2Z/VcPrA+NjVg6jpzHBvhNoVMLrIx1j1v45aCpm5dSve1kHKzaLTH+UoPPbjK2U8Ha/WC7+sJs9M6JbMTAq4C124Cng9Yk9r6ElgkjHFNKl7Cyo4HXt9Zs88I3rqYDXZ/DEbNR9Mgdn94mnfAdn+IRf5lOceZlTeaB5VMTbHTZREa8rKENFvDc5ykW8DtBQxVs44jmmvPwGXTzx8zkVKW4Vr5fAqF9mWmW8zfzc1BluWByKeKmwjimGZ/kjfYKDZ/CQQfIMTdhf5o+ncvjZA1DD2471TQqnl5qdfCsQjme5LZ0u5H4J+7t5Wqp4G9noQRXv42r5+sH14PmlLGY/rlweR/OnmP3tGawa3uLREPrbO58+wAF7PxdAa8Shfsthgyn8Lx56HeFwPO32T2llTOdx+jLMGHZ3iuRpyytEJqBxlGvMNAIPlvsyfcPMUckbZh9VyfunByXxT0PMQWk8LBPAJlZlvI0TT2Oa3p++DL3vASKhx+ZpiuIx76s63meX4Kf/33AqqmvbpTLexukQkWW89Z7/HauQxYwko0lQZ4nh2dv3psI4teU91fYbv1xy+EmVj6UEfpKSQyW8hyP1YsX3JNb3WGBtamgZdseBmSXw7/yMQ6iE16XJsRTQXcd3C2O3xcUJpX71/hwRFtTvhvnsJZ+frYWrd6kmjSXwd57GgtwP383mu7kt1WIUP8wp4Uiw1fdhu/yWOb2TKbUI6nogdXon0ETtbsx7lRA4k2RRwsWP7by/iyDKApZB5bvh7cYywVN9eX9+eiEWmZyFya8C3kp1TKiA967ipSz+YgV3yt58lfN7eJMTOa0Fle8Wv4jzqzzJOnjBvMDpx3AvYz8AsZzTg6QqcShiynfNYFC+GyYcl1x+2DYX7+5pmQwoN80hIc1twyAiJvbXZQ0A/tuTSgwPZ4QEtbvLJI1rd0lVU/HuTRl18a7pYxfv9vu0aMT79OGXdJBh+O/YcYKDp80iW9hvE8/jdJ+F7++FKPTvyxpP33/ZTV5LVLUXucq37gBwfptNiWt3LYUqwegH0N+BepXuNrdMNo8TqCnd9Q/hxPLpbR5rIf+we7N8gNttj2OIebkyOb3XpnjXnIpqd9/oMWL7Jvyqdvdxud2UTBIuxaJ4y2tBuTym0ZZCu458q3KXepOgcDdsrrbPbqNbu7Dh3b6fJKOG2H9y+FS92w4u2va5zUiQiBuR2W/xUOPDszq7zdUDW9VbzqvfQH+/dytfsvuyakTg+VW8ywyhdJdjP0Olu80pCJTu2h2jdDecAUbpLtXdsSH1AbytI5v58Y9Q5a6PrIxdfcw7bQv2F47g5thOXyTzr/oq/uvxm/X7KKD+buJTmkUoKL6/+axH+tm+h2OGC3c5FDd28+/JoDGhPj+SE1TuzvuurZwFGLwN1cMvi4RKd6vDYNv5+2wCtqgep5+rcrc4zLXh9a1CpfLcR6dyQum2C3c9lD60Aa+Fwt0bet0+tRkt4PI712DL5beFUeFu354Yyuap7ueg4oGveIE/Xnj1q6CFl9tOWpjjOkKFu40D/mOTwD9x9FS5OylyDFXuuko7ts9t9mhN9cxzkmweo+2eSmoYfnp9ixa2T23zh5DPczxJFycq8l2w+90vU+mWd6bU7d6Usc3RncMrQL/dUo9vV+mW8UIJ/NvD50MbwPa9vr+oFxuvv1lLW78QZB0C/XedE9o1rql6y3toCnfvZ+nwHtO/2yn87rcObbgoINynyD6o273JgNTt3gxU6naXhzORf955xu+24GqrbLde+E7kN02wRfnYwlC0+3kxPyXGWB9cfubkKV9PUTW7z5+GyF4cz+YUn/Q7kZWVWH15fs4Bi1PWTxrdKaSkYzYo2/Uko263u2PifIYtBZW7g3OagsrdhY96oPlvY5Q90DMf24l+VbkbLss5xHSLX6UfusEMH6H/MDod5/SwlaR0l3rvUOnurXOidNcz8ED1b/y7I7efGvdw7e7xV9b9OaAmKN6tndWg4l3HP1W7WxwlP8rivyppHEKG5J+F8M2DEyp9fX4PCo7DwQ3NojQWFnVuA7aCyt1aPW+U2OMI8SGNv9zL7VvJ4Mpd7/tPt2Hy7VN0HgqWARgcTRUHrh/S4YjycbD6+NwGbs7Ibt/uCdVbdpCp2338AkZr2PPx9fE91yVcuet4yPGPthxfJ2Dp0XdKp8cI5x/kPMrusc1V7e7djB6bgO6H+6c8Pyje7XemOaPTk3665tOPE7R066JksU5nle9vlMTx0T3N183i8TZ+uMV9XRx4xG7rKJf/6lFxXvtblO8uzxfbgHv3YQ3x7v9nBI6MgGutVMEbx2pK93+El+vmTF23RZm4r05SH/isrfw2AyDO/3TPzPug4uOTWNFCEj/D7OQh0otr7Arefe9WWZI/4/yWGVDD66NWgxrexs9/hIt4HbBXGe97jOQrVsp4zVZUlfFWuIxaqOU6FtMPYg9b+fXeoOuVOt5KYX/l53tNEVcKeQsbjOpCXrJoKj/gyzk7tZDRz0+mVVXyuv6tqpLXv3vyBmZgyOicSnkpcauU8rZzG5tfh7DyE76x3XftBchFqoW9AGqst5jXnwYJ1CuPyxo0ayatwSIprlLNW4o/RT/gBfxX/YZvwQWshUM8Gx9KjifnW1b/ii8nDlVX857NdU5xJmX0/eVg/dwm7ybu29wXDnPwhzdqs61H7QeG2+a34u7dW2fvuK3z+fnYqmretqxyJflQpFlVzBvTGu4+wpbP1jGe5I5VqnljeuJS2HU8t6CB3hTx//4fMYFTOQ==","seeds_used":{"reproduce_base":[64900,64907],"choose":[64000,64063],"report":[10000,12047]},"not_done":["no Solidity, engine or gas work: arms read hidden inputs and are not portable","the interior/corner label comes from the hidden rival spot (thr test), not from a replica flag; the gap histogram (diagnostics.threshold_check) shows how clean the split is: an empty 1e-14..1e-11 band and a handful of callbacks in 1e-11..1e-9"]}