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","code_sha256":"301941829a44bb60977bb28181a5e4bfa6544f3696763f987054789b2611d686","contract_name":"inputs_b","port_zb64":"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","port_sha256":"f63227c5e0f63633efd132d6fb402ede665e5c9cb24481911471e8dc20a3362d","port_notes":"ha_port.py = RS2 port + T toxicity anchor (S[16] mean, S[17] count; cap 674, k_lin 0.712696014807650816, k_quad 1.295240753826310656) + 19-8-2 head whose weights are TH[HEAD0:HEAD0+178] (HEAD0=len(RS2 TH)+3). Same strat_init/strat_after API; plug into the replica STRATEGY block via import. Weights zero => exactly T; HA weights with new columns zero => HA.","params":{"W1":[[0.654471,1.616192,-0.786055,3.194473,-2.819834,-0.745799,-1.103005,1.399501,-5.182228,-0.047389,1.840275,-0.34166,0.284462,1.50209,0.230267,-1.204179,0.368945,0.128606,0.322065],[0.495108,1.032922,2.545247,3.022238,0.545921,-0.792076,-0.166156,-0.320584,-0.072802,-0.091527,-0.179354,-0.810304,0.246024,-1.439807,-1.006687,1.317468,0.396225,-0.701713,0.16103],[1.377378,2.844135,0.235131,0.370319,1.065977,1.72645,-1.681601,1.351727,0.715107,-0.937554,0.132692,-3.065224,-0.788429,-0.134986,0.410067,0.637203,-0.584876,-0.838255,-1.118148],[-1.161171,-0.862658,1.44659,-0.347316,-0.560987,1.149734,-1.058577,1.737799,-1.817471,-0.164429,0.328763,-2.122942,-1.647649,-0.104171,0.263169,0.741364,1.831593,0.522629,1.904871],[0.237232,0.675657,2.640259,-0.350353,-2.032207,-0.032464,1.338695,1.061689,-0.090965,0.146196,-0.008601,-2.061813,-0.184058,-0.661903,0.236134,-1.212566,2.5002,-0.715383,-0.162412],[2.414711,-1.219338,1.842294,-0.892567,1.765955,1.503509,0.285132,0.106068,-0.567663,0.363173,-0.175631,3.233143,1.05256,-0.508115,-1.263047,0.58407,0.26797,1.221104,0.152133],[-1.281479,1.354851,0.300801,-2.954143,1.498673,-0.077106,-0.380866,0.016136,0.790167,-0.059733,0.808665,0.275944,-0.94974,0.476579,0.11158,0.203081,0.124231,0.71829,-0.351035],[-2.208804,-1.676579,2.67743,3.026558,-0.39123,-2.586319,-1.282493,2.97081,1.117537,-1.54951,2.703842,-2.273928,0.249918,0.459059,-0.267546,2.343903,0.728739,0.066072,-0.739607]],"b1":[1.843756,-1.958675,-3.109189,-1.951308,-0.923113,-1.635223,-2.724683,-0.957449],"W2":[[0.322713,-1.673628,0.284764,-0.267997,-2.892365,-0.4659,-2.275743,0.886153],[0.343761,-1.317095,-1.335244,0.413609,1.860307,-1.208948,0.636254,0.391827]],"b2":[1.652891,1.962816],"hidden_units":8,"input_count":19,"layout":"flat vector = W1 (8x19 row-major), b1(8), W2 (2x8 row-major: bid row then ask row), b2(2)","weight_fixed_point_scale":1000000,"feature_fixed_point_scale":1000000,"output_unit_fee":0.0001,"feature_order":["sigmaHat","lambdaHat","sizeHat","toxEma","actEma","direction","(spot-staleHat)/staleHat","signed_trade_ratio","arb_probability","first_in_step","uncertainty","min(silent,8)/8","anchor_mean","raw_amountY","step_trade_count","prev_bid_fee","prev_ask_fee","steps_since_arb_like_print","router_implied_vanilla_spot"],"feature_multipliers":[1000,2,500,500,500,1,500,500,2,2,1000000,1,500,0.05,0.5,333.333333,333.333333,0.125,500],"feature_offsets":[-1,-1,-1,-1,-1,-1,0,0,-1,-1,-1,0,-1,-1,-0.5,-1,-1,-1,0],"feature_clip":[-4,4],"hidden_relu_clip":[0,4],"toxicity_anchor":{"count_cap":674,"linear_k_wad":712696014807650816,"quadratic_k_wad":1295240753826310656}},"new_input_formulas":{"raw_amountY (x13)":"trade.amountY/20 - 1 (raw, uncapped Y amount of the print; fixed: amountY/20/1e12 - 1e6)","step_trade_count (x14)":"(stepTradeCount - 1)*0.5 where stepTradeCount is the in-step count after this print, capped 64","prev_bid_fee (x15) / prev_ask_fee (x16)":"fee_before_this_callback/0.003 - 1 (slots[0]/slots[1] read at callback entry)","steps_since_arb_like_print (x17)":"min(timestamp - lastArbTs, 16)*0.125 - 1; a print is arb-like when it is first in its step and the posterior arbProbability >= 0.5; lastArbTs updated on the current print before the feature is computed, 0 before any","router_implied_vanilla_spot (x18)":"on each later-in-step (retail) print, v = isBuy ? spot*(1-feeUsed)/(1-0.003) : spot*(1-0.003)/(1-feeUsed) (router equalizes fee-adjusted marginal prices when the order splits); stored and held; feature = (v - spot)/spot*500, 0 before the first retail print","storage":"lastArbTs (64 bits at <<80) and routerSpot (bits >=144) are packed into slots[16] with the anchor mean (low 64) and count (uint16 at <<64); no new storage slot","clip":"all 19 inputs clipped to [-4,4] like HA","dropped_unobservable":"The true Vanilla spot and true fair price are not callback-observable, so only the router-implied estimate is used; a print is only labelled arb-like via the posterior probability, never via simulator truth. Nothing else was dropped: all requested inputs are observable from TradeInfo and own storage."},"speed":{"replica_with_HA_port_markets_per_s":4181.9,"scope":"512 x 10000-step markets, 22 processes, warmed Numba (measured before search)","search_throughput_markets_per_s":5980.7},"search":{"method":"CMA-ES (pycma), all 178 head parameters","es_seed":20260209,"population":16,"sigma0":0.1,"CMA_stds":"per-group RMS of HA weights (W1 cols incl. new = rms(HA W1), b1, W2, b2), so sigma0 = 0.1 x weight scale","start":"HA weights, 6 new input columns = 0","train_seeds":"480000000 + gen*512 .. +511 (512 fresh markets per generation, common across the population), used 480000000..480465919","monitor_seeds":[30000000,30002047],"monitor_every":10,"generations":910,"market_evaluations":7454720,"search_minutes":20.77,"selection":"best monitor gain among ES mean and generation-best proposal every 10 gens","frozen":{"gain":1.8095714223753105,"gen":890,"src":"genbest"},"weights_rounded":1e-06,"restart":{"frozen_weights":"= 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best monitor improved through gen 890 of 910 when stopped for time"},"gates":{"A":{"sequences":50,"seeds":[310000,310049],"callbacks_incl_init":100050,"swap_callbacks":100000,"max_fee_diff_bps":7.60422131512728e-05,"fees_over_threshold":0,"passed":true,"threshold_bps":0.01,"what":"HA float port (HA weights, new cols 0) vs HA.sol EVM"},"A_extra":{"T_port_vs_T_sol_max_bps":1.2216616607219066e-09,"HA_weights_in_19_input_contract_vs_port_max_bps":7.60422131512728e-05,"note":"19-input contract with HA weights reproduces HA (packing change is inert)"},"B":{"passed":true,"seeds":[6020000,6020511],"replica_HA_minus_T":1.1415,"se":0.0472,"real_reference":1.257,"abs_error":0.1155},"C":{"sequences":50,"seeds":[310000,310049],"callbacks_incl_init":100050,"swap_callbacks":100000,"max_fee_diff_bps":6.64202309125228e-05,"fees_over_threshold":0,"passed":true,"threshold_bps":0.1,"what":"frozen cand.sol EVM vs frozen port, same sequences as A"}},"replica_validation":{"holdout_seeds":[40000000,40008191],"evaluated_once":true,"cand_vs_T":{"n":8192,"mean_diff":1.768984109173352,"se":0.01134997051758743,"retail_edge_delta":5.050611674119493,"arb_edge_delta":-3.2816275649461413},"cand_vs_HA":{"n":8192,"mean_diff":0.6521520466734023,"se":0.007848748423475268,"retail_edge_delta":0.9975757379632462,"arb_edge_delta":-0.3454236912898442},"HA_vs_T":{"n":8192,"mean_diff":1.1168320624999497,"se":0.010208617421586826,"retail_edge_delta":4.053035936156247,"arb_edge_delta":-2.936203873656297},"means":{"T":524.9728358327186,"HA":526.0896678952186,"C":526.7418199418919},"replica_on_real_confirmation_seeds":{"C_vs_T":1.708612109706731,"HA_vs_T":1.1222303487145568,"C_vs_HA":0.5863817609921741},"replica_minus_real_gap_vs_T":-0.0408,"gap_ok_le_0.15":true},"real":{"seeds":[7132768,7133279],"chunk":64,"engine":"amm_sim_rs.run_batch(bytecode, VanillaStrategy, configs, 8), each of T, HA, candidate alone; configs = MatchRunner._build_configs body with seed i -> seed","paired_vs_T":{"n":512,"mean_diff":1.7493637768415764,"se":0.04023343272913579,"worst5":[[7132968,-2.1784639610528984],[7132856,-1.66984730310935],[7132768,-0.5861725795647317],[7133125,-0.48474764017282723],[7133069,-0.45247291668277967]],"retail_volume_y_delta":1847.7843736046352,"arb_volume_y_delta":2274.395186221908,"pnl_delta":1.743885495802246},"paired_vs_HA":{"n":512,"mean_diff":0.5915730521292681,"se":0.030074274386830187,"worst5":[[7132937,-1.8610051877703881],[7133190,-1.5261552069812296],[7132773,-1.4865241904892628],[7132945,-1.3529616660491115],[7132768,-1.1465449046004892]],"retail_volume_y_delta":614.5565211926764,"arb_volume_y_delta":488.4978820229535,"pnl_delta":0.6093007261490229},"HA_vs_T_same_seeds":{"n":512,"mean_diff":1.1577907247123083,"se":0.039183695147935134,"worst5":[[7132856,-5.810410633294168],[7132968,-3.6966011605434232],[7133073,-2.2039406791480474],[7133113,-0.9678532013451786],[7132813,-0.8714872961319315]],"retail_volume_y_delta":1233.2278524119588,"arb_volume_y_delta":1785.8973041989545,"pnl_delta":1.1345847696532232},"mean_edge":{"T":516.2569859793568,"HA":517.4147767040691,"C":518.0063497561983},"retail_arb_note":"LightweightSimResult exposes edges, pnl, retail_volume_y, arb_volume_y but no retail/arb edge split, so the real deltas reported are retail and arb VOLUME (Y) deltas; replica retail/arb EDGE deltas are in replica_validation"},"gas":{"candidate":{"afterSwap_peak":234849,"oog":0,"other_failures":0,"valid":true,"gas_stats":[{"role":"Baseline","callback":"afterInitialize","calls":512,"ok":512,"max_ok_gas":21796,"sum_ok_gas":11159552,"oog":0,"revert":0,"other_halt":0,"tx_error":0,"bad_return":0},{"role":"Baseline","callback":"afterSwap","calls":5435883,"ok":5435883,"max_ok_gas":22592,"sum_ok_gas":122648529288,"oog":0,"revert":0,"other_halt":0,"tx_error":0,"bad_return":0},{"role":"Baseline","callback":"getName","calls":512,"ok":512,"max_ok_gas":21526,"sum_ok_gas":11021312,"oog":0,"revert":0,"other_halt":0,"tx_error":0,"bad_return":0},{"role":"Submission","callback":"afterInitialize","calls":512,"ok":512,"max_ok_gas":243227,"sum_ok_gas":124532224,"oog":0,"revert":0,"other_halt":0,"tx_error":0,"bad_return":0},{"role":"Submission","callback":"afterSwap","calls":5470095,"ok":5470095,"max_ok_gas":234849,"sum_ok_gas":740364720478,"oog":0,"revert":0,"other_halt":0,"tx_error":0,"bad_return":0},{"role":"Submission","callback":"getName","calls":512,"ok":512,"max_ok_gas":21735,"sum_ok_gas":11128320,"oog":0,"revert":0,"other_halt":0,"tx_error":0,"bad_return":0}]},"HA_afterSwap_peak":231418,"T_afterSwap_peak":223348,"HA_oog":0,"T_oog":0},"commands":["git clone github.com/benedictbrady/amm-challenge; git checkout 6b6921f8d463314aa7c96234e1be25a25245f704 (under test/scratch/amm-challenge)","sh scratch/setup.sh (goal SETUP verbatim, succeeded; StarterStrategy validated)",". scratch/env.sh && python diff.py <sol> <weights> <seq0> <seq1> (adapted differential_test_source; 10 shards x 5 sequences, seeds 310000..310049)",". scratch/env.sh && python rep.py (speed + gate B)",". scratch/env.sh && python search.py 400|455|420 (3 segments < 8 min, checkpoint scratch ckpt.pkl)",". scratch/env.sh && python freeze.py; python gen_sol.py frozen.npy cand.sol; amm-match validate cand.sol",". scratch/env.sh && python real.py real.json 7132768 512 T.sol HA.sol cand.sol"],"notes":["All 4 input files and their downloaded JSON match the brief hashes / URL ids.","One real-engine probe ran T on seeds 7000000..7000007 to inspect the result object; the confirmation block 7132768..7133279 was used only once per arm.","Monitor selected gen 890 generation-best proposal (monitor +1.810 vs T, HA +1.152 on same seeds). Search ran 910 generations (~21 min) and the curve was still rising; longer training from params / es_mean restart is the obvious next step.","Replica underestimates HA-T slightly (1.14 vs real 1.26 on 6020000..) but predicted candidate-T on the confirmation seeds within 0.04.","Candidate gain over HA (+0.59) is far from the ~+3.1 over HA the brief says the top 10 needs; no leaderboard placement is claimed.","Which new inputs carry the gain was not ablated (no time)."]}