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arm alone vs 30bps VanillaStrategy, amm_sim_rs.run_batch(bytecode, VanillaStrategy, configs, 8); configs via amm_competition build_config with MatchRunner._build_configs draw order (kit sim.market_config); chunks 8104000+64, +192, +256 (4 arms) and 8104512+512 (Candidate, IB); retail/arb split by kit harness.decompose reconstruction"},"gas":{"candidate":{"afterSwap_peak":235544,"afterInitialize_peak":243237,"OOG":0,"other_failures":0},"per_arm":{"Candidate":{"afterSwap_peak":235544,"afterInitialize_peak":243237,"OOG":0,"other_failures":0},"IB":{"afterSwap_peak":234957,"afterInitialize_peak":243227,"OOG":0,"other_failures":0},"CG":{"afterSwap_peak":235490,"afterInitialize_peak":243237,"OOG":0,"other_failures":0},"HA":{"afterSwap_peak":231445,"afterInitialize_peak":243147,"OOG":0,"other_failures":0}}},"valid":true,"code_zb64":"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","code_sha256":"a487873b90a4b62c0c4f832696158183d392817d1dbfc6622873c9b89b9fd0ed","params":{"architecture":"19-8-2, ReLU clipped [0,4], inputs clipped [-4,4], output in bps added to T-path fees then clamped [0,MAX_FEE]; weights rounded to 1e-6, Solidity signed 1e6 fixed 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anchor and the 6 new-input affine maps are unchanged from CG; only the 178 head weights were searched"}},"weights":[0.183935,0.412231,-0.675434,2.524152,-2.410851,-0.11363,-1.192106,2.278531,-4.232006,-1.666981,-0.917533,-0.938819,0.869562,-1.525542,0.381921,0.068714,-0.794161,-0.18737,1.113129,0.553543,0.91317,1.526981,3.150164,0.401907,0.67139,-0.439201,-0.004957,-0.308064,0.345462,-0.181573,-1.752219,0.137907,-0.490884,-0.02971,1.782079,0.133409,0.008523,0.679152,1.84512,3.479532,-1.072058,1.312458,1.823685,2.619167,-1.938618,1.812089,1.639317,-1.537019,0.125432,-1.118593,-0.725905,0.397141,0.072251,1.446299,-2.678287,0.02865,-1.457535,-0.005262,-1.4715,0.010728,-0.388824,-1.275841,-0.549772,0.130496,0.599701,0.316246,0.35453,0.259633,-1.7073,-0.788408,0.132859,0.13972,-0.472948,2.055882,-0.631008,-0.726679,0.950326,0.56548,4.684042,-0.631247,-2.350769,-1.134616,-0.457635,-0.470568,0.053479,0.068248,0.034879,-2.102229,0.112781,-0.775957,0.202897,-0.286025,2.127138,-0.053741,0.418124,1.350171,-1.785539,1.070412,-2.184929,3.505263,1.266,2.198817,0.961515,-1.197583,-0.445262,-0.097075,3.646629,0.574385,-0.684959,-0.285571,0.427658,-1.711,-0.45424,-0.887615,-2.153413,1.079275,-0.697352,-0.593173,2.579394,1.028645,-1.261567,1.943022,1.174866,-1.067825,1.446443,1.45935,1.374263,-0.134234,-1.311236,-1.371237,0.189431,-1.4787,2.193155,-2.599061,-3.254716,3.631738,3.886597,-0.932778,-0.761886,-1.79068,1.996514,0.772975,-0.149712,2.666145,-2.031979,1.352331,-0.24229,0.614953,1.567412,-0.393395,-1.672913,1.581756,0.948326,-1.56728,-4.716044,-0.611005,-1.518295,-0.867396,-1.379791,0.319834,0.405093,-2.114569,0.291631,0.014186,-3.603033,-0.329155,-1.019662,1.076098,0.69501,-1.544228,-1.249969,3.575971,0.204005,-1.820869,0.629578,0.085065,1.375425,2.031208],"notes":["All four artifact JSONs hash to their URL ids; every decoded file (kit sim/harness/port/diff_test/tests, T, HA, CG code, CG port, IB code) matched its given sha256. Kit unit tests were not rerun here (time); the kit port/sim were used as delivered.","Replica: kit sim.py with its strategy import switched to the CG port (W = [1]+mult+off+W1+b1+W2+b2, 217 entries, from CG params); T baseline from kit port TH_T. Emitting CG params through this item's emitter reproduces CG.sol byte-identically.","Speed 754 markets/s (31-process pool), so per brief: CMA-ES with 512 markets/generation. Each generation took ~22 s, giving 111 generations in 43.3 search minutes.","Result: training gave essentially nothing. Monitor gain over CG never cleared about 2 se (best +0.0020, se 0.0056, ES mean at gen 100, which was frozen). On the holdout it is +0.0070 se 0.0025 over CG, and on the real engine +0.0042 se 0.0098 vs CG (512 seeds). So the candidate is about the same as CG, and CG looks close to a local optimum for this head at the replica's noise level.","Real: candidate and IB on 1024 seeds 8104000..8105023; CG and HA on the first 512 (8104000..8104511), as briefed. Candidate +0.183 se 0.021 over IB, +0.856 se 0.030 over HA. CG vs HA on the same 512 seeds: +0.851.","Gas: candidate 0 OOG, 0 other failures, afterSwap peak 235,544 (<= 245,000). The afterInitialize peak is 243,237 for every 19-input arm (HA 243,147).","Retail/arb deltas on the real engine come from the kit harness.decompose reconstruction (arb from pre-arb constant-k states; retail = edge - arb); max arb-volume reconstruction error is recorded in real.","Holdout seeds 40000000..40008191 were evaluated once after the freeze. over_IB = over_T - 1.7690 as the brief specifies."]}