{"family":"compact analytic displacement/uncertainty; clean-room restart","status":"completed replica structural screening; not qualification","hypothesis":"A moment-matched ambiguous-arb posterior can recover trade-implied fair price; smooth displaced-side fee relief makes corrective arbitrage cheap while the opposite side remains about 35 bps from the estimated price.","source_urls_sha256":[{"url":"https://raw.githubusercontent.com/gtmcknight/amm-sim/main/research/filter-brief.md","sha256":"0ef2200226b68aa58de6aad268442ba09ada710c54b3fb0ec35c77b491f78a4b"},{"url":"https://raw.githubusercontent.com/gtmcknight/amm-sim/main/research/replica.py","sha256":"d079712fa1f4850ad8337e656e7ee8dbdde806cd4762d597fae50f547ef9c7c4"},{"url":"https://raw.githubusercontent.com/gtmcknight/amm-sim/main/research/oracle_table.json","sha256":"3e9d1fc8874c6d796d4c26948d486b587f2a6b5610b20fba99923a60954cf4b5"},{"url":"https://raw.githubusercontent.com/gtmcknight/amm-sim/main/research/setup.sh","sha256":"dc7fa6a1e59f5b043d3954cf68d538318b7affaf5c0db3ca69a1822680f300d4"},{"url":"https://raw.githubusercontent.com/benedictbrady/amm-challenge/6b6921f8d463314aa7c96234e1be25a25245f704/amm_sim_rs/src/lib.rs","sha256":"9fa375f51a8e429933795654112b0d6c59764f64f5783784dfa8969f9e1e074c"},{"url":"https://raw.githubusercontent.com/benedictbrady/amm-challenge/6b6921f8d463314aa7c96234e1be25a25245f704/amm_competition/competition/config.py","sha256":"e4b13d9f271fe27e184157904e6e9f5079db7c49ec5c0e1a31b52daeea2c1f9c"},{"url":"https://raw.githubusercontent.com/benedictbrady/amm-challenge/6b6921f8d463314aa7c96234e1be25a25245f704/amm_competition/competition/match.py","sha256":"78915a1aa36341e61c29f333fb18b31a319f70ce3e8bc2f737ba8b003aea5c17"}],"model":{"parameters":{"base_bps":35,"corrective_rebate_bps":7,"transition_knee_bps":36,"transition_width_bps":11,"opposite_offset_slope":0.28,"diffusion_multiplier":1,"retail_impact_scale_bps":20,"retail_first_intensity":0.7,"router_precision_multiplier":0.35,"uncertainty_width_multiplier":0,"student_t3_arb_likelihood":0},"input_boundary":"initialize(x,y); after_swap(sell_x,amount_x,amount_y,timestamp,post_x,post_y). No fair price, rival reserves, arb labels, or sampled run parameters. Amounts are accepted; the filter uses the equivalent post-reserve displacement.","state":"m=log-price in bps, v=variance in bps^2, previous log spot, last timestamp, previous bid/ask fees, q=innovation variance; arb probability and callback count are diagnostic fields.","filter":"First timestamp trade mixes an arb-implied pin with a truncated-normal retail hypothesis. Repeated timestamps force arb probability zero. Silent gaps use an O(1) moment approximation. Retail likelihood and weak router observation are heuristic.","quote":"Logistic displacement offsets with -7bps corrective-side limit; exact formula in model.py. Fees clipped to [0,.1].","all_constants":"All numeric constants, floors, initialization and updates are literal in the embedded source. No binary weights or downloads."},"protocol":{"engine":"supplied pure-Python replica, unmodified SHA-256 recorded; each arm alone against its fixed-30-bps rival","steps_per_seed":10000,"training_seeds":[20000,20127],"evaluation_seeds":[10000,10255],"ablation_seeds":[10000,10063],"forbidden_confirmation_seeds_used":false,"configuration":"K=1e6; initial x=100,y=10000,p=100; cfg omitted. random.Random(10000000+seed) draws size_mean U[19,21], lambda U[.6,1], sigma U[.000882,.001008], in that order. Price RNG=random.Random(seed); retail RNG=random.Random(seed+1); GBM drift=-sigma^2/2, dt=1; retail lognormal sigma=1.2, buy probability=.5. Full exact simulator code included.","fit":"12 predeclared structural candidates on all 128 training seeds, then 8 diffusion/tail combinations around the best first-stage candidate, also on all 128. Select highest mean edge; freeze before any held-out evaluation. No evaluation-dependent tuning.","oracle":"Exact supplied 17 ask +17 bid knots, linear interpolation/clamped displacement at [-8,8]*30bps, plus .1463 true arb-label bump; true p used only in this offline arm. All arms initialize at 35bps. Fixed arm stays at 35bps.","estimated_table":"Extra diagnostic: same table driven by posterior mean, with the observable soft arb probability replacing the true-label bump. Its returned fees are stored back in the estimator. No retuning.","pairing":"Same seed and random streams/configuration across arms; score is trade edge in Y, not submission minus rival edge. Oracle deficit is oracle edge minus analytic edge. SE=sample SD of per-seed differences/sqrt(n); 95% CI=mean +/-1.96*SE.","rms":"sqrt(total squared log-price errors in bps / total own afterSwap callbacks); computed after updating the model. Event-weighted and endogenous to the arm, not time-weighted or a pre-trade prediction error.","real_engine":"Setup/StarterStrategy compilation validation passed. No real-engine score run: Rust API requires EVM bytecode; this item delivers Python. No CH calibration, Solidity gas measurement, RS2 comparison, leaderboard entry, or 4000-seed qualification 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edges 0.001 Y; volumes rounded to whole Y (max error .5 Y). Per-seed total edge is retail+arb. Aggregate metrics and RMS use unrounded values"},"estimated_table_diagnostic":{"metrics":{"n":256,"mean":526.7555024399737,"se":5.831062481054699,"ci95":[515.3266199771065,538.184384902841],"retail_edge":562.9816597884741,"arb_edge":-36.226157348500394,"retail_volume_y":74713.2223949701,"arb_volume_y":24950.685262385345,"rms_bps_after_own_callbacks":16.87768864629013,"rms_observations":2621726},"oracle_deficit":{"n":256,"mean":8.460985306018356,"se":0.08756348553931848,"ci95":[8.289360874361293,8.63260973767542]},"gain_over_analytic":{"n":256,"mean":2.3975462056819303,"se":0.15957234460154635,"ci95":[2.0847844102628996,2.710308001100961]},"per_seed_edge":[437.487,565.635,521.612,564.51,518.1,521.849,478.827,519.076,652.972,645.637,469.298,363.822,450.304,395.571,694.816,619.855,556.828,488.178,473.739,448.585,489.997,404.181,432.396,626.943,471.091,439.614,340.517,542.372,521.539,436.081,586.076,332.695,629.629,528.211,558.486,625.776,539.465,438.095,611.658,581.962,602.672,624.04,704.796,630.312,412.215,378.639,424.209,590.353,488.811,378.28,368.624,417.318,422.924,528.976,438.387,704.064,576.882,502.91,431.257,500.271,920.455,554.274,598.866,420.949,652.861,577.357,477.616,629.417,496.057,613.99,405.228,428.154,596.614,559.308,523.764,553.754,521.934,421.487,613.617,643.526,479.007,560.444,558.863,458.775,468.948,446.757,565.793,561.544,432.926,408.026,579.523,477.964,551.26,652.792,472.814,587.795,484.458,504.297,496.088,462.063,435.088,529.647,729.19,733.7,696.018,465.388,422.538,483.718,526.077,730.281,567.575,397.254,457.451,428.442,528.454,653.023,517.093,404.787,415.56,408.243,542.025,527.904,580.089,498.094,521.76,447.994,449.938,488.282,726.233,528.571,453.12,489.49,496.521,623.481,528.029,452.929,610.123,614.814,492.097,459.648,546.858,574.948,619.811,464.721,432.724,474.52,754.182,462.046,684.538,512.989,576.504,467.903,409.533,548.967,483.268,469.607,526.224,528.845,430.321,430.378,415.746,451.348,714.698,659.289,677.062,432.68,412.095,486.292,616.347,563.053,381.188,562.728,678.155,390.625,533.583,497.768,439.094,648.469,655.319,373.603,576.805,454.54,567.595,624.422,513.83,534.836,567.312,543.175,524.944,710.956,653.652,655.759,657.654,574.18,502.573,546.981,577.316,433.829,550.814,477.141,555.419,474.473,559.438,610.518,450.438,411.382,514.756,630.19,620.337,544.452,420.007,432.818,620.319,573.638,567.889,450.847,446.608,660.101,625.873,516.24,511.351,529.616,612.355,653.869,428.967,476.0,700.179,437.526,532.986,423.919,623.046,687.377,495.713,441.772,530.59,657.606,435.695,637.036,446.259,593.212,592.892,397.084,417.124,612.672,432.525,591.641,555.19,494.506,491.302,529.404,396.943,379.052,391.124,573.218,493.29,594.818],"seeds":"10000..10255 in order; full splits reproducible by diagnostic command"},"ablation":{"change":"Force first own trade at each timestamp to be arb (probability 1); subsequent same-timestamp trades remain retail. No retuning.","metrics":{"n":64,"mean":377.614377715482,"se":10.444833544214195,"ci95":[357.1425039688222,398.08625146214183],"retail_edge":549.0598450501486,"arb_edge":-171.44546733466674,"retail_volume_y":71108.7110190657,"arb_volume_y":33834.865321329686,"rms_bps_after_own_callbacks":49.22627485386075,"rms_observations":564247},"ablation_minus_analytic":{"n":64,"mean":-138.23574758532882,"se":3.859055357087771,"ci95":[-145.79949608522085,-130.6719990854368]},"oracle_deficit":{"n":64,"mean":148.77544112750414,"se":3.94614037631918,"ci95":[141.04100598991855,156.50987626508973]},"columns":["seed","retail_edge","arb_edge","retail_volume_y","arb_volume_y"],"rows":[[10000,443.614,-136.437,57056,29922],[10001,586.969,-160.981,84817,37516],[10002,522.066,-146.753,63546,32428],[10003,586.857,-193.767,79810,38227],[10004,520.406,-154.698,68576,33778],[10005,540.546,-144.668,69222,32701],[10006,494.722,-138.788,71111,33904],[10007,533.518,-140.894,74323,33994],[10008,665.377,-200.072,90752,39737],[10009,641.661,-162.316,84060,37346],[10010,480.673,-133.908,71747,33190],[10011,382.159,-115.169,57215,29099],[10012,472.023,-133.512,73262,34476],[10013,416.852,-132.547,56774,29171],[10014,697.159,-165.123,77269,34137],[10015,643.636,-176.342,89688,39395],[10016,599.828,-247.572,65375,31831],[10017,501.336,-144.719,67754,33476],[10018,492.779,-125.235,66165,32634],[10019,460.626,-171.582,59390,31047],[10020,499.103,-166.015,64866,32899],[10021,384.374,-102.427,47929,24417],[10022,447.687,-132.458,57439,28958],[10023,641.171,-157.963,81434,36479],[10024,487.577,-154.08,70518,35389],[10025,451.986,-152.284,59288,28825],[10026,356.872,-110.715,52536,27982],[10027,550.61,-150.496,68811,32608],[10028,548.916,-211.825,66167,33785],[10029,442.023,-101.951,69260,31080],[10030,607.819,-161.472,86222,38812],[10031,344.964,-88.045,55953,28988],[10032,638.481,-195.424,80795,36569],[10033,538.092,-129.523,77539,35034],[10034,563.892,-170.807,78263,34835],[10035,649.835,-155.718,89229,38814],[10036,554.004,-160.183,77554,35604],[10037,444.914,-123.676,62754,31992],[10038,632.218,-176.952,89293,39471],[10039,601.573,-160.026,74707,35929],[10040,627.404,-165.101,86655,39321],[10041,638.216,-202.112,81776,37625],[10042,724.792,-243.957,89949,40697],[10043,631.55,-177.966,82330,36358],[10044,409.211,-121.047,54926,28481],[10045,394.72,-101.002,59451,30365],[10046,447.907,-126.547,68588,34521],[10047,608.573,-155.056,87999,38526],[10048,509.663,-153.529,68960,32614],[10049,390.963,-116.026,55096,27734],[10050,382.937,-129.065,56428,29928],[10051,429.067,-149.051,58079,30172],[10052,437.837,-118.493,66232,32803],[10053,538.475,-124.656,67367,31566],[10054,460.778,-167.924,62477,32551],[10055,705.163,-219.527,89688,40406],[10056,594.614,-165.928,80403,36869],[10057,513.366,-165.551,64057,32049],[10058,438.684,-138.509,54006,28438],[10059,521.297,-164.098,69853,34872],[10060,2049.834,-1346.51,83073,39525],[10061,569.097,-165.653,78657,36371],[10062,610.945,-136.427,85427,36875],[10063,435.821,-131.651,61011,30282]]},"training":{"columns":["stage","candidate","mean_edge","rms_bps","weights"],"rows":[["fit",0,503.738496,19.456351,[35.0,7.0,36.0,11.0,0.28,1.0,20.0,0.7,0.35,0.5]],["fit",1,503.352721,19.729946,[35,7,36,11,0.28,1,20,0.7,0,0.5]],["fit",2,510.947159,17.07456,[35,7,36,11,0.28,1,20,0.7,0.35,0]],["fit",3,501.689656,22.436238,[35,7,36,11,0.28,1,20,1.4,0.35,0.5]],["fit",4,502.570097,20.883231,[35,7,36,11,0.28,1,20,0.35,0.35,0.5]],["fit",5,504.678961,19.110799,[35,7,36,11,0.28,1,40,0.7,0.35,0.5]],["fit",6,499.735065,22.148449,[35,7,36,11,0.28,1,10,0.7,0.35,0.5]],["fit",7,476.802353,23.396287,[35,7,36,11,0.28,1,20,0.7,0.35,1.5]],["fit",8,503.823754,17.314275,[35,7,24,8,0.28,1,20,0.7,0.35,0.5]],["fit",9,501.68218,21.734413,[35,7,50,14,0.28,1,20,0.7,0.35,0.5]],["fit",10,507.625081,18.288704,[30,7,36,11,0.28,1,20,0.7,0.35,0.5]],["fit",11,499.364354,20.549974,[40,7,36,11,0.28,1,20,0.7,0.35,0.5]],["refine",0,506.703122,20.988854,[35,7,36,11,0.28,0.5,20,0.7,0.35,0,0]],["refine",1,510.947159,17.07456,[35,7,36,11,0.28,1.0,20,0.7,0.35,0,0]],["refine",2,510.773346,18.207949,[35,7,36,11,0.28,2.0,20,0.7,0.35,0,0]],["refine",3,508.489361,21.057341,[35,7,36,11,0.28,4.0,20,0.7,0.35,0,0]],["refine",4,493.901718,22.97397,[35,7,36,11,0.28,0.5,20,0.7,0.35,0,1]],["refine",5,486.776641,24.949173,[35,7,36,11,0.28,1.0,20,0.7,0.35,0,1]],["refine",6,475.165324,27.790599,[35,7,36,11,0.28,2.0,20,0.7,0.35,0,1]],["refine",7,458.366474,31.549524,[35,7,36,11,0.28,4.0,20,0.7,0.35,0,1]]]},"leakage_tests":{"commands":["python experiment.py leakage","python verify.py"],"results":[{"passed":true,"callbacks":256,"hidden_poison_invariance":true,"repeated_timestamp_arb_probability_zero":true},{"passed":true,"observable_signature":true,"only_math_imported":true,"arb_pin_identity_both_sides":true,"hidden_input_invariance_callbacks":1024,"repeated_timestamp_checks":255,"ambiguous_first_trade_observed":true,"fee_and_state_finite_checks":1024}],"separation":"Only Arm instrumentation receives truth; it calls model.after_swap before recording errors. The model imports math only. Hidden-value poisoning changes instrumentation truth but leaves fees and every model field identical.","artifact_replay":{"commands":["python -S verify.py","python experiment.py eval --start 10000 --count 1 --workers 1"],"result":"PASS: extracted embedded files, verified all six SHA-256 hashes, repeated both leakage tests, and matched all raw fields for analytic/oracle/fixed seed 10000 exactly. verify.py also passes with site packages disabled."}},"runnable":{"requirements":"Python 3.12 standard library only. No pip packages, compiler, network, wheel, or submodule needed for the delivered experiment. Setup-only tools are not runtime dependencies.","extract":"Load this JSON with Python json.load; write every entry of runnable.files verbatim into one empty directory, using its key as filename; run commands there.","commands":["python experiment.py fit --workers 8","python experiment.py refine --workers 8","python experiment.py leakage","python verify.py","python experiment.py eval --start 10000 --count 64 --workers 8","python experiment.py eval --start 10064 --count 64 --workers 8","python experiment.py eval --start 10128 --count 64 --workers 8","python experiment.py eval --start 10192 --count 64 --workers 8","python experiment.py ablation --start 10000 --count 64 --workers 8","python experiment.py diagnostic --start 10000 --count 256 --workers 8"],"resume":"Training checkpoints each candidate; evaluation checkpoints each 64 seeds. Packaged frozen.json permits evaluation without training.","files":{"model.py":"import math\n\n# State is exclusively initialized reserves and own observable trades.\nDEFAULT = [35., 7., 36., 11., .28, 1., 20., .7, .35, .5]\n\ndef bounded(x,a,b): return min(b,max(a,x))\ndef moments(m,v,lo,hi):\n sd=math.sqrt(max(v,1e-10)); a=(lo-m)/sd; b=(hi-m)/sd\n cdf=lambda z: .5*math.erfc(-z/math.sqrt(2))\n pdf=lambda z: math.exp(-.5*z*z)/math.sqrt(2*math.pi)\n mass=max(1e-12,cdf(b)-cdf(a))\n if mass < 1e-8: return bounded(m,lo,hi), min(v,(hi-lo)**2/12),mass\n pa,pb=pdf(a),pdf(b); shift=(pa-pb)/mass\n return m+sd*shift, max(.01,v*(1+(a*pa-b*pb)/mass-shift*shift)),mass\n\nclass Analytic:\n def __init__(self, params=None, ablate=False):\n self.params=list(DEFAULT if params is None else params); self.ablate=ablate\n def initialize(self,x=100.,y=10000.):\n self.m=math.log(y/x)*1e4; self.v=1.; self.s=self.m\n self.last=-1; self.fb=self.fa=.0035; self.q=9.45**2\n self.arbprob=0.; self.count=0\n return self.fb,self.fa\n def after_swap(self,sell_x,ax,ay,t,x,y):\n base,rebate,knee,width,slope,noise,scale,retail_rate,router_gain,uncert=self.params[:10]\n post=math.log(y/x)*1e4; direction=-1 if sell_x else 1\n lo=self.s+math.log1p(-self.fb)*1e4\n hi=self.s-math.log1p(-self.fa)*1e4\n dt=max(0,t-self.last); first=t!=self.last\n # Aggregate moment projection for unobserved silent steps; bounded O(1).\n if dt>1:\n self.v+=self.q*noise*(dt-1)*.5\n self.m,self.v,_=moments(self.m,self.v,lo,hi)\n self.v+=self.q*noise*(dt-1)*.5\n if first: self.v+=self.q*noise\n oldm=self.m; priorv=self.v\n pin=post+(math.log1p(-self.fb)*1e4 if sell_x else -math.log1p(-self.fa)*1e4)\n impact=max(.001,abs(post-self.s))\n # Under retail-first hypothesis the current timestamp had no own arb.\n rm,rv,mass=moments(self.m,self.v,lo,hi) if first else (self.m,self.v,1.)\n # Router provides only a weak, heteroscedastic observation.\n obs=pin-direction*(30.+.5*impact)\n obsvar=(30.+impact)**2/max(.001,router_gain)\n gain=rv/(rv+obsvar)\n rm+=gain*bounded(obs-rm,-3*math.sqrt(rv+obsvar),3*math.sqrt(rv+obsvar))\n rv=max(.01,rv*(1-gain))\n sd=math.sqrt(max(priorv,.01)); z=(pin-oldm)/sd\n arbden=math.exp(-.5*z*z)/(sd*math.sqrt(2*math.pi)) if first else 0.\n if first and len(self.params)>10 and self.params[10]:\n arbden=2/(math.sqrt(3)*math.pi*sd)*(1+z*z/3)**-2\n # Heavy-tailed retail impact density, positive on both sides.\n lz=(math.log(impact/scale)+.5*1.2**2)/1.2\n retden=retail_rate*.5*mass*math.exp(-.5*lz*lz)/(impact*1.2*math.sqrt(2*math.pi))\n w=arbden/(arbden+retden+1e-30) if first else 0.\n if self.ablate: w=1. if first else 0.\n self.m=w*pin+(1-w)*rm\n self.v=max(.01,w*(pin-self.m)**2+(1-w)*(rv+(rm-self.m)**2))\n # Robust innovation-based volatility, only high-confidence own arb pins.\n if first and w>.95 and dt>0:\n innovation=(pin-oldm)**2/max(1,dt)\n self.q=.995*self.q+.005*bounded(innovation,6.**2,14.**2)\n self.arbprob=w; self.last=t; self.s=post; self.count+=1\n d=post-self.m\n spread=base+uncert*math.sqrt(self.v)\n def offset(z):\n logistic=1/(1+math.exp(bounded(-(z-knee)/width,-60,60)))\n # Smooth approximation to positive part.\n pos=.5*(math.sqrt(z*z+25.)-z)\n return spread-(spread+rebate)*logistic+slope*pos\n self.fb=bounded(-math.expm1((-offset(d)-d)/1e4),0.,.1)\n self.fa=bounded(-math.expm1((d-offset(-d))/1e4),0.,.1)\n return self.fb,self.fa\n","experiment.py":"import argparse, json, math, statistics, time, pathlib, concurrent.futures, hashlib, random\nimport replica\nfrom model import Analytic, DEFAULT\nROOT=pathlib.Path(__file__).resolve().parent\nTABLE=json.loads((ROOT/'oracle_table.json').read_text())\nclass Arm:\n def __init__(self,kind,params=None):\n self.kind=kind; self.model=Analytic(params,kind=='ablation'); self.err2=0.; self.n=0\n def init(self,*hidden):\n return self.model.initialize()\n def on_trade(self,sell,ax,ay,t,x,y,arb,p,rival):\n if self.kind=='fixed': return .0035,.0035\n if self.kind=='oracle':\n d=math.log((y/x)/p); z=max(0.,min(16.,d/.003+8)); i=min(15,int(z)); f=z-i\n ask=TABLE['ask'][i]*(1-f)+TABLE['ask'][i+1]*f+TABLE['arb_flag']*arb\n bid=TABLE['bid'][i]*(1-f)+TABLE['bid'][i+1]*f+TABLE['arb_flag']*arb\n return 1-math.exp(-bid*.003-d),1-math.exp(d-ask*.003)\n fees=self.model.after_swap(sell,ax,ay,t,x,y)\n # This line is evaluation-only: truth never reaches the model.\n self.err2+=(self.model.m-math.log(p)*1e4)**2; self.n+=1\n if self.kind=='estimated_table':\n d=math.log(y/x)-self.model.m/1e4; z=max(0.,min(16.,d/.003+8)); i=min(15,int(z)); f=z-i\n ask=TABLE['ask'][i]*(1-f)+TABLE['ask'][i+1]*f+TABLE['arb_flag']*self.model.arbprob\n bid=TABLE['bid'][i]*(1-f)+TABLE['bid'][i+1]*f+TABLE['arb_flag']*self.model.arbprob\n self.model.fb=max(0.,min(.1,1-math.exp(-bid*.003-d)))\n self.model.fa=max(0.,min(.1,1-math.exp(d-ask*.003)))\n return self.model.fb,self.model.fa\n return fees\n\ndef one(job):\n seed,kind,params=job; arm=Arm(kind,params); r=replica.run(seed,arm,10000)\n return [seed,r['edge'],r['retail'],r['arb'],r['rv'],r['av'],arm.err2,arm.n]\n\ndef batch(seeds,kind,params,workers=8):\n with concurrent.futures.ProcessPoolExecutor(max_workers=workers) as pool:\n return list(pool.map(one,[(s,kind,params) for s in seeds]))\n\ndef summary(rows):\n scores=[r[1] for r in rows]; n=len(rows); se=statistics.stdev(scores)/math.sqrt(n) if n>1 else None\n return {'n':n,'mean':statistics.mean(scores),'se':se,'ci95':[statistics.mean(scores)-1.96*se,statistics.mean(scores)+1.96*se] if se else None,'rms_bps_callback':math.sqrt(sum(r[6] for r in rows)/max(1,sum(r[7] for r in rows)))}\n\ndef main():\n ap=argparse.ArgumentParser(); ap.add_argument('mode',choices=['fit','refine','eval','ablation','leakage','diagnostic']); ap.add_argument('--start',type=int,default=10000); ap.add_argument('--count',type=int,default=32); ap.add_argument('--workers',type=int,default=8); a=ap.parse_args()\n if a.mode=='fit':\n # Predeclared structural alternatives, no held-out seeds in selection.\n candidates=[DEFAULT,\n [35,7,36,11,.28,1,20,.7,0,.5],\n [35,7,36,11,.28,1,20,.7,.35,0],\n [35,7,36,11,.28,1,20,1.4,.35,.5],\n [35,7,36,11,.28,1,20,.35,.35,.5],\n [35,7,36,11,.28,1,40,.7,.35,.5],\n [35,7,36,11,.28,1,10,.7,.35,.5],\n [35,7,36,11,.28,1,20,.7,.35,1.5],\n [35,7,24,8,.28,1,20,.7,.35,.5],\n [35,7,50,14,.28,1,20,.7,.35,.5],\n [30,7,36,11,.28,1,20,.7,.35,.5],\n [40,7,36,11,.28,1,20,.7,.35,.5]]\n path=ROOT/'fit.json'; results=json.loads(path.read_text()) if path.exists() else []\n for i,params in enumerate(candidates):\n if i