{"credit":"Taylor (gtmcknight), github.com/gtmcknight/amm-sim@4812a6382311624e1506aca131ca35c378f930d9 research/solved","status":"complete","failed":[{"step":"source-quote checker first invocation","why":"Incorrect relative path to sources.json caused FileNotFoundError; fixed immediately.","recovered":true,"impact":"No policy, seed run, or final source check failed."}],"idea":"Gas-ignored filtering ceiling: rival-only particle output on BASE with 0.35 pull removed or kept; 16-particle reduction. Python replica only.","setup":{"reference":"https://raw.githubusercontent.com/gtmcknight/amm-sim/66604b3426d8e97a73e2c39940b9b4dece51cc73/research","commands":["mkdir -p scratch/amm","curl -fsSL https://raw.githubusercontent.com/gtmcknight/amm-sim/66604b3426d8e97a73e2c39940b9b4dece51cc73/research/tell/fetch.sh -o scratch/fetch.sh","sh scratch/fetch.sh https://raw.githubusercontent.com/gtmcknight/amm-sim/66604b3426d8e97a73e2c39940b9b4dece51cc73/research scratch/amm","python3 -c \"import json,base64,zlib,pathlib; r=json.load(open('result.json')); p=pathlib.Path('scratch/amm'); (p/'stalerival.py').write_bytes(zlib.decompress(base64.b64decode(r['base_code_zb64']))); (p/'rivalbase.py').write_bytes(zlib.decompress(base64.b64decode(r['code_zb64'])))\"","PROCS=8 python3 -I scratch/amm/gates.py","PROCS=8 python3 -I scratch/amm/run.py stalerival:Policy 10000 64 scratch/base.json soft=1 qm=qm_k1e-5.json a0=0.35 ainf=0.35","PROCS=8 python3 -I scratch/amm/run.py rivalbase:Policy 10000 64 scratch/pick.json soft=1 qm=qm_k1e-5.json particles=64 pull=0"],"rebuild_note":"Repeat ordered 64-seed blocks; other arms particles=64 pull=.35 and particles=16 pull=0. run.py chunks eight seeds per worker; measured harness uses chunk=1 with otherwise identical run.run_policy. All custom source payloads are stdlib-only; pinned reference tables are fetched by the provided reference command. No packages installed.","fetch_check":"34 files, 0 mismatched","download_sha256":{"base":"f5a140f51df99423d6ede60b618dbc6eabc37a3050ac439c80dc3824191e9f59","rival":"19bf5534225de5b3d201baaf0e35f9f2a8671120380295ea7726bf6379caedc1"},"base_code_sha256":"176f8115fb67d99340f1de1e6994042dfaa41f830c22e000595df3377cc6a2b3","timing_8_seeds":{"seeds":[64900,64907],"rival64_seconds":27,"method":"run.py single eight-seed chunk; printed rounded wall seconds"}},"rule":{"state":"Unchanged BASE Gaussian price posterior, exact WAD tell, own reserves/fees, and scalar rival tracker. Add N weighted (ln price, ln rival spot) particles, deterministic normal/uniform stream indices, pending-arb flag, particle synced flag, dead flag, sync mass. Particle price mean/variance never feed fee tables. BASE is the exact supplied module, not the two-component lb-4 policy.","constants":{"sigma":0.000945,"rival_gamma":0.997,"orders_per_step":0.8,"log_size_mu":"ln(20)-0.72","log_size_sd":1.2,"gap_step":5e-05,"gap_table_max":400,"rng_seed":20261110,"normal_period":8191,"uniform_period":8209,"silent_step_cap":12,"resample_ess":"< N/2","sync_threshold":0.5},"callback":"First run BASE using WAD-rounded amounts/reserves and its own exact tell. Save pre-callback fees, own spot and timestamp. On dt>=1, compute lo=ln(previous_spot*(1-bid)), hi=ln(previous_spot/(1-ask)); clear particle synced; process pending silent retail remainder, then min(dt-1,12) silent steps; pin on BASE arb classification, otherwise move/cut. Process own retail or set pending-arb. Reseed dead particles from BASE m,v and scalar rival. Take weighted mean log rival, leave BASE m,v unchanged; use particle sync flag for lookup, then restore BASE scalar sync state. Return BASE.round_fees(lookup(...)).","silent_step":"For each live particle move ln p by next fixed normal*sigma; zero weight outside (lo,hi); clip ln rival into ln p +/- -ln(.997). Gap indices ja=clamp(int((hi-r+ln G)/STEP),0,400), jb=clamp(int((r+ln G-lo)/STEP),0,400). Multiply weight by EW[ja]*EW[jb], where EW[j]=exp(-0.4*(1-FT[j])) and FT[j] is lognormal CDF at 10000*expm1(j*STEP/2)/G. One fixed uniform samples a rival-only trade with probabilities .4*FT[ja], .4*FT[jb], changing r by +/- bisect_left(FT,u/.4)*STEP. Normalize and systematic-resample below N/2 ESS.","pin":"Weight *= exp(-(lp-x)^2/(2*sigma^2)); set x=lp and clip r to lp +/- -ln G. If all new pin weights vanish retain old weights, matching upstream. Normalize.","retail":"c=ln(post_spot*(1-old_bid)/G) for pool-buy-X, else ln(post_spot*G/(1-old_ask)). d=r-c for pool-buy-X, else c-r. If d>0, q=ay+1000*exp(r/2)*(exp(d/2)-1)/G, multiply weight by 2*f(q)/f(ay) and set r=c, with f lognormal size density. synced becomes true when the weight mass reset to c is >=0.5; it persists within the timestamp. Normalize/resample.","keep_pull_interpretation":"64 keep arm runs BASE scalar tracker with a0=ainf=.35 (also used if particles die), then applies r += .35*(ln(cand)-r) to particle output only for a nonbinding own retail identity: gap>0 for pool-buy-X, gap<0 otherwise; sets lookup synced=true. Does not move particles by the pull. Replacement arms run latent BASE scalar tracker with zero pull. This is an output composition, not a second particle observation.","disabled":"particles=0 directly delegates to BASE with a0=ainf=.35; all rows exactly equal on 64900..64907.","source_urls":["https://api.imd.fun/artifacts/f5a140f51df99423d6ede60b618dbc6eabc37a3050ac439c80dc3824191e9f59","https://api.imd.fun/artifacts/19bf5534225de5b3d201baaf0e35f9f2a8671120380295ea7726bf6379caedc1","https://raw.githubusercontent.com/gtmcknight/amm-sim/66604b3426d8e97a73e2c39940b9b4dece51cc73/research/tell/tellpol.py","https://raw.githubusercontent.com/gtmcknight/amm-sim/66604b3426d8e97a73e2c39940b9b4dece51cc73/research/solved/real.py"]},"frozen":{"credit":"Taylor (gtmcknight), github.com/gtmcknight/amm-sim@4812a6382311624e1506aca131ca35c378f930d9 research/solved","params":{"credit":"Taylor (gtmcknight), github.com/gtmcknight/amm-sim@4812a6382311624e1506aca131ca35c378f930d9 research/solved","arms":[{"name":"rival64_replace","particles":64,"pull":0},{"name":"rival64_keep","particles":64,"pull":0.35},{"name":"rival16_replace","particles":16,"pull":0}],"common":{"soft":1,"qm":"qm_k1e-5.json","a0":0.35,"ainf":0.35},"choose_seeds":[64000,64063],"timing_and_disabled_seeds":[64900,64907],"report_start":10000,"report_block":64,"selection":"highest paired mean vs BASE among non-dropped arms; drop if mean+3se<0","code_sha256":"982a535e66238ba9e123ad7fddef89d3c6d783154f268c7b57b1bfc45169d435"},"code_sha256":"982a535e66238ba9e123ad7fddef89d3c6d783154f268c7b57b1bfc45169d435","base_sha256":"176f8115fb67d99340f1de1e6994042dfaa41f830c22e000595df3377cc6a2b3","pick":"rival64_replace","active":["rival64_replace","rival64_keep","rival16_replace"],"utc":"2026-11-10T15:22:45.261427+00:00","params_sha256":"9d88234957cf1e1c9325d631e0c79c473d46835dfe05097db2f11d3b3278fbc1"},"choose":{"credit":"Taylor (gtmcknight), github.com/gtmcknight/amm-sim@4812a6382311624e1506aca131ca35c378f930d9 research/solved","seeds":[64000,64063],"table":[{"n":64,"mean_diff":0.09408933727230107,"se":0.040783801108020795,"worst5":[{"seed":64045,"diff":-0.6479896517113275},{"seed":64023,"diff":-0.6057949994033152},{"seed":64008,"diff":-0.4791806365242337},{"seed":64038,"diff":-0.36920631616283117},{"seed":64018,"diff":-0.3124119305608133}],"name":"rival64_replace","drop":false},{"n":64,"mean_diff":0.07306306072925928,"se":0.033439241018150066,"worst5":[{"seed":64044,"diff":-0.6016005108037916},{"seed":64038,"diff":-0.4588102331721302},{"seed":64041,"diff":-0.4139382418389914},{"seed":64045,"diff":-0.3850604568036715},{"seed":64008,"diff":-0.32996997946145257}],"name":"rival64_keep","drop":false},{"n":64,"mean_diff":0.0672956612796014,"se":0.043305984565102755,"worst5":[{"seed":64044,"diff":-0.8977964582709319},{"seed":64045,"diff":-0.5146227552986602},{"seed":64012,"diff":-0.4572138683499247},{"seed":64008,"diff":-0.3844967116170892},{"seed":64041,"diff":-0.34483825434153914}],"name":"rival16_replace","drop":false}]},"report":{"seeds":[10000,12047],"n":2048,"block_size":64,"block_starts":[10000,10064,10128,10192,10256,10320,10384,10448,10512,10576,10640,10704,10768,10832,10896,10960,11024,11088,11152,11216,11280,11344,11408,11472,11536,11600,11664,11728,11792,11856,11920,11984],"pick":"rival64_replace","arms":[{"name":"rival64_replace","minus_base":{"n":2048,"mean_diff":0.04229773867606773,"se":0.00585021205982825,"worst5":[{"seed":11701,"diff":-2.2182635639769614},{"seed":10971,"diff":-1.1319096599542036},{"seed":11303,"diff":-1.0522714973990333},{"seed":11947,"diff":-0.8917244899740808},{"seed":11856,"diff":-0.8560466519819556}],"min":-2.2182635639769614}},{"name":"rival64_keep","minus_base":{"n":2048,"mean_diff":0.04347573177940975,"se":0.005563821129090098,"worst5":[{"seed":11701,"diff":-2.02536883167636},{"seed":11303,"diff":-1.0560800260585665},{"seed":10971,"diff":-0.9176736556618152},{"seed":12003,"diff":-0.9088837069882629},{"seed":11947,"diff":-0.856661260977603}],"min":-2.02536883167636}},{"name":"rival16_replace","minus_base":{"n":2048,"mean_diff":0.01795174128622906,"se":0.006055229468621942,"worst5":[{"seed":11701,"diff":-2.3738122457045847},{"seed":10166,"diff":-1.5164545615825205},{"seed":11852,"diff":-1.239142519413008},{"seed":11907,"diff":-1.115696605893163},{"seed":11947,"diff":-0.998718875730674}],"min":-2.3738122457045847}}],"dropped":{},"base_mean_edge":536.6208103550729,"interpretation":"Paired pure-Python replica differences against exact supplied BASE; not engine evidence or a leaderboard score. Report seeds never used for selection; gate reference runs used 10000..10002 without candidate evaluation."},"cost":{"scope":"Analytical conservative operation bounds for added Python particle layer; gas ignored, no Solidity compilation.","extra_storage_words":{"as_built_scalar_model_rival64":201,"as_built_scalar_model_rival16":57,"minimal_port_rival64":198,"minimal_port_rival16":54,"layout":"3*N particle words + two stream indices + pending-arb/synced/dead flags + sync mass + N/pull configuration and diagnostic reseed counter. Minimal port puts configuration in code and drops diagnostic counter. Python object overhead is not an EVM storage estimate."},"constant_tables":"401 FT + 401 EW + 8191 normal + 8209 uniform scalars (17202 total); fixed pseudo-random tables are immutable code/data, excluded from mutable words. Actual EVM implementation would require redesign.","worst_ops_per_callback":{"N":"16 or 64","particle_transition_sweeps":15,"normalization_and_possible_resampling_passes":15,"normal_draws_upper_bound":"15*N","uniform_draws_upper_bound":"13*N+15","exp_upper_bound":"3*N+16","log_upper_bound":"N+16","sqrt_upper_bound":4,"binary_search_comparisons_upper_bound":"117*N","other_scalar_arithmetic_and_comparison_upper_bound":"2048*N+1024","extra_fee_lookup_upper_bound":1,"transient_particle_arrays":"O(N), in addition to retained 3*N; Python overhead excluded"},"note":"Bounds derived from at most 13 silent-retail sweeps (12 missing timestamps plus pending remainder), cut/pin, retail, optional reseed, and estimate. Retained BASE computations and normal fee lookup are excluded. These are conservative bounds, not measured EVM gas."},"diagnostics":{"P":{"pass":true,"output":"seed   perfect-info      gaussian+plain    gaussian+qm       lattice+qm\n10000  448.261189  443.455598  443.522608  443.663520\n10001  576.779199  571.463409  571.623551  571.977293\n10002  531.886497  525.168028  525.865859  526.411631\n","reference_url":"https://raw.githubusercontent.com/gtmcknight/amm-sim/66604b3426d8e97a73e2c39940b9b4dece51cc73/research/solved/README.md"},"disabled_exact_rows":8,"disabled_seeds":[64900,64907],"hidden_inputs":{"pass":true,"seeds":[64900,64907],"arms":3,"callbacks":259716,"method":"Twin instances see identical contract observations. Through run.Blind, replace init sigma,lam,size with 999,888,777; invert arb and replace p,s2,j by 123456.789,987654.321,-123456 on every callback. Assert every bid/ask float pair identical. Hidden values affect wrapper diagnostics only, never policy. needs_flag=False; policy has no p,s2,j or run configuration 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source-validator relative path was one directory too high; corrected path and all nine URL quotes passed. No experiment failure."],"report_order":"Each block BASE then three frozen arms; finalize cumulative summaries and drop rule before next block. No rerun or parameter changes.","scope":"Replica experiment; Solidity T/F/G and fresh engine E not applicable and not 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