{"hypothesis":"Wholly silent steps convey price evidence through the absence of our retail fills as well as absence of arbitrage. Router competition makes the no-fill probability vary with latent price.","exact_rule":{"scope":"Only the existing min(dt-1,12) wholly silent steps, before the current step variance increment. Observed-trade updates, integer tell test, integer-binade chance, posterior odds, WAD fee roundtrip, rival tracking and tables are unchanged.","model":"Fixed midpoint Poisson intensity lambda=0.8; quote order size Q lognormal(log(20)-0.72,1.2). This is a model constant, never a per-run input. Given log-price ell, p=exp(ell), rival spot r=clamp(stored_s2h,p*0.997,p/0.997), estimated rival reserves xr=sqrt(1e6/r),yr=sqrt(1e6*r). Own reserves x,y come from saved callback WAD integers.","router":"For gA=1-fa,gB=1-fb and R_A=sqrt(x*y*gA/(1e6*0.997)), R_B=sqrt(x*y*gB/(1e6*0.997)), thresholds are qA=(((y+(gA+R_A*0.997)*1e-4)/R_A)-yr)/0.997 and qB=p*(((x+(gB+R_B*0.997)*1e-4)/R_B)-xr)/0.997. Tail(q)=1 for q<=0, otherwise 0.5*erfc((log(q)-log(20)+0.72)/(1.2*sqrt(2))).","likelihood":"P(no own retail fill|p) approx exp(-0.4*(Tail(qA)+Tail(qB))). Weight the Gaussian prediction inside the existing own no-arb interval by this probability raised to frozen strength; normalize and rematch mean and variance.","quadrature":"16-point Gauss-Legendre per interval, split at log(stored_s2h*0.997) and log(stored_s2h/0.997) if interior. Integrate within own no-arb interval intersected with m +/- 8 sd; at most 48 nodes per silent step. Existing trunc fallback retained for prior mass<1e-12 or empty bounds.","approximation":"Poisson thinning uses frozen estimated rival quotes during a silent step; rival-only retail trades can move them. This is an approximation, not an exact marginalized retail likelihood."},"baseline_params":{"policy":"stalerival:Policy","a0":0.35,"ainf":0.35,"soft":1,"qm":"qm_k1e-5.json"},"variant_params":{"policy":"silentretail:Policy","strength":1.0,"other_params":"identical to baseline_params"},"frozen":{"strength":1.0,"tuning":[{"strength":0.5,"paired_vs_base":{"n":256,"mean_diff":0.0009989109301304921,"se":0.005663525859965456,"sum":0.255721198113406,"sumsq":2.094145725568815}},{"strength":1.0,"paired_vs_base":{"n":256,"mean_diff":0.013312805639348557,"se":0.006173443706063784,"sum":3.4080782436732306,"sumsq":2.5332837447514622}},{"strength":2.0,"paired_vs_base":{"n":256,"mean_diff":0.004619968595089841,"se":0.006651719312910535,"sum":1.1827119603429992,"sumsq":2.893801833816345}}],"hashes":{"silentretail.py":"2ebf7761322b26c3c5913a5e6a8fa94e6f78af4faf584099536279319f86828f","driver.py":"125e6905a5056f306eff941ce80fe45e31f3083b8d24f4bf45672d3eb80f041d","stalerival.py":"176f8115fb67d99340f1de1e6994042dfaa41f830c22e000595df3377cc6a2b3","real.py":"7fc9f4bdf9a9af094ec2eb0e9a8e8eec8b343c5226bf0ebaa30c798785cb7c58","tellpol.py":"6e62a0e070d9778537e746438f03ea7a4b44cb1d9652ae13702b801997db5f6d","tellref.py":"72679df55e2ba46a903075543654b685e7b2bf86dfbef3d5122dff6368d4a4f2","run.py":"54d19a9194ebedee8b947dfaaf8626b1e4ef6f7d1b5aa12e738ec01a5ce401ac","replica2.py":"6e37b4e61e1ffa5ffb1e43912ddb8659105989aa986eb48fcd1d7608f8dca685","evalpol.py":"27fa051b1dfc3a5da824596c59ed5140823049ab05fd18185a4ece1995d65df1","par.py":"d81d358cf2f54303d120da34c01022e62428c2e9d6b1577f8c30099b2bfd1483","pol_mid.json":"efbb5ee102db4d312f64565d9bf7232b6becbe02b624164f744a402bbbf2983b","qm_k1e-5.json":"5ff53e42ca87390e5f65d1caa3f7a480e67a8212fb0670274dfd562b05ba1afd","tell_qm.json":"ad382c07e6c2b3a5a8055ea767e140b7461105f29dafee69b7c1766c90d0634f"},"declaration_sha256":"e77617af30267bd4b4af30751b0610e7e7d7275737c759fa04179c4ad8a847ae","params_sha256":"89398cd78a86b590e75529506c31d893cec025a84edc5fed717bbb025e3a54fc"},"tuning_summary":{"range":[64600,64855],"n":256,"settings_predeclared":[0.5,1.0,2.0],"selection":"maximum paired mean gain vs BASE; all 256 seeds for each setting; no holdout retuning","rows_path":"frozen.tuning"},"holdout":{"completed_seeds":[10000,12047],"remaining_seeds":[],"variant_vs_base":{"n":2048,"mean_diff":-0.0011969826742102008,"se":0.0020685995973798565,"sum":-2.451420516782491,"sumsq":17.942034960207323,"worst5":[{"seed":11630,"diff":-0.6449846088005415},{"seed":10944,"diff":-0.509432515090225},{"seed":10015,"diff":-0.451353481767228},{"seed":11702,"diff":-0.45074645091960974},{"seed":10118,"diff":-0.4179985143610452}]},"variant_vs_tell_qm":{"n":2048,"mean_diff":0.08740573093931753,"se":0.00708842210603848,"sum":179.0069369637223,"sumsq":226.28918665542656,"worst5":[{"seed":11696,"diff":-2.091463500701593},{"seed":10822,"diff":-1.6190696818714514},{"seed":11277,"diff":-1.270432391927102},{"seed":11022,"diff":-1.2353324561864838},{"seed":11413,"diff":-1.2225981302235027}]},"base_vs_tell_qm":{"n":2048,"mean_diff":0.08860271361352773,"se":0.007079197151580293,"sum":181.4583574805048,"sumsq":226.17274777636902,"worst5":[{"seed":11696,"diff":-2.0920680876059805},{"seed":10822,"diff":-1.5921358448919136},{"seed":11022,"diff":-1.4867062508961908},{"seed":11277,"diff":-1.3260298263055574},{"seed":11413,"diff":-1.269064635952418}]},"protocol":"Frozen variant and BASE each scored once through unmodified run.run_policy and run.Blind, 8-seed checkpoint chunks, PROCS=1; no engine run."},"per_seed_rows":{"encoding":"base64(lzma-XZ(byte-shuffle16(little-endian float64 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","decode":"base64 decode then lzma.decompress -> b; raw[16*j+i]=b[i*n+j] for i=0..15,j=0..n-1; unpack each row as <dd; seed=10000+j. tell_qm comparator rows are the immutable dependency identified below."},"diagnostics":{"variant":{"first":14400426,"misread":4106,"misread_rate":0.00028513045377963125,"price_squared_error_sum_bps2":1132437250.7839813,"price_observations":22378770,"price_rmse_bps":7.113592235376349},"base":{"first":14400097,"misread":4260,"misread_rate":0.00029583134058055305,"price_squared_error_sum_bps2":1133913770.1730444,"price_observations":22377806,"price_rmse_bps":7.118381544597329}},"blindness_rule_off_checks":{"no_silent_steps_exact_callbacks":10143,"rule_off_exact_callbacks":11760,"rule_off_state_exact":true,"flags_ignored_callbacks":11760,"needs_flag":false,"scoped_hook_restored":true,"enabled_silent_updates":2719,"quadrature_vs_dense_mean_abs":1.1546319456101628e-13,"quadrature_vs_dense_variance_abs":1.1250530892239216e-15,"flat_likelihood_variance_error_by_sd_half_width":[[1,-9.992007221626409e-16],[2,-1.5543122344752192e-15],[4,1.047421926969605e-08],[6,9.776173463060811e-05],[8,0.009422685884980853]],"diagnostic_seed":64600,"diagnostic_steps":10000},"numerical_limitation":"Post-freeze diagnostics found flat-likelihood variance error of +0.9423% on [-8sd,+8sd] with the frozen 16-node quadrature; at +/-4sd absolute normalized variance error is about 1.05e-8. The split test case is more accurate, but no universal quadrature accuracy bound is claimed. Scoring source and parameters were not changed. Incremental performance includes this numerical integration effect and cannot be attributed exclusively to the retail likelihood.","implementation_checks":{"fetch_files":34,"fetch_sha256_mismatches":0,"base_body_sha256":"806404e8de0d3fee0641a55560fee2001cdf19cceefd05c7b23b9b486faa263f","base_stalerival_sha256":"176f8115fb67d99340f1de1e6994042dfaa41f830c22e000595df3377cc6a2b3","dependencies_unmodified":true,"serial_only":true,"diagnostic_inputs":"Blind supplies only six callback fields; needs_flag=false. True price and flags are used only by the unchanged diagnostic harness."},"cost_estimate_unmeasured":{"per_silent_step":"16..48 quadrature nodes, each up to 2 exp, 2 log, 2 erfc, 2 sqrt, plus arithmetic; at most 12 silent-step updates per callback, hence at most 576 nodes. A few setup sqrt/log operations per silent step.","persistent_storage":"No extra decision state if strength compiled as a constant; baseline reserves/fees/filter/rival state reused. Optional runtime strength: 1 scalar/slot. Diagnostic counter not needed by port.","constant_bytes":"16 abscissas + 16 weights: 256 bytes with float64 representation or 1024 bytes as 32-byte fixed-point constants, excluding code.","code_bytes":"Not measured for Solidity. Python code decoded size is reported below.","gas":"Unmeasured; no engine or Solidity port claimed."},"port_decision":{"qualifies":false,"criterion":"variant-tell_qm mean >= 0.2 and variant-BASE mean > 0","engine_rank_claim":false},"code_encoding":"base64(zlib(UTF-8 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","dependencies":{"fetch_script":"https://raw.githubusercontent.com/gtmcknight/amm-sim/66604b3426d8e97a73e2c39940b9b4dece51cc73/research/tell/fetch.sh","manifests":{"SHA256SUMS.solved":"1eed2f29fdd75ce01be171a81517294b1dafed1df5a1caaaf11d290f9c760e6e","SHA256SUMS.tell":"28f28d8da41310b66410368e70ea7d6dccc45bc3dd26fa87cb7e7ec6d719fdc0"},"published_comparator":{"url":"https://raw.githubusercontent.com/gtmcknight/amm-sim/66604b3426d8e97a73e2c39940b9b4dece51cc73/research/tell/tell_qm.json","sha256":"ad382c07e6c2b3a5a8055ea767e140b7461105f29dafee69b7c1766c90d0634f"},"reproduce":"Fetch the pinned tell+solved folders using fetch.sh and verify manifests; decode bundle files into the same directory; run PROCS=1 python3 driver.py. Checkpoints skip completed chunks. Source/params freeze must match before holdout starts."},"interpretation":"Replica experiment only. Does not meet the requested next-port threshold; no engine rank inference or port recommendation."}