{"credit":"Taylor (gtmcknight), github.com/gtmcknight/amm-sim@4812a6382311624e1506aca131ca35c378f930d9 research/solved; LEANBASE/lean_tell from the pinned efb8e653 artifact. Measurements in this item are local Python replica experiments, not independent engine certification.","first_divergence":{"timestamp":9718,"callback_index_zero_based":11582,"side":"buy (pool buys X)","amounts":{"amountX":"33329487878619766784","amountY":"5101746567861406531584","units":"integer WAD; trader pays X, receives Y"},"reserves":{"previousX":"63133726762474610688","previousY":"15839394429577369485312","afterX":"93130265853232398336","afterY":"10737647861715961905152","units":"integer WAD"},"fees":{"bid_wad":"100000000000000000","ask_wad":"0","bid_bps":1000.0,"ask_bps":0.0},"lean_label":"unsupported","exact":true,"truth":{"arb":true,"price":103.76737344199204},"why":"Buy support requires amountX <= previousX >> 1. Here 33329487878619766784 > 31566863381237305344; amountX/previousX = 0.5279189046452764. The early return occurs before preimage/grid evaluation, not because arithmetic disproves arbitrage and not because budget is exceeded. LEANBASE variant A maps every label other than pos to hit=False. The integer reference returns True.","exact_arithmetic_witness":{"gamma_mantissa":"8106479329266893","gamma_exponent":-53,"grid_exponent":-47,"matching_n":"4221637570985666","handed_paid_wad":"33329487878619766784","chance":1.0},"evidence":"diagnose.py; trace_base/trace_lean, prefix equality asserted by verify.py; reference https://api.imd.fun/artifacts/efb8e653f8fd061cecf3e92b186470c9c96c5188523364b60ed101e9672869aa"},"mechanism":"A large retail sell at t=9717 removes 35.261437382877913088 X and adds 5706.673103238910181376 Y, moving spot far above the price and producing bid=10%, ask=0. Both policies still know the true price. At t=9718 the correcting arb pays more than half the previous X reserve. The unsupported->retail mapping forces pi=0. Its retail/no-arb interval is ln-price [5.41963979474118,5.525000310399006], far above prior mean 4.642753447213877. trunc() sees zero mass and snaps to the nearer interval edge, estimating price 225.79777430615385 instead of 103.76737344199204 (7774.881947867982 log-bps error). Fees flip to bid=0, ask=10%. At t=9719, 9758 and 9759 the lean labels are positive and exact says arb, yet the Gaussian arb likelihood underflows to zero: z=-734.8325403698633, -44.98129037409791, -44.535816972882216. Therefore these correct labels do not re-pin the price. Retail/no-trade truncations gradually drag it down, with silent-step propagation capped at 12. It never recovers in the 282 simulation steps t=9718..9999: there are only 18 further callbacks including the divergence, 14 first callbacks, versus 315 BASE callbacks. None is classified arb, although four are actual arbs. At the final own callback t=9971, the estimate is 106.3066356614786 versus true 103.49964250556515, still 267.59548661210886 log-bps high; no further own callback occurs. The loss is 20.746679834774113 more arb loss plus 14.928249101892106 less retail edge. The fix avoids the initial snap: unknown leaves the ordinary posterior; at t=9718 retail mass is zero and arb density is 344.6681104525439, so pi=1 and the price is pinned correctly. Exact chance is also 1 here, explaining full subsequent trace identity.","frequency":{"seeds":{"start":20000,"end_inclusive":21023,"n":1024},"first_trades":7188394,"disagreements_by_kind":{"neg/exact_True":0,"pos/exact_False":0,"unsupported/exact_True":0,"over/exact_True":0},"disagreement_total":0,"seeds_touched":[],"lean_minus_base_on_touched":[],"table":{"buy/neg/False/False":1645925,"buy/pos/True/False":2320,"buy/pos/True/True":1946855,"sell/neg/False/False":1645296,"sell/pos/True/False":1847,"sell/pos/True/True":1946151},"table_key":"side/lean_label/exact_bool/hidden_arb_bool","definition":"BASE stream; first own callback of each timestamp, including timestamp 0 if present. Binary disagreement means (lean_label == pos) != exact. Unsupported/over are separately counted in table even when exact=False. Hidden truth used only for diagnostics.","counting_command_seconds":276.2546236424241,"evidence":"batch.py count, five disjoint ranges; serialized per-seed counts and differences in measurements.json inside code_zb64"},"fix":{"rule":"Preserve ordinary posterior on unknown: encode neg=0,pos=1,over=2,unsupported=3; if label>=2 set pi_after=pi_prior, else run the existing pos/neg update. Negative remains pi_after=0. Positive retains the existing grid chance c: pi_after=pi_prior/(pi_prior+(1-pi_prior)*c), subject to existing denominator handling. For fixed-point WAD W=10^18, unknown is an identity assignment (no rounding); a likelihood-interface implementation returns (hit=true,chance=W), not hit=false. Python fix.Policy selects existing leanbase variant B, whose on_grid returns (True,1.0) for unknown. No hidden truth or exact test is consulted by FIX.","cost":{"extra_ops":"With the stated 0..3 encoding: one unsigned comparison (label>=2) and one conditional branch; unknown path uses identity assignment / constant neutral likelihood, no new multiply, divide, square root or search. This is a source-level operation estimate, not measured EVM gas.","extra_storage_words":0},"results":{"seed_10942":{"base_edge":1048.7079577430886,"lean_edge":1013.0330288064206,"fix_edge":1048.7079577430886,"fix_minus_base":0.0,"lean_minus_base":-35.67492893666804,"fix_minus_lean":35.67492893666804,"callbacks_identical_to_base":11897},"touched":[],"choose":{"n":256,"mean_diff":0.0,"se":0.0,"min":0.0}},"scope":"Small label-consumption change, leaving lean arithmetic and supported labels unchanged. Measured no loss on requested choose set; not a general equivalence proof."},"keep_lean":true,"seeds_used":{"diagnostic":[10942],"frequency":{"start":20000,"end_inclusive":21023,"n":1024},"choose":{"start":64000,"end_inclusive":64255,"n":256},"fresh_engine":[]},"not_done":["No Solidity implementation, compiled gas/source-size checks, Solidity fidelity gates or fresh-engine tests: this is the assigned Python-only diagnostic and does not authorize submission.","The frequency sample contains no touched seeds, so its touched-seed comparison arrays are empty; this is not an estimate of zero population failure probability.","Recovery beyond the 10000-step replica horizon was not simulated. No recovery occurred within the requested seed."],"interpretation":"keep_lean applies only with the unknown->neutral fix. Unmodified LEANBASE is demonstrably worse on 10942. No requested Python measurements are missing; nothing fabricated.","provenance":{"reference":"https://api.imd.fun/artifacts/efb8e653f8fd061cecf3e92b186470c9c96c5188523364b60ed101e9672869aa","solved_dependencies":"https://raw.githubusercontent.com/gtmcknight/amm-sim/66604b3426d8e97a73e2c39940b9b4dece51cc73/research/solved/SHA256SUMS","options":{"soft":1,"qm":"qm_k1e-5.json","a0":0.35,"ainf":0.35},"units":"Unnormalized replica edge per seed; paired same seeds, each policy alone vs the 30-bps Vanilla rival. SE is sample standard deviation of paired differences divided by sqrt(n).","verification":"verify.py validates complete prefix/full fixed trace identity, unknown and supported label branches, complete requested seed coverage and paired choose diagnostics. No independent behavioral certification claimed."},"code_zb64":"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"}