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At a capped 10% fee, reconstruction can yield 99999999999999999 instead of 100000000000000000; one real detector r changed by one WAD unit, without changing that callback output. This control is not claimed to catch every output mutation."},"zero_r_retail":{"real_first_callbacks_with_r_zero":64467,"false_arb_on_r_zero":0,"nonzero_pi_on_r_zero":0,"underflow_stress":{"integer_retail_mass_wad":0,"float_retail_mass":0.0,"r_wad":0,"integer_pi_wad":0,"float_pi":0.0,"integer_is_arb":false,"float_is_arb":false,"integer_mixture_weight_wad":1000000000000000000,"model_evm_exact":true,"production_gas":97439,"note":"synthetic prior state deliberately outside no-arb interval; production slots edited only in this separate test instance"},"review":"Both observe and MixPolicy force pi=0 when r=0; the lr<=0 pin fallback requires r>0. A zero tell must remain retail even when the retail likelihood underflows."},"mutations":{"scope":"all-tape detector sensitivity counts plus isolated pre-callback state copies on first 256 new-timestamp callbacks per run","whole_tape_detector_changes":{"stale_fees":77133,"swapped_fees":77264,"side":102668,"rounded_fees":77042,"log_reconstructed_fees":1,"amount_plus_one":6817,"missing_applied_annotation":75085},"negative_controls":{"stale_fee":{"trials":5120,"detector_changes":2703,"output_changes":2691,"first_witness":{"seed":67512,"callback":8,"r_wad":3217187800974975,"mutated_r_wad":0,"normal_fees":[1288320298722114,4586134264077453],"mutated_fees":[2003120928305986,3344144537738967]}},"swapped_fee":{"trials":5120,"detector_changes":2706,"output_changes":2695,"first_witness":{"seed":67512,"callback":8,"r_wad":3217187800974975,"mutated_r_wad":0,"normal_fees":[1288320298722114,4586134264077453],"mutated_fees":[2003120928305986,3344144537738967]}},"wrong_side":{"trials":5120,"detector_changes":2963,"output_changes":2834,"first_witness":{"seed":67512,"callback":8,"r_wad":3217187800974975,"mutated_r_wad":0,"normal_fees":[1288320298722114,4586134264077453],"mutated_fees":[2003120928305986,3344144537738967]}},"rounded_fee":{"trials":5120,"detector_changes":2707,"output_changes":2697,"first_witness":{"seed":67512,"callback":8,"r_wad":3217187800974975,"mutated_r_wad":0,"normal_fees":[1288320298722114,4586134264077453],"mutated_fees":[2003120928305986,3344144537738967]}},"post_callback_fee":{"trials":5120,"detector_changes":2706,"output_changes":2697,"first_witness":{"seed":67512,"callback":8,"r_wad":3217187800974975,"mutated_r_wad":0,"normal_fees":[1288320298722114,4586134264077453],"mutated_fees":[2003120928305986,3344144537738967]}},"reconstructed_log_fee":{"trials":5120,"detector_changes":0,"output_changes":0,"first_witness":null},"zero_detector":{"trials":5120,"detector_changes":2697,"output_changes":2691,"first_witness":{"seed":67512,"callback":8,"r_wad":3217187800974975,"mutated_r_wad":0,"normal_fees":[1288320298722114,4586134264077453],"mutated_fees":[2003120928305986,3344144537738967]}},"always_positive_detector":{"trials":5120,"detector_changes":5120,"output_changes":4645,"first_witness":{"seed":67512,"callback":2,"r_wad":0,"mutated_r_wad":1000000000000000000,"normal_fees":[1855106460308297,3631343174612257],"mutated_fees":[1672958660260406,3767892193322712]}}},"rounding_note":"rounded fees use a 1e10-WAD quantum (0.0001 bps); side mutation changes only detector side; stale means the immediately previous callback applied fee. Output-change counters are exact WAD comparisons, not all guaranteed >0.01 bps.","untriggered_control":"The reconstructed-log control did not change fees on the 5120 sampled outputs; a separate full-tape scan found the single detector-only change reported above."},"gas_stats":{"kind":"measured pyrevm 0.3.7 message_call result.gas_used, production bytecode only","callbacks":218199,"after_swap_peak":148733,"after_swap_mean":62018.223571143775,"after_swap_total":13532314365,"real_initialize_peak":229429,"swap_call_limit":195000,"initialize_call_limit":250000,"real_faults":{"reverts":0,"out_of_gas":0},"live_engine_gas":null,"live_engine_gas_estimated":false,"margin_note":"Observed peak is below the 195000 pyrevm target; no claim that the live engine charges the same gas."},"stress":{"separate_from_real_callback_parity":true,"independent_binade_detector":{"cases":1855,"mismatches":0,"positive":869,"zero":986},"independent_initialize":{"cases":14,"reverts":0,"oog":0,"peak_gas":230343,"wad_mismatches":0,"retained_mismatches":0},"independent_tiny_amount_and_gap_sequences":{"callbacks":240,"reverts":0,"oog":0,"peak_gas":84465,"integer_mismatches":0,"retained_mismatches":0},"frozen_extreme_stress_reexecuted":{"sequences":64,"calls":16384,"reverts":0,"oog":0,"peak_gas":163384,"mean_gas":66708,"failures":[]},"scope":"Detector: both sides; before-reserve binades -59,-40,-20,-1,0,1,6,13,30,50,59 with +/-1 WAD boundaries; amounts 0,1,2,1e6,1e12,1e16,1e18; four fees and seven guard cases. Targeted policy stress uses gaps 0,1,2,3,12,13,14,50,10000 and near-binade reserves. Frozen stress separately covers extreme uint256/zero reserves and timestamp reversals. No floating numerical bound is claimed on these synthetic stress cases."},"ideal_double_scope":{"conditional_wad_numerical_scope_pass":true,"autonomous_ideal_double_trajectory_parity_pass":false,"fixed_integer_tape_ideal_double_parity_pass":false,"explanation":"Changing a fee by tiny floating rounding can change this grid detector discontinuously. The WAD numerical test fixes the exact applied detector fee and input WADs, isolating filter/lookup arithmetic. It does not compare two independent policies following their own fees through the replica. The ideal double tell uses replica binary64 amounts, its own fees and a 2.220446e-16 relative envelope; the WAD detector uses its specified widened integer envelope and absolute truncation allowance.","failed_fixed_tape_test":{"runs":20,"callbacks":218199,"over_0_01_bps":218080,"max_fee_error_bps":466.3469481364424,"scope":"Unannotated MixPolicy(wad=0) on the integer-generated tape is off policy; this failed test is not evidence that the conditional-WAD test failed."},"separate_autonomous_test":{"runs":20,"same_seed_pairs":20,"different_wad_callback_trajectories":20,"edge_diff_integer_minus_ideal_mean":0.02348817180698006,"scope":"Separate blind replicas with ideal policies applying their own fees; raw six-field WAD trajectory digests and callback counts recorded. Edge differences are diagnostics, not board or profitability claims."}},"audit_findings":["gate.py numerical comparison correctly clamps the double returned lookup fee before comparing; omitting this cap creates false failures on legitimate real callbacks. The delivered scripts and final results use the same cap.","gate.py without a diagnostic executable sets contract_r from its own model and leaves tell_mismatches at zero; that alone does not prove detector independence. This audit uses a separate rational oracle and actual diagnostic getter on every callback.","gate.py tell_vs_truth counts r>0, not pi>0.9. This audit reports both detector and posterior confusion matrices.","int_model.py matches production on these reachable paths. It uses unbounded Python integers and literal coefficient/table parsing, not a universal proof of unchecked Solidity arithmetic.","Phases 1 and 2 share compressed table data through spec.json map 2s->1s and 2c->1c, explaining the integer model tab selection without separate retail count."],"validation":{"pass":true,"tool":"amm-match validate","subject_source_sha256":"6ee77c053e29d23a89b926c33b13e32365bdd09d880cac5557ce06c043dcf1ac","reference_gates_pass":true,"reference_gates":{"10000":[448.261189,443.455598,443.522608,443.66352],"10001":[576.779199,571.463409,571.623551,571.977293],"10002":[531.886497,525.168028,525.865859,526.411631]},"reference_gate_column_order":["perfect-info","gaussian+plain","gaussian+qm","lattice+qm"],"storage_review":"Only slots[0..11] are written, all literal indices. tell reads dynamic side choice 4/5. Pinned base declares uint256[32]; compiler storage policy passes. No inline assembly.","blindness_extra_values_test":true},"limitations":["This is a complete local 20-run audit, not independent certification of the scripts or a formal equivalence proof.","No Rust-engine seeds, engine match, live engine gas measurement, IB comparison or board entry was run.","Raw tapes remain scratch data; their SHA256 and complete generation scripts are delivered. Large solved tables and the solc/pyrevm environment are external task prerequisites, not bundled dependencies.","Only returned fees are required bit-exact; full internal EVM belief state equivalence is not claimed. Detector checks additionally read a separate getter derivative.","No pass is claimed for autonomous ideal-double parity or for the failed off-policy ideal-double replay.","Synthetic tests exercise sampled edge cases; they do not prove all uint256 paths safe."],"evidence":{"authority":"Local measured evidence, reproducible with bundled scripts; no raw command logs included.","archive_files":["audit/README.md","audit/audit.py","audit/auxiliary.py","audit/detector.py","audit/diagnose.py","audit/evidence.json","audit/frozen/int_model.py","audit/frozen/spec.json","audit/frozen/twin.py","audit/mutations.py","audit/retained.py","audit/sources.json"],"source_indices":{"frozen_policy_and_claim":0,"reference_blindness_and_tables":1,"compiler":2,"setup_versions":3,"engine_executor_limits":4},"full_per_run_metrics":"audit/evidence.json"},"code_zb64":"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