{"credit":"Taylor (gtmcknight), github.com/gtmcknight/amm-sim@4812a6382311624e1506aca131ca35c378f930d9 research/solved","approach":{"name":"QPL1 visit-weighted censored ridge coefficient refit","format":"QPL1, unchanged 540-byte header and 10,160-byte total","target":"Taylor FULL nonnegative fee displacement max(A_full-w,0); bid by mirrored ask","fit_training_seeds":[64000,64127],"visitation":"Every fourth LEANBASE/U1 lookup, both sides splatted to grid nodes; no report data","solver":"Python standard library; 8 censor sweeps and up to 100 coordinate-descent sweeps, tolerance 1e-7 bps","regularization":"Ridge to U1 coefficients; lambda in {1,10,100}; quantize original exponents; clip forward row deltas into [-128,127]","selected_lambda":100.0,"source_artifact":"https://api.imd.fun/artifacts/5893446c82f8bc1473468bba6b156ec2a96ce3df5ddfc897c05c63cf545e1162","adaptation":"Earlier visit refit used QMS1 spline coefficients; this build refits QPL1 trilinear knot values and preserves its byte deltas.","interpretation":"No statistically established improvement on selection. Report determines advance."},"ceiling":{"mean_diff":0.006176661077212903,"se":0.004111669561624127,"n":2048},"choose":{"seeds":[64512,64767],"n":256,"tried":{"cand_1":{"mean_diff":-0.006162939500707454,"se":0.007715727561316553,"min":-0.583308467048937,"n":256},"cand_10":{"mean_diff":-0.002904681756189742,"se":0.0071022687394344634,"min":-0.5809782044905205,"n":256},"cand_100":{"mean_diff":0.00038482843600418093,"se":0.005288533882117668,"min":-0.2898353397971505,"n":256}},"selected":"cand_100","selection_rule":"highest paired mean versus U1 among three refits; no further tuning","full_minus_u1":{"mean_diff":-0.0074023695137963585,"se":0.012135437822788451,"min":-1.0075854444755805},"zero_change_control":{"mean_diff":0,"se":0}},"frozen":{"time_utc":"2026-11-10T17:45:37.391045+00:00","builder_sha256":"8e82b01dfff7f2bbfd9c07d63a58c8fa86888f4fc6e690a225237f18f243ecb4","decoder_sha256":"d9fdf84692d5b7cea010926fca6ab8c827b2197db19fd1c27f549e4f4a0f6b5e","blob_sha256":"2266ecebf8253c332ab19e9131fb8f342178dc38fe4d564a906e4d1be1c43683","settings_sha256":"1b8f226ba621175463b8b932ead6dfca673277319702a1ec3a02a6abdfc9bc77","policy_sha256":"ba015769f7981d7798acbbc9aff8c0e0c59825bb1541d55bcdef49311d7a04bc","candidate_table_sha256":"f5a70c0a706f8a1abc145980ad8bf1c6e05522257886f55e68dc35bb2e9e060a","dependency_sha256":{"builder.py":"8e82b01dfff7f2bbfd9c07d63a58c8fa86888f4fc6e690a225237f18f243ecb4","check.py":"5832fc3266f1aafd211f026f56371d5b74f106a2a231511936eb45f61e2c1b4f","clsdata.py":"4bd79bfad7e6b6bb5471c72019e1c39b8fceaa880c05f4f98378a88823aaa1ce","decoder.py":"d9fdf84692d5b7cea010926fca6ab8c827b2197db19fd1c27f549e4f4a0f6b5e","dp.py":"f362ad97bf49dcc80312ced8f4785259445d615703c22d60bd7a0fc9b14f6578","evalpol.py":"79a96efe8c543aee125c937554688b37ec2e7df44df4f3435265bba6eb012bf3","example_policy.py":"b1b374010c5afe28d27edb529e86bf3274d498f2e4f89aa5a71bf089b6c0de12","experiment.py":"40d17100d84fa7aeadbc0bf5ea156ab84f7bfb5ccea04aaaf0be6bbe614686ea","fillstat.py":"9d3783f163848937b8b429224282f6e77bcbaec0e1d259b4711af6c10cefe0b6","fixedcheck2.py":"71241f0045044b138db6458678e58adaf265c2d4fbc6cc2de594aadf470cae40","gates.py":"27541115f7110e8afdd43d8fea6c690398652534be736c1c87bf544ed2f0eda1","latt.py":"ade2aa83f515f80531e97b797ea7da234c2357ba021d722d3577c266f1953300","lean_tell.py":"b70054f8c0561779e40c4c8df3f41ab08da05b9aef591816ac6c9034beb40a95","leanbase.py":"ba015769f7981d7798acbbc9aff8c0e0c59825bb1541d55bcdef49311d7a04bc","measure.py":"f2ae270a66664c6dcb5d769a9715597677855bbc05b75eeee2db613b6a226f0b","pair.py":"16497bc5d3b596102fb0c0d7b6bf331dc788ee04c42d537e41f0578115f631db","par.py":"bf8a1a2e2a4bf9a23bf4622d726fd815702298ad1f9fa0dd17197230cce63ec1","qmdp.py":"63b8c35048718c37eb0a5dae9e70849fa3af257c335e4a2e3a625c833f1dc1eb","real.py":"6a4a0f0e7f8b43d38e5eaa9d8b2e847a0771aee7f89551b122fe89bfe7969b5f","replica2.py":"54380c8755f4e42e9a0426759e918eacd2c5f915b7f04e5d0501c394c5846b40","report_group.py":"2dfd44248efb19dd55dc5e5d7ae65198104ade3ab69a2fc71548159d937d1a68","run.py":"91133e4ef485626226679d73349b5b4e374d9713ac5d7e9d36a39e1afd75933c","stale.py":"2b6ff558726b79d644b9bcaefcb16bf28d139345b8ec7652a409e64c95e0820a","stalerival.py":"d124a171d35b024daed840e5449d352b2b5e50a68ce98c8a9aaafaf3419d23da","tellpol.py":"7d5961c45bec56292533e65607245ad119b8733c760a5bf28f76a2e07bc3960b","tellref.py":"4867a33106ce2597863b8d7c8fb396288954b98f291714801fae8820f1f47aa3","trace.py":"ec7aabb10732c180e91bd9b2caf31ef9ca87feb15b3a4867967b3ce93b5b75f1","track.py":"898c1eb0b57bad36eb28d2c6db3208e2072f67587b12f4c9ec3314d17168a06f","visit.py":"8dc5fbb7a0f88a8a3d46deaf37898d559539de4783b3e1c5a7c7464d85939287"},"freeze_scope":"Before any report-arm run; the mandated P calibration separately ran upstream reference policies on 10000..10002 before selection."},"report":{"seeds":[10000,12047],"n":2048,"cand_minus_u1":{"mean_diff":-0.0017090242149569446,"se":0.001968350948328457,"min":-1.3910107458685843},"cand_minus_full":{"mean_diff":-0.007885685292169847,"se":0.004118149145331034,"min":-1.2103086644744963},"protocol":"One pass, increasing blocks of 64; U1, FULL, CAND each alone versus 30 bps VanillaStrategy; all arms complete on each block before next.","stop_reason":"Completed requested report window.","block_evidence":"Per-seed differences in evidence_zb64 preserve all ordered 64-seed blocks."},"cost":{"blob_bytes":10160,"extra_blob_bytes":0,"decoder_change":false,"extra_runtime_bytes_estimate":0,"extra_source_characters":0,"extra_gas_estimate":0,"estimate_basis":"Same-length hex literal replacement; identical layout, knots, exponents and decoder loops. Structural estimates only; no EVM compilation or gas measurement."},"advance":false,"tables_zb64":"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passages verified against the cited URLs.","replica":"Unmodified pinned replica2.py and frozen LEANBASE variant A; CPython 3.12.3; 10,000 steps; PROCS=4.","decoder_precision":"Integer WAD-bps grid nodes; float conversion only at Taylor replica JSON boundary. Integer surf equality checked by Python transcription, not Solidity."},"evidence_encoding":"base64(zlib(JSON)); report and selection per-seed paired differences in integer micro-edge units; max rounding error 0.0000005 per difference","evidence_zb64":"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","sources_file":"sources.json"}
