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sHdg7sHdw/uHtw9uHtw9+Duwd2Duwd3D+4e3D24e3D34O7B3YO7B3cP7h7cPbh7cPfg7sHdg7sHdw/uHtw9uHtw9+Duwd2Duwd3D+4e3D24e3D34O7B3YO7B3cP7h7cPbh7cPfg7sHdg7sHdw/uHtw9uHtw9+Duwd2Duwd3D+4e3D24e3D34O7B3YO7B3cP7h7cPbh7cPfg7sHdg7sHdw/uHtw9uHtw9+Duwd2Duwd3D+4e3D24e3D34O7B3YO7B3cP7h7cPbh7cPfg7sHdg7sHdw/uHtw9uHtw9+Duwd2Duwd3D+4e3D24e3D34O7B3YO7B3cP7h7cPbh7cPfg7sHdg7sHdw/uHtw9uHtw9+Duwd2Duwd3D+4e3D24e3D34O7B3YO7B3cP7h7cPbh7cPfg7sHdg7sHdw/uHtw9uHtw9+Duwd2Duwd3D+4e3D24e3D34O7B3YO7B3cP7h7cPbh7cPfg7sHdg7sHdw/uHtw9uHtw9+Duwd2Duwd3D+4e3D24e3D34O7B3YO7B3cP7h7cPbh7cPfg7sHdg7sHdw/uHtw9uHtw9+Duwd2Duwd3D+4e3D24e3D34O7B3YO7B3cP7h7cPbh7cPfg7sHdg7sHdw/uHtw9uHtw9+Duwd2Duwd3D+4e3D24e3D34O7B3YO7B3cP7h7cPbh7cPfg7sHdg7sHdw/uHtw9uHtw9+Duwd2Duwd3D+4e3D24e3D34O7B3YO7B3cP7h7cPbh7cPfg7sHdg7sHdw/uHtw9uHtw9+Duwd2Duwd3D+4e3D24e3D34O7B3YO7B3cP7h7cPbh7cPfg7sHdg7sHdw/uHtw9uHtw9+Duwd2Duwd3D+4e3D24e3D34O7B3YO7B3cP7h7cPbh7cPfg7sHdg7sHdw/uHtw9uHtw9+Duwd2Duwd3D+4e3D24e3D34O7B3YO7B3cP7h7cPbh7cPfg7sHdg7sHdw/uHtw9uHtw9+Duwd2Duwd3D+4e3D24e3D34O7B3YO7B3cP7h7cPbh7cPfg7sHdg7sHdw/uHtw9uHtw9+Duwd2Duwd3D+4e3D24e3D34O7B3YO7B3cP7h7cPbh7cPfg7sHdg7sHdw/uHtw9uHtw9+Duwd2Duwd3D+4e3D24e3D34O7B3YO7B3cP7h7cPbh7cPfg7sHdg7sHdw/uHtw9uHtw9+Duwd2Duwd3D+4e3D24e3D34O7B3YO7B3cP7h7cPbh7cPfg7sHdg7sHdw/uHtw9uHtw9+Duwd2Duwd3D+4e3D24e3D34O7B3YO7B3cP7h7cPbh7cPfg7sHdg7sHdw/uHtw9uHtw9+Duwd2Duwd3D+4e3D24e3D34O7B3YO7B3cP7h7cPbh7cPfg7sHdg7sHdw/uHtw9uHtw9+Duwd2Duwd3D+4e3D24e3D34O7B3YO7B3cP7h7cPbh7cPfg7sHdg7sHdw/uHtw9uHtw9+Duwd2Duwd3D+4e3D24e3D34O7B3YO7B3cP7h7cPbh7cPfg7sHdg7sHdw/uHtw9uHtw9+Duwd2Duwd3D+4e3D24e3D34O7B3YO7B3cP7h7cPbh7cPfg7sHdg7sHdw/uHtw9uHtw9+Duwd2Duwd3D+4e3D24e3D34O7B3YO7B3cP7h7cPbh7cPfg7sHdg7sHdw/uHtw9uHtw9+Duwd2Duwd3D+4e3D24e3D34O7B3YO7B3cP7h7cPbh7cPfg7sHdg7sHdw/uHtw9uHtw9+Duwd2Duwd3D+4e3D24e3D34O7B3YO7B3cP7h7cPbh7cPfg7sHdg7sHdw/uHtw9uHtw9+Duwd376BW+PXx7+Pbw7eHbw7eHbw/fHr49fHv49vDt4dvDt4dvD98evj18e/j28O3h28O3h28P3x6+PXx7+Pbw7eHbw7eHbw/fHr49fHv49vDt4dvDt4dvD98evj18e/j28O3h28O3h28P3x6+PXx7+Pbw7eHbw7eHbw/fHr594NsHvn3g2we+feDbB7594NsHvn3g2we+feDbB7594NsHvn3g2we+feDbB7594NsHvn3g2we+feDbB7594NsHvn3g2we+feDbB7594NsHvn3g2we+feDbB7594NsHvn3g2we+feDbB7594NsHvn3g2we+feDbx3++/Z9/Af5H68g=","setup":{"commands":["mkdir -p test/scratch/scratch","curl -fLsS https://raw.githubusercontent.com/gtmcknight/amm-sim/66604b3426d8e97a73e2c39940b9b4dece51cc73/research/tell/fetch.sh -o test/scratch/scratch/fetch.sh","sh test/scratch/scratch/fetch.sh https://raw.githubusercontent.com/gtmcknight/amm-sim/66604b3426d8e97a73e2c39940b9b4dece51cc73/research test/scratch/scratch/amm","python3 - <<'PY2'\nfrom pathlib import Path\nimport json,zlib,base64,hashlib\nr=json.load(open('artifacts/goal/batches/e02-lean-ref/items/ref/result.json'))\np=Path('test/scratch/scratch/amm')\nfor name,text in json.loads(zlib.decompress(base64.b64decode(r['reference_zb64']))).items():\n    assert Path(name).name == name\n    (p/name).write_text(text)\nassert hashlib.sha256((p/'leanbase.py').read_bytes()).hexdigest()==r['frozen']['code_sha256']\nPY2","cd test/scratch/scratch/amm","PROCS=4 timeout 470 python3 -I experiment.py agreement","PROCS=4 timeout 470 python3 -I experiment.py choose","# Each report block is a separate command; invoke in increasing seed order, once only.","PROCS=4 timeout 110 python3 -I experiment.py report 10000","PROCS=4 timeout 110 python3 -I experiment.py report 10064","PROCS=4 timeout 110 python3 -I experiment.py report 10128","PROCS=4 timeout 110 python3 -I experiment.py report 10192","PROCS=4 timeout 110 python3 -I experiment.py report 10256","PROCS=4 timeout 110 python3 -I experiment.py report 10320","PROCS=4 timeout 110 python3 -I experiment.py report 10384","PROCS=4 timeout 110 python3 -I experiment.py report 10448","PROCS=4 timeout 110 python3 -I experiment.py report 10512","PROCS=4 timeout 110 python3 -I experiment.py report 10576","PROCS=4 timeout 110 python3 -I experiment.py report 10640","PROCS=4 timeout 110 python3 -I experiment.py report 10704","PROCS=4 timeout 110 python3 -I experiment.py report 10768","PROCS=4 timeout 110 python3 -I experiment.py report 10832","PROCS=4 timeout 110 python3 -I experiment.py report 10896","PROCS=4 timeout 110 python3 -I experiment.py report 10960","PROCS=4 timeout 110 python3 -I experiment.py report 11024","PROCS=4 timeout 110 python3 -I experiment.py report 11088","PROCS=4 timeout 110 python3 -I experiment.py report 11152","PROCS=4 timeout 110 python3 -I experiment.py report 11216","PROCS=4 timeout 110 python3 -I experiment.py report 11280","PROCS=4 timeout 110 python3 -I experiment.py report 11344","PROCS=4 timeout 110 python3 -I experiment.py report 11408","PROCS=4 timeout 110 python3 -I experiment.py report 11472","PROCS=4 timeout 110 python3 -I experiment.py report 11536","PROCS=4 timeout 110 python3 -I experiment.py report 11600","PROCS=4 timeout 110 python3 -I experiment.py report 11664","PROCS=4 timeout 110 python3 -I experiment.py report 11728","PROCS=4 timeout 110 python3 -I experiment.py report 11792","PROCS=4 timeout 110 python3 -I experiment.py report 11856","PROCS=4 timeout 110 python3 -I experiment.py report 11920","PROCS=4 timeout 110 python3 -I experiment.py report 11984"],"public_sources_only":true,"requirements":"Python 3 standard library; curl or wget for initial fetch. No installed packages.","reference_encoding":"base64(zlib(UTF-8 JSON path:text map)); paths relative to scratch/amm","tables":"pol_mid.json and qm_k1e-5.json are immutable fetched data; excluded from the module map; validated by pinned SHA256SUMS.solved.","table_sha256":{"pol_mid.json":"efbb5ee102db4d312f64565d9bf7232b6becbe02b624164f744a402bbbf2983b","qm_k1e-5.json":"5ff53e42ca87390e5f65d1caa3f7a480e67a8212fb0670274dfd562b05ba1afd"},"options":{"soft":1,"qm":"qm_k1e-5.json","a0":0.35,"ainf":0.35},"base_source_sha256":"176f8115fb67d99340f1de1e6994042dfaa41f830c22e000595df3377cc6a2b3","base_materialization":"Decode code_zb64 of f5a140f5 artifact as base64/zlib to stalerival.py. Packed copy differs only by the added Taylor credit comment.","lean_materialization":"Decode lb-4 bundle.data as base64/tar.xz; scratch/ref/lean_tell.py is copied verbatim.","timing":{"policy":"BASE","seeds":[64000,64007],"n":8,"seconds_printed":2,"PROCS":4,"note":"Eight-seed timing before agreement/selection; run.py uses one task of eight, hence this timing was serial."}},"seeds_used":{"timing":[64000,64007],"agreement":[64000,64031],"selection":[64032,64287],"report":[10000,12047],"timing_overlaps_diagnostic_only":true,"report_reused_for_selection":false},"checks":{"fetch":{"files":34,"mismatched":0},"original_base_sha256_verified":true,"lb4_bundle_and_lean_tell_sha256_verified":true,"synthetic_variant_label_branches":12,"frozen_dependencies_verified":true,"paired_report_rows_verified":true,"packed_modules_parse":11,"reconstructed_init_and_callback_fees_valid":true},"not_done":[],"limitations":["Python replica only: no Solidity port, gas, engine or leaderboard submission performed.","Agreement is measured on the BASE stream of 32 seeds, not a proof for all possible integer inputs.","The cause of the divergent report seed was not localized; report seeds were not rerun, and B/C were not tested on them.","B retains the normal pre-tell posterior on unknown labels by using hit=True and chance=1. A and B have identical selection scores; tie-break selects A.","Timestamp 0 first callbacks are included in the diagnostic table. BASE itself applies the first-trade posterior only when timestamp advances (dt>=1), unchanged here."],"conclusion":"There are measured seed-level losses; the paired mean is not shown worse than BASE under the requested mean+2*SE criterion. This is not an equivalence proof."}