{"credit":"Taylor (gtmcknight), github.com/gtmcknight/amm-sim@4812a6382311624e1506aca131ca35c378f930d9 research/solved: original replica, Gaussian price filter, posterior fee tables and policy. Research snapshot 66604b3426d8e97a73e2c39940b9b4dece51cc73; LEANBASE and QLITE from the supplied IdentityMD artifacts.","question":"Does carrying rival state between q-lite split hits improve on LEANBASE and QLITE, on Taylor's replica?","answer":"No for the specified and tested carry implementation. Retain QLITE; do not advance CARRY-1.","arms":[{"name":"LEANBASE","definition":"Unchanged supplied leanbase:Policy, variant A; bound update plus 0.35 pull.","parameters":{"soft":1,"qm":"qm_k1e-5.json","a0":0.35,"ainf":0.35},"source":"https://api.imd.fun/artifacts/efb8e653f8fd061cecf3e92b186470c9c96c5188523364b60ed101e9672869aa"},{"name":"QLITE","definition":"Supplied frozen qlgate:Policy over LEANBASE.","parameters":{"soft":1,"qm":"qm_k1e-5.json","a0":0.35,"ainf":0.35,"q":1,"pull":1,"cap":24,"gate":6,"order":"b"},"source":"https://api.imd.fun/artifacts/f5cea5b26056f47110e51ce015e5bb62951669a17aad600b2af2533f21886d8e"},{"name":"CARRY-0","definition":"Carry one rival spot (equivalent to reserves x=sqrt(1e6/spot), y=1e6/x). On EVERY own callback, after the unchanged LEANBASE price update, clip the carried log spot to [m+log(0.997)-beta*sqrt(v), m-log(0.997)+beta*sqrt(v)]. Then apply the QLITE cap24/gate6 split test on non-arb callbacks: a hit replaces the rival spot with own_spot*(1-fee)/0.997 on pool-buy-X, own_spot*0.997/(1-fee) otherwise. Use synced=True for this point estimate. Replace the legacy one-sided bound and 0.35 pull, rather than combining with them. No hidden inputs.","parameters":{"beta":0,"cap":24,"gate":6,"order":"b","soft":1,"qm":"qm_k1e-5.json","synced":true,"clip_frequency":"every callback, including later callbacks of the same step","legacy_bound":false,"legacy_pull":false},"report_status":"selection only"},{"name":"CARRY-1","definition":"Carry one rival spot (equivalent to reserves x=sqrt(1e6/spot), y=1e6/x). On EVERY own callback, after the unchanged LEANBASE price update, clip the carried log spot to [m+log(0.997)-beta*sqrt(v), m-log(0.997)+beta*sqrt(v)]. Then apply the QLITE cap24/gate6 split test on non-arb callbacks: a hit replaces the rival spot with own_spot*(1-fee)/0.997 on pool-buy-X, own_spot*0.997/(1-fee) otherwise. Use synced=True for this point estimate. Replace the legacy one-sided bound and 0.35 pull, rather than combining with them. No hidden inputs.","parameters":{"beta":1,"cap":24,"gate":6,"order":"b","soft":1,"qm":"qm_k1e-5.json","synced":true,"clip_frequency":"every callback, including later callbacks of the same step","legacy_bound":false,"legacy_pull":false},"report_status":"selected"},{"name":"CARRY-2","definition":"Carry one rival spot (equivalent to reserves x=sqrt(1e6/spot), y=1e6/x). On EVERY own callback, after the unchanged LEANBASE price update, clip the carried log spot to [m+log(0.997)-beta*sqrt(v), m-log(0.997)+beta*sqrt(v)]. Then apply the QLITE cap24/gate6 split test on non-arb callbacks: a hit replaces the rival spot with own_spot*(1-fee)/0.997 on pool-buy-X, own_spot*0.997/(1-fee) otherwise. Use synced=True for this point estimate. Replace the legacy one-sided bound and 0.35 pull, rather than combining with them. No hidden inputs.","parameters":{"beta":2,"cap":24,"gate":6,"order":"b","soft":1,"qm":"qm_k1e-5.json","synced":true,"clip_frequency":"every callback, including later callbacks of the same step","legacy_bound":false,"legacy_pull":false},"report_status":"selection only"},{"name":"ORACLE-R","definition":"Same LEANBASE price belief and WAD fee rounding; replace the rival estimate at every fee lookup with the true rival spot supplied by a diagnostic wrapper, synced=True. No true price, arb flag or retail count enters policy decisions.","parameters":{"soft":1,"qm":"qm_k1e-5.json","oracle":"rival spot only"}}],"choose":{"seeds":[64000,64255],"n":256,"paired":{"CARRY-0":{"LEANBASE":{"mean_diff":-1.4522988043531708,"se":0.048838489175927265,"n":256},"QLITE":{"mean_diff":-1.4835250690349207,"se":0.04885411029908889,"n":256}},"CARRY-1":{"LEANBASE":{"mean_diff":-1.4315869337192737,"se":0.04826192776802677,"n":256},"QLITE":{"mean_diff":-1.4628131984010235,"se":0.047623191654163864,"n":256}},"CARRY-2":{"LEANBASE":{"mean_diff":-1.4787221834861168,"se":0.04917840563674654,"n":256},"QLITE":{"mean_diff":-1.5099484481678667,"se":0.049888424082138345,"n":256}},"ORACLE-R":{"LEANBASE":{"mean_diff":0.1272228920125884,"se":0.020265169933024598,"n":256},"QLITE":{"mean_diff":0.09599662733083858,"se":0.016897692926648427,"n":256}},"QLITE":{"LEANBASE":{"mean_diff":0.031226264681749827,"se":0.01003380310795843,"n":256},"QLITE":{"mean_diff":0.0,"se":0.0,"n":256}}},"selection_rule":"Highest paired mean vs QLITE on 64000..64255; ties lower beta. Frozen before report.","settings_evaluated":3,"frozen_hashes":{"carry.py":"8d73c554e4d96f8ed0b5b1fe1bdbd1539f6b5bf6b759ea041cf9c68aebc2bd9f","carry_run.py":"91ba6feba3a7b4cd8a32b768b3bf1bc77dbc59f89702816468d0f54a2104d81c"}},"report":{"CARRY-1":{"LEANBASE":{"mean_diff":-1.389634413474623,"se":0.024056279181664364,"n":1024},"QLITE":{"mean_diff":-1.4274138188747783,"se":0.023663850910789933,"n":1024}},"ORACLE-R":{"LEANBASE":{"mean_diff":0.14321572096371615,"se":0.009519073155560588,"n":1024},"QLITE":{"mean_diff":0.10543631556356103,"se":0.008394035953880422,"n":1024}},"QLITE":{"LEANBASE":{"mean_diff":0.03777940540015512,"se":0.004890203753792384,"n":1024},"QLITE":{"mean_diff":0.0,"se":0.0,"n":1024}},"LEANBASE":{"LEANBASE":{"mean_diff":0.0,"se":0.0,"n":1024},"QLITE":{"mean_diff":-0.03777940540015512,"se":0.004890203753792384,"n":1024}}},"report_method":{"seeds":[10000,11023],"n":1024,"steps_per_seed":10000,"opponent":"Each arm alone against replica2 fixed 30 bps constant-product rival","statistic":"Per-seed edge difference; arithmetic mean; SE=sample standard deviation (ddof=1)/sqrt(n).","report_passes":1,"tuned_on_report":false,"scores_are":"Replica edge units, not leaderboard scores","policy_means":{"LEANBASE":536.1283361809258,"QLITE":536.1661155863259,"CARRY-1":534.7387017674511,"ORACLE-R":536.2715519018894}},"best_live":"CARRY-1","recommended_live":"QLITE","ceiling":{"arm":"ORACLE-R","paired":{"LEANBASE":{"mean_diff":0.14321572096371615,"se":0.009519073155560588,"n":1024},"QLITE":{"mean_diff":0.10543631556356103,"se":0.008394035953880422,"n":1024}},"meaning":"Empirical rival-information ceiling for this fixed fee policy; not a proven upper bound on all possible strategies."},"advance":false,"advance_test":{"threshold":0.02,"mean_minus_2se":-1.474741520696358,"comparison":"CARRY-1 minus QLITE"},"cost":{"scope":"Analytical Python/source-level arithmetic only, no gas claim.","storage_words":{"extra_over_LEANBASE_as_implemented":1,"meaning":"One persistent carried rival spot in addition to inherited s2h; fixed K determines both reserves. Existing own-reserve WADs, fee WADs, timestamp and filter state are reused. Python diagnostic counters are excluded.","possible_reuse":"A port could reuse s2h and save its pre-callback value in a local; not ported or measured."},"arithmetic_a_callback":{"carry_clip":"One sqrt(v), log(carried), log(0.997), exp(clipped); one beta multiplication, three additions/subtractions, min/max and changed-value comparison. log(0.997) is constant-foldable. Then ordinary rival-coordinate log/subtract/scale for lookup.","split_test":"Same q-lite capped binary64 test, cap=24 counted operations, only on non-arb callbacks with dt<=6; a hit additionally computes one division for own spot, fee division/subtraction, and one multiplication/division for the rival spot.","removed":"LEANBASE bound and 0.35 pull are overwritten/bypassed. The research subclass still executes the parent bound before overwriting it; this is not an optimized implementation."}},"checks":{"P_gates":{"matched":true,"digits":6,"output":"seed   perfect-info      gaussian+plain    gaussian+qm       lattice+qm\n10000  448.261189  443.455598  443.522608  443.663520\n10001  576.779199  571.463409  571.623551  571.977293\n10002  531.886497  525.168028  525.865859  526.411631\n","source":"https://raw.githubusercontent.com/gtmcknight/amm-sim/66604b3426d8e97a73e2c39940b9b4dece51cc73/research/solved/README.md"},"frozen_code_unchanged":true,"split_spot_truth_diagnostic":{"seeds":[64000,64003],"threshold_absolute_log_error":1e-09,"counts":{"QLITE":{"hits":21114,"wrong":15,"arb_wrong":0,"retail_wrong":15},"CARRY-0":{"hits":20613,"wrong":13,"arb_wrong":0,"retail_wrong":13},"CARRY-1":{"hits":20514,"wrong":10,"arb_wrong":0,"retail_wrong":10},"CARRY-2":{"hits":20600,"wrong":13,"arb_wrong":0,"retail_wrong":13}},"meaning":"wrong counts detected hits with inferred spot different from replica true spot, not a proof that every other hit was an interior split. All mismatches here were retail. Diagnostic hidden values never enter live decisions.","max_error_bps":{"QLITE":14.386472716441851,"CARRY-0":9.152159276099779,"CARRY-1":3.9261688290937102,"CARRY-2":0.01916959306904112}},"carry_invariant_and_finite_fees":{"seeds":[64000,64003],"settings":[0,1,2],"passed":true,"checks":"All callback fees finite and in [0,0.1]; stored spot reconstructs constant-product reserves at relative tolerance 1e-14; shadow runs reproduce pilot seed edges exactly."},"input_dependency_hashes":{"replica2.py":"54380c8755f4e42e9a0426759e918eacd2c5f915b7f04e5d0501c394c5846b40","evalpol.py":"79a96efe8c543aee125c937554688b37ec2e7df44df4f3435265bba6eb012bf3","par.py":"bf8a1a2e2a4bf9a23bf4622d726fd815702298ad1f9fa0dd17197230cce63ec1","run.py":"91133e4ef485626226679d73349b5b4e374d9713ac5d7e9d36a39e1afd75933c","real.py":"6a4a0f0e7f8b43d38e5eaa9d8b2e847a0771aee7f89551b122fe89bfe7969b5f","tellpol.py":"7d5961c45bec56292533e65607245ad119b8733c760a5bf28f76a2e07bc3960b","tellref.py":"4867a33106ce2597863b8d7c8fb396288954b98f291714801fae8820f1f47aa3","stalerival.py":"d124a171d35b024daed840e5449d352b2b5e50a68ce98c8a9aaafaf3419d23da","leanbase.py":"ba015769f7981d7798acbbc9aff8c0e0c59825bb1541d55bcdef49311d7a04bc","lean_tell.py":"b70054f8c0561779e40c4c8df3f41ab08da05b9aef591816ac6c9034beb40a95","qlgate.py":"0823f2d25728bf95238fc350a8aabefe8e73143cfaf9859e894a7c2c06984de6","qlite.py":"2ab8337870a2375b79f1773619e3b11a472bc6d8b4b9085f5047cec645c57535","qbase.py":"1a6dc9fa4bf27891d91701c9b1fce91c19091b7559dfcc282342c5ab47bb306a","pol_mid.json":"efbb5ee102db4d312f64565d9bf7232b6becbe02b624164f744a402bbbf2983b","qm_k1e-5.json":"5ff53e42ca87390e5f65d1caa3f7a480e67a8212fb0670274dfd562b05ba1afd"}},"reference_zb64":"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JSON {filename: source text})); includes arms, runner and diagnostic checks, each credited to Taylor.","reproduce":{"dependencies":[{"url":"https://raw.githubusercontent.com/gtmcknight/amm-sim/66604b3426d8e97a73e2c39940b9b4dece51cc73/research/tell/fetch.sh","purpose":"Fetch pinned solved and tell files, verify SHA256SUMS."},{"url":"https://api.imd.fun/artifacts/efb8e653f8fd061cecf3e92b186470c9c96c5188523364b60ed101e9672869aa","field":"reference_zb64","purpose":"Overlay LEANBASE modules."},{"url":"https://api.imd.fun/artifacts/f5cea5b26056f47110e51ce015e5bb62951669a17aad600b2af2533f21886d8e","field":"code_zb64","purpose":"Overlay frozen QLITE modules."}],"then":"Extract this reference_zb64 beside those modules. Stdlib Python only, no installed packages. Run disjoint chunks of <=128 seeds, PROCS=4, timeout 470.","command":"PROCS=4 timeout 470 python3 -I carry_run.py SEED0 N OUTPUT.json LEANBASE QLITE CARRY-1 ORACLE-R","selection_command":"PROCS=4 timeout 470 python3 -I carry_run.py SEED0 N OUTPUT.json LEANBASE QLITE CARRY-0 CARRY-1 CARRY-2 ORACLE-R"},"seeds_used":{"selection":[64000,64255],"pilot_and_shadow_diagnostics_reuse_selection_only":[64000,64003],"report":[10000,11023],"published_gate_validation_only":[10000,10002],"engine":[],"report_reused_for_selection":false},"not_done":["No Solidity, gas, code-size or fresh-engine evaluation: this assignment is Python replica only. Nothing submitted to Paradigm.","CARRY-0 and CARRY-2 were not run on report seeds; the single selection winner was frozen before report.","No claim of exact rival state between hits: rival-only retail trades and intervening arbitrage are unobserved. A split hit gives an algebraic reconstruction subject to binary64 rounding and the observed exceptions.","The supplied zero-false-hit claim did not reproduce on these diagnostic trajectories; see checks. Carry tests retain the supplied detector unchanged.","The measured implementation clips at every callback and uses synced point-estimate tables. Clipping only on new steps, retaining the baseline bound/pull, or alternative synced handling were not evaluated; this negative result does not rule out those variants.","No causal ablation attributes the loss to clipping, removed bounds/pull, synced handling, or detector errors.","Large pinned Taylor table files and supplied baseline modules are not duplicated in the bounded output; reproduction requires the listed source artifacts and matching hashes."],"evidence":{"report_columns":["LEANBASE","QLITE","CARRY-1","ORACLE-R"],"report_rows_zb64":"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