{"item":"stack-u1-give","batch":"e04-c-replica","kind":"replica forecast, two frozen variants, nothing tuned","hypothesis":"Forecast of the next contract on the replica. V0 = corrected integer-observation TellPolicy (soft=1) with the QPL1 u1 compressed tables (what the lookup-u1 contract carries) in place of qm_k1e-5.json; expected about level with the corrected qm baseline (table compression cost small). V1 = V0 + GivePolicy two-sided rival pull alpha=alpha2=0.35; expected about +0.09 over V0 as GivePolicy measured over TellPolicy.","verdict":"V0 vs tell_qm.json -0.0084 se 0.0043 (u1 tables cost -0.0046 se 0.0043 vs the corrected qm baseline; the integer-observation wrapper itself -0.0038 se 0.0015 incl. this machine's libm). V1 vs tell_qm.json +0.0821 se 0.0077. Paired V1-V0 +0.0904 se 0.0072: the GivePolicy pull stacks on the u1 tables with its full measured gain (+0.091 in its own item). Below the 0.2 bar alone; the forecast for a u1+give contract is about +0.08 over the shipped tell_qm replica rows.","variants":{"V0":{"run":"python3 -I run.py stack:V0 <s0> <n> out.json soft=1 qm=q_u1.json","class":"stack.V0 (tellpol.TellPolicy + integer wrapper)","params":{"soft":1,"qm":"q_u1.json","pin":0}},"V1":{"run":"python3 -I run.py stack:V1 <s0> <n> out.json soft=1 qm=q_u1.json","class":"stack.V1 (give.GivePolicy + the same integer wrapper)","params":{"soft":1,"qm":"q_u1.json","alpha":0.35,"alpha2":0.35,"pin":0}},"C_reference":{"run":"python3 -I run.py stack:V0 <s0> <n> out.json soft=1 qm=qm_k1e-5.json","note":"corrected baseline (identical wrapper, qm_k1e-5 tables); run on holdout only to label the integer-observation difference, not a selectable variant"}},"frozen":{"frozen_utc":"2026-11-10 04:59:10 (before any holdout seed ran)","code_sha256":{"stack.py":"02f8a3df130f10d984af042ca70214d3c315ac81c8e3c0f96fccb00be1d24778","give.py":"8d871a6b4fe3fda47404282239939f558b1656a651fcc03eb86f1d4f56cc1b3e","tellpol.py":"6e62a0e070d9778537e746438f03ea7a4b44cb1d9652ae13702b801997db5f6d","real.py":"7fc9f4bdf9a9af094ec2eb0e9a8e8eec8b343c5226bf0ebaa30c798785cb7c58","tellref.py":"72679df55e2ba46a903075543654b685e7b2bf86dfbef3d5122dff6368d4a4f2","run.py":"54d19a9194ebedee8b947dfaaf8626b1e4ef6f7d1b5aa12e738ec01a5ce401ac","replica2.py":"6e37b4e61e1ffa5ffb1e43912ddb8659105989aa986eb48fcd1d7608f8dca685"},"table_sha256":{"q_u1.json (decoded)":"d5645b6dd4ed8f1e33cc712d56d9eb916a50db7d5014c285b0f388cd114f96f9","qm_k1e-5.json":"5ff53e42ca87390e5f65d1caa3f7a480e67a8212fb0670274dfd562b05ba1afd","pol_mid.json":"efbb5ee102db4d312f64565d9bf7232b6becbe02b624164f744a402bbbf2983b","tell_qm.json":"ad382c07e6c2b3a5a8055ea767e140b7461105f29dafee69b7c1766c90d0634f"},"param_sha256":"f882869b6fa7a084a48c4d2d0445e0ef3d674567d29d39a6688b4ad75653ca97"},"hash_checks":{"artifact_fd38823c_body_sha256":"match","artifact_e931451c_body_sha256":"match","QPL1 blob in u1 Strategy.sol sha256 830868f9b3e5ad162c4f2070e6faed0782c7b4cc308ab69044b2cf7306f68099":"match (10160 bytes)","decoded q_u1.json sha256 d5645b6d...":"match","give.py (code_zb64 decoded) 8d871a6b...":"match","u1 bundle_zb64 decoded sha256 4f3e0243...":"match","u1 code_zb64 decoded sha256 69c361ef...":"match","research fetch.sh":"34 files, 0 mismatched","u1 meta.json axes == pol_mid.json axes":true},"wrapper":"Identical for V0, V1 and C: inputs ax,ay,x,y -> int(float*1e18) (engine as-i128 truncation); policy sees wad/1e18 only. First trade of a step (t > last timestamp): hit = tellref.is_arb(is_buy, wax, way, wx, wy, prev handed wx, wy (afterInitialize 100e18, 10000e18), last returned integer fees). chance = min(1, 2^(binade(paid) - min(binade(before), binade(after)))) from integer binades; paid==0 -> no hit. Posterior odds kept: off grid pi=0, on grid RHO*chance (TellPolicy), soft=1 blend, no pin. Returned fee -> trunc(f*1e18), clamp [0,1e17], gamma guard [2999999999999948, 3000000000000057] -> nearest outside (ties lower), fee in force = wad/1e18. init fee 2823606104386589 both sides (equals the u1 contract afterInitialize).","tuning_smoke":{"seeds":"61600..61855 (256), nothing tuned, smoke only","V0_vs_C":{"n":256,"mean_diff":-0.001369,"se":0.011894,"sum":-0.350404,"sumsq":9.235889,"worst5":[[61724,-0.601496],[61773,-0.550104],[61786,-0.543403],[61658,-0.444325],[61770,-0.419852]]},"V1_vs_C":{"n":256,"mean_diff":0.059018,"se":0.020491,"sum":15.108658,"sumsq":28.302285,"worst5":[[61850,-1.188365],[61638,-0.916978],[61770,-0.808275],[61836,-0.673192],[61660,-0.623777]]},"V1_minus_V0":{"n":256,"mean_diff":0.060387,"se":0.017665,"sum":15.459062,"sumsq":21.305087,"worst5":[[61850,-0.948794],[61638,-0.74212],[61678,-0.585173],[61660,-0.541406],[61817,-0.527785]]}},"holdout":{"seeds":"10000..12047, each arm once, 8 disjoint 256-seed chunks, no retuning/selection","V0_vs_tell_qm":{"n":2048,"mean_diff":-0.00836,"se":0.004272,"sum":-17.121957,"sumsq":76.66296,"worst5":[[11659,-0.978442],[11821,-0.915444],[11013,-0.786341],[11206,-0.703885],[10710,-0.662631]]},"V1_vs_tell_qm":{"n":2048,"mean_diff":0.082079,"se":0.007715,"sum":168.097817,"sumsq":263.353781,"worst5":[[11696,-2.201061],[10822,-1.454063],[11413,-1.255391],[11277,-1.19944],[10178,-1.098939]]},"paired_V1_minus_V0":{"n":2048,"mean_diff":0.090439,"se":0.007157,"sum":185.219774,"sumsq":231.47667,"worst5":[[11696,-2.206221],[11277,-1.428388],[10178,-1.092432],[10676,-1.061892],[10804,-1.060222]]},"labels":{"integer_observation_plus_libm (C - tell_qm)":{"n":2048,"mean_diff":-0.003793,"se":0.001476,"sum":-7.767828,"sumsq":9.157003,"worst5":[[10303,-1.161856],[11413,-0.452487],[11714,-0.392646],[11477,-0.371507],[11796,-0.359345]]},"u1_tables (V0 - C)":{"n":2048,"mean_diff":-0.004567,"se":0.004317,"sum":-9.354129,"sumsq":78.153649,"worst5":[[11659,-0.967348],[11821,-0.914797],[11013,-0.705834],[11206,-0.666792],[10710,-0.662631]]},"V1 - C":{"n":2048,"mean_diff":0.085872,"se":0.007732,"sum":175.865645,"sumsq":265.745043,"worst5":[[11696,-2.201792],[10822,-1.443643],[11277,-1.219565],[11109,-1.132753],[10178,-1.086525]]}},"vs_HA":{"V0_vs_HA":[6.9664,0.0441],"V1_vs_HA":[7.0569,0.0433],"C_vs_HA":[6.971,0.044],"tell_qm_vs_HA":[6.9748,0.044]},"first_trade_misread":{"V0":{"misread":4203,"first":14350622,"rate":0.0002929},"V1":{"misread":4154,"first":14400261,"rate":0.0002885},"C":{"misread":4150,"first":14350187,"rate":0.0002892},"tell_qm":{"misread":3420,"first":14350251,"rate":0.0002383}},"price_rms_bps":{"V0":7.1551,"V1":7.1131,"C":7.1585,"tell_qm":7.1494}},"libm_note":"This machine: tellpol:TellPolicy soft=1 qm=qm_k1e-5.json on 10000..10002 gives 446.79826, 574.32668, 529.637987 vs README 446.7525, 574.32668, 529.609154 (same as the GivePolicy item saw). C - tell_qm therefore mixes the integer-observation difference with libm; both are small (-0.0038 se 0.0015).","diagnostics_harness_only":{"note":"truth (price, rival spot, arb flag) read by the harness after each callback, never passed to the policy; seeds 61600..61663 (tuning range)","V0":{"seeds":[61600,61663],"first_trades":445955,"first_misread":130,"first_misread_rate":0.0002915092329943604,"arb_first_trades":239704,"arb_read_as_retail":27,"retail_read_as_arb":103,"price_rms_bps_by_steps_since_pin":{"0..0":[0.468,395761],"1..1":[6.949,65557],"2..3":[10.42,83721],"4..7":[12.377,85257],"8..15":[12.864,49721],"16..31":[12.832,10015],"32..63":[11.607,284],"64..inf":[null,0]},"price_rms_bps_after_first_pin":7.153,"rival_spot_rms_bps":2.786},"V1":{"seeds":[61600,61663],"first_trades":447455,"first_misread":126,"first_misread_rate":0.00028159256238057456,"arb_first_trades":241274,"arb_read_as_retail":32,"retail_read_as_arb":94,"price_rms_bps_by_steps_since_pin":{"0..0":[0.384,398470],"1..1":[6.973,65842],"2..3":[10.4,83943],"4..7":[12.359,85371],"8..15":[12.745,49410],"16..31":[12.87,9900],"32..63":[11.275,274],"64..inf":[null,0]},"price_rms_bps_after_first_pin":7.114,"rival_spot_rms_bps":2.27}},"blindness":{"needs_flag":false,"run_through":"unmodified run.py module:Class (sha256 54d19a91...), Blind passes 6 afterSwap args only","policy_inputs":"WAD-converted callback fields, timestamp, own stored integers (prior reserves, returned fees) and own filter state","no truth, rival state, seed, sigma, lambda or size used":true,"oraclearb":"TellPolicy sets o[oraclearb]=1 internally to hand RealPolicy the TEST answer (hit), not the truth; run.py never passes the flag since needs_flag is False","Q_cache":"module-level cache of the table JSON per path; read-only, no decision state"},"solidity_estimate_unmeasured":{"V0":"= lookup-u1 contract (artifact fd38823c) as is: no new state or ops. Its own report: Frozen QPL1 u1 replaces only min-lookup tables/decoder; not re-measured here.","V1":"no new state (s2h=r, fb/fa in force, s, cnt already in slots). Per own-retail callback where the bound does not bind: ln(cand) (ln(s) already computed), one mul by 0.35e18, one exp, done before the single fees() lookup. Estimate +60..150 bytes, +3k..6k gas on affected calls, UNMEASURED; peak still expected under 245,000 if fused before the lookup."},"seeds_completed":{"tuning":"61600..61855 x {V0,V1,C}","holdout":"10000..12047 x {V0,V1,C}","remaining":[]},"per_seed_rows":{"encoding":"base64(zlib(UTF-8 CSV)); first line is the header comment; edges as differences to tell_qm.json rows (sha256 ad382c07...), rounded to 1e-5; paired stats above computed from full-precision 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","sha256":"29280f442e1776375e31a9a2d99ebea94fad150ee629d30976ab760b3a68c2bf"},"code_file":"stack.py (beside run.py; classes V0, V1)","code_sha256":"02f8a3df130f10d984af042ca70214d3c315ac81c8e3c0f96fccb00be1d24778","code_zb64":"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","bundle_encoding":"base64(zlib(UTF-8 JSON path:text map, sort_keys)); put stack.py, give.py beside run.py of the research bundle; decode_u1.py with qpl.py and the u1 Strategy.sol (artifact fd38823c code_zb64) writes q_u1.json","bundle_sha256":"9d49f4d2f63bf2cc80b7b7605bdbe3c4683b174fa66d9b24ee596ac976cf14dd","bundle_zb64":"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","not_done":["No Solidity or engine work (out of scope).","Diagnostics by time since pin on 64 tuning seeds only, not on holdout."]}