{"gate_P": {"status": "PASS", "gates_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", "note": "unmodified `python3 -I gates.py` at amm-sim 4812a638 (git rev-parse confirmed), 12 values equal README table at 6dp; qt1=QMRef and COMBO on seeds 10000..10002 at 10000 steps: qt1 443.101741/571.454483/525.908441, COMBO 444.195330/572.172682/526.274760 (from report.jsonl, equal to brief at 6dp)"}, "hashes": {"artifacts": {"qt1": "8a6e303b662e1a5ea9bb47f31acedd3988ef65b3ddb66d074517aa59ad41ca40", "TC": "d3c849db2ac31e567b57e52b80b86e8c24c842e5b4125b9d75c78e3cb07115b9", "COMBO": "d360bea6c8c982717b7b43a8069a87013169c89ee3e4da25622db4d880b936d4", "QMS1": "17d249ab0c33fc44931d9b105cf3f9e3846a560bd518b67310c2a56defacdecc", "note": "all four byte-sha256 verified equal to their id"}, "files": {"head.py": "4f4e932038c53022cc40744c862b90c31f8d9e9dc7a03a080179244c7ef78b69", "combo.py": "c5b711a0b4fb4783e8879a484bf87a427171f4a11aa5eb5ec3513c24ead1a796", "twocomp.py": "9d3c1833321493fb51bbde4e1b4dabc2f8978bd6d774ee2af8dc8aed13f0f0af", "qmref.py": "1dfc9b7a3c2237ff7a93dd9f0af54b9bdab16f2ce0a7eb00784cb4432598db92", "blob.bin": "3ac7cb8811ecfb6a17f499aa4e9b70dd55f7fb6432b3aec3b904e11a3dece38e", "common.py": "e2040ddb59cfaf243cd1b60d22f58f763a5f692d5fc29a9efbc299cf11ae6ef7", "train.py": "2f4800ffae175141e46aab281989e175df22165a325b99cff5a7be3acb04cc11", "scales.py": "b91fb3080d3dee12de002470cd0d1f483f863adc4f3f0ffac768f549c492752b", "final.py": "9608942b440b5447160f2fb1e2b33dabefae6c91a9d3c68085504b454ec209f1"}, "theta_sha256": "75ba34cc0171018318baade8bb6ee1bf311ab43dff08cddcca5389d599fdeae1"}, "frozen_spec": {"policy": "Head(Combo): 14 standardized inputs -> 16 tanh -> 2 outputs (3*tanh bps) added to COMBO fb,fa, clipped [0,0.1]", "inputs": ["pi", "belief mean minus mid of the no-arb interval of the quotes in force (bps)", "belief sd (bps)", "w=(ln s - m) bps", "R=(ln s2h - m) bps", "phase 0/1/2", "ln(own trade Y size) minus fixed size-law mu over 1.2", "silent steps (min(dt-1,12), first trade of step only)", "comp1 mean-ln s, sd, weight", "comp2 mean-ln s, sd, weight (weight 0 if single component)"], "scales": {"mu": [0.5649648905045734, -0.8504509977299615, 11.781002819550867, 2.072135381999941, 0.97952940262968, 0.816571161267688, -0.5501903109073581, 0.28306630803414806, -1.7825190678312404, 1.0990698519960667, 0.6643684690334004, -1.965185085642375, 9.48585107141055, 0.3356315309665996], "sd": [0.362879256776457, 42.579773488257466, 6.890848367097803, 77.03679516140254, 41.37575825372821, 0.7194275087945189, 0.9752903212353984, 0.8030419596077029, 79.36699396165932, 3.4321269593015353, 0.3299769664454189, 69.77233552990211, 5.089895367551814, 0.3299769664454189], "isd": [2.75573756649316, 0.02348532925558604, 0.14512001233037825, 0.012980809987031059, 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-0.08265600654917378, 0.03839239904952743, -0.32475044376582174, 0.01240811366814129, -0.3831073452475176, -0.22317561472056846, -0.6755629636329357], "param_count": 274, "cma": "sep-CMA-ES diagonal, sigma0 0.3, popsize 12, mu 6, own stdlib implementation, hidden init N(0,1/sqrt(14)) seed 1, output weights/biases 0"}, "choose": {"seeds": "64000..64031", "n_steps": 10000, "C-COMBO": {"n": 32, "mean_diff": -0.5037199048617946, "se": 0.10671990481075704, "worst5": [[64027, -1.9623301946830338], [64026, -1.569901283683464], [64011, -1.534183616323503], [64010, -1.2047498296266212], [64006, -1.1272051959081182]], "wins": 7, "sum_d": -16.119036955577428, "sum_d2": 19.41750473988172}, "C-qt1": {"n": 32, "mean_diff": 0.1426751531118402, "se": 0.15745885332703996, "worst5": [[64011, -1.7044643899248513], [64020, -0.9688956385261918], [64021, -0.9638160208459112], [64027, -0.7169669298505141], [64026, -0.609110311168763]], "wins": 16, "sum_d": 4.5656048995788865, "sum_d2": 25.246342545233336}, "COMBO-qt1": {"n": 32, "mean_diff": 0.6463950579736348, "se": 0.11910201586298923, "worst5": [[64019, -0.2939863742011539], [64022, -0.2883237583646405], [64021, -0.20583503019577165], [64011, -0.1702807736013483], [64013, -0.06523229668772501]], "wins": 26, "sum_d": 20.684641855156315, "sum_d2": 27.442258132294352}, "mean_edge": {"C": 522.4958576755677, "COMBO": 522.9995775804294, "qt1": 522.3531825224559}}, "report": {"seeds": "10000..10031", "n_steps": 10000, "C-COMBO": {"n": 32, "mean_diff": -0.3851878035902718, "se": 0.09058559499532828, "worst5": [[10004, -1.3826218964009058], [10023, -1.1841629390380604], [10005, -1.1746134794574346], [10021, -0.961838760191938], [10016, -0.939066684417071]], "wins": 7, "sum_d": -12.326009714888698, "sum_d2": 12.887932629602714}, "C-qt1": {"n": 32, "mean_diff": 0.1945709761236234, "se": 0.12928502586182258, "worst5": [[10030, -1.2420659356751003], [10023, -1.1540914819016734], [10007, -1.1075330275327815], [10004, -1.0339670321868653], [10021, -0.8813235651627451]], "wins": 21, "sum_d": 6.226271235955949, "sum_d2": 17.792352640785783}, "COMBO-qt1": {"n": 32, "mean_diff": 0.5797587797138952, "se": 0.11375007370569483, "worst5": [[10007, -0.8791390600149498], [10030, -0.7332797896277725], [10003, -0.22960233995479484], [10019, -0.05833277465740139], [10013, -0.04137570451314332]], "wins": 27, "sum_d": 18.552280950844647, "sum_d2": 23.591414398877635}, "mean_edge": {"C": 507.5317476483353, "COMBO": 507.91693545192555, "qt1": 507.3371766722116}}, "training_curve": {"columns": ["gen", "mean_fit_pop", "validation_gain_of_CMA_mean", "max_fit_pop"], "rows": [[1, -0.12733030253885683, null, -0.023198260761086775], [2, -0.11388404527440948, null, -0.002823337726903574], [3, -0.07854176803062773, null, -0.010195898957164662], [4, -0.06937176247240606, -0.06432394775456785, 0.005895159549239359], [5, -0.07096649272372775, null, 0.008582146097820598], [6, -0.05790169329001891, null, 0.0051396517377768625], [7, -0.08007797411030992, null, -0.02338232633771442], [8, -0.101936123203285, -0.09495435568519683, -0.04956077245436896], [9, -0.12377021962935918, null, -0.04971923342976359], [10, -0.156139851919495, null, -0.03321700740957878], [11, -0.09434081434280461, null, 0.04024146468952061]]}, "generations": 11, "training_wall_seconds": 1060.5445120334625, "parameter_count": 274, "zero_head_identity": {"seeds_n_steps": 2000, "rows": [[64000, 100.48876017587767, 100.48876017587767, true], [64001, 92.87593278668221, 92.87593278668221, true], [64002, 109.04579976585599, 109.04579976585599, true], [64003, 97.01404956965875, 97.01404956965875, true]], "note": "[seed, COMBO edge, zero-output Head edge, equal]; all 4 bit-equal. Also leak test."}, "leak_test": {"callbacks_per_trial": 1434, "trials": 4302, "bit_identical": 4302}, "seconds_per_1000_steps": {"head": 0.5333397388458252, "combo": 0.25212955474853516}, "findings": ["The learned head did NOT help: frozen checkpoint (best validation gen 4, validation gain -0.0643 vs COMBO at 1000 steps) is below COMBO and C-qt1 is +0.143 (se 0.157, n=32) choose and +0.195 (se 0.129, n=32) report, i.e. not distinguishable from 0, all of it from COMBO's own lead (COMBO-qt1 +0.646/+0.580); see choose/report pairs (C-COMBO mean_diff choose -0.5037, report -0.3852).", "All evaluated validation points (gen 4: -0.064, gen 8: -0.095) were negative; training fitness stayed negative on average over 11 generations, i.e. 11 generations of population 12 are far too few to learn 274 parameters with CMA from a random hidden layer.", "Zero-output head is bit-identical to COMBO (4 seeds, edges equal) and live fees are bit-identical under randomised arb/p/s2/j and init sigma/lam/size (4302 callbacks).", "COMBO-qt1 on the 32 report seeds is shown for context; COMBO beating qt1 is consistent with the COMBO artifact's published +0.597 on 10000..12047."], "limitations": ["SCALED DOWN, not the specified protocol: this machine gives about one CPU core in effect (8 listed, parallel runs showed no speedup: 8 processes took 8x as long each), so the brief's 12 x 256-seed x 2000-step generations (about 2,900 CPU-s each) were impossible. Actually run: minibatch 16 seeds (rotating through 64000..64511) at n_steps=1000, validation 32 seeds 64512..64543 at 1000 steps every 4 generations, training stopped at 1061 s (cap 1080 s) = 11 generations, not 35 min.", "Choose scoring of the frozen checkpoint covers only 32 seeds 64000..64031 (not 1024) at 10000 steps; report covers only 32 seeds 10000..10031 (not 2048). These are 16 of the seeds in training minibatch rotation for choose (in-sample by design of the choose window). Missing coverage: 64032..65023 and 10032..12047 NOT run; no claim about them.", "Validation used the 1000-step window rather than 2000; the gen-0 zero head (=COMBO, gain 0 by identity) was not among the evaluated checkpoints, so selection among evaluated checkpoints picked a head worse than COMBO; the honest best policy here would be the zero head (= COMBO).", "'mid' in the 'belief mean minus mid' input is my reading: midpoint of the no-arb interval of the quotes in force before the trade (the brief does not define it). Standardisation scales come from 16 choose seeds 64000..64015 at 1000 steps. Own trade size input uses a fixed size law (mean 20, log-sd 1.2), not the run's size.", "Replica only; no contract, engine, gas or Gate F; per brief not required (policy is not >= +0.5 over qt1). Per-seed timing numbers are single-core measurements."], "regeneration": {"setup": "git clone amm-sim, checkout 4812a6382311624e1506aca131ca35c378f930d9; fetch artifacts https://api.imd.fun/artifacts/<h> and verify sha256; twocomp.py = first python fence after \"## Code\" in TC, combo.py = first python fence after \"## Code\" in COMBO (sha256 in hashes.files); blob.bin and qmref.py from identity-md-launches/goal-3170-beat-network-s-amm-entry@31ff8ed1 reference/qt1", "commands": ["python3 -I scales.py", "python3 -I train.py 400  (repeat until done; WALL_CAP=1080)", "python3 -I final.py freeze", "python3 -I final.py choose", "python3 -I final.py report"], "work_dir": "scratch/w with ../src/amm-sim and ../state"}, "source_bundle": {"encoding": "base64(zlib.compress(json.dumps({path:text}).encode(),9))", "sha256_of_json": {"head.py": "4f4e932038c53022cc40744c862b90c31f8d9e9dc7a03a080179244c7ef78b69", "common.py": "e2040ddb59cfaf243cd1b60d22f58f763a5f692d5fc29a9efbc299cf11ae6ef7", "scales.py": "b91fb3080d3dee12de002470cd0d1f483f863adc4f3f0ffac768f549c492752b", "train.py": "2f4800ffae175141e46aab281989e175df22165a325b99cff5a7be3acb04cc11", "final.py": "9608942b440b5447160f2fb1e2b33dabefae6c91a9d3c68085504b454ec209f1"}, "data": 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"}}