{
  "credit": "Taylor (gtmcknight), github.com/gtmcknight/amm-sim@4812a6382311624e1506aca131ca35c378f930d9 research/solved",
  "table": 1,
  "levels": [
    0
  ],
  "gain_vs_base": 0.0,
  "se": 0.0,
  "n": 1024,
  "seeds": [
    64000,
    65023
  ],
  "valid": {
    "gain": 0.0,
    "se": 0.0,
    "n": 4096,
    "seeds": [
      300000,
      304095
    ]
  },
  "nonzero": false,
  "advance": true,
  "source_sha256": "e183e5bc3fb794b7067703b10a7da9920267aa5f4caa968e16ca101b02338f40",
  "delta_zb64": "",
  "delta_sha256": "e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855",
  "bands": [
    {
      "level": 0,
      "axis": "row",
      "knots": [
        0,
        1,
        2,
        3,
        4,
        5,
        6,
        7,
        8,
        9,
        10,
        11,
        12
      ],
      "es_mean_bps": 0.1307495897626891,
      "visit_weight": 69821.18166450837
    },
    {
      "level": 0,
      "axis": "row",
      "knots": [
        13,
        14,
        15,
        16
      ],
      "es_mean_bps": -0.06848487725223792,
      "visit_weight": 62509.36313259764
    },
    {
      "level": 0,
      "axis": "row",
      "knots": [
        17,
        18
      ],
      "es_mean_bps": 0.1915348047360381,
      "visit_weight": 81933.59148219283
    },
    {
      "level": 0,
      "axis": "row",
      "knots": [
        19,
        20
      ],
      "es_mean_bps": -0.1603988590392078,
      "visit_weight": 56932.33550253087
    },
    {
      "level": 0,
      "axis": "row",
      "knots": [
        21,
        22,
        23,
        24,
        25,
        26
      ],
      "es_mean_bps": 0.18930675416839596,
      "visit_weight": 58710.82799445077
    },
    {
      "level": 0,
      "axis": "row",
      "knots": [
        27,
        28,
        29,
        30,
        31,
        32,
        33,
        34,
        35,
        36,
        37,
        38,
        39,
        40,
        41,
        42
      ],
      "es_mean_bps": -0.1558036937356692,
      "visit_weight": 55209.10125393786
    },
    {
      "level": 0,
      "axis": "column",
      "knots": [
        0,
        1,
        2,
        3,
        4
      ],
      "es_mean_bps": -0.05477206287061113,
      "visit_weight": 81367.00333577584
    },
    {
      "level": 0,
      "axis": "column",
      "knots": [
        5,
        6
      ],
      "es_mean_bps": 0.2534454073332082,
      "visit_weight": 73963.30603870659
    },
    {
      "level": 0,
      "axis": "column",
      "knots": [
        7,
        8
      ],
      "es_mean_bps": -0.06216567940003262,
      "visit_weight": 70079.42650008076
    },
    {
      "level": 0,
      "axis": "column",
      "knots": [
        9,
        10,
        11,
        12
      ],
      "es_mean_bps": 0.2972396013717753,
      "visit_weight": 77022.00753226975
    },
    {
      "level": 0,
      "axis": "column",
      "knots": [
        13,
        14,
        15,
        16,
        17,
        18,
        19,
        20,
        21,
        22,
        23,
        24,
        25,
        26,
        27,
        28,
        29
      ],
      "es_mean_bps": -0.3279941675676663,
      "visit_weight": 82684.65762338547
    }
  ],
  "candidates": [
    {
      "name": "mean",
      "nonzero": true,
      "nodes": 500,
      "encoded_sha256": "1ed00fa84ae1345f84adeab69b6731b5638e1f461fca5d45f5c21a4c27c8c0e9",
      "valid": {
        "gain": -0.002670734086141033,
        "se": 0.0018347871962237593,
        "n": 4096
      },
      "early_stop_at_bar_0_03": true,
      "gain": -0.002670734086141033,
      "se": 0.0018347871962237593,
      "n": 4096
    },
    {
      "name": "half",
      "nonzero": false,
      "nodes": 0,
      "encoded_sha256": "830868f9b3e5ad162c4f2070e6faed0782c7b4cc308ab69044b2cf7306f68099",
      "valid": {
        "gain": 0.0,
        "se": 0.0,
        "n": 4096
      },
      "early_stop_at_bar_0_03": true,
      "gain": 0.0,
      "se": 0.0,
      "n": 4096
    },
    {
      "name": "one_and_half",
      "nonzero": true,
      "nodes": 871,
      "encoded_sha256": "87f3e98a674ad91e87d35ecacd5891966ca2ba9a5b59964367d32724273b682e",
      "valid": {
        "gain": -0.002556733414370127,
        "se": 0.0019803423093797557,
        "n": 4096
      },
      "early_stop_at_bar_0_03": true,
      "gain": -0.002556733414370127,
      "se": 0.0019803423093797557,
      "n": 4096
    }
  ],
  "simulations": 99072,
  "limitations": [
    "Replica only; no engine or Solidity tests, no report seeds 10000..12047. This is not an engine promotion claim.",
    "Closest contiguous equal-visit-weight bands minimise squared relative mass error; indivisible stored knots prevent exact equality. Visitation is not causal objective sensitivity.",
    "ES evaluates integer-millibps perturbations; final candidates use qpl.apply(quantize=True) on the 0.5-bps lattice and pass qpl.encode.",
    "Finite stochastic search does not prove optimality. VALID was used for selection only after all TRAIN generations. No VALID feedback entered training.",
    "CHOOSE evaluated once for the retained correction on all 1024 seeds; its first 128 seeds were used for the prescribed visit profile.",
    "Early-stop diagnostics at bar +0.03 computed with score.py after the required full VALID scores; fixed ES completion and full VALID selection followed the item-specific protocol.",
    "Nothing was fabricated.",
    "Initial verbose report hit the 64000-byte size assertion after all VALID scores completed. Repaired by lossless compression of the full TRAIN trace; no search or VALID scoring was repeated. The zero CHOOSE check was then run explicitly on all 1024 seeds."
  ],
  "controls": {
    "credit": "Taylor (gtmcknight), github.com/gtmcknight/amm-sim@4812a6382311624e1506aca131ca35c378f930d9 research/solved",
    "empty_delta_64_equal": true,
    "seeds": [
      200000,
      200063
    ],
    "codec": {
      "byte_identical": true,
      "bytes": 10160,
      "sha256": "830868f9b3e5ad162c4f2070e6faed0782c7b4cc308ab69044b2cf7306f68099",
      "scales_checked": [
        250,
        500,
        500,
        1000,
        500
      ],
      "int16_boundaries": true,
      "int8_boundaries": true,
      "invalid_indices_and_duplicates_rejected": true,
      "atomic_failure": true
    },
    "elapsed_seconds": 7.699404421960935,
    "implementation_sha256": "4d4894474104eac9148e52b18d679b994ce73c6a889bafa18169c8dacca22855",
    "pinned_source_tables_match": true,
    "kept_delta_encode_roundtrip": true
  },
  "search": {
    "plan": {
      "credit": "Taylor (gtmcknight), github.com/gtmcknight/amm-sim@4812a6382311624e1506aca131ca35c378f930d9 research/solved",
      "table": 1,
      "levels": [
        0
      ],
      "bands": [
        [
          [
            0,
            1,
            2,
            3,
            4,
            5,
            6,
            7,
            8,
            9,
            10,
            11,
            12
          ],
          [
            13,
            14,
            15,
            16
          ],
          [
            17,
            18
          ],
          [
            19,
            20
          ],
          [
            21,
            22,
            23,
            24,
            25,
            26
          ],
          [
            27,
            28,
            29,
            30,
            31,
            32,
            33,
            34,
            35,
            36,
            37,
            38,
            39,
            40,
            41,
            42
          ]
        ],
        [
          [
            0,
            1,
            2,
            3,
            4
          ],
          [
            5,
            6
          ],
          [
            7,
            8
          ],
          [
            9,
            10,
            11,
            12
          ],
          [
            13,
            14,
            15,
            16,
            17,
            18,
            19,
            20,
            21,
            22,
            23,
            24,
            25,
            26,
            27,
            28,
            29
          ]
        ]
      ],
      "weights": [
        [
          783.3394322126292,
          2478.8610858372585,
          3579.2615695203012,
          3770.7215380070784,
          3421.8332689654576,
          3216.205029417062,
          3581.1627618635835,
          4759.411032042292,
          3660.776287089456,
          6140.06587617626,
          7869.330483157162,
          11300.402353598896,
          15259.810946620937,
          10642.582200021103,
          13265.670973967439,
          16818.4673469324,
          21782.642611676696,
          35112.63721231703,
          46820.95426987581,
          34116.365998717556,
          22815.969503813314,
          15423.06878214096,
          8774.301854374831,
          7527.724560894616,
          7494.045348411535,
          8734.51528458663,
          10757.172164042198,
          9267.385126652724,
          10045.121293160464,
          11402.106274150436,
          6666.950377575192,
          6548.086890376804,
          5390.065530041217,
          1299.7819622818763,
          998.8474158726459,
          982.611754880601,
          867.0621791600489,
          703.834251496484,
          495.1881940578457,
          379.49186236002,
          162.56814187149806,
          0.0,
          0.0
        ],
        [
          2702.1696018091898,
          19937.50179268299,
          23016.43537693944,
          19859.400147353022,
          15851.496416991196,
          27527.638888713107,
          46435.66714999348,
          39142.451883860944,
          30936.974616219824,
          25742.458388805055,
          20742.224321569247,
          16731.84508344583,
          13805.479738449616,
          13178.919318277394,
          14078.4325760718,
          9408.577826762845,
          8369.293035836812,
          9120.4573393885,
          10045.972081232334,
          3920.0193300580568,
          3853.400464031958,
          3329.8772636158387,
          1915.5631546893844,
          1590.9050788342925,
          1263.5660737023256,
          1059.5993761784837,
          800.8924332048749,
          564.4463904860836,
          176.95296403771047,
          7.782916976773887
        ]
      ],
      "band_weights": [
        [
          69821.18166450837,
          62509.36313259764,
          81933.59148219283,
          56932.33550253087,
          58710.82799445077,
          55209.10125393786
        ],
        [
          81367.00333577584,
          73963.30603870659,
          70079.42650008076,
          77022.00753226975,
          82684.65762338547
        ]
      ],
      "generations": 30,
      "pairs": 5,
      "seeds_per_generation": 256,
      "train": [
        200000,
        207679
      ],
      "random_seed": 104729,
      "sigma_bps": [
        0.3,
        0.075
      ],
      "optimizer": "Adam on raw antithetic ES gradient, beta1=.9 beta2=.999 eps=1e-8; learning rate .035 to .012; clip individual offsets to +/- .5 bps; no validation feedback",
      "controls": {
        "credit": "Taylor (gtmcknight), github.com/gtmcknight/amm-sim@4812a6382311624e1506aca131ca35c378f930d9 research/solved",
        "empty_delta_64_equal": true,
        "seeds": [
          200000,
          200063
        ],
        "codec": {
          "byte_identical": true,
          "bytes": 10160,
          "sha256": "830868f9b3e5ad162c4f2070e6faed0782c7b4cc308ab69044b2cf7306f68099",
          "scales_checked": [
            250,
            500,
            500,
            1000,
            500
          ],
          "int16_boundaries": true,
          "int8_boundaries": true,
          "invalid_indices_and_duplicates_rejected": true,
          "atomic_failure": true
        },
        "elapsed_seconds": 7.699404421960935
      }
    },
    "completed_generations": 30,
    "selected_candidate": "zero",
    "final_mean_bps": [
      0.1307495897626891,
      -0.06848487725223792,
      0.1915348047360381,
      -0.1603988590392078,
      0.18930675416839596,
      -0.1558036937356692,
      -0.05477206287061113,
      0.2534454073332082,
      -0.06216567940003262,
      0.2972396013717753,
      -0.3279941675676663
    ],
    "train_simulations": 84480,
    "valid_simulations": 12288,
    "choose_simulations": 2048,
    "control_and_profile_simulations": 256,
    "history_zb64": "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",
    "history_sha256": "577ff16e3604f4e4d2724aef68ca81b49ebf930ab22d7df2803c8e5e65328be7",
    "history_format": "base64(zlib(UTF-8 JSON array of generation records)); includes paired TRAIN mean differences and SE for all mirrored arms",
    "driver_zb64": "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",
    "driver_sha256": "4881c36b6c4af40e7c96e1882233de8bb0575ce04a19ff883f1ef28e325e33d3",
    "driver_format": "base64(zlib(UTF-8 Python source)); caches and runner paths are relative to test/scratch/e1; packaging repaired separately to fit output limit",
    "zero_choose_driver_zb64": "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",
    "zero_choose_driver_sha256": "c79e397eb3bfea01ede909fd4af03796fbf4ac41f0affa4cbb9ded332692f120",
    "frozen_plan_sha256": "ca6729dfe0b4364e08ac1253291a22f2a67324e01757af0bd6ea230eff1af1bf",
    "training_seconds": 2100.0999965560623,
    "packager_zb64": "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",
    "packager_sha256": "96297912200ed284ec2411f7e1ac1ad13eb99f0cf77b1b54d851a9da63a502b0"
  },
  "bands_format": "One record per band; knots are zero-based stored knot indices, es_mean_bps is that band offset. Node offset is row-band plus column-band offset."
}