{
  "study": "Path A: curriculum head-to-head on a text policy (Qwen3-1.7B-4bit, MLX-LoRA, M5)",
  "protocol": "same model, same oracle (plumb_forge score_predictions), same held-out seed-777 benchmark (n=1000) \u2014 only the training curriculum varies",
  "date": "2026-08-22",
  "hub": {
    "dataset": "https://huggingface.co/datasets/caiotheodoro/plumb",
    "handseeded": "https://huggingface.co/caiotheodoro/plumb-handseeded",
    "ornith": "https://huggingface.co/caiotheodoro/plumb-ornith",
    "blended": "https://huggingface.co/caiotheodoro/plumb-blended",
    "collection": "https://huggingface.co/collections/caiotheodoro/plumb-6a89f2ea18c97d11b0238529"
  },
  "cis_95_bootstrap_10k_seed11": {
    "handseeded": {
      "sw-recall": [
        0.29,
        0.347
      ],
      "precision": [
        0.279,
        0.337
      ],
      "exact": [
        0.154,
        0.203
      ]
    },
    "ornith": {
      "sw-recall": [
        0.214,
        0.268
      ],
      "precision": [
        0.098,
        0.124
      ],
      "exact": [
        0.066,
        0.103
      ]
    },
    "blended": {
      "sw-recall": [
        0.306,
        0.363
      ],
      "precision": [
        0.342,
        0.406
      ],
      "exact": [
        0.201,
        0.254
      ]
    },
    "pow-ornith": {
      "sw-recall": [
        0.139,
        0.186
      ],
      "precision": [
        0.102,
        0.135
      ],
      "exact": [
        0.052,
        0.085
      ]
    },
    "pow-handseeded": {
      "sw-recall": [
        0.154,
        0.203
      ],
      "precision": [
        0.152,
        0.201
      ],
      "exact": [
        0.071,
        0.108
      ]
    },
    "pow-blended": {
      "sw-recall": [
        0.238,
        0.295
      ],
      "precision": [
        0.204,
        0.255
      ],
      "exact": [
        0.135,
        0.184
      ]
    }
  },
  "rows_full_size": [
    {
      "run": "base",
      "n": 1000,
      "train_tasks": 0,
      "parse_rate": 0.127,
      "severity_weighted_recall": 0.02,
      "high_recall": 0.034,
      "precision": 0.118,
      "corrected_figure_exact_match": 0.0
    },
    {
      "run": "handseeded",
      "n": 1000,
      "train_tasks": 223,
      "parse_rate": 1.0,
      "severity_weighted_recall": 0.318,
      "high_recall": 0.336,
      "precision": 0.308,
      "corrected_figure_exact_match": 0.178
    },
    {
      "run": "ornith",
      "n": 1000,
      "train_tasks": 58,
      "parse_rate": 0.997,
      "severity_weighted_recall": 0.241,
      "high_recall": 0.287,
      "precision": 0.111,
      "corrected_figure_exact_match": 0.084
    },
    {
      "run": "blended",
      "n": 1000,
      "train_tasks": 281,
      "parse_rate": 1.0,
      "severity_weighted_recall": 0.334,
      "high_recall": 0.365,
      "precision": 0.374,
      "corrected_figure_exact_match": 0.228
    }
  ],
  "rows_equal_n_58_random": [
    {
      "run": "equal-handseeded",
      "n": 1000,
      "train_tasks": 49,
      "parse_rate": 0.998,
      "severity_weighted_recall": 0.106,
      "high_recall": 0.036,
      "precision": 0.126,
      "corrected_figure_exact_match": 0.023
    },
    {
      "run": "equal-ornith",
      "n": 1000,
      "train_tasks": 49,
      "parse_rate": 0.999,
      "severity_weighted_recall": 0.124,
      "high_recall": 0.083,
      "precision": 0.111,
      "corrected_figure_exact_match": 0.038
    },
    {
      "run": "equal-blended",
      "n": 1000,
      "train_tasks": 49,
      "parse_rate": 0.997,
      "severity_weighted_recall": 0.062,
      "high_recall": 0.013,
      "precision": 0.1,
      "corrected_figure_exact_match": 0.02
    }
  ],
  "rows_equal_n_58_stratified": [
    {
      "run": "strat-handseeded",
      "n": 1000,
      "train_tasks": 49,
      "parse_rate": 0.999,
      "severity_weighted_recall": 0.053,
      "high_recall": 0.081,
      "precision": 0.132,
      "corrected_figure_exact_match": 0.011
    },
    {
      "run": "strat-blended",
      "n": 1000,
      "train_tasks": 49,
      "parse_rate": 0.968,
      "severity_weighted_recall": 0.169,
      "high_recall": 0.094,
      "precision": 0.142,
      "corrected_figure_exact_match": 0.095
    }
  ],
  "rows_exploratory_pow184": [
    {
      "run": "pow-ornith",
      "n": 1000,
      "train_tasks": 184,
      "n_train_split": 156,
      "parse_rate": 1.0,
      "severity_weighted_recall": 0.163,
      "high_recall": 0.164,
      "precision": 0.118,
      "corrected_figure_exact_match": 0.068,
      "val_loss": 0.4697
    },
    {
      "run": "pow-handseeded",
      "n": 1000,
      "train_tasks": 184,
      "n_train_split": 156,
      "parse_rate": 0.997,
      "severity_weighted_recall": 0.178,
      "high_recall": 0.206,
      "precision": 0.176,
      "corrected_figure_exact_match": 0.089,
      "val_loss": 0.4918
    },
    {
      "run": "pow-blended",
      "n": 1000,
      "train_tasks": 184,
      "n_train_split": 156,
      "parse_rate": 0.999,
      "severity_weighted_recall": 0.266,
      "high_recall": 0.365,
      "precision": 0.229,
      "corrected_figure_exact_match": 0.159,
      "val_loss": 0.4803
    }
  ],
  "do_not_publish_as_method": true,
  "exploratory_caveats": [
    "pow-* and the reward-only / instruction-anchored grows fit DeploymentProfile on forge/data/benchmark.jsonl (seed-777) and told the proposer to match the held-out eval \u2014 eval-set leakage. Not a methods result.",
    "Acceptance was r>0, so multiplicative M never filtered. count_match compared P_bench(count) to uniform, so the most common deployment class (single-injection) scored lowest.",
    "D was estimated against the linear placeholder (frontier at difficulty ~0.8), not the 1.7B policy.",
    "N had no floor: late naive-grow accepts had novelty 0.01-0.07. pow-ornith@184 (0.163 sw-recall) is WORSE than ornith@58 (0.241) \u2014 growing a self-proposed buffer without a novelty floor added near-duplicates, not information.",
    "pow-blended / anchored2 / mockanchored were still in flight at harvest of pow-ornith and pow-handseeded; fold in when eval artifacts land, still as exploratory.",
    "The clean protocol (train prior, mix constraint, novelty floor, 1.7B/error-profile D) is the methods experiment."
  ],
  "train_val_loss": {
    "ornith": 0.5948,
    "handseeded": 0.4861,
    "blended": 0.4726,
    "equal-handseeded": 0.6289,
    "equal-ornith": 0.5919,
    "equal-blended": 0.6303,
    "strat-handseeded": 0.6065,
    "strat-blended": 0.6058,
    "pow-ornith": 0.4697,
    "pow-handseeded": 0.4918
  },
  "interpretation": [
    "Full size (powered): Ornith-only underperforms hand-seeded with non-overlapping CIs (sw-recall 0.241 [0.214,0.268] vs 0.318 [0.290,0.347]). Blend beats both on point estimates; vs hand-seeded the recall CIs overlap and precision is the surviving interval (0.374 [0.342,0.406] vs 0.308 [0.279,0.337]).",
    "Equal-N at 58 tasks (underpowered): all curricula collapse to 0.05-0.17 sw-recall \u2014 58 tasks cannot train this task well, and the ordering is noisy/unstable (random vs stratified flips hand-seeded). No conclusion about 'blended-wins-is-just-more-data' can be drawn from an underpowered small-N run.",
    "The one stable small-N signal: the stratified blend@58 (0.169 sw-recall, 0.095 exact) is the best small-N curriculum \u2014 a coverage-preserving mix of matched + Ornith tasks beats either alone, reinforcing the full-size blend conclusion.",
    "Stratifying hand-seeded@58 to force 20 PASS + all 9 types diluted per-type counts and hurt recall (0.053) \u2014 small-N composition is dominated by allocation tradeoffs, i.e., noise.",
    "EXPLORATORY, do not publish as method: naive grow 58\u2192184 hurt (0.241 \u2192 0.163). Novelty too weak. Reward-only M did not steer under accept-any-positive. Clean equal-N with train-prior mix constraint is the follow-up."
  ],
  "artifacts": {
    "per_run": "curriculum/data/eval/eval-{run}.json",
    "head2head": "curriculum/data/eval/head2head.json",
    "equalN": "curriculum/data/eval/equalN-head2head.json",
    "adapters": "curriculum/adapters/text-{handseeded,ornith,blended,equal-*,strat-*,pow-*}"
  },
  "rows_equal_n_clean": [
    {
      "run": "eq-handseeded",
      "n": 1000,
      "n_train_split": 15,
      "parse_rate": 0.88,
      "severity_weighted_recall": 0.06,
      "high_recall": 0.094,
      "precision": 0.11,
      "corrected_figure_exact_match": 0.0,
      "ci95": {
        "severity_weighted_recall": {
          "point": 0.05993606819392648,
          "ci95": [
            0.04429028815368197,
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        },
        "precision": {
          "point": 0.10955710955710955,
          "ci95": [
            0.08168316831683169,
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        },
        "corrected_figure_exact_match": {
          "point": 0.0,
          "ci95": [
            0.0,
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          ]
        }
      }
    },
    {
      "run": "eq-naive",
      "n": 1000,
      "n_train_split": 15,
      "parse_rate": 0.98,
      "severity_weighted_recall": 0.135,
      "high_recall": 0.101,
      "precision": 0.079,
      "corrected_figure_exact_match": 0.025,
      "ci95": {
        "severity_weighted_recall": {
          "point": 0.13465636654235483,
          "ci95": [
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        "precision": {
          "point": 0.07853107344632769,
          "ci95": [
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        "corrected_figure_exact_match": {
          "point": 0.02450719232818327,
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    },
    {
      "run": "eq-mix",
      "n": 1000,
      "n_train_split": 15,
      "parse_rate": 0.843,
      "severity_weighted_recall": 0.075,
      "high_recall": 0.081,
      "precision": 0.082,
      "corrected_figure_exact_match": 0.011,
      "ci95": {
        "severity_weighted_recall": {
          "point": 0.07511987213638785,
          "ci95": [
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        "precision": {
          "point": 0.08167330677290836,
          "ci95": [
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        },
        "corrected_figure_exact_match": {
          "point": 0.010522109749600427,
          "ci95": [
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    },
    {
      "run": "eq-error",
      "n": 1000,
      "n_train_split": 15,
      "parse_rate": 0.99,
      "severity_weighted_recall": 0.031,
      "high_recall": 0.004,
      "precision": 0.116,
      "corrected_figure_exact_match": 0.002,
      "ci95": {
        "severity_weighted_recall": {
          "point": 0.03143313798614811,
          "ci95": [
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        "precision": {
          "point": 0.11572700296735905,
          "ci95": [
            0.08282208588957055,
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        },
        "corrected_figure_exact_match": {
          "point": 0.0019978689397975495,
          "ci95": [
            0.0,
            0.0047694753577106515
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      }
    },
    {
      "run": "eq-mockanchored",
      "n": 1000,
      "n_train_split": 15,
      "parse_rate": 0.987,
      "severity_weighted_recall": 0.125,
      "high_recall": 0.166,
      "precision": 0.108,
      "corrected_figure_exact_match": 0.033,
      "ci95": {
        "severity_weighted_recall": {
          "point": 0.12493340436867342,
          "ci95": [
            0.10391016100662968,
            0.14705111402359108
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        },
        "precision": {
          "point": 0.10797174571140263,
          "ci95": [
            0.08906882591093117,
            0.12763819095477386
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        },
        "corrected_figure_exact_match": {
          "point": 0.03329781566329249,
          "ci95": [
            0.021077591885127124,
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      }
    },
    {
      "run": "eq-blend",
      "n": 1000,
      "n_train_split": 15,
      "parse_rate": 0.98,
      "severity_weighted_recall": 0.055,
      "high_recall": 0.076,
      "precision": 0.101,
      "corrected_figure_exact_match": 0.001,
      "ci95": {
        "severity_weighted_recall": {
          "point": 0.05487480021310603,
          "ci95": [
            0.039577121171049064,
            0.07064079296384197
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        },
        "precision": {
          "point": 0.1010989010989011,
          "ci95": [
            0.07407407407407407,
            0.12873563218390804
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        },
        "corrected_figure_exact_match": {
          "point": 0.0013319126265316996,
          "ci95": [
            0.0,
            0.00411635565312843
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      }
    }
  ],
  "equal_n_clean": 18,
  "equal_n_clean_sizes": {
    "handseeded": 223,
    "naive": 24,
    "mix": 22,
    "error": 18,
    "mockanchored": 22
  },
  "clean_protocol_interpretation": [
    "equal N=18 (15 train / 3 valid); novelty floor 0.3 capped grow at 18-24.",
    "no weak win: mix 0.075 [0.059, 0.093] below naive 0.135 [0.114, 0.156], no overlap.",
    "mock-anchored 0.125 [0.104, 0.147] overlaps naive.",
    "no strong win: error 0.031 [0.022, 0.042] below hand-seeded 0.060 [0.044, 0.076].",
    "no super win: error below blend 0.055 [0.040, 0.071].",
    "mix buffer: 16/22 accepts were controller deficit-fill; 9B proposer mode-collapsed to singles.",
    "N=18 does not touch full-size bars (handseeded 0.318, blend 0.334)."
  ]
}