{
  "schema_version": "vision_question_clusters_resolved_v1",
  "dataset_family": "conditional_image_generation",
  "selection_metric": "test_energy_score",
  "profile_prefix": "conditional_image_generation_template_",
  "source_path": "profiles/image-generation-ranking-clusters-v1.yaml",
  "source_sha256": "9066b70f1272f930381c8c017b7daf9d74ce93bc79d4eb6204395e08c0a0470e",
  "base_profile": "profiles/image-generation-ranking-base-v1.yaml",
  "default_num_choices": 3,
  "discovery_training_seeds": [
    20,
    21,
    22,
    23
  ],
  "confirmation_training_seeds": [
    200,
    201,
    202,
    203,
    204,
    205,
    206,
    207,
    208,
    209,
    210,
    211,
    212,
    213,
    214,
    215
  ],
  "selection_contract": {
    "discovery_split": "validation",
    "confirmation_split": "test",
    "minimum_mean_gap": 0.003,
    "require_nonoverlap": false,
    "multiplicity": "Holm across every frozen adjacent hypothesis including rejected templates",
    "claim_scope": "Fixed synthetic image distribution and fixed independent evaluation sample streams; training-seed robustness only.",
    "difficulty_policy": "Evaluate every admitted triple and report rejected templates without replacement."
  },
  "statistics": {
    "discovery": {
      "n_seeds": 4,
      "split": "validation",
      "method": "direction_estimation",
      "ordering": "mean_validation_energy_score",
      "admission_gate": false,
      "purpose": "Freeze adjacent ranking directions before independent confirmation; no significance claim from four discovery seeds."
    },
    "confirmation": {
      "n_seeds": 16,
      "split": "test",
      "test": "one_sided_paired_sign_test",
      "alpha": 0.05,
      "correction": "holm",
      "minimum_mean_gap": 0.003
    },
    "estimand": "Training-seed paired direction stability conditional on one fixed dataset and fixed evaluation-noise/reference stream, with observed mean energy-score gap; not a test of expected-score mean difference or a natural-image population claim."
  },
  "protocol_config": {
    "schema_version": "vision_ranking_protocol_v1",
    "discovery": {
      "seeds": [
        20,
        21,
        22,
        23
      ],
      "metric": "final_validation_metric",
      "order": "ascending mean; ties use candidate_id",
      "purpose": "Freeze a direction for every template instance before independent confirmation. Weak discovery separation is retained and reported, not an automatic rejection."
    },
    "confirmation": {
      "seeds": [
        200,
        201,
        202,
        203,
        204,
        205,
        206,
        207,
        208,
        209,
        210,
        211,
        212,
        213,
        214,
        215
      ],
      "metric": "final_test_energy_score",
      "higher_is_better": false,
      "test": "one-sided exact paired binomial sign test; ties count as nonpositive",
      "null": "P(oriented paired improvement > 0) <= 0.5 over training seed pairs, conditional on fixed materialized data and training protocol",
      "multiplicity": "Holm across every frozen adjacent comparison, including failed or missing comparisons with p=1",
      "alpha": 0.05,
      "minimum_adjacent_mean_gap": 0.003,
      "require_nonoverlapping_seed_ranges": false,
      "bootstrap": {
        "samples": 10000,
        "confidence": 0.95,
        "seed": 73013,
        "purpose": "Descriptive paired mean-gap interval only; no admission decision uses this interval"
      },
      "failure_policy": "Any absent, duplicate, nonfinite or failed seed rejects that instance. No seed deletion, replacement, added seeds, reordered choices, or extra metric selection after confirmation."
    },
    "admission": "Every adjacent comparison in the discovery-frozen order must pass Holm and the minimum mean-gap gate; both gates use oriented improvement in the frozen direction.",
    "independence": "Confirmation seeds must be disjoint from discovery seeds. Freeze full membership, candidate order, artifact hashes, metrics, gates, and hypothesis family before starting confirmation. Never select questions using tested models responses.",
    "comparison_contract": {
      "same_data": "Every choice shares one dataset instance and identical split file hashes.",
      "budgets": "Equal total_samples_seen unless the cluster explicitly intervenes on budget; always expose steps, batch size and total_samples_seen per choice. Equal samples with different batches does not imply equal optimizer updates.",
      "parameters": "Record measured parameter count. Parameter matching is a separate intervention, never inferred from width matching.",
      "streams": "Use paired training seeds and shared batch index stream when batch sizes agree; seed number alone does not establish matched initialization across architectures.",
      "interventions": "Record every changed spec field and final full-code hashes. Single-axis interpretation requires other fields fixed; multi-axis choices establish joint effects only.",
      "devices": [
        "360-1",
        "ophis"
      ]
    },
    "evaluation": {
      "models": [
        "claude-opus-5-5",
        "gpt-6.1-sol",
        "claude-haiku-5-5",
        "gpt-6-luna"
      ],
      "reasoning_effort": "medium",
      "max_tokens": 8192,
      "tools": false,
      "calls_per_question": 1,
      "system": "Answer the supplied question. Tools are unavailable.",
      "response": "Public explanation followed by exact full ordering. Audit explanation independently of correctness.",
      "metrics": [
        "exact full order",
        "pairwise concordance",
        "valid response rate",
        "answer-explanation consistency"
      ],
      "failure_policy": "Only transport failures without completed answer may retry. Preserve all attempts, identity, prompt hashes and public text. Never replace a completed wrong answer.",
      "baseline": "For k choices, uniform complete-ranking probability is 1/k!; pairwise chance is 1/2.",
      "prompt": "Use canonical renderer with complete shared base and independent diffs; byte-exact reconstruction checked. Fallback to full code when diff readability degrades."
    },
    "difficulty": {
      "surface_heuristics": "Freeze interpretable surface-only rules before LLM responses; report abstentions and tie coverage. Do not use confirmation CE or model errors to tune rules.",
      "coverage": "Report every frozen template instance as accepted/rejected/failed and why. Do not only publish difficult successes.",
      "dependence": "Report results by template and teacher draw. Repeated teacher or dataset conditions are correlated; no independent-question binomial intervals or general model ranking claims.",
      "selection": "Do not use model mistakes to admit or drop items. Any later difficult subset is explicitly exploratory and kept separate from full predeclared batch."
    },
    "causal_scope": "Confirmed performance differences identify the total effect of the exposed intervention at fixed data and training horizon. A mechanism is supported only to the extent that separately logged diagnostic/control evidence rules out alternatives. Image results concern the stated synthetic image distribution only.",
    "statistics": {
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        "split": "validation",
        "method": "direction_estimation",
        "ordering": "mean_validation_energy_score",
        "admission_gate": false,
        "purpose": "Freeze adjacent ranking directions before independent confirmation; no significance claim from four discovery seeds."
      },
      "confirmation": {
        "n_seeds": 16,
        "split": "test",
        "test": "one_sided_paired_sign_test",
        "alpha": 0.05,
        "correction": "holm",
        "minimum_mean_gap": 0.003
      },
      "estimand": "Training-seed paired direction stability conditional on one fixed dataset and fixed evaluation-noise/reference stream, with observed mean energy-score gap; not a test of expected-score mean difference or a natural-image population claim."
    },
    "answer_format": "best_to_worst_v1"
  },
  "surface_heuristics": [
    {
      "id": "more_parameters",
      "field": "parameter_count",
      "higher_is_better": true
    },
    {
      "id": "more_training_steps",
      "field": "training.training_steps",
      "higher_is_better": true
    },
    {
      "id": "more_sampling_steps",
      "field": "training.sampling_steps",
      "higher_is_better": true
    }
  ],
  "prompt_contract": {
    "representation": "shared_complete_base_plus_independent_diff",
    "answer_format": "best_to_worst_v1",
    "analyst_only_fields": [
      "hypothesis",
      "shortcut_checks",
      "explanation_target",
      "admission"
    ]
  },
  "worker_sources": [
    "tools/ntp_h200_worker.py",
    "tools/ntp_anchor_worker.py",
    "tools/vision_h200_worker.py"
  ],
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    "templates": 18,
    "instances": 54,
    "dataset_nodes": 12,
    "candidate_nodes": 162,
    "distinct_models": 25
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