{
  "recipe_id": "pi0fast-base",
  "generated_at": "2026-07-06T15:10:33+00:00",
  "bundle": "build/pi0fast-base/pi0fast-base.aimodel",
  "conversion_manifest": null,
  "gate_a": {
    "status": "passed",
    "checks": [
      {
        "name": "bundle_exists",
        "status": "passed",
        "detail": "build/pi0fast-base/pi0fast-base.aimodel"
      },
      {
        "name": "bundle_files_present",
        "status": "passed",
        "detail": "3 expected file(s) present"
      },
      {
        "name": "metadata_json_parses",
        "status": "passed",
        "detail": "metadata.json parses (3 top-level keys)"
      },
      {
        "name": "metadata_matches_recipe",
        "status": "skipped",
        "detail": "no overlapping keys between recipe expectations and bundle metadata (nothing to compare \u2014 this is not a pass)"
      }
    ]
  },
  "gate_b": {
    "metric": "action_parity",
    "threshold": 0.9,
    "tolerance": 0.05,
    "value": 0.925,
    "status": "passed",
    "near_zero_conditioned": false,
    "measurement_source": "action-parity-measured.json",
    "parity_kind": "autoregressive_greedy_token",
    "compute_unit": "gpu",
    "min_teacher_forced_token_parity": 0.85,
    "mean_teacher_forced_token_parity": 0.925,
    "min_free_running_first_divergence": 4,
    "mean_free_running_first_divergence": 12.0,
    "per_obs": [
      {
        "obs": 0,
        "n_steps": 20,
        "teacher_forced_parity": 0.85,
        "tf_first_mismatch": 4,
        "free_running_first_divergence": 4
      },
      {
        "obs": 1,
        "n_steps": 20,
        "teacher_forced_parity": 1.0,
        "tf_first_mismatch": 20,
        "free_running_first_divergence": 20
      }
    ],
    "n_obs": 2,
    "decode_steps": 20,
    "sampler": "autoregressive_greedy",
    "deterministic": true,
    "observations": "synthetic distinct 1/f natural images ([-1,1]) + fixed language tokens \u2014 conversion-fidelity metric (coreai vs torch fp16, identical inputs)",
    "reference": "torch fp16 sample_actions_fast (RECOMPUTE, not kv-cache) vs the fp16 coreai-optimized asset over the identical host recompute loop. Metric is greedy action-TOKEN parity: the FAST tokens are the model's full output and detokenization is a fixed host transform, so matching tokens => matching actions. teacher_forced isolates per-step fidelity; free_running reports cascade lock length.",
    "runner": "coreai-fabric-parity-runner/0.1.0",
    "environment": {
      "platform": "darwin-arm64",
      "accelerator": "apple_silicon",
      "runtime_version": "1.0.0b2",
      "coreai_torch": "0.4.1",
      "torch": "2.9.0",
      "transformers": "4.57.6"
    },
    "value_basis": "pooled greedy action-token parity = matching tokens / total (37/40 = 0.925); per-obs = {diverse obs: 1.0 (free-running bit-locked 20/20), low-entropy repetitive obs: 0.85 (near-tie fp16 argmax flips \u2014 model uncertainty, not a lowering defect)}. min disclosed as min_teacher_forced_token_parity. Same metric class as the LLM greedy_parity lane (threshold 0.9, tol 0.05)."
  },
  "overall": "passed"
}