{
  "run_id": "phrugsa-limbunlom--deltaflow-claude-haiku-5-5-1793170996249152880",
  "checker": {
    "claims": [
      {
        "claim": "Every probability path returns the interpolated state x_t and its conditional target velocity u_t (interpolant example prints x_t and u_t and passes boundary checks)",
        "command": "/tmp/dfvenv/bin/python examples/00-foundations/01-linear-interpolant/main.py",
        "evidence": "Boundary conditions OK.\n",
        "expected": "x_t and u_t printed for each t, boundary conditions OK",
        "input": "examples/00-foundations/01-linear-interpolant/main.py",
        "kind": "claim",
        "outcome": "Real input produced the expected result: Boundary conditions OK.",
        "returncode": 0,
        "source": "Every path returns the interpolated state $x_t$ and its conditional target velocity $u_t$.",
        "source_checked": true,
        "source_file": "README.md",
        "verdict": "yes"
      },
      {
        "claim": "The examples tree is a runnable curriculum (linear interpolant example runs)",
        "command": "/tmp/dfvenv/bin/python examples/00-foundations/01-linear-interpolant/main.py",
        "evidence": "t=1.00  x_t[0]=[1.5409960746765137, -0.293428897857666]  u_t[0]=[2.260253667831421, 0.109914630651474]\nBoundary conditions OK.\n",
        "expected": "t=1.00 row printed with x_t endpoint equal to x1",
        "input": "examples/00-foundations/01-linear-interpolant/main.py",
        "kind": "claim",
        "outcome": "Real input produced the expected result: t=1.00  x_t[0]=[1.5409960746765137, -0.293428897857666]",
        "returncode": 0,
        "source": "The [`examples/`](https://github.com/phrugsa-limbunlom/deltaflow/tree/main/examples) tree is a tiered, runnable curriculum.",
        "source_checked": true,
        "source_file": "README.md",
        "verdict": "yes"
      },
      {
        "claim": "Flow matching regresses a velocity field onto the conditional velocity of the path (training example runs and reports loss)",
        "command": "/tmp/dfvenv/bin/python examples/20-training/01-flow-matching/main.py",
        "evidence": "final loss: 4.1941\n",
        "expected": "final loss printed",
        "input": "examples/20-training/01-flow-matching/main.py",
        "kind": "claim",
        "outcome": "Real input produced the expected result: final loss: 4.1941",
        "returncode": 0,
        "source": "Regress a velocity field onto the conditional velocity of a probability path.",
        "source_checked": true,
        "source_file": "README.md",
        "verdict": "yes"
      },
      {
        "claim": "A learned field is integrated with a simple Euler or Heun solver to produce samples",
        "command": "/tmp/dfvenv/bin/python examples/10-sampling/01-euler-flow/main.py",
        "evidence": "sample mean: [0.019584309309720993, 0.05306430160999298]\nsample std: [1.97209894657135, 0.18536506593227386]\n",
        "expected": "sample mean and std printed",
        "input": "examples/10-sampling/01-euler-flow/main.py",
        "kind": "claim",
        "outcome": "Real input produced the expected result: sample mean: [0.019584309309720993, 0.05306430160999298]",
        "returncode": 0,
        "source": "transporting the whole source density onto the data density with a simple Euler or Heun solver.",
        "source_checked": true,
        "source_file": "README.md",
        "verdict": "yes"
      },
      {
        "claim": "Posterior sampling for an inverse problem reuses the pretrained field and reports observed-region MSE",
        "command": "/tmp/dfvenv/bin/python examples/30-inverse/01-posterior/main.py",
        "evidence": "\u2026observed-region MSE, independent-coupled model: 0.0633\n  observed-region MSE, OT-coupled model:          0.0710\n\nNote: this toy setup is not tuned; it demonstrates the API wiring, not SOTA quality.\n",
        "expected": "observed-region MSE printed for independent-coupled model",
        "input": "examples/30-inverse/01-posterior/main.py",
        "kind": "claim",
        "outcome": "Real input produced the expected result: observed-region MSE, independent-coupled model: 0.0633",
        "returncode": 0,
        "source": "posterior sampling for an inverse problem, reusing the very same pretrained field.",
        "source_checked": true,
        "source_file": "README.md",
        "verdict": "yes"
      }
    ],
    "cycle_checks": [
      {
        "claim": "Scheduled or looping main behavior",
        "command": "",
        "evidence": "Inspected README.md and package entrypoints. DeltaFlow is a PyTorch library with no CLI, daemon, cron, webhook, or --once/--now scheduler. The only while True found is deltaflow/trainer/loop.py training loop, which is not a product cycle.",
        "expected": "identify the product trigger",
        "input": "source inspection",
        "kind": "cycle",
        "outcome": "Inspected README.md and package entrypoints. DeltaFlow is a PyTorch library with no CLI, daemon, cron, webhook, or --once/--now scheduler. The only while True found is deltaflow/trainer/loop.py training loop, which is not a product cycle.",
        "source": "repository entry points",
        "verdict": "yes"
      }
    ],
    "details": {
      "argv": [
        "/tmp/dfvenv/bin/python",
        "examples/10-sampling/01-euler-flow/main.py"
      ],
      "kind": "cli",
      "needs_browser": false,
      "needs_credentials": false,
      "returncode": 0,
      "scope": "product operation",
      "stderr": "",
      "stdout": "step=   0  loss=2.9880\nstep= 100  loss=1.7197\nstep= 200  loss=1.6778\nstep= 300  loss=1.6499\nstep= 400  loss=1.6405\nsample mean: [0.019584309309720993, 0.05306430160999298]\nsample std: [1.97209894657135, 0.18536506593227386]",
      "working": true
    },
    "evidence_strength": "readme_checked",
    "example_checks": [
      {
        "claim": "Posterior sampling example runs with a pretrained field",
        "command": "/tmp/dfvenv/bin/python examples/30-inverse/01-posterior/main.py",
        "deferred": false,
        "evidence": "/ 100 steps\n[deltaflow.trainer] step     300 | loss 0.1096 | 2.1s / 100 steps\n[deltaflow.trainer] step     100 | loss 0.1989 | 2.8s / 100 steps\n[deltaflow.trainer] step     200 | loss 0.0747 | 2.1s / 100 steps\n[deltaflow.trainer] step     300 | loss 0.0625 | 2.0s / 100 steps\n[2/3] Unconditional Euler sampling (sanity check)...\n[3/3] Posterior sampling under a centre-mask inpainting measurement...\n\n  observed-region MSE, independent-coupled model: 0.0633\n  observed-region MSE, OT-coupled model:          0.0710\n\nNote: this toy setup is not tuned; it demonstrates the API wiring, not SOTA quality.",
        "expected": "observed-region MSE printed",
        "input": "none",
        "kind": "example",
        "outcome": "command completed successfully",
        "returncode": 0,
        "source": "examples/30-inverse/01-posterior/main.py",
        "verdict": "yes"
      },
      {
        "claim": "Development test suite from README Development section passes",
        "command": "/tmp/dfvenv/bin/pytest -q",
        "deferred": false,
        "evidence": ".............................. [ 85%]\n............                                                             [100%]\n=============================== warnings summary ===============================\ntests/test_trainer.py::test_train_loop_runs_and_checkpoints\n  /tmp/dfvenv/lib/python3.12/site-packages/torch/utils/data/dataloader.py:759: UserWarning: 'pin_memory' argument is set as true but no accelerator is found, then device pinned memory won't be used.\n    super().__init__(loader)\n\n-- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html\n81 passed, 3 skipped, 1 warning in 6.46s",
        "expected": "tests pass",
        "input": "pytest",
        "kind": "example",
        "outcome": "command completed successfully",
        "returncode": 0,
        "source": "README.md Development",
        "verdict": "yes"
      }
    ],
    "status": "PASS"
  },
  "diagnosis": null
}