{
  "checker": {
    "claims": [
      {
        "claim": "TabArena computes a leaderboard from results.",
        "command": ".venv/bin/python examples/plots/run_generate_custom_leaderboard.py",
        "evidence": "=== Leaderboard ===\n           elo  elo+  elo-  rank   winrate  improvability  improvability+  improvability-       mrr  metric_error  time_train_s  time_infer_s  median_rank  median_improvability  median_loss_rescaled  median_metric_error  median_time_train_s  median_time_infer_s\nmethod                                                                                                                                                                                                                                                               \nModelA  1161.2  60.7  47.9  1.80  0.733333       0.02753",
        "expected": "prints a leaderboard ranking methods",
        "input": "synthetic example results generated by the documented script",
        "kind": "claim",
        "outcome": "Real input produced the expected result: === Leaderboard ===",
        "returncode": 0,
        "source": "TabArena ranks **models** (one method, tuned by TabArena under a shared protocol) and **systems**",
        "source_checked": true,
        "source_file": "README.md",
        "verdict": "yes"
      }
    ],
    "cycle_checks": [
      {
        "claim": "Scheduled or looping main behavior",
        "command": "",
        "evidence": "Inspected README quickstart, examples index, and repository scripts; product behavior is invoked as finite benchmark or leaderboard commands, with no product scheduler or recurring cycle.",
        "expected": "identify the product trigger",
        "input": "source inspection",
        "kind": "cycle",
        "outcome": "Inspected README quickstart, examples index, and repository scripts; product behavior is invoked as finite benchmark or leaderboard commands, with no product scheduler or recurring cycle.",
        "source": "repository entry points",
        "verdict": "yes"
      }
    ],
    "details": {
      "argv": [
        ".venv/bin/python",
        "examples/plots/run_generate_custom_leaderboard.py"
      ],
      "kind": "cli",
      "needs_browser": false,
      "needs_credentials": false,
      "returncode": 0,
      "scope": "product operation",
      "stderr": "",
      "stdout": "Synthetic results (head):\nmethod    task  seed  metric_error  time_train_s  time_infer_s\nModelA task_01     0      0.319539      9.336413      1.333594\nModelB task_01     0      0.325628     16.444140      1.166359\nModelC task_01     0      0.294925     16.230843      1.677682\nModelD task_01     0      0.299650     21.288854      1.701920\nModelA task_01     1      0.303044     14.043352      1.037219\nModelB task_01     1      0.310581     21.670556      2.040347\nModelC task_01     1      0.292780     35.715085      2.222946\nModelD task_01     1      0.338301     25.729447      1.637356\n\nRow count: 240 (should be tasks * seeds * methods = 240 )\n[excerpt truncated]",
      "working": true
    },
    "evidence_strength": "readme_checked",
    "example_checks": [
      {
        "claim": "Generate a custom leaderboard from synthetic results.",
        "command": ".venv/bin/python examples/plots/run_generate_custom_leaderboard.py",
        "deferred": false,
        "evidence": "            0.750011             0.296726            24.270589             1.759914\nModelB  1000.0  35.8  57.7  2.65  0.450000          0.502097     17.851416      1.521589     2.666667              0.064467              0.458040             0.283038            16.516307             1.539750\n\n=== Pairwise win-rate matrix (results_per_task) ===\n           ModelA     ModelC     ModelB     ModelD\nModelA        NaN  70.000000  78.300003  71.699997\nModelC  30.000000        NaN  46.700001  63.299999\nModelB  21.700001  53.299999        NaN  60.000000\nModelD  28.299999  36.700001  40.000000        NaN",
        "expected": "prints leaderboard, Elo and ranks",
        "input": "documented custom leaderboard script",
        "kind": "example",
        "outcome": "command completed successfully",
        "returncode": 0,
        "source": "examples/README.md, Generating Plots and Leaderboards",
        "verdict": "yes"
      }
    ],
    "status": "PASS"
  },
  "deviations": [
    "The quickstart benchmark model requires costly model dependencies and downloaded dataset assets; used the documented no-download custom leaderboard example instead."
  ],
  "fault_owner": null,
  "main_blocker": null,
  "operation": "Generate a custom benchmark leaderboard",
  "operation_scope": "README compatible Python 3.12.3 environment with uv and the plot extra installed",
  "outcome": "working",
  "plain_failure": null,
  "steps": [
    "Created Python 3.12 virtual environment with pip and uv",
    "Installed documented evaluation-only dependency path: uv pip install --prerelease=allow -e './packages/tabarena[plot]'",
    "Ran .venv/bin/python examples/plots/run_generate_custom_leaderboard.py",
    "Validated and replayed probe, README claim, example and cycle evidence successfully"
  ],
  "stuck_step": null,
  "suggested_fix": null
}