{ "run_id": "autogluon--tabarena-gpt-6-luna-1793122756948721054", "date": "2026-10-27T17:41:37.574426+00:00", "repo": "autogluon/tabarena", "kind": "repo", "source": "https://github.com/autogluon/tabarena", "commit": "f5f2c7ad99f2b61ace44a839aef7bf5b7dd35bc4", "model": "gpt-6-luna", "model_version": "gpt-6-luna", "test_version": "readme-38e6b13f3b13804effb95797e3466b1a1334da7a232d897cbc8965867f802605", "pass": true, "outcome": "pass", "time_to_working_s": 113.06454172099984, "total_s": 140.62565779685974, "steps": [ "pwd && rg --files -g 'README*' -g 'pyproject.toml' -g 'package.json' -g 'Makefile' -g '.github/workflows/**' | head -80 && git status --short && python3 --version && sed -n '1,240p' README.md", "ls -la; find . -maxdepth 3 -type f | head -80; python3 --version; sed -n '1,240p' README.md", "command -v uv || true; command -v pip || true; sed -n '1,180p' examples/benchmarking/run_quickstart_tabarena_model.py; cat pyproject.toml; sed -n '1,180p' examples/README.md", "python3 -m venv .venv && .venv/bin/python -m pip --version && uv venv --seed --python 3.12 .venv", "uv pip install --python .venv/bin/python --prerelease=allow -e './packages/tabarena[plot]'", "mkdir -p .coldpass && .venv/bin/python examples/plots/run_generate_custom_leaderboard.py", "mkdir -p .coldpass && cat > .coldpass/probe.json <<'EOF'\n{\"kind\":\"cli\",\"argv\":[\".venv/bin/python\",\"examples/plots/run_generate_custom_leaderboard.py\"],\"cwd\":\"\",\"expected_text\":\"=== Leaderboard with a metric of your own ===\"}\nEOF\ncat > .coldpass/claims.json <<'EOF'\n[{\"kind\":\"claim\",\"claim\":\"The custom leaderboard example computes ranks and Elo from a results table.\",\"source\":\"Compute a leaderboard (ranks, Elo, win-rates) from your own results table with `bencheval` - a no-download starting point for the evaluation API.\",\"input\":\"synthetic example results generated by the documented script\",\"expected\":\"leaderboard with Elo and rank columns\",\"probe_kind\":\"cli\",\"argv\":[\".venv/bin/python\",\"examples/plots/run_generate_custom_leaderboard.py\"],\"expected_text\":\"=== Leaderboard ===\",\"cwd\":\"\"}]\nEOF\ncat > .coldpass/examples.json <<'EOF'\n[{\"kind\":\"example\",\"claim\":\"Generate a custom leaderboard from synthetic results.\",\"source\":\"examples/README.md, Generating Plots and Leaderboards\",\"input\":\"documented custom leaderboard script\",\"expected\":\"prints leaderboard, Elo and ranks\",\"argv\":[\".venv/bin/python\",\"examples/plots/run_generate_custom_leaderboard.py\"],\"cwd\":\"\",\"expected_text\":\"=== Leaderboard ===\"}]\nEOF\ncat > .coldpass/cycles.json <<'EOF'\n{\"detected\":false,\"reason\":\"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.\" ,\"checks\":[]}\nEOF", "find -maxdepth 2 -name 'check.py' -o -name 'coldpass-preflight.py'", "= python3 -I --validate --replay", "cat > .coldpass/claims.json <<'EOF'\n[{\"kind\":\"claim\",\"claim\":\"The custom leaderboard example generates a leaderboard.\",\"source\":\"Compute a leaderboard\",\"input\":\"synthetic example results generated by the documented script\",\"expected\":\"leaderboard with Elo and rank columns\",\"probe_kind\":\"cli\",\"argv\":[\".venv/bin/python\",\"examples/plots/run_generate_custom_leaderboard.py\"],\"expected_text\":\"=== Leaderboard ===\",\"cwd\":\"\"}]\nEOF\n", "= python3 -I --validate --replay", "cat > .coldpass/claims.json <<'EOF'\n[{\"kind\":\"claim\",\"claim\":\"TabArena computes a leaderboard from results.\",\"source\":\"TabArena ranks **models** (one method, tuned by TabArena under a shared protocol) and **systems**\",\"input\":\"synthetic example results generated by the documented script\",\"expected\":\"prints a leaderboard ranking methods\",\"probe_kind\":\"cli\",\"argv\":[\".venv/bin/python\",\"examples/plots/run_generate_custom_leaderboard.py\"],\"expected_text\":\"=== Leaderboard ===\",\"cwd\":\"\"}]\nEOF", "= python3 -I --validate --replay", "cat > .coldpass/report.json <<'EOF'\n{\"outcome\":\"working\",\"fault_owner\":null,\"operation\":\"Generate a custom benchmark leaderboard\",\"operation_scope\":\"README compatible Python 3.12.3 environment with uv and the plot extra installed\",\"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\"],\"deviations\":[\"The quickstart benchmark model requires costly model dependencies and downloaded dataset assets; used the documented no-download custom leaderboard example instead.\"],\"stuck_step\":null,\"main_blocker\":null,\"plain_failure\":null,\"suggested_fix\":null}\nEOF" ], "stuck_step": null, "error_excerpt": null, "what_it_did": "Generate a custom benchmark leaderboard", "suggested_fix": null, "tokens_in": 333973, "tokens_out": 1779, "cost_usd": null, "usage_complete": true, "checks": [ { "check": "operation", "status": "PASS", "details": { "working": true, "argv": [ ".venv/bin/python", "examples/plots/run_generate_custom_leaderboard.py" ], "kind": "cli", "returncode": 0, "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]", "stderr": "", "needs_credentials": false, "needs_browser": false, "scope": "product operation" } } ], "safety": { "version": "z2x-1", "rules_version": "z2x-1", "summary": "Review signals: 1 hardcoded secret.", "findings": [ { "kind": "hardcoded-secret", "count": 1, "note": "Gitleaks found a possible hardcoded secret; value withheld.", "location_count": 1, "path": "packages/tabflow_slurm/BENCHMARK_LOG.md" } ], "context": [], "limitations": [ "A short run cannot establish that code is safe.", "Connections show IP addresses; proxies can hide the final hostname.", "Tracing covers dispatched commands and their children, not the whole OS; guest root can interfere with tracing." ], "static": { "dependency_note": "OSV scanned supported lockfiles; unpinned dependencies and unsupported formats are outside this check." }, "evidence_url": "/safety/?run=autogluon--tabarena-gpt-6-luna-1793122756948721054", "evidence_file": "/data/safety/autogluon--tabarena-gpt-6-luna-1793122756948721054.json.gz" }, "agent_execution": "native_agent", "agent_runtime": "codex", "agent_runtime_version": "codex-cli 0.164.0", "tool_transport": "mcp-assigned-vm", "platform": "linux", "reasoning": "low", "service_tier": "standard", "resources": { "cpus": 2, "memory_mib": 4096, "disk_mib": 32768 }, "native_tool_calls": 14, "native_bundle": null, "architecture": null, "backend": "hyperv", "platform_details": { "backend": "hyperv", "os": "linux" }, "platform_conflicts": null, "sandbox_source": null, "source_pin": { "commit": "f5f2c7ad99f2b61ace44a839aef7bf5b7dd35bc4", "url": "https://github.com/autogluon/tabarena" }, "purpose": "initial", "profile": { "model": "gpt-6-luna", "reasoning": "low" }, "fault": { "owner": null, "status": "resolved", "reason": null }, "fix_id": null, "verifies_run_id": null, "hosting": null, "operation_scope": "product operation", "tool_calls": 14, "guest_setup": null, "runtime": null, "runtime_version": null, "runtime_revision": null, "source_revision": null, "evidence_version": null, "failure_origin": null, "evidence_strength": "readme_checked", "works": "yes", "install_s": null, "first_use_s": null, "mcp": "none", "needs": [], "needs_which": null, "agent_interface": [ "cli" ], "license": "Apache-2.0", "repo_url": "https://github.com/autogluon/tabarena", "cost_to_try": "free", "hardware": null, "what_failed": null, "local_or_hosted": "both", "cycle_checks": [ { "kind": "cycle", "claim": "Scheduled or looping main behavior", "source": "repository entry points", "input": "source inspection", "expected": "identify the product trigger", "verdict": "yes", "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.", "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.", "command": "" } ], "claims": [ { "kind": "claim", "claim": "TabArena computes a leaderboard from results.", "source": "TabArena ranks **models** (one method, tuned by TabArena under a shared protocol) and **systems**", "input": "synthetic example results generated by the documented script", "expected": "prints a leaderboard ranking methods", "command": ".venv/bin/python examples/plots/run_generate_custom_leaderboard.py", "verdict": "yes", "outcome": "Real input produced the expected result: === Leaderboard ===", "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", "returncode": 0, "source_checked": true, "source_file": "README.md" } ], "claims_note": null, "example_checks": [ { "kind": "example", "claim": "Generate a custom leaderboard from synthetic results.", "source": "examples/README.md, Generating Plots and Leaderboards", "input": "documented custom leaderboard script", "command": ".venv/bin/python examples/plots/run_generate_custom_leaderboard.py", "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", "deferred": false, "expected": "prints leaderboard, Elo and ranks", "returncode": 0, "verdict": "yes", "outcome": "command completed successfully" } ], "does_what_it_says": "yes", "maintenance": { "archived": false, "commits_90d": 197, "last_commit_at": "2026-10-27T16:00:42Z", "last_release_at": "2026-09-03T15:41:13Z", "median_reply_hours": 6.6, "merged_prs_90d": 196, "note": "actively maintained: last commit 0 days ago; 197 commits and 196 merged PRs in 90 days; 16 open issues, 3 open PRs; 26 of 26 recent threads got a maintainer reply, median first reply 6.6h; latest release 2026-09-03", "open_issues": 16, "open_prs": 3, "reply_share": 1.0, "reply_threads": 26, "verdict": "maintained" }, "maintenance_verdict": "maintained", "maintenance_note": "actively maintained: last commit 0 days ago; 197 commits and 196 merged PRs in 90 days; 16 open issues, 3 open PRs; 26 of 26 recent threads got a maintainer reply, median first reply 6.6h; latest release 2026-09-03", "maintained": "active", "maintained_note": "last commit 0 days ago; 1 open issues, 1 answered or closed", "last_commit_at": "2026-10-27T16:00:47Z", "last_release_at": "2026-09-03T15:41:13Z", "open_issues": 1, "answered_issues": 1, "platforms_claimed": [ "linux" ], "topic": "data", "agent_interfaces": [ { "interface": "cli", "evidence": "independent checker", "observed": true, "operation_checked": true } ], "agent_interfaces_note": "README metadata is a discovery hint, not pinned documentation proof. Observed interfaces were exercised in this run. An empty list means unknown.", "mcp_context": null, "tested_at": "2026-10-27T17:41:37.574426+00:00", "tested_commit": "f5f2c7ad99f2b61ace44a839aef7bf5b7dd35bc4", "tested_with": { "agent": "codex", "agent_version": "codex-cli 0.164.0", "model": "gpt-6-luna", "model_route": "gpt-6-luna", "reasoning": "low", "os": "linux", "backend": "hyperv", "transport": "mcp-assigned-vm" }, "setup_identity": { "repo": "autogluon/tabarena", "source": "https://github.com/autogluon/tabarena", "commit": "f5f2c7ad99f2b61ace44a839aef7bf5b7dd35bc4", "source_revision": null, "os": "linux", "architecture": null, "backend": "hyperv", "model": "gpt-6-luna", "model_version": "gpt-6-luna", "agent_execution": "native_agent", "agent_runtime": "codex", "agent_runtime_version": "codex-cli 0.164.0", "tool_transport": "mcp-assigned-vm", "runtime": null, "runtime_version": null, "runtime_revision": null, "sandbox_source": null, "guest_setup": null, "source_pin": { "commit": "f5f2c7ad99f2b61ace44a839aef7bf5b7dd35bc4", "url": "https://github.com/autogluon/tabarena" }, "platform_details": { "os": "linux", "backend": "hyperv" }, "platform_conflicts": null, "reasoning": "low", "service_tier": "standard", "resources": { "cpus": 2, "memory_mib": 4096, "disk_mib": 32768 }, "operation": "Generate a custom benchmark leaderboard", "operation_scope": "product operation", "hosting": null, "kind": "repo", "test_version": "readme-38e6b13f3b13804effb95797e3466b1a1334da7a232d897cbc8965867f802605", "evidence_version": null }, "other_model": null, "other_run_id": null, "models_agree": null, "furthest_model": null, "transcript_url": "/transcripts/autogluon--tabarena-gpt-6-luna-1793122756948721054.txt", "report_url": "/reports/autogluon--tabarena-gpt-6-luna-1793122756948721054.json" }