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      "request_ids": [
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        "chatcmpl-b158f9f2fb688782",
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        "chatcmpl-baa0265c51f5fb10",
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      "tokens": {
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      "trajectory": {
        "path": "<OPERATOR_HOME>/benchmark-runs/glm-v091-trial-20261016/glm-v091-restored-baseline-t0-swe5-r1/work/mini-output/pylint-dev__pylint-8898/pylint-dev__pylint-8898.traj.json",
        "sha256": "b7312daddaa4bd4199d2757d9cc95c7d708914106dda288d2ed9aa87661c42df"
      }
    }
  ],
  "official_grader_complete": true,
  "ownership_id": "glm-v091-trial-20261016",
  "paired_image_ids": {
    "django__django-13512": "sha256:730eab5b64c03ed8e8c94064da483db36fbd79d2465ffa98809acc6282bcfd82",
    "pydata__xarray-4356": "sha256:6783a6b3a574ea663b66d11da371b6fe00a18c6501e361e1384c8daeb978bf5c",
    "pylint-dev__pylint-8898": "sha256:a78e6034f627c1ae69c67b5a25ae5420192afb7279a4fd032a74095752e707d7",
    "sphinx-doc__sphinx-9658": "sha256:f8847eb2c89ef395c61e91c1f00166b274b2742aee6f310e3a315a10999f1e93",
    "sympy__sympy-12096": "sha256:278dcceabbf7775dee7ee59434fd829e857d5476ac9e547fe9f9f204b2c3f4fe"
  },
  "plan_sha256": "7b5066fa41a7cd5101d4cc442ed9a94cd75488cf6f29df1ffe88afaefcfc60c7",
  "profile": "scout",
  "profile_sha256": "fce0e6e1e7497585bdfc6f76507d3baaff99a6b4f174d841d268de20c68de7f6",
  "promotion": {
    "authorized": false,
    "message": "Benchmark evidence does not authorize model promotion."
  },
  "request_controls": {
    "reasoning_effort": "max",
    "sampling": {
      "temperature": {
        "effective_request": 0.0,
        "requested": 0.0,
        "sent": true
      },
      "top_p": {
        "effective_request": 0.95,
        "requested": 0.95,
        "sent": true
      }
    },
    "thinking_mode": "default"
  },
  "run_id": "glm-v091-restored-baseline-t0-swe5-r1",
  "schema": "anvil-serving.swe-run/v1",
  "selection": {
    "instance_ids": [
      "sphinx-doc__sphinx-9658",
      "sympy__sympy-12096",
      "django__django-13512",
      "pydata__xarray-4356",
      "pylint-dev__pylint-8898"
    ],
    "kind": "explicit_instance_ids",
    "sha256": "622934396fb97d950f803dfbb0791f6a398a5a6fe0fd94cc27b329f1a953cac4"
  },
  "split": "test",
  "stages": [
    {
      "command": [
        "<OPERATOR_HOME>/benchmark-harness-cache/swe-python-environments/656cef6c79321ff3244984321b336422f2d5913dadd685f3e00241a4ed181190/bin/python",
        "<OPERATOR_HOME>/benchmark-harness-cache/mini-swe-agent/a83fcae82d2a08f0ee0c688f9d137b3566c097f8/src/minisweagent/run/benchmarks/swebench.py",
        "--output",
        "<OPERATOR_HOME>/benchmark-runs/glm-v091-trial-20261016/glm-v091-restored-baseline-t0-swe5-r1/work/mini-output",
        "--model",
        "openai/glm53-flash-exl3-4bpw-v84-fp8-tp2-dcp1-c4-327k-r11-vision8-apc-nospec",
        "--subset",
        "<OPERATOR_HOME>/benchmark-harness-cache/swe-bench-verified/c104f840cc67f8b6eec6f759ebc8b2693d585d4a<PRIVATE_PATH>",
        "--split",
        "test",
        "--filter",
        "^(?:sphinx\\-doc__sphinx\\-9658|sympy__sympy\\-12096|django__django\\-13512|pydata__xarray\\-4356|pylint\\-dev__pylint\\-8898)$",
        "--workers",
        "1",
        "--environment-class",
        "docker",
        "--config",
        "<OPERATOR_HOME>/benchmark-harness-cache/mini-swe-agent/a83fcae82d2a08f0ee0c688f9d137b3566c097f8/src/minisweagent/config/benchmarks/swebench.yaml",
        "--config",
        "<OPERATOR_HOME>/benchmark-runs/glm-v091-trial-20261016/glm-v091-restored-baseline-t0-swe5-r1/work/anvil-swe-config.yaml"
      ],
      "duration_s": 2398.387541,
      "name": "agent",
      "output_truncated": false,
      "returncode": 0,
      "status": "completed",
      "stderr": "",
      "stdout": "minisweagent: INFO: Results will be saved to                                    \n<OPERATOR_HOME>/benchm\nark-runs/glm-v091-trial-20261016/glm-v091-restored-baseline-t0-swe5-r1/work/mini\n-output                                                                         \nLogging to '<OPERATOR_HOME>/benchmark-runs/glm-v091-trial-20261016/glm-v091-restored-baseline-t0-swe5-r1/work/mini-output/minisweagent.log'\nminisweagent: INFO: Loading dataset                                             \n<OPERATOR_HOME>/benchm\nark-harness-cache/swe-bench-verified/c104f840cc67f8b6eec6f759ebc8b2693d585d4a/da\nta, split test...                                                               \nminisweagent: INFO: Instance filter: 500 -> 5 instances                         \nminisweagent: INFO: Running on 5 instances...                                   \nminisweagent: INFO: Building agent config from specs:                           \n['<OPERATOR_HOME>/benc\nhmark-harness-cache/mini-swe-agent/a83fcae82d2a08f0ee0c688f9d137b3566c097f8/src/\nminisweagent/config/benchmarks/swebench.yaml',                                  \n'<OPERATOR_HOME>/bench\nmark-runs/glm-v091-trial-20261016/glm-v091-restored-baseline-t0-swe5-r1/work/anv\nil-swe-config.yaml']                                                            \nminisweagent.environment: DEBUG: Starting container with command:               \n/usr/bin/docker run -d --name minisweagent-REDACTED -w /testbed --platform      \nlinux/amd64 --network none --rm --memory 8g --memory-swap 8g --cpus 4           \n--pids-limit 512 --pull never                                                   \ndocker.io/swebench/sweb.eval.x86_64.django_1776_django-13512:latest sleep 2h    \nminisweagent.environment: INFO: Started container minisweagent-REDACTED with ID \nCONTAINER_ID_REDACTED                \nminisweagent: INFO: Saved trajectory to                                         \n'<OPERATOR_HOME>/bench\nmark-runs/glm-v091-trial-20261016/glm-v091-restored-baseline-t0-swe5-r1/work/min\ni-output/django__django-13512/django__django-13512.traj.json'                   \nminisweagent.environment: DEBUG: Starting container with command:               \n/usr/bin/docker run -d --name minisweagent-REDACTED -w /testbed --platform      \nlinux/amd64 --network none --rm --memory 8g --memory-swap 8g --cpus 4           \n--pids-limit 512 --pull never                                                   \ndocker.io/swebench/sweb.eval.x86_64.pydata_1776_xarray-4356:latest sleep 2h     \nminisweagent.environment: INFO: Started container minisweagent-REDACTED with ID \nCONTAINER_ID_REDACTED                \nminisweagent: INFO: Saved trajectory to                                         \n'<OPERATOR_HOME>/bench\nmark-runs/glm-v091-trial-20261016/glm-v091-restored-baseline-t0-swe5-r1/work/min\ni-output/pydata__xarray-4356/pydata__xarray-4356.traj.json'                     \nminisweagent.environment: DEBUG: Starting container with command:               \n/usr/bin/docker run -d --name minisweagent-REDACTED -w /testbed --platform      \nlinux/amd64 --network none --rm --memory 8g --memory-swap 8g --cpus 4           \n--pids-limit 512 --pull never                                                   \ndocker.io/swebench/sweb.eval.x86_64.pylint-dev_1776_pylint-8898:latest sleep 2h \nminisweagent.environment: INFO: Started container minisweagent-REDACTED with ID \nCONTAINER_ID_REDACTED                \nminisweagent: INFO: Saved trajectory to                                         \n'<OPERATOR_HOME>/bench\nmark-runs/glm-v091-trial-20261016/glm-v091-restored-baseline-t0-swe5-r1/work/min\ni-output/pylint-dev__pylint-8898/pylint-dev__pylint-8898.traj.json'             \nminisweagent.environment: DEBUG: Starting container with command:               \n/usr/bin/docker run -d --name minisweagent-REDACTED -w /testbed --platform      \nlinux/amd64 --network none --rm --memory 8g --memory-swap 8g --cpus 4           \n--pids-limit 512 --pull never                                                   \ndocker.io/swebench/sweb.eval.x86_64.sphinx-doc_1776_sphinx-9658:latest sleep 2h \nminisweagent.environment: INFO: Started container minisweagent-REDACTED with ID \nCONTAINER_ID_REDACTED                \nminisweagent: INFO: Saved trajectory to                                         \n'<OPERATOR_HOME>/bench\nmark-runs/glm-v091-trial-20261016/glm-v091-restored-baseline-t0-swe5-r1/work/min\ni-output/sphinx-doc__sphinx-9658/sphinx-doc__sphinx-9658.traj.json'             \nminisweagent.environment: DEBUG: Starting container with command:               \n/usr/bin/docker run -d --name minisweagent-REDACTED -w /testbed --platform      \nlinux/amd64 --network none --rm --memory 8g --memory-swap 8g --cpus 4           \n--pids-limit 512 --pull never                                                   \ndocker.io/swebench/sweb.eval.x86_64.sympy_1776_sympy-12096:latest sleep 2h      \nminisweagent.environment: INFO: Started container minisweagent-REDACTED with ID \nCONTAINER_ID_REDACTED                \nminisweagent: INFO: Saved trajectory to                                         \n'<OPERATOR_HOME>/bench\nmark-runs/glm-v091-trial-20261016/glm-v091-restored-baseline-t0-swe5-r1/work/min\ni-output/sympy__sympy-12096/sympy__sympy-12096.traj.json'                       \n  Overall Progress ($0.00) \u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 5/5 100% 0:39:56 eta: 0:00:00\n\u250f\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2533\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2513\n\u2503 Exit Status \u2503 Count \u2503 Most recent instances                                  \u2503\n\u2521\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2547\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2529\n\u2502 Submitted   \u2502     5 \u2502 sympy__sympy-12096, sphinx-doc__sphinx-9658,           \u2502\n\u2502             \u2502       \u2502 pylint-...                                             \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518"
    },
    {
      "command": [
        "<OPERATOR_HOME>/benchmark-harness-cache/swe-python-environments/656cef6c79321ff3244984321b336422f2d5913dadd685f3e00241a4ed181190/bin/python",
        "<PRIVATE_PATH>s/glm-v091-trial/anvil-serving/anvil_serving/benchmarking/swe_grader_adapter.py",
        "--grader-root",
        "<OPERATOR_HOME>/benchmark-harness-cache/swe-bench/f7bbbb2ccdf479001d6467c9e34af59e44a840f9",
        "--adapter-sha256",
        "23c56b27d62b79c39aca6dcfdd4c504030e241e3a6941620cdc6e947b9035a7b",
        "--receipt",
        "<OPERATOR_HOME>/benchmark-runs/glm-v091-trial-20261016/glm-v091-restored-baseline-t0-swe5-r1/work/official-grader/containment.json",
        "--image-identities",
        "<OPERATOR_HOME>/benchmark-runs/glm-v091-trial-20261016/glm-v091-restored-baseline-t0-swe5-r1/work/paired-image-ids.json",
        "--image-identities-sha256",
        "e2880b3b24051b0658dd3095f7564ae592ab970e110fed76028f3a8a301f9b65",
        "--",
        "--dataset_name",
        "<OPERATOR_HOME>/benchmark-harness-cache/swe-bench-verified/c104f840cc67f8b6eec6f759ebc8b2693d585d4a<PRIVATE_PATH>",
        "--split",
        "test",
        "--predictions_path",
        "<OPERATOR_HOME>/benchmark-runs/glm-v091-trial-20261016/glm-v091-restored-baseline-t0-swe5-r1/work/predictions.jsonl",
        "--max_workers",
        "1",
        "--namespace",
        "swebench",
        "--run_id",
        "glm-v091-restored-baseline-t0-swe5-r1",
        "--timeout",
        "1800",
        "--report_dir",
        "<OPERATOR_HOME>/benchmark-runs/glm-v091-trial-20261016/glm-v091-restored-baseline-t0-swe5-r1/work/official-grader",
        "--instance_ids",
        "sphinx-doc__sphinx-9658",
        "sympy__sympy-12096",
        "django__django-13512",
        "pydata__xarray-4356",
        "pylint-dev__pylint-8898"
      ],
      "duration_s": 112.868923,
      "name": "official_grader",
      "output_truncated": false,
      "returncode": 0,
      "status": "completed",
      "stderr": "<frozen runpy>:128: RuntimeWarning: 'swebench.harness.run_evaluation' found in sys.modules after import of package 'swebench.harness', but prior to execution of 'swebench.harness.run_evaluation'; this may result in unpredictable behaviour\n\rEvaluation:   0%|          | 0/5 [00:00<?, ?it/s, error=0, \u2713=0, \u2716=0]\rEvaluation:   0%|          | 0/5 [00:19<?, ?it/s, \u2713=0, \u2716=1, error=0]\rEvaluation:  20%|\u2588\u2588        | 1/5 [00:19<01:18, 19.68s/it, \u2713=0, \u2716=1, error=0]\rEvaluation:  20%|\u2588\u2588        | 1/5 [00:42<01:18, 19.68s/it, \u2713=1, \u2716=1, error=0]\rEvaluation:  40%|\u2588\u2588\u2588\u2588      | 2/5 [00:42<01:05, 21.75s/it, \u2713=1, \u2716=1, error=0]\rEvaluation:  40%|\u2588\u2588\u2588\u2588      | 2/5 [01:08<01:05, 21.75s/it, \u2713=1, \u2716=2, error=0]\rEvaluation:  60%|\u2588\u2588\u2588\u2588\u2588\u2588    | 3/5 [01:08<00:47, 23.74s/it, \u2713=1, \u2716=2, error=0]\rEvaluation:  60%|\u2588\u2588\u2588\u2588\u2588\u2588    | 3/5 [01:26<00:47, 23.74s/it, \u2713=2, \u2716=2, error=0]\rEvaluation:  80%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588  | 4/5 [01:26<00:21, 21.29s/it, \u2713=2, \u2716=2, error=0]\rEvaluation:  80%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588  | 4/5 [01:44<00:21, 21.29s/it, \u2713=3, \u2716=2, error=0]\rEvaluation: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5/5 [01:44<00:00, 20.07s/it, \u2713=3, \u2716=2, error=0]\rEvaluation: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 5/5 [01:44<00:00, 20.89s/it, \u2713=3, \u2716=2, error=0]\n",
      "stdout": "Found 5 existing instance images. Will reuse them.\nRunning 5 instances...\nAll instances run.\nCleaning cached images...\nRemoved 0 images.\nTotal instances: 5\nInstances submitted: 5\nInstances completed: 5\nInstances incomplete: 0\nInstances resolved: 3\nInstances unresolved: 2\nInstances with empty patches: 0\nInstances with errors: 0\nUnstopped containers: 0\nUnremoved images: 5\nReport written to openai__glm53-flash-exl3-4bpw-v84-fp8-tp2-dcp1-c4-327k-r11-vision8-apc-nospec.glm-v091-restored-baseline-t0-swe5-r1.json\n"
    }
  ],
  "state": "completed",
  "summary": {
    "attempted": 5,
    "graded": 5,
    "resolve_rate": 0.6,
    "resolved": 3
  },
  "task_container_network": "none"
}