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        "chatcmpl-80217f0253b7ee01",
        "chatcmpl-82dd98140cc4f7e6",
        "chatcmpl-8318bd87df6ff7df",
        "chatcmpl-899626d2f0f5bdfc",
        "chatcmpl-8ac06f30acde0cab",
        "chatcmpl-8ce9e96b31698131",
        "chatcmpl-8f231cad5fcc1f56",
        "chatcmpl-8fd0c37b68c705d5",
        "chatcmpl-9108b9db978dd2e9",
        "chatcmpl-92ca463a85c6cbf3",
        "chatcmpl-93aa528a369b0133",
        "chatcmpl-956029ccba0fbd30",
        "chatcmpl-957ff8ed8797a391",
        "chatcmpl-9606b12bb03ca4c0",
        "chatcmpl-966b4a64a9afed0d",
        "chatcmpl-9bac4294073e9ec5",
        "chatcmpl-9fce7286d03aa9aa",
        "chatcmpl-a45e52a8e5d024e5",
        "chatcmpl-a4d6e487cd1444ed",
        "chatcmpl-a7a3a26ea417eff4",
        "chatcmpl-a7cf88b26bfd3f17",
        "chatcmpl-a86900d00c0711cb",
        "chatcmpl-a8b94ad32667f4a3",
        "chatcmpl-ab602f00f4f51c7b",
        "chatcmpl-aba75d8e771ffa1c",
        "chatcmpl-ac57ff6a2c49f613",
        "chatcmpl-accbafef8826bf07",
        "chatcmpl-ae938c3de85c9ae1",
        "chatcmpl-afa158991006e88c",
        "chatcmpl-b2889cc9d7fb11ac",
        "chatcmpl-b81158cc2d0ada96",
        "chatcmpl-b896e496aa56425d",
        "chatcmpl-b9b39df1a0f1e374",
        "chatcmpl-bd15c366a1ce9efd",
        "chatcmpl-bd51c3ea4f416eb0",
        "chatcmpl-bdb547798d696c23",
        "chatcmpl-bf3eba0ad0fb9fd4"
      ],
      "tokens": {
        "completion_tokens": null,
        "prompt_tokens": null,
        "total_tokens": null
      },
      "trajectory": {
        "path": "<PRIVATE_PATH>",
        "sha256": "1656be1eaf695bd01a36ea4130f84d6788738c29b8c569b0ddd3ae14fa329ac9"
      }
    }
  ],
  "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": "a19f2fd3af6600893b0655cf6a17b35cf3047c8dc9701cdd3cfe24d5dc3c40a4",
  "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-r2-t0-swe5",
  "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": [
        "<PRIVATE_PATH>",
        "<PRIVATE_PATH>",
        "--output",
        "<PRIVATE_PATH>",
        "--model",
        "openai/glm53-v091-tp2-327k-c4-vision8-nospec-r2",
        "--subset",
        "<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",
        "<PRIVATE_PATH>",
        "--config",
        "<PRIVATE_PATH>"
      ],
      "duration_s": 2290.77528,
      "name": "agent",
      "output_truncated": false,
      "returncode": 0,
      "status": "completed",
      "stderr": "",
      "stdout": "minisweagent: INFO: Results will be saved to                                    \n<PRIVATE_PATH>\nark-runs/glm-v091-trial-20261016/glm-v091-r2-t0-swe5/work/mini-output           \nLogging to '<PRIVATE_PATH>\nminisweagent: INFO: Loading dataset                                             \n<PRIVATE_PATH>\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['<PRIVATE_PATH>\nhmark-harness-cache/mini-swe-agent/a83fcae82d2a08f0ee0c688f9d137b3566c097f8/src/\nminisweagent/config/benchmarks/swebench.yaml',                                  \n'<PRIVATE_PATH>\nmark-runs/glm-v091-trial-20261016/glm-v091-r2-t0-swe5/work/anvil-swe-config.yaml\n']                                                                              \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'<PRIVATE_PATH>\nmark-runs/glm-v091-trial-20261016/glm-v091-r2-t0-swe5/work/mini-output/django__d\njango-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'<PRIVATE_PATH>\nmark-runs/glm-v091-trial-20261016/glm-v091-r2-t0-swe5/work/mini-output/pydata__x\narray-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'<PRIVATE_PATH>\nmark-runs/glm-v091-trial-20261016/glm-v091-r2-t0-swe5/work/mini-output/pylint-de\nv__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'<PRIVATE_PATH>\nmark-runs/glm-v091-trial-20261016/glm-v091-r2-t0-swe5/work/mini-output/sphinx-do\nc__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'<PRIVATE_PATH>\nmark-runs/glm-v091-trial-20261016/glm-v091-r2-t0-swe5/work/mini-output/sympy__sy\nmpy-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% 0:38:09 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": [
        "<PRIVATE_PATH>",
        "<PRIVATE_PATH>",
        "--grader-root",
        "<PRIVATE_PATH>",
        "--adapter-sha256",
        "23c56b27d62b79c39aca6dcfdd4c504030e241e3a6941620cdc6e947b9035a7b",
        "--receipt",
        "<PRIVATE_PATH>",
        "--image-identities",
        "<PRIVATE_PATH>",
        "--image-identities-sha256",
        "e2880b3b24051b0658dd3095f7564ae592ab970e110fed76028f3a8a301f9b65",
        "--",
        "--dataset_name",
        "<PRIVATE_PATH>",
        "--split",
        "test",
        "--predictions_path",
        "<PRIVATE_PATH>",
        "--max_workers",
        "1",
        "--namespace",
        "swebench",
        "--run_id",
        "glm-v091-r2-t0-swe5",
        "--timeout",
        "1800",
        "--report_dir",
        "<PRIVATE_PATH>",
        "--instance_ids",
        "sphinx-doc__sphinx-9658",
        "sympy__sympy-12096",
        "django__django-13512",
        "pydata__xarray-4356",
        "pylint-dev__pylint-8898"
      ],
      "duration_s": 111.80655,
      "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, 19.59s/it, \u2713=0, \u2716=1, error=0]\rEvaluation:  20%|\u2588\u2588        | 1/5 [00:42<01:18, 19.59s/it, \u2713=1, \u2716=1, error=0]\rEvaluation:  40%|\u2588\u2588\u2588\u2588      | 2/5 [00:42<01:05, 21.81s/it, \u2713=1, \u2716=1, error=0]\rEvaluation:  40%|\u2588\u2588\u2588\u2588      | 2/5 [01:09<01:05, 21.81s/it, \u2713=1, \u2716=2, error=0]\rEvaluation:  60%|\u2588\u2588\u2588\u2588\u2588\u2588    | 3/5 [01:09<00:47, 23.81s/it, \u2713=1, \u2716=2, error=0]\rEvaluation:  60%|\u2588\u2588\u2588\u2588\u2588\u2588    | 3/5 [01:26<00:47, 23.81s/it, \u2713=2, \u2716=2, error=0]\rEvaluation:  80%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588  | 4/5 [01:26<00:21, 21.38s/it, \u2713=2, \u2716=2, error=0]\rEvaluation:  80%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588  | 4/5 [01:44<00:21, 21.38s/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, 20.15s/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, 20.95s/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-v091-tp2-327k-c4-vision8-nospec-r2.glm-v091-r2-t0-swe5.json\n"
    }
  ],
  "state": "completed",
  "summary": {
    "attempted": 5,
    "graded": 5,
    "resolve_rate": 0.6,
    "resolved": 3
  },
  "task_container_network": "none"
}