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data/adapter_stats.json

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data/openeval/deepseek/deepseek-llm-67b-chat/8b2d9859-4828-4cbe-a815-6570e79b7fd0.json

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data/openeval/deepseek/deepseek-r1-0528/4f660c58-5534-4384-aafe-53970bac5da2.json

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data/openeval/deepseek/deepseek-r1-0528/7f598637-1520-4cc3-8e2f-4cfa0fa49e98.json

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data/openeval/deepseek/deepseek-r1-0528/c36614b4-48bf-4d93-b5f5-4ab9b4a49bd0.json

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data/openeval/deepseek/deepseek-r1-0528/fe486e3d-7e1f-4be1-b8f8-b5799fd18ae8.json

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data/openeval/deepseek/deepseek-r1-0528/ff8fc85a-6b4f-44b7-bd1d-f16798bd1e94.json

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data/openeval/deepseek/deepseek-r1-hide-reasoning/daab28a0-0db6-41e9-a102-2553040d6e6d.json

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data/openeval/deepseek/deepseek-r1/02f84949-80ff-4f1b-bb12-aac7a3a924e6.json

@@ -0,0 +1,175 @@
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data/openeval/deepseek/deepseek-r1/2ace2ae2-ac58-4f65-85ea-8849ce41afb9.json

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data/openeval/deepseek/deepseek-r1/2ce6f45c-643e-47f7-a1e8-de29d41f5321.json

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data/openeval/deepseek/deepseek-r1/4e463ea7-5461-4d74-9685-3bd915c7cef4.json

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data/openeval/deepseek/deepseek-r1/614ef45a-ec2c-450f-ad65-3b36161f83d9.json

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data/openeval/deepseek/deepseek-r1/617eb01a-65db-44c4-bddf-187efbb6b4e2.json

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data/openeval/deepseek/deepseek-r1/bfb5bf72-9654-4eff-b005-6fc342211a4a.json

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data/openeval/deepseek/deepseek-r1/edca2e2e-f6db-4920-a584-9256e42bc273.json

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data/openeval/deepseek/deepseek-v3-0324/1ff45190-d0ab-4491-ba74-843bd7a03e13.json

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data/openeval/deepseek/deepseek-v3/1f0d502e-8078-4627-86a1-fad8d0d65d69.json

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data/openeval/deepseek/deepseek-v3/88c5c6c7-726b-4967-b533-19828a6cc987.json

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data/openeval/deepseek/deepseek-v3/b45e4336-eebe-4426-a947-982197bbf92b.json

@@ -0,0 +1,319 @@
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data/openeval/deepseek/deepseek-v3/cccd1d38-1ee0-44b6-9ddb-cb2efd6140b8.json

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data/openeval/deepseek/deepseek-v3/f81abd95-60a3-4b8d-b302-a0e492c256c1.json

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data/openeval/eleutherai/pythia-12b-v0/340a4876-b895-49b2-9411-42a6e3ee9b23.json

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data/openeval/eleutherai/pythia-12b-v0/43a7c0eb-559c-4938-aeee-c171498feb3b.json

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data/openeval/eleutherai/pythia-12b-v0/b1cfd4fb-3cba-4951-89c2-b9f855872b90.json

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data/openeval/eleutherai/pythia-12b-v0/ca42afd9-6b04-4827-9307-72df27278635.json

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data/openeval/eleutherai/pythia-12b-v0/e2d1a123-4aff-40de-9a66-9e8b781c8a32.json

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data/openeval/eleutherai/pythia-12b-v0/ebc12c3d-661f-4403-8691-6f718546aedf.json

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data/openeval/eleutherai/pythia-12b-v0/f89e5d28-9227-4732-901f-8f72ec1b6d02.json

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data/openeval/eleutherai/pythia-1b-v0/04c5c88a-012b-4602-bf69-865520c82b18.json

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data/openeval/eleutherai/pythia-1b-v0/3cd307e5-2cd4-4279-9c1e-0dd29028eedb.json

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data/openeval/eleutherai/pythia-1b-v0/49674065-21cc-4f63-9112-534909b64aa5.json

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data/openeval/eleutherai/pythia-1b-v0/89de9ebc-c481-40e8-90ab-4016bb98eff3.json

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data/openeval/eleutherai/pythia-1b-v0/8a1ef494-28f9-4201-b7a5-5687ff74b15a.json

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data/openeval/eleutherai/pythia-1b-v0/ea934942-4c08-4314-872e-d43c0eaf13e3.json

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data/openeval/eleutherai/pythia-2.8b-v0/136211ed-62a5-49b3-82c5-9bc8a704372d.json

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data/openeval/eleutherai/pythia-2.8b-v0/2c4c0e7d-635d-4470-a6bf-cb9f0e7eb4c9.json

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data/openeval/eleutherai/pythia-2.8b-v0/5bb49a50-3ce4-409e-86b6-956316c65019.json

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data/openeval/eleutherai/pythia-2.8b-v0/80e26116-5d85-4b70-b53b-77a50bd87c00.json

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data/openeval/eleutherai/pythia-2.8b-v0/c595ac69-f75b-4cc8-80c6-17e22a606f45.json

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data/openeval/eleutherai/pythia-2.8b-v0/dc78aa63-10f9-401a-a98d-f11cbf46e4f7.json

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data/openeval/eleutherai/pythia-6.9b/268ba127-dca0-41c0-9694-d2ed30e05240.json

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data/openeval/eleutherai/pythia-6.9b/362792a0-0474-445a-82e5-681fdc9eaa04.json

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data/openeval/eleutherai/pythia-6.9b/3a512022-6829-434a-bac4-4c1caf1143a8.json

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data/openeval/eleutherai/pythia-6.9b/8b5cc5dd-60bd-4981-b3b2-ce5a0204d0af.json

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data/openeval/eleutherai/pythia-6.9b/a51a1d3a-918f-46f2-ae7e-0f79298fd473.json

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+      },
+      "metric_config": {
+        "evaluation_description": "Mean OpenEval score for self_bleu on disinformation.",
+        "metric_id": "openeval.disinformation.self-bleu",
+        "metric_name": "self_bleu",
+        "metric_kind": "benchmark_score",
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+        }
+      }
+    }
+  ]
+}

data/openeval/eleutherai/pythia-6.9b/f76eb0e4-112c-407d-a24b-765b54be70b9.json

@@ -0,0 +1,316 @@
+{
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+    "source_type": "evaluation_run",
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+    "source_organization_url": "https://open-eval.github.io/",
+    "evaluator_relationship": "third_party",
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+      "hf_dataset_url": "https://huggingface.co/datasets/human-centered-eval/OpenEval",
+      "github_url": "https://github.com/open-eval/OpenEval",
+      "hf_revision": "main",
+      "hf_commit": "91a9a3a78257a4b4c04e45f5a493796f8a2966b1",
+      "downloaded_response_shards": "118",
+      "total_response_shards": "118",
+      "include_instances": "false",
+      "partial_export": "false",
+      "allow_unknown_benchmark": "false",
+      "source_role": "aggregator"
+    }
+  },
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+    "name": "OpenEval",
+    "version": "unknown"
+  },
+  "model_info": {
+    "name": "pythia-6.9b",
+    "id": "eleutherai/pythia-6.9b",
+    "developer": "eleutherai",
+    "additional_details": {
+      "raw_model_name": "pythia-6.9b",
+      "model_size": "6.9b"
+    }
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+          "dataset_url": "https://huggingface.co/datasets/heegyu/bbq",
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+        }
+      },
+      "metric_config": {
+        "evaluation_description": "Mean OpenEval score for exact_match on bbq.",
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+          "score_values_are_binary": "true",
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+        },
+        "uncertainty": {
+          "standard_error": {
+            "value": 0.015435082648728969,
+            "method": "analytic"
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+          "standard_deviation": 0.48785605930102066,
+          "num_samples": 999
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+      },
+      "generation_config": {
+        "generation_args": {
+          "temperature": 0.0,
+          "top_p": 1.0,
+          "top_k": 5.0,
+          "max_tokens": 1,
+          "max_attempts": 1
+        },
+        "additional_details": {
+          "do_sample": "false",
+          "generation_config_hash": "1aeb65201d6c"
+        }
+      }
+    },
+    {
+      "evaluation_result_id": "bbq::prefix-exact-match",
+      "evaluation_name": "openeval.bbq.prefix-exact-match",
+      "source_data": {
+        "dataset_name": "bbq",
+        "source_type": "hf_dataset",
+        "hf_repo": "human-centered-eval/OpenEval",
+        "hf_split": "train",
+        "samples_number": 999,
+        "additional_details": {
+          "benchmark_name": "bbq",
+          "paper_url": "https://aclanthology.org/2022.findings-acl.165/",
+          "dataset_url": "https://huggingface.co/datasets/heegyu/bbq",
+          "source_urls_json": "[\"https://huggingface.co/datasets/human-centered-eval/OpenEval\",\"https://github.com/open-eval/OpenEval\",\"https://huggingface.co/datasets/heegyu/bbq\"]"
+        }
+      },
+      "metric_config": {
+        "evaluation_description": "Mean OpenEval score for prefix_exact_match on bbq.",
+        "metric_id": "openeval.bbq.prefix-exact-match",
+        "metric_name": "prefix_exact_match",
+        "metric_kind": "benchmark_score",
+        "metric_unit": "proportion",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "additional_details": {
+          "benchmark_tags_json": "[\"safety\",\"bias\"]",
+          "aggregation": "mean",
+          "raw_metric_name": "prefix_exact_match",
+          "response_count": "999",
+          "unique_sample_count": "999",
+          "score_values_are_binary": "true",
+          "bounds_source": "binary_values",
+          "metric_models_json": "[]",
+          "extra_artifact_types_json": "[]"
+        }
+      },
+      "score_details": {
+        "score": 0.3893893893893894,
+        "details": {
+          "min_instance_score": "0.0",
+          "max_instance_score": "1.0",
+          "response_count": "999",
+          "example_response_ids_json": "[\"bbq_20260331T215816Z_33368_pythia-6.9b_0\",\"bbq_20260331T215816Z_23084_pythia-6.9b_0\",\"bbq_20260331T215816Z_50349_pythia-6.9b_0\",\"bbq_20260331T215816Z_44933_pythia-6.9b_0\",\"bbq_20260331T215816Z_41828_pythia-6.9b_0\"]"
+        },
+        "uncertainty": {
+          "standard_error": {
+            "value": 0.015435082648728969,
+            "method": "analytic"
+          },
+          "standard_deviation": 0.48785605930102066,
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+        }
+      },
+      "generation_config": {
+        "generation_args": {
+          "temperature": 0.0,
+          "top_p": 1.0,
+          "top_k": 5.0,
+          "max_tokens": 1,
+          "max_attempts": 1
+        },
+        "additional_details": {
+          "do_sample": "false",
+          "generation_config_hash": "1aeb65201d6c"
+        }
+      }
+    },
+    {
+      "evaluation_result_id": "bbq::quasi-exact-match",
+      "evaluation_name": "openeval.bbq.quasi-exact-match",
+      "source_data": {
+        "dataset_name": "bbq",
+        "source_type": "hf_dataset",
+        "hf_repo": "human-centered-eval/OpenEval",
+        "hf_split": "train",
+        "samples_number": 999,
+        "additional_details": {
+          "benchmark_name": "bbq",
+          "paper_url": "https://aclanthology.org/2022.findings-acl.165/",
+          "dataset_url": "https://huggingface.co/datasets/heegyu/bbq",
+          "source_urls_json": "[\"https://huggingface.co/datasets/human-centered-eval/OpenEval\",\"https://github.com/open-eval/OpenEval\",\"https://huggingface.co/datasets/heegyu/bbq\"]"
+        }
+      },
+      "metric_config": {
+        "evaluation_description": "Mean OpenEval score for quasi_exact_match on bbq.",
+        "metric_id": "openeval.bbq.quasi-exact-match",
+        "metric_name": "quasi_exact_match",
+        "metric_kind": "benchmark_score",
+        "metric_unit": "proportion",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "additional_details": {
+          "benchmark_tags_json": "[\"safety\",\"bias\"]",
+          "aggregation": "mean",
+          "raw_metric_name": "quasi_exact_match",
+          "response_count": "999",
+          "unique_sample_count": "999",
+          "score_values_are_binary": "true",
+          "bounds_source": "binary_values",
+          "metric_models_json": "[]",
+          "extra_artifact_types_json": "[]"
+        }
+      },
+      "score_details": {
+        "score": 0.3893893893893894,
+        "details": {
+          "min_instance_score": "0.0",
+          "max_instance_score": "1.0",
+          "response_count": "999",
+          "example_response_ids_json": "[\"bbq_20260331T215816Z_33368_pythia-6.9b_0\",\"bbq_20260331T215816Z_23084_pythia-6.9b_0\",\"bbq_20260331T215816Z_50349_pythia-6.9b_0\",\"bbq_20260331T215816Z_44933_pythia-6.9b_0\",\"bbq_20260331T215816Z_41828_pythia-6.9b_0\"]"
+        },
+        "uncertainty": {
+          "standard_error": {
+            "value": 0.015435082648728969,
+            "method": "analytic"
+          },
+          "standard_deviation": 0.48785605930102066,
+          "num_samples": 999
+        }
+      },
+      "generation_config": {
+        "generation_args": {
+          "temperature": 0.0,
+          "top_p": 1.0,
+          "top_k": 5.0,
+          "max_tokens": 1,
+          "max_attempts": 1
+        },
+        "additional_details": {
+          "do_sample": "false",
+          "generation_config_hash": "1aeb65201d6c"
+        }
+      }
+    },
+    {
+      "evaluation_result_id": "bbq::quasi-prefix-exact-match",
+      "evaluation_name": "openeval.bbq.quasi-prefix-exact-match",
+      "source_data": {
+        "dataset_name": "bbq",
+        "source_type": "hf_dataset",
+        "hf_repo": "human-centered-eval/OpenEval",
+        "hf_split": "train",
+        "samples_number": 999,
+        "additional_details": {
+          "benchmark_name": "bbq",
+          "paper_url": "https://aclanthology.org/2022.findings-acl.165/",
+          "dataset_url": "https://huggingface.co/datasets/heegyu/bbq",
+          "source_urls_json": "[\"https://huggingface.co/datasets/human-centered-eval/OpenEval\",\"https://github.com/open-eval/OpenEval\",\"https://huggingface.co/datasets/heegyu/bbq\"]"
+        }
+      },
+      "metric_config": {
+        "evaluation_description": "Mean OpenEval score for quasi_prefix_exact_match on bbq.",
+        "metric_id": "openeval.bbq.quasi-prefix-exact-match",
+        "metric_name": "quasi_prefix_exact_match",
+        "metric_kind": "benchmark_score",
+        "metric_unit": "proportion",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "additional_details": {
+          "benchmark_tags_json": "[\"safety\",\"bias\"]",
+          "aggregation": "mean",
+          "raw_metric_name": "quasi_prefix_exact_match",
+          "response_count": "999",
+          "unique_sample_count": "999",
+          "score_values_are_binary": "true",
+          "bounds_source": "binary_values",
+          "metric_models_json": "[]",
+          "extra_artifact_types_json": "[]"
+        }
+      },
+      "score_details": {
+        "score": 0.3893893893893894,
+        "details": {
+          "min_instance_score": "0.0",
+          "max_instance_score": "1.0",
+          "response_count": "999",
+          "example_response_ids_json": "[\"bbq_20260331T215816Z_33368_pythia-6.9b_0\",\"bbq_20260331T215816Z_23084_pythia-6.9b_0\",\"bbq_20260331T215816Z_50349_pythia-6.9b_0\",\"bbq_20260331T215816Z_44933_pythia-6.9b_0\",\"bbq_20260331T215816Z_41828_pythia-6.9b_0\"]"
+        },
+        "uncertainty": {
+          "standard_error": {
+            "value": 0.015435082648728969,
+            "method": "analytic"
+          },
+          "standard_deviation": 0.48785605930102066,
+          "num_samples": 999
+        }
+      },
+      "generation_config": {
+        "generation_args": {
+          "temperature": 0.0,
+          "top_p": 1.0,
+          "top_k": 5.0,
+          "max_tokens": 1,
+          "max_attempts": 1
+        },
+        "additional_details": {
+          "do_sample": "false",
+          "generation_config_hash": "1aeb65201d6c"
+        }
+      }
+    }
+  ]
+}

data/run_report.json

@@ -1,16 +1,10 @@
 {
-  "date": "2026-07-04",
+  "date": "2026-07-05",
   "adapters": {
     "arc_agi": {
       "execution_failed": true,
       "error": "HTTP Error 404: Not Found"
     },
-    "bfcl": {
-      "execution_failed": false,
-      "valid_files": 109,
-      "failed_files": 0,
-      "errors": []
-    },
     "cocoabench": {
       "execution_failed": true,
       "error": "usage: adapter.py [-h] --csv CSV [--row-map ROW_MAP] [--output-dir OUTPUT_DIR]\n                  [--benchmark-version BENCHMARK_VERSION]\n                  [--eval-library-version EVAL_LIBRARY_VERSION]\n                  [--evaluation-timestamp EVALUATION_TIMESTAMP]\n                  [--public-source-url PUBLIC_SOURCE_URLS]\n                  [--benchmark-reference-url BENCHMARK_REFERENCE_URLS]\nadapter.py: error: the following arguments are required: --csv\n"
@@ -19,20 +13,29 @@
       "execution_failed": true,
       "error": "python3.12/site-packages/datasets/load.py\", line 1652, in dataset_module_factory\n    raise e1 from None\n  File \"/home/runner/work/every_eval_ever/every_eval_ever/.venv/lib/python3.12/site-packages/datasets/load.py\", line 1578, in dataset_module_factory\n    raise DatasetNotFoundError(f\"Dataset '{path}' doesn't exist on the Hub or cannot be accessed.\") from e\ndatasets.exceptions.DatasetNotFoundError: Dataset 'Exgentic/open-agent-leaderboard-results' doesn't exist on the Hub or cannot be accessed.\n"
     },
+    "helm": {
+      "execution_failed": false,
+      "valid_files": 0,
+      "failed_files": 0,
+      "error": "Validation failed: 0 valid, 0 failed.",
+      "errors": []
+    },
     "livecodebenchpro": {
       "execution_failed": true,
       "error": "\", line 198, in _run_module_as_main\n  File \"<frozen runpy>\", line 88, in _run_code\n  File \"/home/runner/work/every_eval_ever/every_eval_ever/utils/livecodebenchpro/adapter.py\", line 94, in <module>\n    main()\n  File \"/home/runner/work/every_eval_ever/every_eval_ever/utils/livecodebenchpro/adapter.py\", line 82, in main\n    raise FileNotFoundError(f'No JSON files found in {DATA_DIR}')\nFileNotFoundError: No JSON files found in /home/runner/work/every_eval_ever/every_eval_ever/data/livecodebenchpro\n"
     },
-    "mmlu_pro": {
+    "multi_swe_bench": {
       "execution_failed": false,
-      "valid_files": 261,
+      "valid_files": 0,
       "failed_files": 0,
+      "error": "Validation failed: 0 valid, 0 failed.",
       "errors": []
     },
-    "sciarena": {
+    "rewardbench": {
       "execution_failed": false,
-      "valid_files": 38,
+      "valid_files": 0,
       "failed_files": 0,
+      "error": "Validation failed: 0 valid, 0 failed.",
       "errors": []
     },
     "swe_bench_verified": {
@@ -62,23 +65,22 @@
       "failed_files": 0,
       "errors": []
     },
-    "global-mmlu-lite": {
+    "bfcl": {
       "execution_failed": false,
-      "valid_files": 28,
+      "valid_files": 109,
       "failed_files": 0,
       "errors": []
     },
-    "hal": {
+    "global-mmlu-lite": {
       "execution_failed": false,
-      "valid_files": 246,
+      "valid_files": 28,
       "failed_files": 0,
       "errors": []
     },
-    "helm": {
+    "hal": {
       "execution_failed": false,
-      "valid_files": 0,
+      "valid_files": 246,
       "failed_files": 0,
-      "error": "Validation failed: 0 valid, 0 failed.",
       "errors": []
     },
     "hfopenllm_v2": {
@@ -95,21 +97,20 @@
     },
     "llm_stats": {
       "execution_failed": false,
-      "valid_files": 287,
+      "valid_files": 324,
       "failed_files": 0,
       "errors": []
     },
-    "mt_bench": {
+    "mmlu_pro": {
       "execution_failed": false,
-      "valid_files": 34,
+      "valid_files": 261,
       "failed_files": 0,
       "errors": []
     },
-    "multi_swe_bench": {
+    "mt_bench": {
       "execution_failed": false,
-      "valid_files": 0,
+      "valid_files": 34,
       "failed_files": 0,
-      "error": "Validation failed: 0 valid, 0 failed.",
       "errors": []
     },
     "openeval": {
@@ -118,11 +119,10 @@
       "failed_files": 0,
       "errors": []
     },
-    "rewardbench": {
+    "sciarena": {
       "execution_failed": false,
-      "valid_files": 0,
+      "valid_files": 38,
       "failed_files": 0,
-      "error": "Validation failed: 0 valid, 0 failed.",
       "errors": []
     },
     "vals_ai": {