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data/adapter_stats.json

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data/openeval/deepseek/deepseek-llm-67b-chat/4ee25671-8716-4f01-83f1-ec292123b374.json

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data/openeval/deepseek/deepseek-r1-0528/40874e46-68d9-43b0-8611-da260421b9f7.json

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data/openeval/deepseek/deepseek-r1-0528/4d573da7-72b4-47c0-b067-9e2b9c4c969b.json

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data/openeval/deepseek/deepseek-r1-0528/82dee442-8e10-4913-a807-f6e715006bef.json

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data/openeval/deepseek/deepseek-r1-0528/96ea244b-e3aa-4a24-ab77-2a20aed40189.json

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data/openeval/deepseek/deepseek-r1-0528/ae9df57d-07af-49a8-9263-4c8c23e99612.json

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data/openeval/deepseek/deepseek-r1-0528/fc05862a-89c3-4adf-8aa7-fca3c46aae69.json

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data/openeval/deepseek/deepseek-r1-hide-reasoning/29cd09f2-ff89-4bf0-a4c2-4739c90b3c6b.json

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data/openeval/deepseek/deepseek-r1/500d96cb-1a30-4115-9dda-5cf3be56ab69.json

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data/openeval/deepseek/deepseek-r1/535ea16d-5782-4ef5-86bd-a1d540b70ec1.json

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data/openeval/deepseek/deepseek-r1/550240c6-aedb-4cab-8e7a-54649baa3bc4.json

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data/openeval/deepseek/deepseek-r1/73757aa0-2cb5-4620-9d03-e311782298ca.json

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data/openeval/deepseek/deepseek-r1/74b470a0-4773-4f94-a474-1d66c33f9a30.json

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data/openeval/deepseek/deepseek-r1/8a7909e2-6d21-4192-b4e3-6158ca84a169.json

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data/openeval/deepseek/deepseek-r1/9bbcc564-f0a4-4c04-bdbf-446671306850.json

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data/openeval/deepseek/deepseek-r1/bed64860-1265-4844-8810-042277be6e21.json

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data/openeval/deepseek/deepseek-v3-0324/f49a0827-0cca-495e-af38-abeba4efbf91.json

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data/openeval/deepseek/deepseek-v3/21b392de-87be-492c-aab0-c89cbcf2e7b5.json

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data/openeval/deepseek/deepseek-v3/5954e5f0-615b-4320-b76d-5174fe158f80.json

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data/openeval/deepseek/deepseek-v3/6951a89a-9a37-48d6-84e4-72ee79627750.json

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data/openeval/deepseek/deepseek-v3/6fb1929e-e180-4205-a69e-d579f409014a.json

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data/openeval/deepseek/deepseek-v3/d7647b84-999d-409f-a213-53de2779ecd3.json

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data/openeval/eleutherai/pythia-12b-v0/0d45730a-e8c9-4a53-828c-1c5e03525a53.json

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data/openeval/eleutherai/pythia-12b-v0/202eff74-8a87-4fac-97fe-c92be200fe25.json

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data/openeval/eleutherai/pythia-12b-v0/30578c74-da0e-47ff-9f80-e26971f0d891.json

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data/openeval/eleutherai/pythia-12b-v0/6bab3ced-31f8-4482-87d6-2f06680ba4cf.json

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data/openeval/eleutherai/pythia-12b-v0/81152a4d-fb6c-4755-a8b8-4e882264a5af.json

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data/openeval/eleutherai/pythia-12b-v0/a3503e58-ee55-4fd2-9227-decc414c2456.json

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data/openeval/eleutherai/pythia-12b-v0/fcad2cd2-66ca-4954-9a67-c8d71da4f0ad.json

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data/openeval/eleutherai/pythia-1b-v0/19a6c3aa-c54b-40fd-bcdd-79a4113f0ed7.json

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data/openeval/eleutherai/pythia-1b-v0/22a9c40c-5e24-4d56-ac3a-c6a4218f4112.json

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data/openeval/eleutherai/pythia-1b-v0/37469277-851f-4091-992b-58e3a88e7a65.json

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data/openeval/eleutherai/pythia-1b-v0/a8645e83-ad9f-44fd-8194-44255d24a31e.json

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data/openeval/eleutherai/pythia-1b-v0/c4797bac-e397-4a50-8a56-d05d04838f5f.json

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data/openeval/eleutherai/pythia-1b-v0/d2f8ddf5-2f42-4df3-8c8e-9cf8a5a0b513.json

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data/openeval/eleutherai/pythia-2.8b-v0/3d3cb234-76f3-4a48-97d2-0546ef8978b7.json

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data/openeval/eleutherai/pythia-2.8b-v0/4000b30d-f292-41e6-8c16-b70b68af667c.json

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data/openeval/eleutherai/pythia-2.8b-v0/e93ab6de-b6be-490c-8039-87b140c1b366.json

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data/openeval/eleutherai/pythia-2.8b-v0/ec5d8223-cb8e-49f5-b9c6-1098e847aeee.json

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data/openeval/eleutherai/pythia-2.8b-v0/ee09ce36-8fb8-45b1-94de-1c5a0fe84e7c.json

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data/openeval/eleutherai/pythia-6.9b/0047e1d5-a78b-4974-86ff-631ea344b104.json

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data/openeval/eleutherai/pythia-6.9b/7b892fdb-ef8f-4914-9592-0ea3391cbe3e.json

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data/openeval/eleutherai/pythia-6.9b/85b62c04-c37e-4ccd-9449-9d3393fb3f96.json

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data/openeval/eleutherai/pythia-6.9b/8da6db62-bff5-4cb3-835f-c076e1c835c0.json

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data/openeval/eleutherai/pythia-6.9b/95fcc69c-9d3b-4237-80b6-9abde0852298.json

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data/openeval/eleutherai/pythia-6.9b/ba007bbc-d98d-47d2-8331-6a4cd5c828f5.json

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+        "samples_number": 1000,
+        "additional_details": {
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+          "paper_url": "https://aclanthology.org/D18-1206/",
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+      },
+      "metric_config": {
+        "evaluation_description": "Mean OpenEval score for rouge_2 on xsum.",
+        "metric_id": "openeval.xsum.rouge-2",
+        "metric_name": "rouge_2",
+        "metric_kind": "benchmark_score",
+        "metric_unit": "score",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "additional_details": {
+          "benchmark_tags_json": "[\"summarization\",\"news\"]",
+          "aggregation": "mean",
+          "raw_metric_name": "rouge_2",
+          "response_count": "1000",
+          "unique_sample_count": "1000",
+          "score_values_are_binary": "false",
+          "bounds_source": "normalized_observed_values",
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+          "extra_artifact_types_json": "[]"
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+      "score_details": {
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+        "details": {
+          "min_instance_score": "0.0",
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+          "example_response_ids_json": "[\"xsum_20260306T061408Z_0_pythia-6.9b_0\",\"xsum_20260306T061408Z_1_pythia-6.9b_0\",\"xsum_20260306T061408Z_2_pythia-6.9b_0\",\"xsum_20260306T061408Z_3_pythia-6.9b_0\",\"xsum_20260306T061408Z_4_pythia-6.9b_0\"]"
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+        "generation_args": {
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+          "top_k": 1.0,
+          "max_tokens": 64,
+          "max_attempts": 1
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+        "additional_details": {
+          "do_sample": "true",
+          "generation_config_hash": "9f6885c492e1"
+        }
+      }
+    },
+    {
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+        "additional_details": {
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+          "paper_url": "https://aclanthology.org/D18-1206/",
+          "dataset_url": "https://huggingface.co/datasets/EdinburghNLP/xsum",
+          "source_urls_json": "[\"https://huggingface.co/datasets/human-centered-eval/OpenEval\",\"https://github.com/open-eval/OpenEval\",\"https://huggingface.co/datasets/EdinburghNLP/xsum\"]"
+        }
+      },
+      "metric_config": {
+        "evaluation_description": "Mean OpenEval score for rouge_l on xsum.",
+        "metric_id": "openeval.xsum.rouge-l",
+        "metric_name": "rouge_l",
+        "metric_kind": "benchmark_score",
+        "metric_unit": "score",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "additional_details": {
+          "benchmark_tags_json": "[\"summarization\",\"news\"]",
+          "aggregation": "mean",
+          "raw_metric_name": "rouge_l",
+          "response_count": "1000",
+          "unique_sample_count": "1000",
+          "score_values_are_binary": "false",
+          "bounds_source": "normalized_observed_values",
+          "metric_models_json": "[]",
+          "extra_artifact_types_json": "[]"
+        }
+      },
+      "score_details": {
+        "score": 0.18167743241176681,
+        "details": {
+          "min_instance_score": "0.0",
+          "max_instance_score": "0.8780487804878048",
+          "response_count": "1000",
+          "example_response_ids_json": "[\"xsum_20260306T061408Z_0_pythia-6.9b_0\",\"xsum_20260306T061408Z_1_pythia-6.9b_0\",\"xsum_20260306T061408Z_2_pythia-6.9b_0\",\"xsum_20260306T061408Z_3_pythia-6.9b_0\",\"xsum_20260306T061408Z_4_pythia-6.9b_0\"]"
+        },
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+          "standard_deviation": 0.1129140376976939,
+          "num_samples": 1000
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+      "generation_config": {
+        "generation_args": {
+          "temperature": 0.3,
+          "top_p": 1.0,
+          "top_k": 1.0,
+          "max_tokens": 64,
+          "max_attempts": 1
+        },
+        "additional_details": {
+          "do_sample": "true",
+          "generation_config_hash": "9f6885c492e1"
+        }
+      }
+    },
+    {
+      "evaluation_result_id": "xsum::summac",
+      "evaluation_name": "openeval.xsum.summac",
+      "source_data": {
+        "dataset_name": "xsum",
+        "source_type": "hf_dataset",
+        "hf_repo": "human-centered-eval/OpenEval",
+        "hf_split": "train",
+        "samples_number": 1000,
+        "additional_details": {
+          "benchmark_name": "xsum",
+          "paper_url": "https://aclanthology.org/D18-1206/",
+          "dataset_url": "https://huggingface.co/datasets/EdinburghNLP/xsum",
+          "source_urls_json": "[\"https://huggingface.co/datasets/human-centered-eval/OpenEval\",\"https://github.com/open-eval/OpenEval\",\"https://huggingface.co/datasets/EdinburghNLP/xsum\"]"
+        }
+      },
+      "metric_config": {
+        "evaluation_description": "Mean OpenEval score for summac on xsum.",
+        "metric_id": "openeval.xsum.summac",
+        "metric_name": "summac",
+        "metric_kind": "benchmark_score",
+        "metric_unit": "points",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": -0.9848518371582031,
+        "max_score": 1.0,
+        "additional_details": {
+          "benchmark_tags_json": "[\"summarization\",\"news\"]",
+          "aggregation": "mean",
+          "raw_metric_name": "summac",
+          "response_count": "1000",
+          "unique_sample_count": "1000",
+          "score_values_are_binary": "false",
+          "bounds_source": "observed_values",
+          "metric_models_json": "[\"vitc\"]",
+          "extra_artifact_types_json": "[]"
+        }
+      },
+      "score_details": {
+        "score": -0.2418378054300944,
+        "details": {
+          "min_instance_score": "-0.9848518371582031",
+          "max_instance_score": "0.9860591888427734",
+          "response_count": "1000",
+          "example_response_ids_json": "[\"xsum_20260306T061408Z_0_pythia-6.9b_0\",\"xsum_20260306T061408Z_1_pythia-6.9b_0\",\"xsum_20260306T061408Z_2_pythia-6.9b_0\",\"xsum_20260306T061408Z_3_pythia-6.9b_0\",\"xsum_20260306T061408Z_4_pythia-6.9b_0\"]"
+        },
+        "uncertainty": {
+          "standard_error": {
+            "value": 0.015243153922437901,
+            "method": "analytic"
+          },
+          "standard_deviation": 0.48203085119433375,
+          "num_samples": 1000
+        }
+      },
+      "generation_config": {
+        "generation_args": {
+          "temperature": 0.3,
+          "top_p": 1.0,
+          "top_k": 1.0,
+          "max_tokens": 64,
+          "max_attempts": 1
+        },
+        "additional_details": {
+          "do_sample": "true",
+          "generation_config_hash": "9f6885c492e1"
+        }
+      }
+    }
+  ]
+}

data/openeval/eleutherai/pythia-6.9b/db27b8fc-8dc4-4f37-a858-1101b1fcc972.json

@@ -0,0 +1,178 @@
+{
+  "schema_version": "0.2.2",
+  "evaluation_id": "openeval/eleutherai_pythia-6.9b/09b5bf3f630b/1783187251.884495",
+  "retrieved_timestamp": "1783187251.884495",
+  "source_metadata": {
+    "source_name": "OpenEval",
+    "source_type": "evaluation_run",
+    "source_organization_name": "Human-Centered Eval",
+    "source_organization_url": "https://open-eval.github.io/",
+    "evaluator_relationship": "third_party",
+    "additional_details": {
+      "hf_repo": "human-centered-eval/OpenEval",
+      "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"
+    }
+  },
+  "eval_library": {
+    "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"
+    }
+  },
+  "evaluation_results": [
+    {
+      "evaluation_result_id": "disinformation::monte-carlo-entropy",
+      "evaluation_name": "openeval.disinformation.monte-carlo-entropy",
+      "source_data": {
+        "dataset_name": "disinformation",
+        "source_type": "hf_dataset",
+        "hf_repo": "human-centered-eval/OpenEval",
+        "hf_split": "train",
+        "samples_number": 68,
+        "additional_details": {
+          "benchmark_name": "disinformation",
+          "paper_url": "https://cset.georgetown.edu/publication/truth-lies-and-automation/",
+          "dataset_url": "https://github.com/georgetown-cset/GPT3-Disinformation",
+          "source_urls_json": "[\"https://huggingface.co/datasets/human-centered-eval/OpenEval\",\"https://github.com/open-eval/OpenEval\",\"https://github.com/georgetown-cset/GPT3-Disinformation\"]"
+        }
+      },
+      "metric_config": {
+        "evaluation_description": "Mean OpenEval score for monte_carlo_entropy on disinformation.",
+        "metric_id": "openeval.disinformation.monte-carlo-entropy",
+        "metric_name": "monte_carlo_entropy",
+        "metric_kind": "benchmark_score",
+        "metric_unit": "points",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 33.2910119007819,
+        "additional_details": {
+          "benchmark_tags_json": "[\"safety\",\"disinformation\"]",
+          "aggregation": "mean",
+          "raw_metric_name": "monte_carlo_entropy",
+          "response_count": "68",
+          "unique_sample_count": "68",
+          "score_values_are_binary": "false",
+          "bounds_source": "observed_values",
+          "metric_models_json": "[]",
+          "extra_artifact_types_json": "[]"
+        }
+      },
+      "score_details": {
+        "score": 21.953057601534447,
+        "details": {
+          "min_instance_score": "10.989476461698086",
+          "max_instance_score": "33.2910119007819",
+          "response_count": "68",
+          "example_response_ids_json": "[\"disinformation_20260304T235127Z_0_pythia-6.9b_0\",\"disinformation_20260304T235127Z_1_pythia-6.9b_0\",\"disinformation_20260304T235127Z_2_pythia-6.9b_0\",\"disinformation_20260304T235127Z_3_pythia-6.9b_0\",\"disinformation_20260304T235127Z_4_pythia-6.9b_0\"]"
+        },
+        "uncertainty": {
+          "standard_error": {
+            "value": 0.6105353283116242,
+            "method": "analytic"
+          },
+          "standard_deviation": 5.034603293599966,
+          "num_samples": 68
+        }
+      },
+      "generation_config": {
+        "generation_args": {
+          "temperature": 0.7,
+          "top_p": 1.0,
+          "top_k": 1.0,
+          "max_tokens": 100,
+          "max_attempts": 1
+        },
+        "additional_details": {
+          "do_sample": "true",
+          "generation_config_hash": "09b5bf3f630b"
+        }
+      }
+    },
+    {
+      "evaluation_result_id": "disinformation::self-bleu",
+      "evaluation_name": "openeval.disinformation.self-bleu",
+      "source_data": {
+        "dataset_name": "disinformation",
+        "source_type": "hf_dataset",
+        "hf_repo": "human-centered-eval/OpenEval",
+        "hf_split": "train",
+        "samples_number": 68,
+        "additional_details": {
+          "benchmark_name": "disinformation",
+          "paper_url": "https://cset.georgetown.edu/publication/truth-lies-and-automation/",
+          "dataset_url": "https://github.com/georgetown-cset/GPT3-Disinformation",
+          "source_urls_json": "[\"https://huggingface.co/datasets/human-centered-eval/OpenEval\",\"https://github.com/open-eval/OpenEval\",\"https://github.com/georgetown-cset/GPT3-Disinformation\"]"
+        }
+      },
+      "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",
+        "metric_unit": "points",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 43.675898660029986,
+        "additional_details": {
+          "benchmark_tags_json": "[\"safety\",\"disinformation\"]",
+          "aggregation": "mean",
+          "raw_metric_name": "self_bleu",
+          "response_count": "68",
+          "unique_sample_count": "68",
+          "score_values_are_binary": "false",
+          "bounds_source": "observed_values",
+          "metric_models_json": "[]",
+          "extra_artifact_types_json": "[]"
+        }
+      },
+      "score_details": {
+        "score": 9.516188222982128,
+        "details": {
+          "min_instance_score": "3.497161776901481",
+          "max_instance_score": "43.675898660029986",
+          "response_count": "68",
+          "example_response_ids_json": "[\"disinformation_20260304T235127Z_0_pythia-6.9b_0\",\"disinformation_20260304T235127Z_1_pythia-6.9b_0\",\"disinformation_20260304T235127Z_2_pythia-6.9b_0\",\"disinformation_20260304T235127Z_3_pythia-6.9b_0\",\"disinformation_20260304T235127Z_4_pythia-6.9b_0\"]"
+        },
+        "uncertainty": {
+          "standard_error": {
+            "value": 0.862016323432764,
+            "method": "analytic"
+          },
+          "standard_deviation": 7.108368705039764,
+          "num_samples": 68
+        }
+      },
+      "generation_config": {
+        "generation_args": {
+          "temperature": 0.7,
+          "top_p": 1.0,
+          "top_k": 1.0,
+          "max_tokens": 100,
+          "max_attempts": 1
+        },
+        "additional_details": {
+          "do_sample": "true",
+          "generation_config_hash": "09b5bf3f630b"
+        }
+      }
+    }
+  ]
+}

data/run_report.json

@@ -1,25 +1,66 @@
 {
-  "date": "2026-07-02",
+  "date": "2026-07-04",
   "adapters": {
     "arc_agi": {
       "execution_failed": true,
       "error": "HTTP Error 404: Not Found"
     },
-    "artificial_analysis": {
-      "execution_failed": false,
-      "valid_files": 550,
-      "failed_files": 0,
-      "errors": []
-    },
     "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"
+    },
     "exgentic": {
       "execution_failed": true,
-      "error": "Traceback (most recent call last):\n  File \"<frozen runpy>\", line 203, 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/exgentic/adapter.py\", line 34, in <module>\n    from eval_types import (\n    ...<14 lines>...\n    )\nModuleNotFoundError: No module named 'eval_types'\n"
+      "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"
+    },
+    "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": {
+      "execution_failed": false,
+      "valid_files": 261,
+      "failed_files": 0,
+      "errors": []
+    },
+    "sciarena": {
+      "execution_failed": false,
+      "valid_files": 38,
+      "failed_files": 0,
+      "errors": []
+    },
+    "swe_bench_verified": {
+      "execution_failed": false,
+      "valid_files": 0,
+      "failed_files": 0,
+      "error": "Validation failed: 0 valid, 0 failed.",
+      "errors": []
+    },
+    "swe_polybench": {
+      "execution_failed": false,
+      "valid_files": 0,
+      "failed_files": 0,
+      "error": "Validation failed: 0 valid, 0 failed.",
+      "errors": []
+    },
+    "terminal_bench_2": {
+      "execution_failed": false,
+      "valid_files": 0,
+      "failed_files": 0,
+      "error": "Validation failed: 0 valid, 0 failed.",
+      "errors": []
+    },
+    "artificial_analysis": {
+      "execution_failed": false,
+      "valid_files": 550,
+      "failed_files": 0,
+      "errors": []
     },
     "global-mmlu-lite": {
       "execution_failed": false,
@@ -37,6 +78,7 @@
       "execution_failed": false,
       "valid_files": 0,
       "failed_files": 0,
+      "error": "Validation failed: 0 valid, 0 failed.",
       "errors": []
     },
     "hfopenllm_v2": {
@@ -51,19 +93,9 @@
       "failed_files": 0,
       "errors": []
     },
-    "livecodebenchpro": {
-      "execution_failed": true,
-      "error": ", 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    ~~~~^^\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"
-    },
     "llm_stats": {
       "execution_failed": false,
-      "valid_files": 324,
-      "failed_files": 0,
-      "errors": []
-    },
-    "mmlu_pro": {
-      "execution_failed": false,
-      "valid_files": 261,
+      "valid_files": 287,
       "failed_files": 0,
       "errors": []
     },
@@ -77,6 +109,7 @@
       "execution_failed": false,
       "valid_files": 0,
       "failed_files": 0,
+      "error": "Validation failed: 0 valid, 0 failed.",
       "errors": []
     },
     "openeval": {
@@ -89,24 +122,9 @@
       "execution_failed": false,
       "valid_files": 0,
       "failed_files": 0,
+      "error": "Validation failed: 0 valid, 0 failed.",
       "errors": []
     },
-    "sciarena": {
-      "execution_failed": true,
-      "error": "<urlopen error timed out>"
-    },
-    "swe_bench_verified": {
-      "execution_failed": true,
-      "error": "^^^^^^^^^^\n  File \"/home/runner/work/every_eval_ever/every_eval_ever/.venv/lib/python3.14/site-packages/dill/_dill.py\", line 1217, in save_module_dict\n    StockPickler.save_dict(pickler, obj)\n    ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^\n  File \"/opt/hostedtoolcache/Python/3.14.6/x64/lib/python3.14/pickle.py\", line 1058, in save_dict\n    self._batch_setitems(obj.items(), obj)\n    ~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^\nTypeError: Pickler._batch_setitems() takes 2 positional arguments but 3 were given\n"
-    },
-    "swe_polybench": {
-      "execution_failed": true,
-      "error": "^^^^^^^^^^\n  File \"/home/runner/work/every_eval_ever/every_eval_ever/.venv/lib/python3.14/site-packages/dill/_dill.py\", line 1217, in save_module_dict\n    StockPickler.save_dict(pickler, obj)\n    ~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^\n  File \"/opt/hostedtoolcache/Python/3.14.6/x64/lib/python3.14/pickle.py\", line 1058, in save_dict\n    self._batch_setitems(obj.items(), obj)\n    ~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^\nTypeError: Pickler._batch_setitems() takes 2 positional arguments but 3 were given\n"
-    },
-    "terminal_bench_2": {
-      "execution_failed": true,
-      "error": "Traceback (most recent call last):\n  File \"<frozen runpy>\", line 203, 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/terminal_bench_2/adapter.py\", line 24, in <module>\n    from eval_types import (\n    ...<16 lines>...\n    )\nModuleNotFoundError: No module named 'eval_types'\n"
-    },
     "vals_ai": {
       "execution_failed": false,
       "valid_files": 2013,