Add files using upload-large-folder tool

flat/objects/05/00/0500545e-63dc-4205-bbfe-bdc080fd5d43.json

@@ -0,0 +1,89 @@
+{
+  "schema_version": "0.2.2",
+  "evaluation_id": "interpreter/openrouter_deepseek_deepseek-chat/1742523019.0",
+  "evaluation_timestamp": "1742523019.0",
+  "retrieved_timestamp": "1775628399.124279",
+  "source_metadata": {
+    "source_name": "inspect_ai",
+    "source_type": "evaluation_run",
+    "source_organization_name": "Arcadia Impact",
+    "evaluator_relationship": "third_party"
+  },
+  "eval_library": {
+    "name": "inspect_ai",
+    "version": "inspect_ai:0.3.75"
+  },
+  "model_info": {
+    "name": "openrouter/deepseek/deepseek-chat",
+    "id": "deepseek/deepseek-chat",
+    "developer": "deepseek",
+    "inference_platform": "openrouter"
+  },
+  "evaluation_results": [
+    {
+      "evaluation_name": "cyse2_interpreter_abuse",
+      "source_data": {
+        "dataset_name": "cyse2_interpreter_abuse",
+        "source_type": "url",
+        "url": [
+          "https://github.com/UKGovernmentBEIS/inspect_evals/tree/main/src/inspect_evals/cyberseceval_2/interpreter_abuse"
+        ],
+        "additional_details": {
+          "shuffled": "False"
+        }
+      },
+      "evaluation_timestamp": "1742523019.0",
+      "metric_config": {
+        "evaluation_description": "accuracy",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "additional_details": {
+          "raw_evaluation_name": "accuracy on inspect_evals/cyse2_cyse2_interpreter_abuse_abuse for scorer model_graded_qa"
+        },
+        "metric_id": "accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion"
+      },
+      "score_details": {
+        "score": 0.281,
+        "uncertainty": {
+          "standard_error": {
+            "value": 0.015829692300124893
+          },
+          "num_samples": 2000
+        }
+      },
+      "generation_config": {
+        "generation_args": {
+          "reasoning": false,
+          "agentic_eval_config": {
+            "available_tools": []
+          },
+          "eval_plan": {
+            "name": "plan",
+            "steps": [
+              "{\"solver\": \"generate\", \"params\": {}, \"params_passed\": {}}"
+            ],
+            "config": {
+              "max_tokens": "2048",
+              "temperature": "0.75"
+            }
+          },
+          "eval_limits": {},
+          "sandbox": {}
+        }
+      },
+      "evaluation_result_id": "interpreter/openrouter_deepseek_deepseek-chat/1742523019.0#cyse2_interpreter_abuse#accuracy"
+    }
+  ],
+  "detailed_evaluation_results": {
+    "format": "jsonl",
+    "file_path": "./0500545e-63dc-4205-bbfe-bdc080fd5d43_samples.jsonl",
+    "hash_algorithm": "sha256",
+    "checksum": "a86e1b25d5019431190289eede3a7c76d7df7b61736d56d77bc580a58bf95f00",
+    "total_rows": 2000
+  }
+}

flat/objects/05/05/050584f9-2f86-4e5e-8d72-9195ec9be92f.json

@@ -0,0 +1,144 @@
+{
+  "schema_version": "0.2.2",
+  "evaluation_id": "facts-grounding/xai/grok-4-fast-reasoning/1777067886.443135",
+  "retrieved_timestamp": "1777067886.443135",
+  "source_metadata": {
+    "source_name": "FACTS Grounding Leaderboard",
+    "source_type": "documentation",
+    "source_organization_name": "Google DeepMind",
+    "source_organization_url": "https://kaggle.com/benchmarks/google/facts-grounding",
+    "evaluator_relationship": "third_party",
+    "additional_details": {
+      "api_endpoint": "https://www.kaggle.com/api/v1/benchmarks/google/facts-grounding/leaderboard",
+      "license": "Apache-2.0",
+      "attribution": "Google DeepMind, FACTS Grounding",
+      "paper_title": "The FACTS Grounding Leaderboard: Benchmarking LLMs' Ability to Ground Responses to Long-Form Input",
+      "paper_arxiv_id": "2501.03200",
+      "paper_authors_lead": "Alon Jacovi, Andrew Wang, Chris Alberti, et al. (26 authors)"
+    }
+  },
+  "eval_library": {
+    "name": "kaggle leaderboard",
+    "version": "unknown"
+  },
+  "model_info": {
+    "name": "grok-4-fast-reasoning",
+    "id": "xai/grok-4-fast-reasoning",
+    "developer": "xai",
+    "inference_platform": "unknown",
+    "additional_details": {
+      "display_name": "grok-4-fast-reasoning",
+      "kaggle_model_version_slug": "grok-4-fast-reasoning"
+    }
+  },
+  "evaluation_results": [
+    {
+      "evaluation_result_id": "facts_grounding.score_overall",
+      "evaluation_name": "Score",
+      "source_data": {
+        "dataset_name": "facts-grounding",
+        "source_type": "url",
+        "url": [
+          "https://www.kaggle.com/api/v1/benchmarks/google/facts-grounding/leaderboard"
+        ]
+      },
+      "metric_config": {
+        "evaluation_description": "FACTS Grounding overall accuracy across public + private splits.",
+        "metric_id": "facts_grounding.score_overall",
+        "metric_name": "Score",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "additional_details": {
+          "bound_strategy": "fixed",
+          "evaluation_date": "2025-11-14T00:00:00Z"
+        }
+      },
+      "score_details": {
+        "score": 0.4314868804664723,
+        "uncertainty": {
+          "confidence_interval": {
+            "lower": -0.023440672768366387,
+            "upper": 0.023440672768366387,
+            "method": "unknown"
+          }
+        }
+      }
+    },
+    {
+      "evaluation_result_id": "facts_grounding.score_public",
+      "evaluation_name": "Public Score",
+      "source_data": {
+        "dataset_name": "facts-grounding",
+        "source_type": "url",
+        "url": [
+          "https://www.kaggle.com/api/v1/benchmarks/google/facts-grounding/leaderboard"
+        ]
+      },
+      "metric_config": {
+        "evaluation_description": "FACTS Grounding accuracy on the public split (860 examples).",
+        "metric_id": "facts_grounding.score_public",
+        "metric_name": "Public Score",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "additional_details": {
+          "bound_strategy": "fixed",
+          "evaluation_date": "2025-11-12T00:00:00Z"
+        }
+      },
+      "score_details": {
+        "score": 0.42998833138856474,
+        "uncertainty": {
+          "confidence_interval": {
+            "lower": -0.03314575289017124,
+            "upper": 0.03314575289017124,
+            "method": "unknown"
+          }
+        }
+      }
+    },
+    {
+      "evaluation_result_id": "facts_grounding.score_private",
+      "evaluation_name": "Private Score",
+      "source_data": {
+        "dataset_name": "facts-grounding",
+        "source_type": "url",
+        "url": [
+          "https://www.kaggle.com/api/v1/benchmarks/google/facts-grounding/leaderboard"
+        ]
+      },
+      "metric_config": {
+        "evaluation_description": "FACTS Grounding accuracy on the private held-out split (859 examples).",
+        "metric_id": "facts_grounding.score_private",
+        "metric_name": "Private Score",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "additional_details": {
+          "bound_strategy": "fixed",
+          "evaluation_date": "2025-11-14T00:00:00Z"
+        }
+      },
+      "score_details": {
+        "score": 0.432983682983683,
+        "uncertainty": {
+          "confidence_interval": {
+            "lower": -0.03315415706673056,
+            "upper": 0.03315415706673056,
+            "method": "unknown"
+          }
+        }
+      }
+    }
+  ]
+}

flat/objects/05/09/05095260-f687-4d70-87b9-e656a42c076f.json

@@ -0,0 +1,840 @@
+{
+  "schema_version": "0.2.2",
+  "evaluation_id": "vals-ai/mgsm/openai_gpt-5.1-2025-11-13/1777395187.3170502",
+  "retrieved_timestamp": "1777395187.3170502",
+  "source_metadata": {
+    "source_name": "Vals.ai Leaderboard - MGSM",
+    "source_type": "documentation",
+    "source_organization_name": "Vals.ai",
+    "source_organization_url": "https://www.vals.ai",
+    "evaluator_relationship": "third_party",
+    "additional_details": {
+      "benchmark_slug": "mgsm",
+      "benchmark_name": "MGSM",
+      "benchmark_updated": "2026-01-09",
+      "dataset_type": "public",
+      "industry": "math",
+      "leaderboard_page_url": "https://www.vals.ai/benchmarks/mgsm",
+      "extraction_method": "static_astro_benchmark_view_props"
+    }
+  },
+  "eval_library": {
+    "name": "Vals.ai",
+    "version": "unknown"
+  },
+  "model_info": {
+    "name": "gpt-5.1-2025-11-13",
+    "id": "openai/gpt-5.1-2025-11-13",
+    "developer": "openai",
+    "additional_details": {
+      "vals_model_id": "openai/gpt-5.1-2025-11-13",
+      "vals_provider": "OpenAI"
+    }
+  },
+  "evaluation_results": [
+    {
+      "evaluation_result_id": "mgsm:mgsm_bn:openai/gpt-5.1-2025-11-13:score",
+      "evaluation_name": "vals_ai.mgsm.mgsm_bn",
+      "source_data": {
+        "dataset_name": "MGSM - Bengali",
+        "source_type": "url",
+        "url": [
+          "https://www.vals.ai/benchmarks/mgsm"
+        ],
+        "additional_details": {
+          "benchmark_slug": "mgsm",
+          "task_key": "mgsm_bn",
+          "dataset_type": "public",
+          "leaderboard_page_url": "https://www.vals.ai/benchmarks/mgsm"
+        }
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy reported by Vals.ai for MGSM (Bengali).",
+        "metric_id": "vals_ai.mgsm.mgsm_bn.accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "percent",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "additional_details": {
+          "score_scale": "percent_0_to_100",
+          "max_score_source": "fixed_percentage_bound",
+          "leaderboard_page_url": "https://www.vals.ai/benchmarks/mgsm"
+        }
+      },
+      "score_details": {
+        "score": 93.2,
+        "details": {
+          "benchmark_slug": "mgsm",
+          "benchmark_name": "MGSM",
+          "benchmark_updated": "2026-01-09",
+          "task_key": "mgsm_bn",
+          "task_name": "Bengali",
+          "dataset_type": "public",
+          "industry": "math",
+          "raw_score": "93.2",
+          "raw_stderr": "1.592",
+          "latency": "8.319",
+          "cost_per_test": "0.006064",
+          "max_output_tokens": "128000",
+          "reasoning_effort": "high",
+          "provider": "OpenAI"
+        },
+        "uncertainty": {
+          "standard_error": {
+            "value": 1.592,
+            "method": "vals_reported"
+          }
+        }
+      },
+      "generation_config": {
+        "generation_args": {
+          "max_tokens": 128000,
+          "max_attempts": 1
+        },
+        "additional_details": {
+          "reasoning_effort": "high"
+        }
+      }
+    },
+    {
+      "evaluation_result_id": "mgsm:mgsm_de:openai/gpt-5.1-2025-11-13:score",
+      "evaluation_name": "vals_ai.mgsm.mgsm_de",
+      "source_data": {
+        "dataset_name": "MGSM - German",
+        "source_type": "url",
+        "url": [
+          "https://www.vals.ai/benchmarks/mgsm"
+        ],
+        "additional_details": {
+          "benchmark_slug": "mgsm",
+          "task_key": "mgsm_de",
+          "dataset_type": "public",
+          "leaderboard_page_url": "https://www.vals.ai/benchmarks/mgsm"
+        }
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy reported by Vals.ai for MGSM (German).",
+        "metric_id": "vals_ai.mgsm.mgsm_de.accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "percent",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "additional_details": {
+          "score_scale": "percent_0_to_100",
+          "max_score_source": "fixed_percentage_bound",
+          "leaderboard_page_url": "https://www.vals.ai/benchmarks/mgsm"
+        }
+      },
+      "score_details": {
+        "score": 93.6,
+        "details": {
+          "benchmark_slug": "mgsm",
+          "benchmark_name": "MGSM",
+          "benchmark_updated": "2026-01-09",
+          "task_key": "mgsm_de",
+          "task_name": "German",
+          "dataset_type": "public",
+          "industry": "math",
+          "raw_score": "93.6",
+          "raw_stderr": "1.548",
+          "latency": "7.833",
+          "cost_per_test": "0.004707",
+          "max_output_tokens": "128000",
+          "reasoning_effort": "high",
+          "provider": "OpenAI"
+        },
+        "uncertainty": {
+          "standard_error": {
+            "value": 1.548,
+            "method": "vals_reported"
+          }
+        }
+      },
+      "generation_config": {
+        "generation_args": {
+          "max_tokens": 128000,
+          "max_attempts": 1
+        },
+        "additional_details": {
+          "reasoning_effort": "high"
+        }
+      }
+    },
+    {
+      "evaluation_result_id": "mgsm:mgsm_en:openai/gpt-5.1-2025-11-13:score",
+      "evaluation_name": "vals_ai.mgsm.mgsm_en",
+      "source_data": {
+        "dataset_name": "MGSM - English",
+        "source_type": "url",
+        "url": [
+          "https://www.vals.ai/benchmarks/mgsm"
+        ],
+        "additional_details": {
+          "benchmark_slug": "mgsm",
+          "task_key": "mgsm_en",
+          "dataset_type": "public",
+          "leaderboard_page_url": "https://www.vals.ai/benchmarks/mgsm"
+        }
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy reported by Vals.ai for MGSM (English).",
+        "metric_id": "vals_ai.mgsm.mgsm_en.accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "percent",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "additional_details": {
+          "score_scale": "percent_0_to_100",
+          "max_score_source": "fixed_percentage_bound",
+          "leaderboard_page_url": "https://www.vals.ai/benchmarks/mgsm"
+        }
+      },
+      "score_details": {
+        "score": 98.8,
+        "details": {
+          "benchmark_slug": "mgsm",
+          "benchmark_name": "MGSM",
+          "benchmark_updated": "2026-01-09",
+          "task_key": "mgsm_en",
+          "task_name": "English",
+          "dataset_type": "public",
+          "industry": "math",
+          "raw_score": "98.8",
+          "raw_stderr": "0.689",
+          "latency": "6.378",
+          "cost_per_test": "0.003675",
+          "max_output_tokens": "128000",
+          "reasoning_effort": "high",
+          "provider": "OpenAI"
+        },
+        "uncertainty": {
+          "standard_error": {
+            "value": 0.689,
+            "method": "vals_reported"
+          }
+        }
+      },
+      "generation_config": {
+        "generation_args": {
+          "max_tokens": 128000,
+          "max_attempts": 1
+        },
+        "additional_details": {
+          "reasoning_effort": "high"
+        }
+      }
+    },
+    {
+      "evaluation_result_id": "mgsm:mgsm_es:openai/gpt-5.1-2025-11-13:score",
+      "evaluation_name": "vals_ai.mgsm.mgsm_es",
+      "source_data": {
+        "dataset_name": "MGSM - Spanish",
+        "source_type": "url",
+        "url": [
+          "https://www.vals.ai/benchmarks/mgsm"
+        ],
+        "additional_details": {
+          "benchmark_slug": "mgsm",
+          "task_key": "mgsm_es",
+          "dataset_type": "public",
+          "leaderboard_page_url": "https://www.vals.ai/benchmarks/mgsm"
+        }
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy reported by Vals.ai for MGSM (Spanish).",
+        "metric_id": "vals_ai.mgsm.mgsm_es.accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "percent",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "additional_details": {
+          "score_scale": "percent_0_to_100",
+          "max_score_source": "fixed_percentage_bound",
+          "leaderboard_page_url": "https://www.vals.ai/benchmarks/mgsm"
+        }
+      },
+      "score_details": {
+        "score": 91.6,
+        "details": {
+          "benchmark_slug": "mgsm",
+          "benchmark_name": "MGSM",
+          "benchmark_updated": "2026-01-09",
+          "task_key": "mgsm_es",
+          "task_name": "Spanish",
+          "dataset_type": "public",
+          "industry": "math",
+          "raw_score": "91.6",
+          "raw_stderr": "1.754",
+          "latency": "8.999",
+          "cost_per_test": "0.005862",
+          "max_output_tokens": "128000",
+          "reasoning_effort": "high",
+          "provider": "OpenAI"
+        },
+        "uncertainty": {
+          "standard_error": {
+            "value": 1.754,
+            "method": "vals_reported"
+          }
+        }
+      },
+      "generation_config": {
+        "generation_args": {
+          "max_tokens": 128000,
+          "max_attempts": 1
+        },
+        "additional_details": {
+          "reasoning_effort": "high"
+        }
+      }
+    },
+    {
+      "evaluation_result_id": "mgsm:mgsm_fr:openai/gpt-5.1-2025-11-13:score",
+      "evaluation_name": "vals_ai.mgsm.mgsm_fr",
+      "source_data": {
+        "dataset_name": "MGSM - French",
+        "source_type": "url",
+        "url": [
+          "https://www.vals.ai/benchmarks/mgsm"
+        ],
+        "additional_details": {
+          "benchmark_slug": "mgsm",
+          "task_key": "mgsm_fr",
+          "dataset_type": "public",
+          "leaderboard_page_url": "https://www.vals.ai/benchmarks/mgsm"
+        }
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy reported by Vals.ai for MGSM (French).",
+        "metric_id": "vals_ai.mgsm.mgsm_fr.accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "percent",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "additional_details": {
+          "score_scale": "percent_0_to_100",
+          "max_score_source": "fixed_percentage_bound",
+          "leaderboard_page_url": "https://www.vals.ai/benchmarks/mgsm"
+        }
+      },
+      "score_details": {
+        "score": 82.4,
+        "details": {
+          "benchmark_slug": "mgsm",
+          "benchmark_name": "MGSM",
+          "benchmark_updated": "2026-01-09",
+          "task_key": "mgsm_fr",
+          "task_name": "French",
+          "dataset_type": "public",
+          "industry": "math",
+          "raw_score": "82.4",
+          "raw_stderr": "2.408",
+          "latency": "9.373",
+          "cost_per_test": "0.006635",
+          "max_output_tokens": "128000",
+          "reasoning_effort": "high",
+          "provider": "OpenAI"
+        },
+        "uncertainty": {
+          "standard_error": {
+            "value": 2.408,
+            "method": "vals_reported"
+          }
+        }
+      },
+      "generation_config": {
+        "generation_args": {
+          "max_tokens": 128000,
+          "max_attempts": 1
+        },
+        "additional_details": {
+          "reasoning_effort": "high"
+        }
+      }
+    },
+    {
+      "evaluation_result_id": "mgsm:mgsm_ja:openai/gpt-5.1-2025-11-13:score",
+      "evaluation_name": "vals_ai.mgsm.mgsm_ja",
+      "source_data": {
+        "dataset_name": "MGSM - Japanese",
+        "source_type": "url",
+        "url": [
+          "https://www.vals.ai/benchmarks/mgsm"
+        ],
+        "additional_details": {
+          "benchmark_slug": "mgsm",
+          "task_key": "mgsm_ja",
+          "dataset_type": "public",
+          "leaderboard_page_url": "https://www.vals.ai/benchmarks/mgsm"
+        }
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy reported by Vals.ai for MGSM (Japanese).",
+        "metric_id": "vals_ai.mgsm.mgsm_ja.accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "percent",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "additional_details": {
+          "score_scale": "percent_0_to_100",
+          "max_score_source": "fixed_percentage_bound",
+          "leaderboard_page_url": "https://www.vals.ai/benchmarks/mgsm"
+        }
+      },
+      "score_details": {
+        "score": 93.2,
+        "details": {
+          "benchmark_slug": "mgsm",
+          "benchmark_name": "MGSM",
+          "benchmark_updated": "2026-01-09",
+          "task_key": "mgsm_ja",
+          "task_name": "Japanese",
+          "dataset_type": "public",
+          "industry": "math",
+          "raw_score": "93.2",
+          "raw_stderr": "1.592",
+          "latency": "10.355",
+          "cost_per_test": "0.008581",
+          "max_output_tokens": "128000",
+          "reasoning_effort": "high",
+          "provider": "OpenAI"
+        },
+        "uncertainty": {
+          "standard_error": {
+            "value": 1.592,
+            "method": "vals_reported"
+          }
+        }
+      },
+      "generation_config": {
+        "generation_args": {
+          "max_tokens": 128000,
+          "max_attempts": 1
+        },
+        "additional_details": {
+          "reasoning_effort": "high"
+        }
+      }
+    },
+    {
+      "evaluation_result_id": "mgsm:mgsm_ru:openai/gpt-5.1-2025-11-13:score",
+      "evaluation_name": "vals_ai.mgsm.mgsm_ru",
+      "source_data": {
+        "dataset_name": "MGSM - Russian",
+        "source_type": "url",
+        "url": [
+          "https://www.vals.ai/benchmarks/mgsm"
+        ],
+        "additional_details": {
+          "benchmark_slug": "mgsm",
+          "task_key": "mgsm_ru",
+          "dataset_type": "public",
+          "leaderboard_page_url": "https://www.vals.ai/benchmarks/mgsm"
+        }
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy reported by Vals.ai for MGSM (Russian).",
+        "metric_id": "vals_ai.mgsm.mgsm_ru.accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "percent",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "additional_details": {
+          "score_scale": "percent_0_to_100",
+          "max_score_source": "fixed_percentage_bound",
+          "leaderboard_page_url": "https://www.vals.ai/benchmarks/mgsm"
+        }
+      },
+      "score_details": {
+        "score": 97.2,
+        "details": {
+          "benchmark_slug": "mgsm",
+          "benchmark_name": "MGSM",
+          "benchmark_updated": "2026-01-09",
+          "task_key": "mgsm_ru",
+          "task_name": "Russian",
+          "dataset_type": "public",
+          "industry": "math",
+          "raw_score": "97.2",
+          "raw_stderr": "1.043",
+          "latency": "6.241",
+          "cost_per_test": "0.004853",
+          "max_output_tokens": "128000",
+          "reasoning_effort": "high",
+          "provider": "OpenAI"
+        },
+        "uncertainty": {
+          "standard_error": {
+            "value": 1.043,
+            "method": "vals_reported"
+          }
+        }
+      },
+      "generation_config": {
+        "generation_args": {
+          "max_tokens": 128000,
+          "max_attempts": 1
+        },
+        "additional_details": {
+          "reasoning_effort": "high"
+        }
+      }
+    },
+    {
+      "evaluation_result_id": "mgsm:mgsm_sw:openai/gpt-5.1-2025-11-13:score",
+      "evaluation_name": "vals_ai.mgsm.mgsm_sw",
+      "source_data": {
+        "dataset_name": "MGSM - Swahili",
+        "source_type": "url",
+        "url": [
+          "https://www.vals.ai/benchmarks/mgsm"
+        ],
+        "additional_details": {
+          "benchmark_slug": "mgsm",
+          "task_key": "mgsm_sw",
+          "dataset_type": "public",
+          "leaderboard_page_url": "https://www.vals.ai/benchmarks/mgsm"
+        }
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy reported by Vals.ai for MGSM (Swahili).",
+        "metric_id": "vals_ai.mgsm.mgsm_sw.accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "percent",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "additional_details": {
+          "score_scale": "percent_0_to_100",
+          "max_score_source": "fixed_percentage_bound",
+          "leaderboard_page_url": "https://www.vals.ai/benchmarks/mgsm"
+        }
+      },
+      "score_details": {
+        "score": 95.6,
+        "details": {
+          "benchmark_slug": "mgsm",
+          "benchmark_name": "MGSM",
+          "benchmark_updated": "2026-01-09",
+          "task_key": "mgsm_sw",
+          "task_name": "Swahili",
+          "dataset_type": "public",
+          "industry": "math",
+          "raw_score": "95.6",
+          "raw_stderr": "1.297",
+          "latency": "7.332",
+          "cost_per_test": "0.005489",
+          "max_output_tokens": "128000",
+          "reasoning_effort": "high",
+          "provider": "OpenAI"
+        },
+        "uncertainty": {
+          "standard_error": {
+            "value": 1.297,
+            "method": "vals_reported"
+          }
+        }
+      },
+      "generation_config": {
+        "generation_args": {
+          "max_tokens": 128000,
+          "max_attempts": 1
+        },
+        "additional_details": {
+          "reasoning_effort": "high"
+        }
+      }
+    },
+    {
+      "evaluation_result_id": "mgsm:mgsm_te:openai/gpt-5.1-2025-11-13:score",
+      "evaluation_name": "vals_ai.mgsm.mgsm_te",
+      "source_data": {
+        "dataset_name": "MGSM - Telugu",
+        "source_type": "url",
+        "url": [
+          "https://www.vals.ai/benchmarks/mgsm"
+        ],
+        "additional_details": {
+          "benchmark_slug": "mgsm",
+          "task_key": "mgsm_te",
+          "dataset_type": "public",
+          "leaderboard_page_url": "https://www.vals.ai/benchmarks/mgsm"
+        }
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy reported by Vals.ai for MGSM (Telugu).",
+        "metric_id": "vals_ai.mgsm.mgsm_te.accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "percent",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "additional_details": {
+          "score_scale": "percent_0_to_100",
+          "max_score_source": "fixed_percentage_bound",
+          "leaderboard_page_url": "https://www.vals.ai/benchmarks/mgsm"
+        }
+      },
+      "score_details": {
+        "score": 89.6,
+        "details": {
+          "benchmark_slug": "mgsm",
+          "benchmark_name": "MGSM",
+          "benchmark_updated": "2026-01-09",
+          "task_key": "mgsm_te",
+          "task_name": "Telugu",
+          "dataset_type": "public",
+          "industry": "math",
+          "raw_score": "89.6",
+          "raw_stderr": "1.931",
+          "latency": "11.426",
+          "cost_per_test": "0.009245",
+          "max_output_tokens": "128000",
+          "reasoning_effort": "high",
+          "provider": "OpenAI"
+        },
+        "uncertainty": {
+          "standard_error": {
+            "value": 1.931,
+            "method": "vals_reported"
+          }
+        }
+      },
+      "generation_config": {
+        "generation_args": {
+          "max_tokens": 128000,
+          "max_attempts": 1
+        },
+        "additional_details": {
+          "reasoning_effort": "high"
+        }
+      }
+    },
+    {
+      "evaluation_result_id": "mgsm:mgsm_th:openai/gpt-5.1-2025-11-13:score",
+      "evaluation_name": "vals_ai.mgsm.mgsm_th",
+      "source_data": {
+        "dataset_name": "MGSM - Thai",
+        "source_type": "url",
+        "url": [
+          "https://www.vals.ai/benchmarks/mgsm"
+        ],
+        "additional_details": {
+          "benchmark_slug": "mgsm",
+          "task_key": "mgsm_th",
+          "dataset_type": "public",
+          "leaderboard_page_url": "https://www.vals.ai/benchmarks/mgsm"
+        }
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy reported by Vals.ai for MGSM (Thai).",
+        "metric_id": "vals_ai.mgsm.mgsm_th.accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "percent",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "additional_details": {
+          "score_scale": "percent_0_to_100",
+          "max_score_source": "fixed_percentage_bound",
+          "leaderboard_page_url": "https://www.vals.ai/benchmarks/mgsm"
+        }
+      },
+      "score_details": {
+        "score": 94.4,
+        "details": {
+          "benchmark_slug": "mgsm",
+          "benchmark_name": "MGSM",
+          "benchmark_updated": "2026-01-09",
+          "task_key": "mgsm_th",
+          "task_name": "Thai",
+          "dataset_type": "public",
+          "industry": "math",
+          "raw_score": "94.4",
+          "raw_stderr": "1.454",
+          "latency": "8.603",
+          "cost_per_test": "0.006629",
+          "max_output_tokens": "128000",
+          "reasoning_effort": "high",
+          "provider": "OpenAI"
+        },
+        "uncertainty": {
+          "standard_error": {
+            "value": 1.454,
+            "method": "vals_reported"
+          }
+        }
+      },
+      "generation_config": {
+        "generation_args": {
+          "max_tokens": 128000,
+          "max_attempts": 1
+        },
+        "additional_details": {
+          "reasoning_effort": "high"
+        }
+      }
+    },
+    {
+      "evaluation_result_id": "mgsm:mgsm_zh:openai/gpt-5.1-2025-11-13:score",
+      "evaluation_name": "vals_ai.mgsm.mgsm_zh",
+      "source_data": {
+        "dataset_name": "MGSM - Chinese",
+        "source_type": "url",
+        "url": [
+          "https://www.vals.ai/benchmarks/mgsm"
+        ],
+        "additional_details": {
+          "benchmark_slug": "mgsm",
+          "task_key": "mgsm_zh",
+          "dataset_type": "public",
+          "leaderboard_page_url": "https://www.vals.ai/benchmarks/mgsm"
+        }
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy reported by Vals.ai for MGSM (Chinese).",
+        "metric_id": "vals_ai.mgsm.mgsm_zh.accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "percent",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "additional_details": {
+          "score_scale": "percent_0_to_100",
+          "max_score_source": "fixed_percentage_bound",
+          "leaderboard_page_url": "https://www.vals.ai/benchmarks/mgsm"
+        }
+      },
+      "score_details": {
+        "score": 93.2,
+        "details": {
+          "benchmark_slug": "mgsm",
+          "benchmark_name": "MGSM",
+          "benchmark_updated": "2026-01-09",
+          "task_key": "mgsm_zh",
+          "task_name": "Chinese",
+          "dataset_type": "public",
+          "industry": "math",
+          "raw_score": "93.2",
+          "raw_stderr": "1.592",
+          "latency": "6.955",
+          "cost_per_test": "0.004917",
+          "max_output_tokens": "128000",
+          "reasoning_effort": "high",
+          "provider": "OpenAI"
+        },
+        "uncertainty": {
+          "standard_error": {
+            "value": 1.592,
+            "method": "vals_reported"
+          }
+        }
+      },
+      "generation_config": {
+        "generation_args": {
+          "max_tokens": 128000,
+          "max_attempts": 1
+        },
+        "additional_details": {
+          "reasoning_effort": "high"
+        }
+      }
+    },
+    {
+      "evaluation_result_id": "mgsm:overall:openai/gpt-5.1-2025-11-13:score",
+      "evaluation_name": "vals_ai.mgsm.overall",
+      "source_data": {
+        "dataset_name": "MGSM - Overall",
+        "source_type": "url",
+        "url": [
+          "https://www.vals.ai/benchmarks/mgsm"
+        ],
+        "additional_details": {
+          "benchmark_slug": "mgsm",
+          "task_key": "overall",
+          "dataset_type": "public",
+          "leaderboard_page_url": "https://www.vals.ai/benchmarks/mgsm"
+        }
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy reported by Vals.ai for MGSM (Overall).",
+        "metric_id": "vals_ai.mgsm.overall.accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "percent",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "additional_details": {
+          "score_scale": "percent_0_to_100",
+          "max_score_source": "fixed_percentage_bound",
+          "leaderboard_page_url": "https://www.vals.ai/benchmarks/mgsm"
+        }
+      },
+      "score_details": {
+        "score": 92.982,
+        "details": {
+          "benchmark_slug": "mgsm",
+          "benchmark_name": "MGSM",
+          "benchmark_updated": "2026-01-09",
+          "task_key": "overall",
+          "task_name": "Overall",
+          "dataset_type": "public",
+          "industry": "math",
+          "raw_score": "92.982",
+          "raw_stderr": "1.536",
+          "latency": "8.347",
+          "cost_per_test": "0.00606",
+          "max_output_tokens": "128000",
+          "reasoning_effort": "high",
+          "provider": "OpenAI"
+        },
+        "uncertainty": {
+          "standard_error": {
+            "value": 1.536,
+            "method": "vals_reported"
+          }
+        }
+      },
+      "generation_config": {
+        "generation_args": {
+          "max_tokens": 128000,
+          "max_attempts": 1
+        },
+        "additional_details": {
+          "reasoning_effort": "high"
+        }
+      }
+    }
+  ]
+}

flat/objects/05/0a/050a139d-35b8-4f82-a237-b95d9d0e03e2.json

@@ -0,0 +1,148 @@
+{
+  "schema_version": "0.2.2",
+  "evaluation_id": "Next Edit Prediction/Claude 3.7 Sonnet/1771591481.616601",
+  "retrieved_timestamp": "1771591481.616601",
+  "source_metadata": {
+    "source_name": "alphaXiv State of the Art",
+    "source_type": "documentation",
+    "source_organization_name": "alphaXiv",
+    "source_organization_url": "https://alphaxiv.org",
+    "evaluator_relationship": "third_party",
+    "additional_details": {
+      "alphaxiv_dataset_org": "The Chinese University of Hong Kong",
+      "alphaxiv_dataset_type": "text",
+      "scrape_source": "https://github.com/alphaXiv/feedback/issues/189"
+    }
+  },
+  "model_info": {
+    "id": "Claude 3.7 Sonnet",
+    "name": "Claude 3.7 Sonnet",
+    "developer": "unknown"
+  },
+  "evaluation_results": [
+    {
+      "evaluation_name": "Next Edit Prediction",
+      "source_data": {
+        "dataset_name": "Next Edit Prediction",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2508.10074"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Measures the percentage of predictions where the single ground truth edit chunk is present in the model's generated output. This metric is less strict than Exact Match and allows for cases where the model generates additional correct or incorrect edits, as long as the intended one is included.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Partial Match (%)",
+          "alphaxiv_is_primary": "True",
+          "raw_evaluation_name": "Next Edit Prediction: Partial Match"
+        },
+        "metric_id": "next_edit_prediction_partial_match",
+        "metric_name": "Next Edit Prediction: Partial Match",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 59.52
+      },
+      "evaluation_result_id": "Next Edit Prediction/Claude 3.7 Sonnet/1771591481.616601#next_edit_prediction#next_edit_prediction_partial_match"
+    },
+    {
+      "evaluation_name": "Next Edit Prediction",
+      "source_data": {
+        "dataset_name": "Next Edit Prediction",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2508.10074"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Measures the percentage of predictions where the model's generated code is identical to the ground truth next version of the code. This is the strictest metric, requiring a perfect character-for-character match. Results include one-shot prompted baselines and fine-tuned models.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Exact Match (%)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Next Edit Prediction: Exact Match"
+        },
+        "metric_id": "next_edit_prediction_exact_match",
+        "metric_name": "Next Edit Prediction: Exact Match",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 33.81
+      },
+      "evaluation_result_id": "Next Edit Prediction/Claude 3.7 Sonnet/1771591481.616601#next_edit_prediction#next_edit_prediction_exact_match"
+    },
+    {
+      "evaluation_name": "Next Edit Prediction",
+      "source_data": {
+        "dataset_name": "Next Edit Prediction",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2508.10074"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Measures the semantic and logical correctness of the predicted edit, as evaluated by an external LLM (GPT-4.1 mini). This score captures the qualitative aspect of the prediction, assessing whether it is a reasonable and helpful edit even if it doesn't match the ground truth exactly.",
+        "additional_details": {
+          "alphaxiv_y_axis": "LLM-as-a-Judge Score (%)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Next Edit Prediction: LLM-as-a-Judge"
+        },
+        "metric_id": "next_edit_prediction_llm_as_a_judge",
+        "metric_name": "Next Edit Prediction: LLM-as-a-Judge",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 61.9
+      },
+      "evaluation_result_id": "Next Edit Prediction/Claude 3.7 Sonnet/1771591481.616601#next_edit_prediction#next_edit_prediction_llm_as_a_judge"
+    },
+    {
+      "evaluation_name": "Next Edit Prediction",
+      "source_data": {
+        "dataset_name": "Next Edit Prediction",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2508.10074"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Measures the percentage of predictions where the model correctly identifies the location of the next edit, regardless of the generated content. This metric isolates the model's ability to localize developer intent.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Position Match (%)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Next Edit Prediction: Position Match"
+        },
+        "metric_id": "next_edit_prediction_position_match",
+        "metric_name": "Next Edit Prediction: Position Match",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 75.71
+      },
+      "evaluation_result_id": "Next Edit Prediction/Claude 3.7 Sonnet/1771591481.616601#next_edit_prediction#next_edit_prediction_position_match"
+    }
+  ],
+  "eval_library": {
+    "name": "alphaxiv",
+    "version": "unknown"
+  }
+}

flat/objects/05/0a/050a1a41-824a-48a7-9c4d-0f2a50566055.json

@@ -0,0 +1,148 @@
+{
+  "schema_version": "0.2.2",
+  "evaluation_id": "IFEvalCode/grok-3-mini-fast/1771591481.616601",
+  "retrieved_timestamp": "1771591481.616601",
+  "source_metadata": {
+    "source_name": "alphaXiv State of the Art",
+    "source_type": "documentation",
+    "source_organization_name": "alphaXiv",
+    "source_organization_url": "https://alphaxiv.org",
+    "evaluator_relationship": "third_party",
+    "additional_details": {
+      "alphaxiv_dataset_org": "Beihang University",
+      "alphaxiv_dataset_type": "text",
+      "scrape_source": "https://github.com/alphaXiv/feedback/issues/189"
+    }
+  },
+  "model_info": {
+    "id": "grok-3-mini-fast",
+    "name": "grok-3-mini-fast",
+    "developer": "unknown"
+  },
+  "evaluation_results": [
+    {
+      "evaluation_name": "IFEvalCode",
+      "source_data": {
+        "dataset_name": "IFEvalCode",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2507.22462"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Average instruction-following Pass@1 score across 8 programming languages (Python, Java, C++, C#, TypeScript, JavaScript, PHP, Shell) on the IFEvalCode benchmark using English queries. This metric measures the percentage of generated code snippets that correctly adhere to all human-provided constraints (e.g., style, variable naming, algorithm usage), which is the key innovation of this benchmark.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Instruction-Following Avg. Pass@1 (%)",
+          "alphaxiv_is_primary": "True",
+          "raw_evaluation_name": "IFEvalCode: Average Instruction-Following on English Queries"
+        },
+        "metric_id": "ifevalcode_average_instruction_following_on_english_queries",
+        "metric_name": "IFEvalCode: Average Instruction-Following on English Queries",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 16.8
+      },
+      "evaluation_result_id": "IFEvalCode/grok-3-mini-fast/1771591481.616601#ifevalcode#ifevalcode_average_instruction_following_on_english_queries"
+    },
+    {
+      "evaluation_name": "IFEvalCode",
+      "source_data": {
+        "dataset_name": "IFEvalCode",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2507.22462"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Average correctness Pass@1 score across 8 programming languages (Python, Java, C++, C#, TypeScript, JavaScript, PHP, Shell) on the IFEvalCode benchmark using Chinese queries. This metric measures the percentage of generated code snippets that are functionally correct and pass standard unit tests, testing the multilingual capabilities of models.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Correctness Avg. Pass@1 (%)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "IFEvalCode: Average Correctness on Chinese Queries"
+        },
+        "metric_id": "ifevalcode_average_correctness_on_chinese_queries",
+        "metric_name": "IFEvalCode: Average Correctness on Chinese Queries",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 22
+      },
+      "evaluation_result_id": "IFEvalCode/grok-3-mini-fast/1771591481.616601#ifevalcode#ifevalcode_average_correctness_on_chinese_queries"
+    },
+    {
+      "evaluation_name": "IFEvalCode",
+      "source_data": {
+        "dataset_name": "IFEvalCode",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2507.22462"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Average correctness Pass@1 score across 8 programming languages (Python, Java, C++, C#, TypeScript, JavaScript, PHP, Shell) on the IFEvalCode benchmark using English queries. This metric measures the percentage of generated code snippets that are functionally correct and pass standard unit tests.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Correctness Avg. Pass@1 (%)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "IFEvalCode: Average Correctness on English Queries"
+        },
+        "metric_id": "ifevalcode_average_correctness_on_english_queries",
+        "metric_name": "IFEvalCode: Average Correctness on English Queries",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 28.8
+      },
+      "evaluation_result_id": "IFEvalCode/grok-3-mini-fast/1771591481.616601#ifevalcode#ifevalcode_average_correctness_on_english_queries"
+    },
+    {
+      "evaluation_name": "IFEvalCode",
+      "source_data": {
+        "dataset_name": "IFEvalCode",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2507.22462"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Average instruction-following Pass@1 score across 8 programming languages (Python, Java, C++, C#, TypeScript, JavaScript, PHP, Shell) on the IFEvalCode benchmark using Chinese queries. This metric measures the percentage of generated code snippets that correctly adhere to all human-provided constraints (e.g., style, variable naming, algorithm usage), testing the multilingual capabilities of models.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Instruction-Following Avg. Pass@1 (%)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "IFEvalCode: Average Instruction-Following on Chinese Queries"
+        },
+        "metric_id": "ifevalcode_average_instruction_following_on_chinese_queries",
+        "metric_name": "IFEvalCode: Average Instruction-Following on Chinese Queries",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 15.8
+      },
+      "evaluation_result_id": "IFEvalCode/grok-3-mini-fast/1771591481.616601#ifevalcode#ifevalcode_average_instruction_following_on_chinese_queries"
+    }
+  ],
+  "eval_library": {
+    "name": "alphaxiv",
+    "version": "unknown"
+  }
+}

flat/objects/05/0a/050afc2d-4bca-40ea-8c31-922d7e44189f.json

@@ -0,0 +1,538 @@
+{
+  "schema_version": "0.2.2",
+  "evaluation_id": "MathBode/Qwen3 235B Instruct/1771591481.616601",
+  "retrieved_timestamp": "1771591481.616601",
+  "source_metadata": {
+    "source_name": "alphaXiv State of the Art",
+    "source_type": "documentation",
+    "source_organization_name": "alphaXiv",
+    "source_organization_url": "https://alphaxiv.org",
+    "evaluator_relationship": "third_party",
+    "additional_details": {
+      "alphaxiv_dataset_org": "Columbia University",
+      "alphaxiv_dataset_type": "text",
+      "scrape_source": "https://github.com/alphaXiv/feedback/issues/189"
+    }
+  },
+  "model_info": {
+    "id": "Qwen3 235B Instruct",
+    "name": "Qwen3 235B Instruct",
+    "developer": "unknown"
+  },
+  "evaluation_results": [
+    {
+      "evaluation_name": "MathBode",
+      "source_data": {
+        "dataset_name": "MathBode",
+        "source_type": "url",
+        "url": [
+          "https://huggingface.co/datasets/cognitive-metrology-lab/MathBode"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "The MB-Core score is a summary metric from the MathBode benchmark, aggregating mid-band gain and phase performance (at frequencies of 4 & 8 cycles per 64 steps) across five mathematical reasoning families. It provides a compact measure of a model's dynamic reasoning fidelity. Higher scores are better.",
+        "additional_details": {
+          "alphaxiv_y_axis": "MB-Core Score",
+          "alphaxiv_is_primary": "True",
+          "raw_evaluation_name": "Overall MathBode Core (MB-Core) Score"
+        },
+        "metric_id": "overall_mathbode_core_mb_core_score",
+        "metric_name": "Overall MathBode Core (MB-Core) Score",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 0.782
+      },
+      "evaluation_result_id": "MathBode/Qwen3 235B Instruct/1771591481.616601#mathbode#overall_mathbode_core_mb_core_score"
+    },
+    {
+      "evaluation_name": "MathBode",
+      "source_data": {
+        "dataset_name": "MathBode",
+        "source_type": "url",
+        "url": [
+          "https://huggingface.co/datasets/cognitive-metrology-lab/MathBode"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": true,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Mean absolute deviation of gain from the ideal value of 1 (|G-1|) at mid-frequencies on the 'Linear Solve' task. A lower value indicates better amplitude tracking. This is a supplementary diagnostic metric from the paper's appendix.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Mean |G-1| (Linear Solve)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Mean Gain Error |G-1| on Linear Solve Task"
+        },
+        "metric_id": "mean_gain_error_g_1_on_linear_solve_task",
+        "metric_name": "Mean Gain Error |G-1| on Linear Solve Task",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 0.009
+      },
+      "evaluation_result_id": "MathBode/Qwen3 235B Instruct/1771591481.616601#mathbode#mean_gain_error_g_1_on_linear_solve_task"
+    },
+    {
+      "evaluation_name": "MathBode",
+      "source_data": {
+        "dataset_name": "MathBode",
+        "source_type": "url",
+        "url": [
+          "https://huggingface.co/datasets/cognitive-metrology-lab/MathBode"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": true,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Mean absolute deviation of gain from the ideal value of 1 (|G-1|) at mid-frequencies on the 'Linear System' task. A lower value indicates better amplitude tracking. This is a supplementary diagnostic metric from the paper's appendix.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Mean |G-1| (Linear System)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Mean Gain Error |G-1| on Linear System Task"
+        },
+        "metric_id": "mean_gain_error_g_1_on_linear_system_task",
+        "metric_name": "Mean Gain Error |G-1| on Linear System Task",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 1.453
+      },
+      "evaluation_result_id": "MathBode/Qwen3 235B Instruct/1771591481.616601#mathbode#mean_gain_error_g_1_on_linear_system_task"
+    },
+    {
+      "evaluation_name": "MathBode",
+      "source_data": {
+        "dataset_name": "MathBode",
+        "source_type": "url",
+        "url": [
+          "https://huggingface.co/datasets/cognitive-metrology-lab/MathBode"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": true,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Mean absolute deviation of gain from the ideal value of 1 (|G-1|) at mid-frequencies on the 'Ratio Saturation' task. A lower value indicates better amplitude tracking. This is a supplementary diagnostic metric from the paper's appendix.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Mean |G-1| (Ratio Saturation)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Mean Gain Error |G-1| on Ratio Saturation Task"
+        },
+        "metric_id": "mean_gain_error_g_1_on_ratio_saturation_task",
+        "metric_name": "Mean Gain Error |G-1| on Ratio Saturation Task",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 0.005
+      },
+      "evaluation_result_id": "MathBode/Qwen3 235B Instruct/1771591481.616601#mathbode#mean_gain_error_g_1_on_ratio_saturation_task"
+    },
+    {
+      "evaluation_name": "MathBode",
+      "source_data": {
+        "dataset_name": "MathBode",
+        "source_type": "url",
+        "url": [
+          "https://huggingface.co/datasets/cognitive-metrology-lab/MathBode"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": true,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Mean absolute deviation of gain from the ideal value of 1 (|G-1|) at mid-frequencies on the 'Similar Triangles' task. A lower value indicates better amplitude tracking. This is a supplementary diagnostic metric from the paper's appendix.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Mean |G-1| (Similar Triangles)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Mean Gain Error |G-1| on Similar Triangles Task"
+        },
+        "metric_id": "mean_gain_error_g_1_on_similar_triangles_task",
+        "metric_name": "Mean Gain Error |G-1| on Similar Triangles Task",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 0
+      },
+      "evaluation_result_id": "MathBode/Qwen3 235B Instruct/1771591481.616601#mathbode#mean_gain_error_g_1_on_similar_triangles_task"
+    },
+    {
+      "evaluation_name": "MathBode",
+      "source_data": {
+        "dataset_name": "MathBode",
+        "source_type": "url",
+        "url": [
+          "https://huggingface.co/datasets/cognitive-metrology-lab/MathBode"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Per-family MB-Core score on the 'Exponential Interest' task, measuring dynamic reasoning fidelity for problems of the form A(1+p)^t. This task is challenging and reveals amplitude distortions in some models.",
+        "additional_details": {
+          "alphaxiv_y_axis": "MB-Core Score (Exponential Interest)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "MB-Core Score on Exponential Interest Task"
+        },
+        "metric_id": "mb_core_score_on_exponential_interest_task",
+        "metric_name": "MB-Core Score on Exponential Interest Task",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 0.467
+      },
+      "evaluation_result_id": "MathBode/Qwen3 235B Instruct/1771591481.616601#mathbode#mb_core_score_on_exponential_interest_task"
+    },
+    {
+      "evaluation_name": "MathBode",
+      "source_data": {
+        "dataset_name": "MathBode",
+        "source_type": "url",
+        "url": [
+          "https://huggingface.co/datasets/cognitive-metrology-lab/MathBode"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Per-family MB-Core score on the 'Linear Solve' task (ax+b=c), measuring dynamic reasoning fidelity for simple algebraic equations. Most top-tier models perform well on this task.",
+        "additional_details": {
+          "alphaxiv_y_axis": "MB-Core Score (Linear Solve)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "MB-Core Score on Linear Solve Task"
+        },
+        "metric_id": "mb_core_score_on_linear_solve_task",
+        "metric_name": "MB-Core Score on Linear Solve Task",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 0.982
+      },
+      "evaluation_result_id": "MathBode/Qwen3 235B Instruct/1771591481.616601#mathbode#mb_core_score_on_linear_solve_task"
+    },
+    {
+      "evaluation_name": "MathBode",
+      "source_data": {
+        "dataset_name": "MathBode",
+        "source_type": "url",
+        "url": [
+          "https://huggingface.co/datasets/cognitive-metrology-lab/MathBode"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Per-family MB-Core score on the 'Linear System' task (solving a 2x2 system), measuring dynamic reasoning fidelity. This task proves difficult for most models and acts as a timing bottleneck for some.",
+        "additional_details": {
+          "alphaxiv_y_axis": "MB-Core Score (Linear System)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "MB-Core Score on Linear System Task"
+        },
+        "metric_id": "mb_core_score_on_linear_system_task",
+        "metric_name": "MB-Core Score on Linear System Task",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 0.471
+      },
+      "evaluation_result_id": "MathBode/Qwen3 235B Instruct/1771591481.616601#mathbode#mb_core_score_on_linear_system_task"
+    },
+    {
+      "evaluation_name": "MathBode",
+      "source_data": {
+        "dataset_name": "MathBode",
+        "source_type": "url",
+        "url": [
+          "https://huggingface.co/datasets/cognitive-metrology-lab/MathBode"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Per-family MB-Core score on the 'Ratio Saturation' task (p/(p+k)), measuring dynamic reasoning fidelity for saturating transforms. Mixtral 8x7B shows a notable collapse on this task.",
+        "additional_details": {
+          "alphaxiv_y_axis": "MB-Core Score (Ratio Saturation)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "MB-Core Score on Ratio Saturation Task"
+        },
+        "metric_id": "mb_core_score_on_ratio_saturation_task",
+        "metric_name": "MB-Core Score on Ratio Saturation Task",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 0.99
+      },
+      "evaluation_result_id": "MathBode/Qwen3 235B Instruct/1771591481.616601#mathbode#mb_core_score_on_ratio_saturation_task"
+    },
+    {
+      "evaluation_name": "MathBode",
+      "source_data": {
+        "dataset_name": "MathBode",
+        "source_type": "url",
+        "url": [
+          "https://huggingface.co/datasets/cognitive-metrology-lab/MathBode"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Per-family MB-Core score on the 'Similar Triangles' task (proportional scaling), measuring dynamic reasoning fidelity. All top-tier models perform nearly perfectly, serving as an instrument check for the benchmark.",
+        "additional_details": {
+          "alphaxiv_y_axis": "MB-Core Score (Similar Triangles)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "MB-Core Score on Similar Triangles Task"
+        },
+        "metric_id": "mb_core_score_on_similar_triangles_task",
+        "metric_name": "MB-Core Score on Similar Triangles Task",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 1
+      },
+      "evaluation_result_id": "MathBode/Qwen3 235B Instruct/1771591481.616601#mathbode#mb_core_score_on_similar_triangles_task"
+    },
+    {
+      "evaluation_name": "MathBode",
+      "source_data": {
+        "dataset_name": "MathBode",
+        "source_type": "url",
+        "url": [
+          "https://huggingface.co/datasets/cognitive-metrology-lab/MathBode"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "The MB-Plus score is a summary metric from the MathBode benchmark that extends MB-Core by additionally downweighting poor performance on certain features. It offers an alternative compact comparison of models' dynamic reasoning abilities. Higher scores are better.",
+        "additional_details": {
+          "alphaxiv_y_axis": "MB-Plus Score",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Overall MathBode Plus (MB-Plus) Score"
+        },
+        "metric_id": "overall_mathbode_plus_mb_plus_score",
+        "metric_name": "Overall MathBode Plus (MB-Plus) Score",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 0.576
+      },
+      "evaluation_result_id": "MathBode/Qwen3 235B Instruct/1771591481.616601#mathbode#overall_mathbode_plus_mb_plus_score"
+    },
+    {
+      "evaluation_name": "MathBode",
+      "source_data": {
+        "dataset_name": "MathBode",
+        "source_type": "url",
+        "url": [
+          "https://huggingface.co/datasets/cognitive-metrology-lab/MathBode"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": true,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Mean absolute phase error in degrees at mid-frequencies on the 'Exponential Interest' task. A lower value indicates more accurate timing and less lag/lead. This is a supplementary diagnostic metric from the paper's appendix.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Mean |Phase Error| (deg, Exponential Interest)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Mean Phase Error on Exponential Interest Task"
+        },
+        "metric_id": "mean_phase_error_on_exponential_interest_task",
+        "metric_name": "Mean Phase Error on Exponential Interest Task",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 2.97
+      },
+      "evaluation_result_id": "MathBode/Qwen3 235B Instruct/1771591481.616601#mathbode#mean_phase_error_on_exponential_interest_task"
+    },
+    {
+      "evaluation_name": "MathBode",
+      "source_data": {
+        "dataset_name": "MathBode",
+        "source_type": "url",
+        "url": [
+          "https://huggingface.co/datasets/cognitive-metrology-lab/MathBode"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": true,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Mean absolute phase error in degrees at mid-frequencies on the 'Linear Solve' task. A lower value indicates more accurate timing and less lag/lead. This is a supplementary diagnostic metric from the paper's appendix.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Mean |Phase Error| (deg, Linear Solve)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Mean Phase Error on Linear Solve Task"
+        },
+        "metric_id": "mean_phase_error_on_linear_solve_task",
+        "metric_name": "Mean Phase Error on Linear Solve Task",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 0.03
+      },
+      "evaluation_result_id": "MathBode/Qwen3 235B Instruct/1771591481.616601#mathbode#mean_phase_error_on_linear_solve_task"
+    },
+    {
+      "evaluation_name": "MathBode",
+      "source_data": {
+        "dataset_name": "MathBode",
+        "source_type": "url",
+        "url": [
+          "https://huggingface.co/datasets/cognitive-metrology-lab/MathBode"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": true,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Mean absolute phase error in degrees at mid-frequencies on the 'Linear System' task. A lower value indicates more accurate timing and less lag/lead. This is a supplementary diagnostic metric from the paper's appendix.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Mean |Phase Error| (deg, Linear System)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Mean Phase Error on Linear System Task"
+        },
+        "metric_id": "mean_phase_error_on_linear_system_task",
+        "metric_name": "Mean Phase Error on Linear System Task",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 2.61
+      },
+      "evaluation_result_id": "MathBode/Qwen3 235B Instruct/1771591481.616601#mathbode#mean_phase_error_on_linear_system_task"
+    },
+    {
+      "evaluation_name": "MathBode",
+      "source_data": {
+        "dataset_name": "MathBode",
+        "source_type": "url",
+        "url": [
+          "https://huggingface.co/datasets/cognitive-metrology-lab/MathBode"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": true,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Mean absolute phase error in degrees at mid-frequencies on the 'Ratio Saturation' task. A lower value indicates more accurate timing and less lag/lead. This is a supplementary diagnostic metric from the paper's appendix.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Mean |Phase Error| (deg, Ratio Saturation)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Mean Phase Error on Ratio Saturation Task"
+        },
+        "metric_id": "mean_phase_error_on_ratio_saturation_task",
+        "metric_name": "Mean Phase Error on Ratio Saturation Task",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 0.01
+      },
+      "evaluation_result_id": "MathBode/Qwen3 235B Instruct/1771591481.616601#mathbode#mean_phase_error_on_ratio_saturation_task"
+    },
+    {
+      "evaluation_name": "MathBode",
+      "source_data": {
+        "dataset_name": "MathBode",
+        "source_type": "url",
+        "url": [
+          "https://huggingface.co/datasets/cognitive-metrology-lab/MathBode"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": true,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Mean absolute deviation of gain from the ideal value of 1 (|G-1|) at mid-frequencies on the 'Exponential Interest' task. A lower value indicates better amplitude tracking. This is a supplementary diagnostic metric from the paper's appendix.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Mean |G-1| (Exponential Interest)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Mean Gain Error |G-1| on Exponential Interest Task"
+        },
+        "metric_id": "mean_gain_error_g_1_on_exponential_interest_task",
+        "metric_name": "Mean Gain Error |G-1| on Exponential Interest Task",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 0.323
+      },
+      "evaluation_result_id": "MathBode/Qwen3 235B Instruct/1771591481.616601#mathbode#mean_gain_error_g_1_on_exponential_interest_task"
+    },
+    {
+      "evaluation_name": "MathBode",
+      "source_data": {
+        "dataset_name": "MathBode",
+        "source_type": "url",
+        "url": [
+          "https://huggingface.co/datasets/cognitive-metrology-lab/MathBode"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": true,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Mean absolute phase error in degrees at mid-frequencies on the 'Similar Triangles' task. A lower value indicates more accurate timing and less lag/lead. This is a supplementary diagnostic metric from the paper's appendix.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Mean |Phase Error| (deg, Similar Triangles)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Mean Phase Error on Similar Triangles Task"
+        },
+        "metric_id": "mean_phase_error_on_similar_triangles_task",
+        "metric_name": "Mean Phase Error on Similar Triangles Task",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 0
+      },
+      "evaluation_result_id": "MathBode/Qwen3 235B Instruct/1771591481.616601#mathbode#mean_phase_error_on_similar_triangles_task"
+    }
+  ],
+  "eval_library": {
+    "name": "alphaxiv",
+    "version": "unknown"
+  }
+}

flat/objects/05/0d/050d565e-90ad-4878-b18e-659bfa2e7f1a.json

@@ -0,0 +1,358 @@
+{
+  "schema_version": "0.2.2",
+  "evaluation_id": "Stanford-ORB/NVDiffRecMC/1771591481.616601",
+  "retrieved_timestamp": "1771591481.616601",
+  "source_metadata": {
+    "source_name": "alphaXiv State of the Art",
+    "source_type": "documentation",
+    "source_organization_name": "alphaXiv",
+    "source_organization_url": "https://alphaxiv.org",
+    "evaluator_relationship": "third_party",
+    "additional_details": {
+      "alphaxiv_dataset_org": "Stanford University",
+      "alphaxiv_dataset_type": "image",
+      "scrape_source": "https://github.com/alphaXiv/feedback/issues/189"
+    }
+  },
+  "model_info": {
+    "id": "NVDiffRecMC",
+    "name": "NVDiffRecMC",
+    "developer": "unknown"
+  },
+  "evaluation_results": [
+    {
+      "evaluation_name": "Stanford-ORB",
+      "source_data": {
+        "dataset_name": "Stanford-ORB",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2310.16044"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Evaluates material recovery by rendering the object under novel, unseen real-world lighting. This metric is the Peak Signal-to-Noise Ratio on HDR images (PSNR-H), comparing the relit image to a ground-truth photo. Higher scores indicate more accurate material and lighting disentanglement.",
+        "additional_details": {
+          "alphaxiv_y_axis": "PSNR-H",
+          "alphaxiv_is_primary": "True",
+          "raw_evaluation_name": "Novel Scene Relighting: Image Quality (PSNR-H)"
+        },
+        "metric_id": "novel_scene_relighting_image_quality_psnr_h",
+        "metric_name": "Novel Scene Relighting: Image Quality (PSNR-H)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 24.43
+      },
+      "evaluation_result_id": "Stanford-ORB/NVDiffRecMC/1771591481.616601#stanford_orb#novel_scene_relighting_image_quality_psnr_h"
+    },
+    {
+      "evaluation_name": "Stanford-ORB",
+      "source_data": {
+        "dataset_name": "Stanford-ORB",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2310.16044"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Measures the accuracy of reconstructed 3D object geometry by computing the cosine distance between predicted surface normal maps and ground-truth normals. Lower values indicate more accurate surface normal estimation.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Normal Cosine Distance",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Geometry Estimation: Surface Normal Accuracy"
+        },
+        "metric_id": "geometry_estimation_surface_normal_accuracy",
+        "metric_name": "Geometry Estimation: Surface Normal Accuracy",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 0.04
+      },
+      "evaluation_result_id": "Stanford-ORB/NVDiffRecMC/1771591481.616601#stanford_orb#geometry_estimation_surface_normal_accuracy"
+    },
+    {
+      "evaluation_name": "Stanford-ORB",
+      "source_data": {
+        "dataset_name": "Stanford-ORB",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2310.16044"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Measures the accuracy of the final 3D mesh reconstruction using the bi-directional Chamfer Distance between the predicted mesh and the ground-truth scan. The score is reported multiplied by 1000. Lower values indicate a more accurate 3D shape.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Shape Chamfer Distance (x10⁻³)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Geometry Estimation: 3D Shape Chamfer Distance"
+        },
+        "metric_id": "geometry_estimation_3d_shape_chamfer_distance",
+        "metric_name": "Geometry Estimation: 3D Shape Chamfer Distance",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 0.51
+      },
+      "evaluation_result_id": "Stanford-ORB/NVDiffRecMC/1771591481.616601#stanford_orb#geometry_estimation_3d_shape_chamfer_distance"
+    },
+    {
+      "evaluation_name": "Stanford-ORB",
+      "source_data": {
+        "dataset_name": "Stanford-ORB",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2310.16044"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": true,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Evaluates material recovery by rendering the object under novel lighting and measuring the Learned Perceptual Image Patch Similarity (LPIPS) between the rendered LDR image and the ground truth. Lower scores indicate higher perceptual similarity.",
+        "additional_details": {
+          "alphaxiv_y_axis": "LPIPS",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Novel Scene Relighting: Perceptual Similarity (LPIPS)"
+        },
+        "metric_id": "novel_scene_relighting_perceptual_similarity_lpips",
+        "metric_name": "Novel Scene Relighting: Perceptual Similarity (LPIPS)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 0.036
+      },
+      "evaluation_result_id": "Stanford-ORB/NVDiffRecMC/1771591481.616601#stanford_orb#novel_scene_relighting_perceptual_similarity_lpips"
+    },
+    {
+      "evaluation_name": "Stanford-ORB",
+      "source_data": {
+        "dataset_name": "Stanford-ORB",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2310.16044"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Evaluates material recovery by rendering the object under novel, unseen real-world lighting. This metric is the Peak Signal-to-Noise Ratio on LDR images (PSNR-L) after tone mapping, comparing the relit image to a ground-truth photo. Higher scores indicate more accurate material decomposition.",
+        "additional_details": {
+          "alphaxiv_y_axis": "PSNR-L",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Novel Scene Relighting: Image Quality (PSNR-L)"
+        },
+        "metric_id": "novel_scene_relighting_image_quality_psnr_l",
+        "metric_name": "Novel Scene Relighting: Image Quality (PSNR-L)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 31.6
+      },
+      "evaluation_result_id": "Stanford-ORB/NVDiffRecMC/1771591481.616601#stanford_orb#novel_scene_relighting_image_quality_psnr_l"
+    },
+    {
+      "evaluation_name": "Stanford-ORB",
+      "source_data": {
+        "dataset_name": "Stanford-ORB",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2310.16044"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Evaluates material recovery by rendering the object under novel lighting and measuring the Structural Similarity Index Measure (SSIM) between the rendered LDR image and the ground truth. Higher values (closer to 1) indicate better structural preservation.",
+        "additional_details": {
+          "alphaxiv_y_axis": "SSIM",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Novel Scene Relighting: Structural Similarity (SSIM)"
+        },
+        "metric_id": "novel_scene_relighting_structural_similarity_ssim",
+        "metric_name": "Novel Scene Relighting: Structural Similarity (SSIM)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 0.972
+      },
+      "evaluation_result_id": "Stanford-ORB/NVDiffRecMC/1771591481.616601#stanford_orb#novel_scene_relighting_structural_similarity_ssim"
+    },
+    {
+      "evaluation_name": "Stanford-ORB",
+      "source_data": {
+        "dataset_name": "Stanford-ORB",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2310.16044"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": true,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Evaluates appearance modeling by rendering novel viewpoints and measuring the Learned Perceptual Image Patch Similarity (LPIPS) on LDR images. Lower scores indicate higher perceptual similarity.",
+        "additional_details": {
+          "alphaxiv_y_axis": "LPIPS",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Novel View Synthesis: Perceptual Similarity (LPIPS)"
+        },
+        "metric_id": "novel_view_synthesis_perceptual_similarity_lpips",
+        "metric_name": "Novel View Synthesis: Perceptual Similarity (LPIPS)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 0.028
+      },
+      "evaluation_result_id": "Stanford-ORB/NVDiffRecMC/1771591481.616601#stanford_orb#novel_view_synthesis_perceptual_similarity_lpips"
+    },
+    {
+      "evaluation_name": "Stanford-ORB",
+      "source_data": {
+        "dataset_name": "Stanford-ORB",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2310.16044"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Evaluates appearance modeling by rendering novel, unobserved viewpoints of an object within the same scene it was trained on. This metric is the Peak Signal-to-Noise Ratio on HDR images (PSNR-H). Higher scores indicate more accurate appearance reconstruction.",
+        "additional_details": {
+          "alphaxiv_y_axis": "PSNR-H",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Novel View Synthesis: Image Quality (PSNR-H)"
+        },
+        "metric_id": "novel_view_synthesis_image_quality_psnr_h",
+        "metric_name": "Novel View Synthesis: Image Quality (PSNR-H)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 28.03
+      },
+      "evaluation_result_id": "Stanford-ORB/NVDiffRecMC/1771591481.616601#stanford_orb#novel_view_synthesis_image_quality_psnr_h"
+    },
+    {
+      "evaluation_name": "Stanford-ORB",
+      "source_data": {
+        "dataset_name": "Stanford-ORB",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2310.16044"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Evaluates appearance modeling by rendering novel viewpoints within the same scene. This metric is the Peak Signal-to-Noise Ratio on LDR images (PSNR-L) after tone mapping. Higher scores indicate more accurate appearance reconstruction.",
+        "additional_details": {
+          "alphaxiv_y_axis": "PSNR-L",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Novel View Synthesis: Image Quality (PSNR-L)"
+        },
+        "metric_id": "novel_view_synthesis_image_quality_psnr_l",
+        "metric_name": "Novel View Synthesis: Image Quality (PSNR-L)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 36.4
+      },
+      "evaluation_result_id": "Stanford-ORB/NVDiffRecMC/1771591481.616601#stanford_orb#novel_view_synthesis_image_quality_psnr_l"
+    },
+    {
+      "evaluation_name": "Stanford-ORB",
+      "source_data": {
+        "dataset_name": "Stanford-ORB",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2310.16044"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": true,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Measures the accuracy of reconstructed 3D object geometry by comparing the predicted depth maps to ground-truth scans. The metric is Scale-Invariant Mean Squared Error (SI-MSE), reported multiplied by 1000. Lower values indicate better geometric accuracy.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Depth Error (x10⁻³)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Geometry Estimation: Scale-Invariant Depth Error"
+        },
+        "metric_id": "geometry_estimation_scale_invariant_depth_error",
+        "metric_name": "Geometry Estimation: Scale-Invariant Depth Error",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 0.32
+      },
+      "evaluation_result_id": "Stanford-ORB/NVDiffRecMC/1771591481.616601#stanford_orb#geometry_estimation_scale_invariant_depth_error"
+    },
+    {
+      "evaluation_name": "Stanford-ORB",
+      "source_data": {
+        "dataset_name": "Stanford-ORB",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2310.16044"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Evaluates appearance modeling by rendering novel viewpoints and measuring the Structural Similarity Index Measure (SSIM) on LDR images. Higher values (closer to 1) indicate better structural preservation.",
+        "additional_details": {
+          "alphaxiv_y_axis": "SSIM",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Novel View Synthesis: Structural Similarity (SSIM)"
+        },
+        "metric_id": "novel_view_synthesis_structural_similarity_ssim",
+        "metric_name": "Novel View Synthesis: Structural Similarity (SSIM)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 0.982
+      },
+      "evaluation_result_id": "Stanford-ORB/NVDiffRecMC/1771591481.616601#stanford_orb#novel_view_synthesis_structural_similarity_ssim"
+    }
+  ],
+  "eval_library": {
+    "name": "alphaxiv",
+    "version": "unknown"
+  }
+}

flat/objects/05/0d/050d81f1-b04c-41fe-b7c7-dc3909da6efe.json

@@ -0,0 +1,224 @@
+{
+  "schema_version": "0.2.2",
+  "retrieved_timestamp": "1777540249.8057818",
+  "source_metadata": {
+    "source_type": "evaluation_run",
+    "source_organization_name": "Writer, Inc.",
+    "evaluator_relationship": "third_party",
+    "source_name": "wasp (Writer's Assessor of System Performance)",
+    "additional_details": {}
+  },
+  "model_info": {
+    "name": "MiniMaxAI/MiniMax-M2.1",
+    "id": "MiniMaxAI/MiniMax-M2.1",
+    "developer": "MiniMax",
+    "inference_platform": "sglang",
+    "inference_engine": {
+      "name": "sglang"
+    },
+    "additional_details": {
+      "wasp_model_name": "minimax-m2.1-sglang",
+      "served_model": "sglang/MiniMaxAI/MiniMax-M2.1"
+    }
+  },
+  "eval_library": {
+    "name": "wasp",
+    "version": "0.3.0",
+    "additional_details": {}
+  },
+  "evaluation_results": [
+    {
+      "evaluation_name": "Factuality",
+      "source_data": {
+        "dataset_name": "RewardBench 2",
+        "source_type": "hf_dataset",
+        "hf_repo": "allenai/reward-bench-2",
+        "hf_split": "test",
+        "additional_details": {}
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "evaluation_description": "Factuality accuracy on RewardBench 2",
+        "metric_id": "accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion",
+        "metric_parameters": {},
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "additional_details": {}
+      },
+      "score_details": {
+        "score": 0.6589473684210526,
+        "details": {}
+      },
+      "evaluation_result_id": "reward-bench-2/MiniMaxAI_MiniMax-M2.1/1777540249.8057818#factuality#accuracy",
+      "evaluation_timestamp": "2026-02-18T09:24:30Z",
+      "generation_config": {
+        "generation_args": {
+          "temperature": 1.0,
+          "top_p": 0.95,
+          "top_k": 40
+        },
+        "additional_details": {}
+      }
+    },
+    {
+      "evaluation_name": "Precise IF",
+      "source_data": {
+        "dataset_name": "RewardBench 2",
+        "source_type": "hf_dataset",
+        "hf_repo": "allenai/reward-bench-2",
+        "hf_split": "test",
+        "additional_details": {}
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "evaluation_description": "Precise instruction-following accuracy on RewardBench 2",
+        "metric_id": "accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion",
+        "metric_parameters": {},
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "additional_details": {}
+      },
+      "score_details": {
+        "score": 0.478125,
+        "details": {}
+      },
+      "evaluation_result_id": "reward-bench-2/MiniMaxAI_MiniMax-M2.1/1777540249.8057818#precise_if#accuracy",
+      "evaluation_timestamp": "2026-02-18T09:24:37Z",
+      "generation_config": {
+        "generation_args": {
+          "temperature": 1.0,
+          "top_p": 0.95,
+          "top_k": 40
+        },
+        "additional_details": {}
+      }
+    },
+    {
+      "evaluation_name": "Math",
+      "source_data": {
+        "dataset_name": "RewardBench 2",
+        "source_type": "hf_dataset",
+        "hf_repo": "allenai/reward-bench-2",
+        "hf_split": "test",
+        "additional_details": {}
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "evaluation_description": "Math accuracy on RewardBench 2",
+        "metric_id": "accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion",
+        "metric_parameters": {},
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "additional_details": {}
+      },
+      "score_details": {
+        "score": 0.7773224043715847,
+        "details": {}
+      },
+      "evaluation_result_id": "reward-bench-2/MiniMaxAI_MiniMax-M2.1/1777540249.8057818#math#accuracy",
+      "evaluation_timestamp": "2026-02-17T20:01:15Z",
+      "generation_config": {
+        "generation_args": {
+          "temperature": 1.0,
+          "top_p": 0.95,
+          "top_k": 40
+        },
+        "additional_details": {}
+      }
+    },
+    {
+      "evaluation_name": "Safety",
+      "source_data": {
+        "dataset_name": "RewardBench 2",
+        "source_type": "hf_dataset",
+        "hf_repo": "allenai/reward-bench-2",
+        "hf_split": "test",
+        "additional_details": {}
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "evaluation_description": "Safety accuracy on RewardBench 2",
+        "metric_id": "accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion",
+        "metric_parameters": {},
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "additional_details": {}
+      },
+      "score_details": {
+        "score": 0.8622222222222222,
+        "details": {}
+      },
+      "evaluation_result_id": "reward-bench-2/MiniMaxAI_MiniMax-M2.1/1777540249.8057818#safety#accuracy",
+      "evaluation_timestamp": "2026-02-17T20:29:38Z",
+      "generation_config": {
+        "generation_args": {
+          "temperature": 1.0,
+          "top_p": 0.95,
+          "top_k": 40
+        },
+        "additional_details": {}
+      }
+    },
+    {
+      "evaluation_name": "Focus",
+      "source_data": {
+        "dataset_name": "RewardBench 2",
+        "source_type": "hf_dataset",
+        "hf_repo": "allenai/reward-bench-2",
+        "hf_split": "test",
+        "additional_details": {}
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "evaluation_description": "Focus accuracy on RewardBench 2",
+        "metric_id": "accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion",
+        "metric_parameters": {},
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "additional_details": {}
+      },
+      "score_details": {
+        "score": 0.8883838383838384,
+        "details": {}
+      },
+      "evaluation_result_id": "reward-bench-2/MiniMaxAI_MiniMax-M2.1/1777540249.8057818#focus#accuracy",
+      "evaluation_timestamp": "2026-02-17T20:37:53Z",
+      "generation_config": {
+        "generation_args": {
+          "temperature": 1.0,
+          "top_p": 0.95,
+          "top_k": 40
+        },
+        "additional_details": {}
+      }
+    }
+  ],
+  "evaluation_timestamp": "2026-02-18T09:24:37Z",
+  "detailed_evaluation_results": {
+    "format": "jsonl",
+    "file_path": "050d81f1-b04c-41fe-b7c7-dc3909da6efe_samples.jsonl",
+    "total_rows": 1763,
+    "additional_details": {}
+  },
+  "evaluation_id": "reward-bench-2/MiniMaxAI_MiniMax-M2.1/1777540249.8057818"
+}

flat/objects/05/0e/050e8e0a-536c-4a77-829a-75254cbc3035.json

@@ -0,0 +1,88 @@
+{
+  "schema_version": "0.2.2",
+  "evaluation_id": "Argus Inspection/Doubao-1.5-Vision-Pro-250328/1771591481.616601",
+  "retrieved_timestamp": "1771591481.616601",
+  "source_metadata": {
+    "source_name": "alphaXiv State of the Art",
+    "source_type": "documentation",
+    "source_organization_name": "alphaXiv",
+    "source_organization_url": "https://alphaxiv.org",
+    "evaluator_relationship": "third_party",
+    "additional_details": {
+      "alphaxiv_dataset_org": "Shanghai Artificial Intelligence Laboratory",
+      "alphaxiv_dataset_type": "image",
+      "scrape_source": "https://github.com/alphaXiv/feedback/issues/189"
+    }
+  },
+  "model_info": {
+    "id": "Doubao-1.5-Vision-Pro-250328",
+    "name": "Doubao-1.5-Vision-Pro-250328",
+    "developer": "unknown"
+  },
+  "evaluation_results": [
+    {
+      "evaluation_name": "Argus Inspection",
+      "source_data": {
+        "dataset_name": "Argus Inspection",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2506.14805"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Overall performance score on the Argus Inspection benchmark's basic level. This benchmark evaluates the visual fine-grained perception and commonsense causal inference of Multimodal Large Language Models (MLLMs). The score is calculated using the 'Eye of Panoptes' framework, which combines a model's ability to identify visual 'trap elements' with its capacity to reason about the correct action. Scores range from approximately 0 to 1.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Overall Score (Basic)",
+          "alphaxiv_is_primary": "True",
+          "raw_evaluation_name": "Argus Inspection: Overall Performance (Basic Level)"
+        },
+        "metric_id": "argus_inspection_overall_performance_basic_level",
+        "metric_name": "Argus Inspection: Overall Performance (Basic Level)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 0.365223
+      },
+      "evaluation_result_id": "Argus Inspection/Doubao-1.5-Vision-Pro-250328/1771591481.616601#argus_inspection#argus_inspection_overall_performance_basic_level"
+    },
+    {
+      "evaluation_name": "Argus Inspection",
+      "source_data": {
+        "dataset_name": "Argus Inspection",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2506.14805"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Overall performance score on the Argus Inspection benchmark's deceptive level. This level introduces moral or emotional disturbances in the textual prompt to challenge the MLLM's cross-modal reasoning by attempting to distract from visual traps. The score is calculated using the 'Eye of Panoptes' framework. Scores range from approximately 0 to 1.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Overall Score (Deceptive)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Argus Inspection: Overall Performance (Deceptive Level)"
+        },
+        "metric_id": "argus_inspection_overall_performance_deceptive_level",
+        "metric_name": "Argus Inspection: Overall Performance (Deceptive Level)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 0.284327
+      },
+      "evaluation_result_id": "Argus Inspection/Doubao-1.5-Vision-Pro-250328/1771591481.616601#argus_inspection#argus_inspection_overall_performance_deceptive_level"
+    }
+  ],
+  "eval_library": {
+    "name": "alphaxiv",
+    "version": "unknown"
+  }
+}

flat/objects/05/10/0510aba9-c171-4cac-be58-1ed539ba4a5d.json

@@ -0,0 +1,208 @@
+{
+  "schema_version": "0.2.2",
+  "evaluation_id": "EESE/GPT-4o/1771591481.616601",
+  "retrieved_timestamp": "1771591481.616601",
+  "source_metadata": {
+    "source_name": "alphaXiv State of the Art",
+    "source_type": "documentation",
+    "source_organization_name": "alphaXiv",
+    "source_organization_url": "https://alphaxiv.org",
+    "evaluator_relationship": "third_party",
+    "additional_details": {
+      "alphaxiv_dataset_org": "Shanghai Artificial Intelligence Laboratory",
+      "alphaxiv_dataset_type": "text",
+      "scrape_source": "https://github.com/alphaXiv/feedback/issues/189"
+    }
+  },
+  "model_info": {
+    "id": "GPT-4o",
+    "name": "GPT-4o",
+    "developer": "unknown"
+  },
+  "evaluation_results": [
+    {
+      "evaluation_name": "EESE",
+      "source_data": {
+        "dataset_name": "EESE",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2507.16514"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Measures model performance on the Engineering and Technological Sciences (ETS) subset of the EESE benchmark, which includes computer science, mechanical engineering, and materials science.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Accuracy on EESE - Engineering & Tech Sciences",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Accuracy on EESE: Engineering & Technological Sciences (ETS)"
+        },
+        "metric_id": "accuracy_on_eese_engineering_technological_sciences_ets",
+        "metric_name": "Accuracy on EESE: Engineering & Technological Sciences (ETS)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 0.2354
+      },
+      "evaluation_result_id": "EESE/GPT-4o/1771591481.616601#eese#accuracy_on_eese_engineering_technological_sciences_ets"
+    },
+    {
+      "evaluation_name": "EESE",
+      "source_data": {
+        "dataset_name": "EESE",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2507.16514"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Measures model performance on the Medical Sciences (MS) subset of the EESE benchmark, covering basic and clinical medicine, pharmacy, and public health.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Accuracy on EESE - Medical Sciences",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Accuracy on EESE: Medical Sciences (MS)"
+        },
+        "metric_id": "accuracy_on_eese_medical_sciences_ms",
+        "metric_name": "Accuracy on EESE: Medical Sciences (MS)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 0.3041
+      },
+      "evaluation_result_id": "EESE/GPT-4o/1771591481.616601#eese#accuracy_on_eese_medical_sciences_ms"
+    },
+    {
+      "evaluation_name": "EESE",
+      "source_data": {
+        "dataset_name": "EESE",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2507.16514"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Measures model performance on the Natural Sciences (NS) subset of the EESE benchmark, testing capabilities in physics, chemistry, biology, and earth science.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Accuracy on EESE - Natural Sciences",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Accuracy on EESE: Natural Sciences (NS)"
+        },
+        "metric_id": "accuracy_on_eese_natural_sciences_ns",
+        "metric_name": "Accuracy on EESE: Natural Sciences (NS)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 0.2216
+      },
+      "evaluation_result_id": "EESE/GPT-4o/1771591481.616601#eese#accuracy_on_eese_natural_sciences_ns"
+    },
+    {
+      "evaluation_name": "EESE",
+      "source_data": {
+        "dataset_name": "EESE",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2507.16514"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Measures model performance on the Social Sciences and Humanities (SSH) subset of the EESE benchmark, testing knowledge in fields like philosophy, law, and economics.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Accuracy on EESE - Social Sciences & Humanities",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Accuracy on EESE: Social Sciences & Humanities (SSH)"
+        },
+        "metric_id": "accuracy_on_eese_social_sciences_humanities_ssh",
+        "metric_name": "Accuracy on EESE: Social Sciences & Humanities (SSH)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 0.2029
+      },
+      "evaluation_result_id": "EESE/GPT-4o/1771591481.616601#eese#accuracy_on_eese_social_sciences_humanities_ssh"
+    },
+    {
+      "evaluation_name": "EESE",
+      "source_data": {
+        "dataset_name": "EESE",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2507.16514"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Measures model performance on the Agricultural Sciences (AS) subset of the EESE benchmark, which includes topics like agronomy, forestry, and veterinary science.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Accuracy on EESE - Agricultural Sciences",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Accuracy on EESE: Agricultural Sciences (AS)"
+        },
+        "metric_id": "accuracy_on_eese_agricultural_sciences_as",
+        "metric_name": "Accuracy on EESE: Agricultural Sciences (AS)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 0.2448
+      },
+      "evaluation_result_id": "EESE/GPT-4o/1771591481.616601#eese#accuracy_on_eese_agricultural_sciences_as"
+    },
+    {
+      "evaluation_name": "EESE",
+      "source_data": {
+        "dataset_name": "EESE",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2507.16514"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Overall accuracy scores from the original EESE paper, comparing 32 open- and closed-source models on their ability to answer scientific questions across five major disciplines.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Overall Accuracy on EESE (Paper)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Overall Accuracy on the EESE Benchmark (from Paper)"
+        },
+        "metric_id": "overall_accuracy_on_the_eese_benchmark_from_paper",
+        "metric_name": "Overall Accuracy on the EESE Benchmark (from Paper)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 0.2397
+      },
+      "evaluation_result_id": "EESE/GPT-4o/1771591481.616601#eese#overall_accuracy_on_the_eese_benchmark_from_paper"
+    }
+  ],
+  "eval_library": {
+    "name": "alphaxiv",
+    "version": "unknown"
+  }
+}

flat/objects/05/12/0512ac1a-a93f-489c-9f8e-3e6616505513.json

@@ -0,0 +1,88 @@
+{
+  "schema_version": "0.2.2",
+  "evaluation_id": "CodeEval-Pro/Yi-Coder-9B/1771591481.616601",
+  "retrieved_timestamp": "1771591481.616601",
+  "source_metadata": {
+    "source_name": "alphaXiv State of the Art",
+    "source_type": "documentation",
+    "source_organization_name": "alphaXiv",
+    "source_organization_url": "https://alphaxiv.org",
+    "evaluator_relationship": "third_party",
+    "additional_details": {
+      "alphaxiv_dataset_org": "Tsinghua University",
+      "alphaxiv_dataset_type": "text",
+      "scrape_source": "https://github.com/alphaXiv/feedback/issues/189"
+    }
+  },
+  "model_info": {
+    "id": "Yi-Coder-9B",
+    "name": "Yi-Coder-9B",
+    "developer": "unknown"
+  },
+  "evaluation_results": [
+    {
+      "evaluation_name": "CodeEval-Pro",
+      "source_data": {
+        "dataset_name": "CodeEval-Pro",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2412.21199"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Measures the pass@1 score on the BigCodeBench-Lite Pro benchmark using a 1-shot prompting strategy. This benchmark extends the self-invoking code generation task to a different set of problems, testing the generalizability of this reasoning capability.",
+        "additional_details": {
+          "alphaxiv_y_axis": "pass@1 (%)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Code Generation on BigCodeBench-Lite Pro (1-shot)"
+        },
+        "metric_id": "code_generation_on_bigcodebench_lite_pro_1_shot",
+        "metric_name": "Code Generation on BigCodeBench-Lite Pro (1-shot)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 57.9
+      },
+      "evaluation_result_id": "CodeEval-Pro/Yi-Coder-9B/1771591481.616601#codeeval_pro#code_generation_on_bigcodebench_lite_pro_1_shot"
+    },
+    {
+      "evaluation_name": "CodeEval-Pro",
+      "source_data": {
+        "dataset_name": "CodeEval-Pro",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2412.21199"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Measures the pass@1 score on the BigCodeBench-Lite Pro benchmark using a 0-shot prompting strategy. This benchmark extends the self-invoking code generation task to a different set of problems, testing the generalizability of this reasoning capability without providing examples.",
+        "additional_details": {
+          "alphaxiv_y_axis": "pass@1 (%)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Code Generation on BigCodeBench-Lite Pro (0-shot)"
+        },
+        "metric_id": "code_generation_on_bigcodebench_lite_pro_0_shot",
+        "metric_name": "Code Generation on BigCodeBench-Lite Pro (0-shot)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 21.1
+      },
+      "evaluation_result_id": "CodeEval-Pro/Yi-Coder-9B/1771591481.616601#codeeval_pro#code_generation_on_bigcodebench_lite_pro_0_shot"
+    }
+  ],
+  "eval_library": {
+    "name": "alphaxiv",
+    "version": "unknown"
+  }
+}

flat/objects/05/13/05134e61-64e4-43dc-9c0a-42c56d77dfd2.json

@@ -0,0 +1,88 @@
+{
+  "schema_version": "0.2.2",
+  "evaluation_id": "LiveCodeBench/anthropic/Claude-3.5-Sonnet-20241022/1771591481.616601",
+  "retrieved_timestamp": "1771591481.616601",
+  "source_metadata": {
+    "source_name": "alphaXiv State of the Art",
+    "source_type": "documentation",
+    "source_organization_name": "alphaXiv",
+    "source_organization_url": "https://alphaxiv.org",
+    "evaluator_relationship": "third_party",
+    "additional_details": {
+      "alphaxiv_dataset_org": "UC Berkeley",
+      "alphaxiv_dataset_type": "text",
+      "scrape_source": "https://github.com/alphaXiv/feedback/issues/189"
+    }
+  },
+  "model_info": {
+    "id": "anthropic/Claude-3.5-Sonnet-20241022",
+    "name": "Claude-3.5-Sonnet-20241022",
+    "developer": "anthropic"
+  },
+  "evaluation_results": [
+    {
+      "evaluation_name": "LiveCodeBench",
+      "source_data": {
+        "dataset_name": "LiveCodeBench",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2403.07974"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Measures the overall Pass@1 score for code generation on the LiveCodeBench. Pass@1 is the percentage of problems for which a model generates a correct solution on the first attempt. These results are from the live leaderboard, evaluating models on 454 problems released between August 2024 and May 2025, ensuring a contamination-free assessment of the latest models.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Pass@1 (%) - Live Leaderboard",
+          "alphaxiv_is_primary": "True",
+          "raw_evaluation_name": "LiveCodeBench: Overall Code Generation Performance (Live Leaderboard)"
+        },
+        "metric_id": "livecodebench_overall_code_generation_performance_live_leaderboard",
+        "metric_name": "LiveCodeBench: Overall Code Generation Performance (Live Leaderboard)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 36.4
+      },
+      "evaluation_result_id": "LiveCodeBench/anthropic/Claude-3.5-Sonnet-20241022/1771591481.616601#livecodebench#livecodebench_overall_code_generation_performance_live_leaderboard"
+    },
+    {
+      "evaluation_name": "LiveCodeBench",
+      "source_data": {
+        "dataset_name": "LiveCodeBench",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2403.07974"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Measures the Pass@1 score for code generation specifically on 'Hard' difficulty problems from the LiveCodeBench. This metric highlights model performance on the most challenging coding tasks. These results are from the live leaderboard (Aug 2024 - May 2025).",
+        "additional_details": {
+          "alphaxiv_y_axis": "Pass@1 (%) on Hard Problems - Live Leaderboard",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "LiveCodeBench: Performance on Hard Problems (Live Leaderboard)"
+        },
+        "metric_id": "livecodebench_performance_on_hard_problems_live_leaderboard",
+        "metric_name": "LiveCodeBench: Performance on Hard Problems (Live Leaderboard)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 8.2
+      },
+      "evaluation_result_id": "LiveCodeBench/anthropic/Claude-3.5-Sonnet-20241022/1771591481.616601#livecodebench#livecodebench_performance_on_hard_problems_live_leaderboard"
+    }
+  ],
+  "eval_library": {
+    "name": "alphaxiv",
+    "version": "unknown"
+  }
+}

flat/objects/05/13/0513bc6e-c83b-4a77-98a2-3d43a4a13963.json

@@ -0,0 +1,69 @@
+{
+  "schema_version": "0.2.2",
+  "evaluation_id": "fibble1_arena/meta-llama/llama-3/1773248706",
+  "retrieved_timestamp": "1773248706",
+  "source_metadata": {
+    "source_name": "Fibble Arena (1 lie)",
+    "source_type": "evaluation_run",
+    "source_organization_name": "Dr. Chang Liu's Lab",
+    "source_organization_url": "https://drchangliu.github.io/WordleArenas/",
+    "evaluator_relationship": "first_party"
+  },
+  "eval_library": {
+    "name": "wordle_arena",
+    "version": "1.0.0",
+    "additional_details": {
+      "arena_type": "fibble",
+      "num_lies": "1",
+      "max_guesses": "8"
+    }
+  },
+  "model_info": {
+    "name": "Llama 3",
+    "id": "meta-llama/llama-3",
+    "developer": "Meta",
+    "inference_platform": "ollama"
+  },
+  "evaluation_results": [
+    {
+      "evaluation_name": "fibble1_arena",
+      "evaluation_result_id": "fibble1_arena/meta-llama/llama-3/1773248706#fibble1_arena#fibble1_arena_win_rate",
+      "source_data": {
+        "dataset_name": "Fibble Arena (1 lie) Word Set",
+        "source_type": "url",
+        "url": [
+          "https://drchangliu.github.io/WordleArenas/"
+        ]
+      },
+      "metric_config": {
+        "evaluation_description": "Win rate on Fibble Arena (1 lie) puzzles (1 lies, 8 max guesses)",
+        "metric_id": "fibble1_arena.win_rate",
+        "metric_name": "Win Rate",
+        "metric_unit": "proportion",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "additional_details": {
+          "raw_evaluation_name": "fibble1_arena_win_rate"
+        },
+        "metric_kind": "win_rate"
+      },
+      "score_details": {
+        "score": 0.0,
+        "uncertainty": {
+          "num_samples": 11
+        },
+        "details": {
+          "games_played": "11",
+          "games_won": "0"
+        }
+      }
+    }
+  ],
+  "detailed_evaluation_results": {
+    "format": "jsonl",
+    "file_path": "0513bc6e-c83b-4a77-98a2-3d43a4a13963_samples.jsonl",
+    "total_rows": 11
+  }
+}

flat/objects/05/13/0513cfb5-dfc7-4a13-9046-5b0d210ef90c.json

@@ -0,0 +1,169 @@
+{
+  "schema_version": "0.2.2",
+  "evaluation_id": "hfopenllm_v2/jaspionjader_Auro-Kosmos-EVAA-v2-8B/1773936498.240187",
+  "retrieved_timestamp": "1773936498.240187",
+  "source_metadata": {
+    "source_name": "HF Open LLM v2",
+    "source_type": "documentation",
+    "source_organization_name": "Hugging Face",
+    "evaluator_relationship": "third_party"
+  },
+  "eval_library": {
+    "name": "lm-evaluation-harness",
+    "version": "0.4.0",
+    "additional_details": {
+      "fork": "https://github.com/huggingface/lm-evaluation-harness/tree/adding_all_changess"
+    }
+  },
+  "model_info": {
+    "name": "Auro-Kosmos-EVAA-v2-8B",
+    "id": "jaspionjader/Auro-Kosmos-EVAA-v2-8B",
+    "developer": "jaspionjader",
+    "inference_platform": "unknown",
+    "additional_details": {
+      "precision": "bfloat16",
+      "architecture": "LlamaForCausalLM",
+      "params_billions": "8.03"
+    }
+  },
+  "evaluation_results": [
+    {
+      "evaluation_name": "IFEval",
+      "source_data": {
+        "dataset_name": "IFEval",
+        "source_type": "hf_dataset",
+        "hf_repo": "google/IFEval"
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy on IFEval",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "metric_id": "accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion"
+      },
+      "score_details": {
+        "score": 0.4778
+      },
+      "evaluation_result_id": "hfopenllm_v2/jaspionjader_Auro-Kosmos-EVAA-v2-8B/1773936498.240187#ifeval#accuracy"
+    },
+    {
+      "evaluation_name": "BBH",
+      "source_data": {
+        "dataset_name": "BBH",
+        "source_type": "hf_dataset",
+        "hf_repo": "SaylorTwift/bbh"
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy on BBH",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "metric_id": "accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion"
+      },
+      "score_details": {
+        "score": 0.5447
+      },
+      "evaluation_result_id": "hfopenllm_v2/jaspionjader_Auro-Kosmos-EVAA-v2-8B/1773936498.240187#bbh#accuracy"
+    },
+    {
+      "evaluation_name": "MATH Level 5",
+      "source_data": {
+        "dataset_name": "MATH Level 5",
+        "source_type": "hf_dataset",
+        "hf_repo": "DigitalLearningGmbH/MATH-lighteval"
+      },
+      "metric_config": {
+        "evaluation_description": "Exact Match on MATH Level 5",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "metric_id": "exact_match",
+        "metric_name": "Exact Match",
+        "metric_kind": "exact_match",
+        "metric_unit": "proportion"
+      },
+      "score_details": {
+        "score": 0.1412
+      },
+      "evaluation_result_id": "hfopenllm_v2/jaspionjader_Auro-Kosmos-EVAA-v2-8B/1773936498.240187#math_level_5#exact_match"
+    },
+    {
+      "evaluation_name": "GPQA",
+      "source_data": {
+        "dataset_name": "GPQA",
+        "source_type": "hf_dataset",
+        "hf_repo": "Idavidrein/gpqa"
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy on GPQA",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "metric_id": "accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion"
+      },
+      "score_details": {
+        "score": 0.3154
+      },
+      "evaluation_result_id": "hfopenllm_v2/jaspionjader_Auro-Kosmos-EVAA-v2-8B/1773936498.240187#gpqa#accuracy"
+    },
+    {
+      "evaluation_name": "MUSR",
+      "source_data": {
+        "dataset_name": "MUSR",
+        "source_type": "hf_dataset",
+        "hf_repo": "TAUR-Lab/MuSR"
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy on MUSR",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "metric_id": "accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion"
+      },
+      "score_details": {
+        "score": 0.425
+      },
+      "evaluation_result_id": "hfopenllm_v2/jaspionjader_Auro-Kosmos-EVAA-v2-8B/1773936498.240187#musr#accuracy"
+    },
+    {
+      "evaluation_name": "MMLU-PRO",
+      "source_data": {
+        "dataset_name": "MMLU-PRO",
+        "source_type": "hf_dataset",
+        "hf_repo": "TIGER-Lab/MMLU-Pro"
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy on MMLU-PRO",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "metric_id": "accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion"
+      },
+      "score_details": {
+        "score": 0.3858
+      },
+      "evaluation_result_id": "hfopenllm_v2/jaspionjader_Auro-Kosmos-EVAA-v2-8B/1773936498.240187#mmlu_pro#accuracy"
+    }
+  ]
+}

flat/objects/05/15/05154b56-b016-488c-acb1-9cd91b38defe.json

@@ -0,0 +1,148 @@
+{
+  "schema_version": "0.2.2",
+  "evaluation_id": "XBOUND/OS-Atlas-4B-Pro/1771591481.616601",
+  "retrieved_timestamp": "1771591481.616601",
+  "source_metadata": {
+    "source_name": "alphaXiv State of the Art",
+    "source_type": "documentation",
+    "source_organization_name": "alphaXiv",
+    "source_organization_url": "https://alphaxiv.org",
+    "evaluator_relationship": "third_party",
+    "additional_details": {
+      "alphaxiv_dataset_org": "Shanghai Jiao Tong University",
+      "alphaxiv_dataset_type": "image",
+      "scrape_source": "https://github.com/alphaXiv/feedback/issues/189"
+    }
+  },
+  "model_info": {
+    "id": "OS-Atlas-4B-Pro",
+    "name": "OS-Atlas-4B-Pro",
+    "developer": "unknown"
+  },
+  "evaluation_results": [
+    {
+      "evaluation_name": "XBOUND",
+      "source_data": {
+        "dataset_name": "XBOUND",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2505.21279"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Measures a device-control agent's mastery of a UI state using the Exploration Metric (EM) on the Multi-Widget Action Matching (MWAM) task. MWAM evaluates the agent's ability to discriminate between multiple distinct instructions mapped to different UI widgets within the same screen. The EM is the paper's novel metric, averaging action accuracy across all instructions associated with a state. Higher is better.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Exploration Metric (MWAM, %)",
+          "alphaxiv_is_primary": "True",
+          "raw_evaluation_name": "XBOUND: Exploration Metric on Multi-Widget Action Matching (MWAM)"
+        },
+        "metric_id": "xbound_exploration_metric_on_multi_widget_action_matching_mwam",
+        "metric_name": "XBOUND: Exploration Metric on Multi-Widget Action Matching (MWAM)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 41.92
+      },
+      "evaluation_result_id": "XBOUND/OS-Atlas-4B-Pro/1771591481.616601#xbound#xbound_exploration_metric_on_multi_widget_action_matching_mwam"
+    },
+    {
+      "evaluation_name": "XBOUND",
+      "source_data": {
+        "dataset_name": "XBOUND",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2505.21279"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Measures a device-control agent's step-wise success rate (SR) on the Multi-Widget Action Matching (MWAM) task. MWAM evaluates the agent's ability to discriminate between multiple distinct instructions mapped to different UI widgets within the same screen. Higher is better.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Success Rate (MWAM, %)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "XBOUND: Success Rate on Multi-Widget Action Matching (MWAM)"
+        },
+        "metric_id": "xbound_success_rate_on_multi_widget_action_matching_mwam",
+        "metric_name": "XBOUND: Success Rate on Multi-Widget Action Matching (MWAM)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 42.82
+      },
+      "evaluation_result_id": "XBOUND/OS-Atlas-4B-Pro/1771591481.616601#xbound#xbound_success_rate_on_multi_widget_action_matching_mwam"
+    },
+    {
+      "evaluation_name": "XBOUND",
+      "source_data": {
+        "dataset_name": "XBOUND",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2505.21279"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Measures a device-control agent's mastery of a UI state using the Exploration Metric (EM) on the Uni-Widget Instruction Unification (UWIU) task. UWIU evaluates the agent's ability to unify semantically diverse instructions that should result in the same action on a single UI widget. The EM is the paper's novel metric, averaging action accuracy across all instructions associated with a state. Higher is better.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Exploration Metric (UWIU, %)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "XBOUND: Exploration Metric on Uni-Widget Instruction Unification (UWIU)"
+        },
+        "metric_id": "xbound_exploration_metric_on_uni_widget_instruction_unification_uwiu",
+        "metric_name": "XBOUND: Exploration Metric on Uni-Widget Instruction Unification (UWIU)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 31.8
+      },
+      "evaluation_result_id": "XBOUND/OS-Atlas-4B-Pro/1771591481.616601#xbound#xbound_exploration_metric_on_uni_widget_instruction_unification_uwiu"
+    },
+    {
+      "evaluation_name": "XBOUND",
+      "source_data": {
+        "dataset_name": "XBOUND",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2505.21279"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Measures a device-control agent's step-wise success rate (SR) on the Uni-Widget Instruction Unification (UWIU) task. UWIU evaluates the agent's ability to unify semantically diverse instructions that should result in the same action on a single UI widget. Higher is better.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Success Rate (UWIU, %)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "XBOUND: Success Rate on Uni-Widget Instruction Unification (UWIU)"
+        },
+        "metric_id": "xbound_success_rate_on_uni_widget_instruction_unification_uwiu",
+        "metric_name": "XBOUND: Success Rate on Uni-Widget Instruction Unification (UWIU)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 30.24
+      },
+      "evaluation_result_id": "XBOUND/OS-Atlas-4B-Pro/1771591481.616601#xbound#xbound_success_rate_on_uni_widget_instruction_unification_uwiu"
+    }
+  ],
+  "eval_library": {
+    "name": "alphaxiv",
+    "version": "unknown"
+  }
+}

flat/objects/05/17/05179ce0-dfc8-46a7-a206-4686cc835ce8.json

@@ -0,0 +1,169 @@
+{
+  "schema_version": "0.2.2",
+  "evaluation_id": "hfopenllm_v2/DreadPoor_Incidental-8B-Model_Stock/1773936498.240187",
+  "retrieved_timestamp": "1773936498.240187",
+  "source_metadata": {
+    "source_name": "HF Open LLM v2",
+    "source_type": "documentation",
+    "source_organization_name": "Hugging Face",
+    "evaluator_relationship": "third_party"
+  },
+  "eval_library": {
+    "name": "lm-evaluation-harness",
+    "version": "0.4.0",
+    "additional_details": {
+      "fork": "https://github.com/huggingface/lm-evaluation-harness/tree/adding_all_changess"
+    }
+  },
+  "model_info": {
+    "name": "Incidental-8B-Model_Stock",
+    "id": "DreadPoor/Incidental-8B-Model_Stock",
+    "developer": "DreadPoor",
+    "inference_platform": "unknown",
+    "additional_details": {
+      "precision": "bfloat16",
+      "architecture": "LlamaForCausalLM",
+      "params_billions": "8.03"
+    }
+  },
+  "evaluation_results": [
+    {
+      "evaluation_name": "IFEval",
+      "source_data": {
+        "dataset_name": "IFEval",
+        "source_type": "hf_dataset",
+        "hf_repo": "google/IFEval"
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy on IFEval",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "metric_id": "accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion"
+      },
+      "score_details": {
+        "score": 0.7482
+      },
+      "evaluation_result_id": "hfopenllm_v2/DreadPoor_Incidental-8B-Model_Stock/1773936498.240187#ifeval#accuracy"
+    },
+    {
+      "evaluation_name": "BBH",
+      "source_data": {
+        "dataset_name": "BBH",
+        "source_type": "hf_dataset",
+        "hf_repo": "SaylorTwift/bbh"
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy on BBH",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "metric_id": "accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion"
+      },
+      "score_details": {
+        "score": 0.5452
+      },
+      "evaluation_result_id": "hfopenllm_v2/DreadPoor_Incidental-8B-Model_Stock/1773936498.240187#bbh#accuracy"
+    },
+    {
+      "evaluation_name": "MATH Level 5",
+      "source_data": {
+        "dataset_name": "MATH Level 5",
+        "source_type": "hf_dataset",
+        "hf_repo": "DigitalLearningGmbH/MATH-lighteval"
+      },
+      "metric_config": {
+        "evaluation_description": "Exact Match on MATH Level 5",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "metric_id": "exact_match",
+        "metric_name": "Exact Match",
+        "metric_kind": "exact_match",
+        "metric_unit": "proportion"
+      },
+      "score_details": {
+        "score": 0.1616
+      },
+      "evaluation_result_id": "hfopenllm_v2/DreadPoor_Incidental-8B-Model_Stock/1773936498.240187#math_level_5#exact_match"
+    },
+    {
+      "evaluation_name": "GPQA",
+      "source_data": {
+        "dataset_name": "GPQA",
+        "source_type": "hf_dataset",
+        "hf_repo": "Idavidrein/gpqa"
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy on GPQA",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "metric_id": "accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion"
+      },
+      "score_details": {
+        "score": 0.3029
+      },
+      "evaluation_result_id": "hfopenllm_v2/DreadPoor_Incidental-8B-Model_Stock/1773936498.240187#gpqa#accuracy"
+    },
+    {
+      "evaluation_name": "MUSR",
+      "source_data": {
+        "dataset_name": "MUSR",
+        "source_type": "hf_dataset",
+        "hf_repo": "TAUR-Lab/MuSR"
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy on MUSR",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "metric_id": "accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion"
+      },
+      "score_details": {
+        "score": 0.424
+      },
+      "evaluation_result_id": "hfopenllm_v2/DreadPoor_Incidental-8B-Model_Stock/1773936498.240187#musr#accuracy"
+    },
+    {
+      "evaluation_name": "MMLU-PRO",
+      "source_data": {
+        "dataset_name": "MMLU-PRO",
+        "source_type": "hf_dataset",
+        "hf_repo": "TIGER-Lab/MMLU-Pro"
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy on MMLU-PRO",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "metric_id": "accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion"
+      },
+      "score_details": {
+        "score": 0.3873
+      },
+      "evaluation_result_id": "hfopenllm_v2/DreadPoor_Incidental-8B-Model_Stock/1773936498.240187#mmlu_pro#accuracy"
+    }
+  ]
+}

flat/objects/05/19/0519a39e-db9b-4559-a727-8c6264a5956b.json

@@ -0,0 +1,169 @@
+{
+  "schema_version": "0.2.2",
+  "evaluation_id": "hfopenllm_v2/lesubra_ECE-PRYMMAL-3B-SLERP_2-V2/1773936498.240187",
+  "retrieved_timestamp": "1773936498.240187",
+  "source_metadata": {
+    "source_name": "HF Open LLM v2",
+    "source_type": "documentation",
+    "source_organization_name": "Hugging Face",
+    "evaluator_relationship": "third_party"
+  },
+  "eval_library": {
+    "name": "lm-evaluation-harness",
+    "version": "0.4.0",
+    "additional_details": {
+      "fork": "https://github.com/huggingface/lm-evaluation-harness/tree/adding_all_changess"
+    }
+  },
+  "model_info": {
+    "name": "ECE-PRYMMAL-3B-SLERP_2-V2",
+    "id": "lesubra/ECE-PRYMMAL-3B-SLERP_2-V2",
+    "developer": "lesubra",
+    "inference_platform": "unknown",
+    "additional_details": {
+      "precision": "float16",
+      "architecture": "Phi3ForCausalLM",
+      "params_billions": "3.821"
+    }
+  },
+  "evaluation_results": [
+    {
+      "evaluation_name": "IFEval",
+      "source_data": {
+        "dataset_name": "IFEval",
+        "source_type": "hf_dataset",
+        "hf_repo": "google/IFEval"
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy on IFEval",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "metric_id": "accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion"
+      },
+      "score_details": {
+        "score": 0.3664
+      },
+      "evaluation_result_id": "hfopenllm_v2/lesubra_ECE-PRYMMAL-3B-SLERP_2-V2/1773936498.240187#ifeval#accuracy"
+    },
+    {
+      "evaluation_name": "BBH",
+      "source_data": {
+        "dataset_name": "BBH",
+        "source_type": "hf_dataset",
+        "hf_repo": "SaylorTwift/bbh"
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy on BBH",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "metric_id": "accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion"
+      },
+      "score_details": {
+        "score": 0.5411
+      },
+      "evaluation_result_id": "hfopenllm_v2/lesubra_ECE-PRYMMAL-3B-SLERP_2-V2/1773936498.240187#bbh#accuracy"
+    },
+    {
+      "evaluation_name": "MATH Level 5",
+      "source_data": {
+        "dataset_name": "MATH Level 5",
+        "source_type": "hf_dataset",
+        "hf_repo": "DigitalLearningGmbH/MATH-lighteval"
+      },
+      "metric_config": {
+        "evaluation_description": "Exact Match on MATH Level 5",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "metric_id": "exact_match",
+        "metric_name": "Exact Match",
+        "metric_kind": "exact_match",
+        "metric_unit": "proportion"
+      },
+      "score_details": {
+        "score": 0.1677
+      },
+      "evaluation_result_id": "hfopenllm_v2/lesubra_ECE-PRYMMAL-3B-SLERP_2-V2/1773936498.240187#math_level_5#exact_match"
+    },
+    {
+      "evaluation_name": "GPQA",
+      "source_data": {
+        "dataset_name": "GPQA",
+        "source_type": "hf_dataset",
+        "hf_repo": "Idavidrein/gpqa"
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy on GPQA",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "metric_id": "accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion"
+      },
+      "score_details": {
+        "score": 0.3213
+      },
+      "evaluation_result_id": "hfopenllm_v2/lesubra_ECE-PRYMMAL-3B-SLERP_2-V2/1773936498.240187#gpqa#accuracy"
+    },
+    {
+      "evaluation_name": "MUSR",
+      "source_data": {
+        "dataset_name": "MUSR",
+        "source_type": "hf_dataset",
+        "hf_repo": "TAUR-Lab/MuSR"
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy on MUSR",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "metric_id": "accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion"
+      },
+      "score_details": {
+        "score": 0.4661
+      },
+      "evaluation_result_id": "hfopenllm_v2/lesubra_ECE-PRYMMAL-3B-SLERP_2-V2/1773936498.240187#musr#accuracy"
+    },
+    {
+      "evaluation_name": "MMLU-PRO",
+      "source_data": {
+        "dataset_name": "MMLU-PRO",
+        "source_type": "hf_dataset",
+        "hf_repo": "TIGER-Lab/MMLU-Pro"
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy on MMLU-PRO",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "metric_id": "accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion"
+      },
+      "score_details": {
+        "score": 0.399
+      },
+      "evaluation_result_id": "hfopenllm_v2/lesubra_ECE-PRYMMAL-3B-SLERP_2-V2/1773936498.240187#mmlu_pro#accuracy"
+    }
+  ]
+}

flat/objects/05/19/0519d9fb-f220-40ab-8257-f20ed98a8b47.json

@@ -0,0 +1,171 @@
+{
+  "schema_version": "0.2.2",
+  "eval_library": {
+    "name": "rewardbench",
+    "version": "0.1.3",
+    "additional_details": {
+      "subsets": "Chat, Chat Hard, Safety, Reasoning",
+      "hf_space": "allenai/reward-bench"
+    }
+  },
+  "evaluation_id": "reward-bench/allenai_OLMo-7B-Instruct/1766412838.146816",
+  "retrieved_timestamp": "1766412838.146816",
+  "source_metadata": {
+    "source_name": "RewardBench",
+    "source_type": "documentation",
+    "source_organization_name": "Allen Institute for AI",
+    "source_organization_url": "https://allenai.org",
+    "evaluator_relationship": "third_party"
+  },
+  "model_info": {
+    "name": "allenai/OLMo-7B-Instruct",
+    "id": "allenai/OLMo-7B-Instruct",
+    "developer": "allenai",
+    "additional_details": {
+      "model_type": "DPO"
+    }
+  },
+  "evaluation_results": [
+    {
+      "evaluation_name": "RewardBench",
+      "metric_config": {
+        "evaluation_description": "Overall RewardBench Score",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "additional_details": {
+          "raw_evaluation_name": "Score"
+        },
+        "metric_id": "rewardbench.score",
+        "metric_name": "Score",
+        "metric_kind": "score",
+        "metric_unit": "proportion"
+      },
+      "score_details": {
+        "score": 0.6727
+      },
+      "source_data": {
+        "dataset_name": "RewardBench",
+        "source_type": "hf_dataset",
+        "hf_repo": "allenai/reward-bench"
+      },
+      "evaluation_result_id": "reward-bench/allenai_OLMo-7B-Instruct/1766412838.146816#rewardbench#rewardbench_score"
+    },
+    {
+      "evaluation_name": "Chat",
+      "metric_config": {
+        "evaluation_description": "Chat accuracy - includes easy chat subsets",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "metric_id": "accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion"
+      },
+      "score_details": {
+        "score": 0.8966
+      },
+      "source_data": {
+        "dataset_name": "RewardBench",
+        "source_type": "hf_dataset",
+        "hf_repo": "allenai/reward-bench"
+      },
+      "evaluation_result_id": "reward-bench/allenai_OLMo-7B-Instruct/1766412838.146816#chat#accuracy"
+    },
+    {
+      "evaluation_name": "Chat Hard",
+      "metric_config": {
+        "evaluation_description": "Chat Hard accuracy - includes hard chat subsets",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "metric_id": "accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion"
+      },
+      "score_details": {
+        "score": 0.5066
+      },
+      "source_data": {
+        "dataset_name": "RewardBench",
+        "source_type": "hf_dataset",
+        "hf_repo": "allenai/reward-bench"
+      },
+      "evaluation_result_id": "reward-bench/allenai_OLMo-7B-Instruct/1766412838.146816#chat_hard#accuracy"
+    },
+    {
+      "evaluation_name": "Safety",
+      "metric_config": {
+        "evaluation_description": "Safety accuracy - includes safety subsets",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "metric_id": "accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion"
+      },
+      "score_details": {
+        "score": 0.6486
+      },
+      "source_data": {
+        "dataset_name": "RewardBench",
+        "source_type": "hf_dataset",
+        "hf_repo": "allenai/reward-bench"
+      },
+      "evaluation_result_id": "reward-bench/allenai_OLMo-7B-Instruct/1766412838.146816#safety#accuracy"
+    },
+    {
+      "evaluation_name": "Reasoning",
+      "metric_config": {
+        "evaluation_description": "Reasoning accuracy - includes code and math subsets",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "metric_id": "accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion"
+      },
+      "score_details": {
+        "score": 0.7168
+      },
+      "source_data": {
+        "dataset_name": "RewardBench",
+        "source_type": "hf_dataset",
+        "hf_repo": "allenai/reward-bench"
+      },
+      "evaluation_result_id": "reward-bench/allenai_OLMo-7B-Instruct/1766412838.146816#reasoning#accuracy"
+    },
+    {
+      "evaluation_name": "Prior Sets (0.5 weight)",
+      "metric_config": {
+        "evaluation_description": "Prior Sets score (weighted 0.5) - includes test sets",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "metric_id": "score",
+        "metric_name": "Score",
+        "metric_kind": "score",
+        "metric_unit": "proportion"
+      },
+      "score_details": {
+        "score": 0.5173
+      },
+      "source_data": {
+        "dataset_name": "RewardBench",
+        "source_type": "hf_dataset",
+        "hf_repo": "allenai/reward-bench"
+      },
+      "evaluation_result_id": "reward-bench/allenai_OLMo-7B-Instruct/1766412838.146816#prior_sets_0_5_weight#score"
+    }
+  ]
+}

flat/objects/05/1b/051b5e13-4e57-4fc5-8bb9-60de091c2948.json

@@ -0,0 +1,358 @@
+{
+  "schema_version": "0.2.2",
+  "evaluation_id": "SKA-Bench/DeepSeek-v3/1771591481.616601",
+  "retrieved_timestamp": "1771591481.616601",
+  "source_metadata": {
+    "source_name": "alphaXiv State of the Art",
+    "source_type": "documentation",
+    "source_organization_name": "alphaXiv",
+    "source_organization_url": "https://alphaxiv.org",
+    "evaluator_relationship": "third_party",
+    "additional_details": {
+      "alphaxiv_dataset_org": "Ant Group",
+      "alphaxiv_dataset_type": "text",
+      "scrape_source": "https://github.com/alphaXiv/feedback/issues/189"
+    }
+  },
+  "model_info": {
+    "id": "DeepSeek-v3",
+    "name": "DeepSeek-v3",
+    "developer": "unknown"
+  },
+  "evaluation_results": [
+    {
+      "evaluation_name": "SKA-Bench",
+      "source_data": {
+        "dataset_name": "SKA-Bench",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2507.17178"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": true,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Average rejection rate across four subsets (KG, Table, KG+Text, Table+Text) of the SKA-Bench. This testbed evaluates an LLM's ability to refuse to answer when the provided knowledge base contains only irrelevant, noisy information. A higher score indicates a lower tendency to hallucinate.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Rejection Rate (%)",
+          "alphaxiv_is_primary": "True",
+          "raw_evaluation_name": "SKA-Bench: Average Negative Rejection Rate"
+        },
+        "metric_id": "ska_bench_average_negative_rejection_rate",
+        "metric_name": "SKA-Bench: Average Negative Rejection Rate",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 69.26
+      },
+      "evaluation_result_id": "SKA-Bench/DeepSeek-v3/1771591481.616601#ska_bench#ska_bench_average_negative_rejection_rate"
+    },
+    {
+      "evaluation_name": "SKA-Bench",
+      "source_data": {
+        "dataset_name": "SKA-Bench",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2507.17178"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": true,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Rejection rate on the SKA-Bench-KG subset, evaluating an LLM's ability to refuse to answer when the provided knowledge graph contains only irrelevant, noisy information. A higher score indicates a lower tendency to hallucinate.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Rejection Rate (%)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "SKA-Bench: Negative Rejection Rate (KG)"
+        },
+        "metric_id": "ska_bench_negative_rejection_rate_kg",
+        "metric_name": "SKA-Bench: Negative Rejection Rate (KG)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 78.54
+      },
+      "evaluation_result_id": "SKA-Bench/DeepSeek-v3/1771591481.616601#ska_bench#ska_bench_negative_rejection_rate_kg"
+    },
+    {
+      "evaluation_name": "SKA-Bench",
+      "source_data": {
+        "dataset_name": "SKA-Bench",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2507.17178"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": true,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Rejection rate on the SKA-Bench-Table subset, evaluating an LLM's ability to refuse to answer when the provided table contains only irrelevant, noisy information. A higher score indicates a lower tendency to hallucinate.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Rejection Rate (%)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "SKA-Bench: Negative Rejection Rate (Table)"
+        },
+        "metric_id": "ska_bench_negative_rejection_rate_table",
+        "metric_name": "SKA-Bench: Negative Rejection Rate (Table)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 69.83
+      },
+      "evaluation_result_id": "SKA-Bench/DeepSeek-v3/1771591481.616601#ska_bench#ska_bench_negative_rejection_rate_table"
+    },
+    {
+      "evaluation_name": "SKA-Bench",
+      "source_data": {
+        "dataset_name": "SKA-Bench",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2507.17178"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Appendix result showing Macro-F1 score on the KG subset with 'Mixed Noise', where noisy knowledge units have diverse semantic similarity to the question. This corresponds to the main noise robustness experiment setting but is presented for a subset of models.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Macro-F1 Score",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "SKA-Bench Appendix: Performance on Mixed Noise (KG)"
+        },
+        "metric_id": "ska_bench_appendix_performance_on_mixed_noise_kg",
+        "metric_name": "SKA-Bench Appendix: Performance on Mixed Noise (KG)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 73.93
+      },
+      "evaluation_result_id": "SKA-Bench/DeepSeek-v3/1771591481.616601#ska_bench#ska_bench_appendix_performance_on_mixed_noise_kg"
+    },
+    {
+      "evaluation_name": "SKA-Bench",
+      "source_data": {
+        "dataset_name": "SKA-Bench",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2507.17178"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Appendix result showing Macro-F1 score on the KG+Text subset with 'Mixed Noise', where noisy knowledge units have diverse semantic similarity to the question. This corresponds to the main noise robustness experiment setting but is presented for a subset of models.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Macro-F1 Score",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "SKA-Bench Appendix: Performance on Mixed Noise (KG+Text)"
+        },
+        "metric_id": "ska_bench_appendix_performance_on_mixed_noise_kg_text",
+        "metric_name": "SKA-Bench Appendix: Performance on Mixed Noise (KG+Text)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 77.12
+      },
+      "evaluation_result_id": "SKA-Bench/DeepSeek-v3/1771591481.616601#ska_bench#ska_bench_appendix_performance_on_mixed_noise_kg_text"
+    },
+    {
+      "evaluation_name": "SKA-Bench",
+      "source_data": {
+        "dataset_name": "SKA-Bench",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2507.17178"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Macro-F1 score on the SKA-Bench-KG subset with a context length of 4k tokens. This test evaluates the model's ability to answer questions accurately when the knowledge base contains a high proportion of noisy, irrelevant knowledge graph triples.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Macro-F1 Score",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "SKA-Bench: Noise Robustness on Knowledge Graphs (4k context)"
+        },
+        "metric_id": "ska_bench_noise_robustness_on_knowledge_graphs_4k_context",
+        "metric_name": "SKA-Bench: Noise Robustness on Knowledge Graphs (4k context)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 73.93
+      },
+      "evaluation_result_id": "SKA-Bench/DeepSeek-v3/1771591481.616601#ska_bench#ska_bench_noise_robustness_on_knowledge_graphs_4k_context"
+    },
+    {
+      "evaluation_name": "SKA-Bench",
+      "source_data": {
+        "dataset_name": "SKA-Bench",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2507.17178"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Macro-F1 score on the SKA-Bench-KG+Text subset with a context length of 12k tokens. This test evaluates the model's ability to answer questions using a hybrid knowledge base of knowledge graph triples and text paragraphs containing significant noise.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Macro-F1 Score",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "SKA-Bench: Noise Robustness on KG+Text (12k context)"
+        },
+        "metric_id": "ska_bench_noise_robustness_on_kg_text_12k_context",
+        "metric_name": "SKA-Bench: Noise Robustness on KG+Text (12k context)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 74.96
+      },
+      "evaluation_result_id": "SKA-Bench/DeepSeek-v3/1771591481.616601#ska_bench#ska_bench_noise_robustness_on_kg_text_12k_context"
+    },
+    {
+      "evaluation_name": "SKA-Bench",
+      "source_data": {
+        "dataset_name": "SKA-Bench",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2507.17178"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Macro-F1 score on the SKA-Bench-Table subset with a context length of 4k tokens. This test evaluates the model's ability to answer questions accurately when the knowledge base contains a high proportion of noisy, irrelevant table rows.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Macro-F1 Score",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "SKA-Bench: Noise Robustness on Tables (4k context)"
+        },
+        "metric_id": "ska_bench_noise_robustness_on_tables_4k_context",
+        "metric_name": "SKA-Bench: Noise Robustness on Tables (4k context)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 51.83
+      },
+      "evaluation_result_id": "SKA-Bench/DeepSeek-v3/1771591481.616601#ska_bench#ska_bench_noise_robustness_on_tables_4k_context"
+    },
+    {
+      "evaluation_name": "SKA-Bench",
+      "source_data": {
+        "dataset_name": "SKA-Bench",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2507.17178"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Macro-F1 score on the SKA-Bench-Table+Text subset with a context length of 12k tokens. This test evaluates the model's ability to answer questions using a hybrid knowledge base of table rows and text paragraphs containing significant noise.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Macro-F1 Score",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "SKA-Bench: Noise Robustness on Table+Text (12k context)"
+        },
+        "metric_id": "ska_bench_noise_robustness_on_table_text_12k_context",
+        "metric_name": "SKA-Bench: Noise Robustness on Table+Text (12k context)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 53.61
+      },
+      "evaluation_result_id": "SKA-Bench/DeepSeek-v3/1771591481.616601#ska_bench#ska_bench_noise_robustness_on_table_text_12k_context"
+    },
+    {
+      "evaluation_name": "SKA-Bench",
+      "source_data": {
+        "dataset_name": "SKA-Bench",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2507.17178"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Macro-F1 score on the SKA-Bench-KG subset where positive knowledge units are randomly shuffled within the context. This evaluates the 'lost in the middle' problem, testing if models can find relevant information regardless of its position. The context size is 4k tokens.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Macro-F1 Score",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "SKA-Bench: Order Insensitivity on Knowledge Graphs (Random Order)"
+        },
+        "metric_id": "ska_bench_order_insensitivity_on_knowledge_graphs_random_order",
+        "metric_name": "SKA-Bench: Order Insensitivity on Knowledge Graphs (Random Order)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 73.93
+      },
+      "evaluation_result_id": "SKA-Bench/DeepSeek-v3/1771591481.616601#ska_bench#ska_bench_order_insensitivity_on_knowledge_graphs_random_order"
+    },
+    {
+      "evaluation_name": "SKA-Bench",
+      "source_data": {
+        "dataset_name": "SKA-Bench",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2507.17178"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Macro-F1 score on the SKA-Bench-Table subset where positive knowledge units are randomly shuffled within the context. This evaluates the 'lost in the middle' problem, testing if models can find relevant information regardless of its position. The context size is 4k tokens.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Macro-F1 Score",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "SKA-Bench: Order Insensitivity on Tables (Random Order)"
+        },
+        "metric_id": "ska_bench_order_insensitivity_on_tables_random_order",
+        "metric_name": "SKA-Bench: Order Insensitivity on Tables (Random Order)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 44.75
+      },
+      "evaluation_result_id": "SKA-Bench/DeepSeek-v3/1771591481.616601#ska_bench#ska_bench_order_insensitivity_on_tables_random_order"
+    }
+  ],
+  "eval_library": {
+    "name": "alphaxiv",
+    "version": "unknown"
+  }
+}

flat/objects/05/1c/051c5c53-97c1-4e34-ab7b-9bc9d2e6aff2.json

@@ -0,0 +1,224 @@
+{
+  "schema_version": "0.2.2",
+  "retrieved_timestamp": "1777540088.3871834",
+  "source_metadata": {
+    "source_type": "evaluation_run",
+    "source_organization_name": "Writer, Inc.",
+    "evaluator_relationship": "third_party",
+    "source_name": "wasp (Writer's Assessor of System Performance)",
+    "additional_details": {}
+  },
+  "model_info": {
+    "name": "google/gemma-3-12b-it",
+    "id": "google/gemma-3-12b-it",
+    "developer": "google",
+    "inference_platform": "sglang",
+    "inference_engine": {
+      "name": "sglang"
+    },
+    "additional_details": {
+      "wasp_model_name": "gemma-3-12b-sglang",
+      "served_model": "sglang/google/gemma-3-12b-it"
+    }
+  },
+  "eval_library": {
+    "name": "wasp",
+    "version": "0.3.0",
+    "additional_details": {}
+  },
+  "evaluation_results": [
+    {
+      "evaluation_name": "Factuality",
+      "source_data": {
+        "dataset_name": "RewardBench 2",
+        "source_type": "hf_dataset",
+        "hf_repo": "allenai/reward-bench-2",
+        "hf_split": "test",
+        "additional_details": {}
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "evaluation_description": "Factuality accuracy on RewardBench 2",
+        "metric_id": "accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion",
+        "metric_parameters": {},
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "additional_details": {}
+      },
+      "score_details": {
+        "score": 0.3878947368421053,
+        "details": {}
+      },
+      "evaluation_result_id": "reward-bench-2/google_gemma-3-12b-it/1777540088.3871834#factuality#accuracy",
+      "evaluation_timestamp": "2026-02-21T09:04:22Z",
+      "generation_config": {
+        "generation_args": {
+          "temperature": 0.3,
+          "top_p": 0.95,
+          "max_tokens": 16384
+        },
+        "additional_details": {}
+      }
+    },
+    {
+      "evaluation_name": "Precise IF",
+      "source_data": {
+        "dataset_name": "RewardBench 2",
+        "source_type": "hf_dataset",
+        "hf_repo": "allenai/reward-bench-2",
+        "hf_split": "test",
+        "additional_details": {}
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "evaluation_description": "Precise instruction-following accuracy on RewardBench 2",
+        "metric_id": "accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion",
+        "metric_parameters": {},
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "additional_details": {}
+      },
+      "score_details": {
+        "score": 0.3390625,
+        "details": {}
+      },
+      "evaluation_result_id": "reward-bench-2/google_gemma-3-12b-it/1777540088.3871834#precise_if#accuracy",
+      "evaluation_timestamp": "2026-02-21T09:04:55Z",
+      "generation_config": {
+        "generation_args": {
+          "temperature": 0.3,
+          "top_p": 0.95,
+          "max_tokens": 16384
+        },
+        "additional_details": {}
+      }
+    },
+    {
+      "evaluation_name": "Math",
+      "source_data": {
+        "dataset_name": "RewardBench 2",
+        "source_type": "hf_dataset",
+        "hf_repo": "allenai/reward-bench-2",
+        "hf_split": "test",
+        "additional_details": {}
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "evaluation_description": "Math accuracy on RewardBench 2",
+        "metric_id": "accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion",
+        "metric_parameters": {},
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "additional_details": {}
+      },
+      "score_details": {
+        "score": 0.5163934426229508,
+        "details": {}
+      },
+      "evaluation_result_id": "reward-bench-2/google_gemma-3-12b-it/1777540088.3871834#math#accuracy",
+      "evaluation_timestamp": "2026-02-21T09:05:36Z",
+      "generation_config": {
+        "generation_args": {
+          "temperature": 0.3,
+          "top_p": 0.95,
+          "max_tokens": 16384
+        },
+        "additional_details": {}
+      }
+    },
+    {
+      "evaluation_name": "Safety",
+      "source_data": {
+        "dataset_name": "RewardBench 2",
+        "source_type": "hf_dataset",
+        "hf_repo": "allenai/reward-bench-2",
+        "hf_split": "test",
+        "additional_details": {}
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "evaluation_description": "Safety accuracy on RewardBench 2",
+        "metric_id": "accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion",
+        "metric_parameters": {},
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "additional_details": {}
+      },
+      "score_details": {
+        "score": 0.7688888888888888,
+        "details": {}
+      },
+      "evaluation_result_id": "reward-bench-2/google_gemma-3-12b-it/1777540088.3871834#safety#accuracy",
+      "evaluation_timestamp": "2026-02-21T09:07:01Z",
+      "generation_config": {
+        "generation_args": {
+          "temperature": 0.3,
+          "top_p": 0.95,
+          "max_tokens": 16384
+        },
+        "additional_details": {}
+      }
+    },
+    {
+      "evaluation_name": "Focus",
+      "source_data": {
+        "dataset_name": "RewardBench 2",
+        "source_type": "hf_dataset",
+        "hf_repo": "allenai/reward-bench-2",
+        "hf_split": "test",
+        "additional_details": {}
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "evaluation_description": "Focus accuracy on RewardBench 2",
+        "metric_id": "accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion",
+        "metric_parameters": {},
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "additional_details": {}
+      },
+      "score_details": {
+        "score": 0.7373737373737373,
+        "details": {}
+      },
+      "evaluation_result_id": "reward-bench-2/google_gemma-3-12b-it/1777540088.3871834#focus#accuracy",
+      "evaluation_timestamp": "2026-02-21T09:08:59Z",
+      "generation_config": {
+        "generation_args": {
+          "temperature": 0.3,
+          "top_p": 0.95,
+          "max_tokens": 16384
+        },
+        "additional_details": {}
+      }
+    }
+  ],
+  "evaluation_timestamp": "2026-02-21T09:08:59Z",
+  "detailed_evaluation_results": {
+    "format": "jsonl",
+    "file_path": "051c5c53-97c1-4e34-ab7b-9bc9d2e6aff2_samples.jsonl",
+    "total_rows": 1763,
+    "additional_details": {}
+  },
+  "evaluation_id": "reward-bench-2/google_gemma-3-12b-it/1777540088.3871834"
+}

flat/objects/05/1f/051f379f-2f77-4ffe-9b5b-580308e56cb3.json

@@ -0,0 +1,58 @@
+{
+  "schema_version": "0.2.2",
+  "evaluation_id": "MathVerse/Human Performance/1771591481.616601",
+  "retrieved_timestamp": "1771591481.616601",
+  "source_metadata": {
+    "source_name": "alphaXiv State of the Art",
+    "source_type": "documentation",
+    "source_organization_name": "alphaXiv",
+    "source_organization_url": "https://alphaxiv.org",
+    "evaluator_relationship": "third_party",
+    "additional_details": {
+      "alphaxiv_dataset_org": "UCLA",
+      "alphaxiv_dataset_type": "image",
+      "scrape_source": "https://github.com/alphaXiv/feedback/issues/189"
+    }
+  },
+  "model_info": {
+    "id": "Human Performance",
+    "name": "Human Performance",
+    "developer": "unknown"
+  },
+  "evaluation_results": [
+    {
+      "evaluation_name": "MathVerse",
+      "source_data": {
+        "dataset_name": "MathVerse",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2403.14624"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Overall performance on the MATHVERSE benchmark using standard final answer accuracy, averaged across five visual problem versions (Text Dominant, Text Lite, Vision Intensive, Vision Dominant, Vision Only). This metric, referred to as 'w/o CoT-E', measures only if the final answer is correct. Higher scores are better.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Accuracy (%)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "MathVerse Overall Performance (Accuracy)"
+        },
+        "metric_id": "mathverse_overall_performance_accuracy",
+        "metric_name": "MathVerse Overall Performance (Accuracy)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 64.9
+      },
+      "evaluation_result_id": "MathVerse/Human Performance/1771591481.616601#mathverse#mathverse_overall_performance_accuracy"
+    }
+  ],
+  "eval_library": {
+    "name": "alphaxiv",
+    "version": "unknown"
+  }
+}

flat/objects/05/1f/051f791e-277f-47a3-8bac-844069e11fd9.json

@@ -0,0 +1,118 @@
+{
+  "schema_version": "0.2.2",
+  "evaluation_id": "MathConstruct/GPT-4O-MINI/1771591481.616601",
+  "retrieved_timestamp": "1771591481.616601",
+  "source_metadata": {
+    "source_name": "alphaXiv State of the Art",
+    "source_type": "documentation",
+    "source_organization_name": "alphaXiv",
+    "source_organization_url": "https://alphaxiv.org",
+    "evaluator_relationship": "third_party",
+    "additional_details": {
+      "alphaxiv_dataset_org": "ETH Zurich",
+      "alphaxiv_dataset_type": "text",
+      "scrape_source": "https://github.com/alphaXiv/feedback/issues/189"
+    }
+  },
+  "model_info": {
+    "id": "GPT-4O-MINI",
+    "name": "GPT-4O-MINI",
+    "developer": "unknown"
+  },
+  "evaluation_results": [
+    {
+      "evaluation_name": "MathConstruct",
+      "source_data": {
+        "dataset_name": "MathConstruct",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2502.10197"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Average accuracy on the MATHCONSTRUCT benchmark. This is calculated by first finding the accuracy across all variations of a single problem, and then averaging these per-problem accuracies across all 121 problems. This metric reflects a model's overall performance.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Average Accuracy (%)",
+          "alphaxiv_is_primary": "True",
+          "raw_evaluation_name": "MATHCONSTRUCT Average Accuracy"
+        },
+        "metric_id": "mathconstruct_average_accuracy",
+        "metric_name": "MATHCONSTRUCT Average Accuracy",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 4.55
+      },
+      "evaluation_result_id": "MathConstruct/GPT-4O-MINI/1771591481.616601#mathconstruct#mathconstruct_average_accuracy"
+    },
+    {
+      "evaluation_name": "MathConstruct",
+      "source_data": {
+        "dataset_name": "MathConstruct",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2502.10197"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "The monetary cost in USD of API calls required for running the models on the MATHCONSTRUCT benchmark. Models with a value of -1 were marked N/A in the paper, noted as being free at a limited rate.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Cost (USD)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "MATHCONSTRUCT Evaluation Cost"
+        },
+        "metric_id": "mathconstruct_evaluation_cost",
+        "metric_name": "MATHCONSTRUCT Evaluation Cost",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 0.3
+      },
+      "evaluation_result_id": "MathConstruct/GPT-4O-MINI/1771591481.616601#mathconstruct#mathconstruct_evaluation_cost"
+    },
+    {
+      "evaluation_name": "MathConstruct",
+      "source_data": {
+        "dataset_name": "MathConstruct",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2502.10197"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "A stricter metric where a model is considered to have solved a problem only if it correctly answers all of its generated variations. This evaluates the model's ability to generalize a solution across different parameter instantiations.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Robust Accuracy (%)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "MATHCONSTRUCT Robust Accuracy"
+        },
+        "metric_id": "mathconstruct_robust_accuracy",
+        "metric_name": "MATHCONSTRUCT Robust Accuracy",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 1.65
+      },
+      "evaluation_result_id": "MathConstruct/GPT-4O-MINI/1771591481.616601#mathconstruct#mathconstruct_robust_accuracy"
+    }
+  ],
+  "eval_library": {
+    "name": "alphaxiv",
+    "version": "unknown"
+  }
+}

flat/objects/05/25/0525e620-4f29-4f3e-95e1-fed2d12a5056.json

@@ -0,0 +1,58 @@
+{
+  "schema_version": "0.2.2",
+  "evaluation_id": "OptiBench/Deepseek-V2/1771591481.616601",
+  "retrieved_timestamp": "1771591481.616601",
+  "source_metadata": {
+    "source_name": "alphaXiv State of the Art",
+    "source_type": "documentation",
+    "source_organization_name": "alphaXiv",
+    "source_organization_url": "https://alphaxiv.org",
+    "evaluator_relationship": "third_party",
+    "additional_details": {
+      "alphaxiv_dataset_org": "Huawei Noah’s Ark Lab",
+      "alphaxiv_dataset_type": "text",
+      "scrape_source": "https://github.com/alphaXiv/feedback/issues/189"
+    }
+  },
+  "model_info": {
+    "id": "Deepseek-V2",
+    "name": "Deepseek-V2",
+    "developer": "unknown"
+  },
+  "evaluation_results": [
+    {
+      "evaluation_name": "OptiBench",
+      "source_data": {
+        "dataset_name": "OptiBench",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2407.09887"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Supplementary zero-shot comparison from Appendix A showing model performance on OPTIBENCH. This result is presented alongside results on a modified NL4OPT-E benchmark to highlight the increased difficulty of OPTIBENCH.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Answer Accuracy (%)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "OPTIBENCH: Zero-shot Answer Accuracy (Appendix Comparison)"
+        },
+        "metric_id": "optibench_zero_shot_answer_accuracy_appendix_comparison",
+        "metric_name": "OPTIBENCH: Zero-shot Answer Accuracy (Appendix Comparison)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 34.4
+      },
+      "evaluation_result_id": "OptiBench/Deepseek-V2/1771591481.616601#optibench#optibench_zero_shot_answer_accuracy_appendix_comparison"
+    }
+  ],
+  "eval_library": {
+    "name": "alphaxiv",
+    "version": "unknown"
+  }
+}

flat/objects/05/28/0528478d-cd3d-489d-9ac6-27685022ff1d.json

@@ -0,0 +1,169 @@
+{
+  "schema_version": "0.2.2",
+  "evaluation_id": "hfopenllm_v2/Triangle104_Dolphin3-Llama3.2-Smart/1773936498.240187",
+  "retrieved_timestamp": "1773936498.240187",
+  "source_metadata": {
+    "source_name": "HF Open LLM v2",
+    "source_type": "documentation",
+    "source_organization_name": "Hugging Face",
+    "evaluator_relationship": "third_party"
+  },
+  "eval_library": {
+    "name": "lm-evaluation-harness",
+    "version": "0.4.0",
+    "additional_details": {
+      "fork": "https://github.com/huggingface/lm-evaluation-harness/tree/adding_all_changess"
+    }
+  },
+  "model_info": {
+    "name": "Dolphin3-Llama3.2-Smart",
+    "id": "Triangle104/Dolphin3-Llama3.2-Smart",
+    "developer": "Triangle104",
+    "inference_platform": "unknown",
+    "additional_details": {
+      "precision": "bfloat16",
+      "architecture": "LlamaForCausalLM",
+      "params_billions": "3.213"
+    }
+  },
+  "evaluation_results": [
+    {
+      "evaluation_name": "IFEval",
+      "source_data": {
+        "dataset_name": "IFEval",
+        "source_type": "hf_dataset",
+        "hf_repo": "google/IFEval"
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy on IFEval",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "metric_id": "accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion"
+      },
+      "score_details": {
+        "score": 0.4137
+      },
+      "evaluation_result_id": "hfopenllm_v2/Triangle104_Dolphin3-Llama3.2-Smart/1773936498.240187#ifeval#accuracy"
+    },
+    {
+      "evaluation_name": "BBH",
+      "source_data": {
+        "dataset_name": "BBH",
+        "source_type": "hf_dataset",
+        "hf_repo": "SaylorTwift/bbh"
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy on BBH",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "metric_id": "accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion"
+      },
+      "score_details": {
+        "score": 0.3975
+      },
+      "evaluation_result_id": "hfopenllm_v2/Triangle104_Dolphin3-Llama3.2-Smart/1773936498.240187#bbh#accuracy"
+    },
+    {
+      "evaluation_name": "MATH Level 5",
+      "source_data": {
+        "dataset_name": "MATH Level 5",
+        "source_type": "hf_dataset",
+        "hf_repo": "DigitalLearningGmbH/MATH-lighteval"
+      },
+      "metric_config": {
+        "evaluation_description": "Exact Match on MATH Level 5",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "metric_id": "exact_match",
+        "metric_name": "Exact Match",
+        "metric_kind": "exact_match",
+        "metric_unit": "proportion"
+      },
+      "score_details": {
+        "score": 0.0438
+      },
+      "evaluation_result_id": "hfopenllm_v2/Triangle104_Dolphin3-Llama3.2-Smart/1773936498.240187#math_level_5#exact_match"
+    },
+    {
+      "evaluation_name": "GPQA",
+      "source_data": {
+        "dataset_name": "GPQA",
+        "source_type": "hf_dataset",
+        "hf_repo": "Idavidrein/gpqa"
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy on GPQA",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "metric_id": "accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion"
+      },
+      "score_details": {
+        "score": 0.2693
+      },
+      "evaluation_result_id": "hfopenllm_v2/Triangle104_Dolphin3-Llama3.2-Smart/1773936498.240187#gpqa#accuracy"
+    },
+    {
+      "evaluation_name": "MUSR",
+      "source_data": {
+        "dataset_name": "MUSR",
+        "source_type": "hf_dataset",
+        "hf_repo": "TAUR-Lab/MuSR"
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy on MUSR",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "metric_id": "accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion"
+      },
+      "score_details": {
+        "score": 0.3922
+      },
+      "evaluation_result_id": "hfopenllm_v2/Triangle104_Dolphin3-Llama3.2-Smart/1773936498.240187#musr#accuracy"
+    },
+    {
+      "evaluation_name": "MMLU-PRO",
+      "source_data": {
+        "dataset_name": "MMLU-PRO",
+        "source_type": "hf_dataset",
+        "hf_repo": "TIGER-Lab/MMLU-Pro"
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy on MMLU-PRO",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "metric_id": "accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion"
+      },
+      "score_details": {
+        "score": 0.2195
+      },
+      "evaluation_result_id": "hfopenllm_v2/Triangle104_Dolphin3-Llama3.2-Smart/1773936498.240187#mmlu_pro#accuracy"
+    }
+  ]
+}

flat/objects/05/29/0529a415-d4e4-4ab8-b251-99acd08acab6.json

@@ -0,0 +1,312 @@
+{
+  "schema_version": "0.2.2",
+  "evaluation_id": "vals-ai/lcb/openai_gpt-4o-mini-2024-07-18/1777395187.3170502",
+  "retrieved_timestamp": "1777395187.3170502",
+  "source_metadata": {
+    "source_name": "Vals.ai Leaderboard - LiveCodeBench",
+    "source_type": "documentation",
+    "source_organization_name": "Vals.ai",
+    "source_organization_url": "https://www.vals.ai",
+    "evaluator_relationship": "third_party",
+    "additional_details": {
+      "benchmark_slug": "lcb",
+      "benchmark_name": "LiveCodeBench",
+      "benchmark_updated": "2026-04-21",
+      "dataset_type": "public",
+      "industry": "coding",
+      "leaderboard_page_url": "https://www.vals.ai/benchmarks/lcb",
+      "extraction_method": "static_astro_benchmark_view_props"
+    }
+  },
+  "eval_library": {
+    "name": "Vals.ai",
+    "version": "unknown"
+  },
+  "model_info": {
+    "name": "gpt-4o-mini-2024-07-18",
+    "id": "openai/gpt-4o-mini-2024-07-18",
+    "developer": "openai",
+    "additional_details": {
+      "vals_model_id": "openai/gpt-4o-mini-2024-07-18",
+      "vals_provider": "OpenAI"
+    }
+  },
+  "evaluation_results": [
+    {
+      "evaluation_result_id": "lcb:easy:openai/gpt-4o-mini-2024-07-18:score",
+      "evaluation_name": "vals_ai.lcb.easy",
+      "source_data": {
+        "dataset_name": "LiveCodeBench - Easy",
+        "source_type": "url",
+        "url": [
+          "https://www.vals.ai/benchmarks/lcb"
+        ],
+        "additional_details": {
+          "benchmark_slug": "lcb",
+          "task_key": "easy",
+          "dataset_type": "public",
+          "leaderboard_page_url": "https://www.vals.ai/benchmarks/lcb"
+        }
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy reported by Vals.ai for LiveCodeBench (Easy).",
+        "metric_id": "vals_ai.lcb.easy.accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "percent",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "additional_details": {
+          "score_scale": "percent_0_to_100",
+          "max_score_source": "fixed_percentage_bound",
+          "leaderboard_page_url": "https://www.vals.ai/benchmarks/lcb"
+        }
+      },
+      "score_details": {
+        "score": 60.87,
+        "details": {
+          "benchmark_slug": "lcb",
+          "benchmark_name": "LiveCodeBench",
+          "benchmark_updated": "2026-04-21",
+          "task_key": "easy",
+          "task_name": "Easy",
+          "dataset_type": "public",
+          "industry": "coding",
+          "raw_score": "60.87",
+          "raw_stderr": "2.72",
+          "latency": "6.066",
+          "cost_per_test": "0.000237",
+          "temperature": "1",
+          "max_output_tokens": "16384",
+          "reasoning_effort": "high",
+          "provider": "OpenAI"
+        },
+        "uncertainty": {
+          "standard_error": {
+            "value": 2.72,
+            "method": "vals_reported"
+          }
+        }
+      },
+      "generation_config": {
+        "generation_args": {
+          "temperature": 1.0,
+          "max_tokens": 16384,
+          "max_attempts": 1
+        },
+        "additional_details": {
+          "reasoning_effort": "high"
+        }
+      }
+    },
+    {
+      "evaluation_result_id": "lcb:hard:openai/gpt-4o-mini-2024-07-18:score",
+      "evaluation_name": "vals_ai.lcb.hard",
+      "source_data": {
+        "dataset_name": "LiveCodeBench - Hard",
+        "source_type": "url",
+        "url": [
+          "https://www.vals.ai/benchmarks/lcb"
+        ],
+        "additional_details": {
+          "benchmark_slug": "lcb",
+          "task_key": "hard",
+          "dataset_type": "public",
+          "leaderboard_page_url": "https://www.vals.ai/benchmarks/lcb"
+        }
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy reported by Vals.ai for LiveCodeBench (Hard).",
+        "metric_id": "vals_ai.lcb.hard.accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "percent",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "additional_details": {
+          "score_scale": "percent_0_to_100",
+          "max_score_source": "fixed_percentage_bound",
+          "leaderboard_page_url": "https://www.vals.ai/benchmarks/lcb"
+        }
+      },
+      "score_details": {
+        "score": 1.429,
+        "details": {
+          "benchmark_slug": "lcb",
+          "benchmark_name": "LiveCodeBench",
+          "benchmark_updated": "2026-04-21",
+          "task_key": "hard",
+          "task_name": "Hard",
+          "dataset_type": "public",
+          "industry": "coding",
+          "raw_score": "1.429",
+          "raw_stderr": "0.634",
+          "latency": "8.949",
+          "cost_per_test": "0.000362",
+          "temperature": "1",
+          "max_output_tokens": "16384",
+          "reasoning_effort": "high",
+          "provider": "OpenAI"
+        },
+        "uncertainty": {
+          "standard_error": {
+            "value": 0.634,
+            "method": "vals_reported"
+          }
+        }
+      },
+      "generation_config": {
+        "generation_args": {
+          "temperature": 1.0,
+          "max_tokens": 16384,
+          "max_attempts": 1
+        },
+        "additional_details": {
+          "reasoning_effort": "high"
+        }
+      }
+    },
+    {
+      "evaluation_result_id": "lcb:medium:openai/gpt-4o-mini-2024-07-18:score",
+      "evaluation_name": "vals_ai.lcb.medium",
+      "source_data": {
+        "dataset_name": "LiveCodeBench - Medium",
+        "source_type": "url",
+        "url": [
+          "https://www.vals.ai/benchmarks/lcb"
+        ],
+        "additional_details": {
+          "benchmark_slug": "lcb",
+          "task_key": "medium",
+          "dataset_type": "public",
+          "leaderboard_page_url": "https://www.vals.ai/benchmarks/lcb"
+        }
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy reported by Vals.ai for LiveCodeBench (Medium).",
+        "metric_id": "vals_ai.lcb.medium.accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "percent",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "additional_details": {
+          "score_scale": "percent_0_to_100",
+          "max_score_source": "fixed_percentage_bound",
+          "leaderboard_page_url": "https://www.vals.ai/benchmarks/lcb"
+        }
+      },
+      "score_details": {
+        "score": 16.971,
+        "details": {
+          "benchmark_slug": "lcb",
+          "benchmark_name": "LiveCodeBench",
+          "benchmark_updated": "2026-04-21",
+          "task_key": "medium",
+          "task_name": "Medium",
+          "dataset_type": "public",
+          "industry": "coding",
+          "raw_score": "16.971",
+          "raw_stderr": "1.918",
+          "latency": "7.076",
+          "cost_per_test": "0.000295",
+          "temperature": "1",
+          "max_output_tokens": "16384",
+          "reasoning_effort": "high",
+          "provider": "OpenAI"
+        },
+        "uncertainty": {
+          "standard_error": {
+            "value": 1.918,
+            "method": "vals_reported"
+          }
+        }
+      },
+      "generation_config": {
+        "generation_args": {
+          "temperature": 1.0,
+          "max_tokens": 16384,
+          "max_attempts": 1
+        },
+        "additional_details": {
+          "reasoning_effort": "high"
+        }
+      }
+    },
+    {
+      "evaluation_result_id": "lcb:overall:openai/gpt-4o-mini-2024-07-18:score",
+      "evaluation_name": "vals_ai.lcb.overall",
+      "source_data": {
+        "dataset_name": "LiveCodeBench - Overall",
+        "source_type": "url",
+        "url": [
+          "https://www.vals.ai/benchmarks/lcb"
+        ],
+        "additional_details": {
+          "benchmark_slug": "lcb",
+          "task_key": "overall",
+          "dataset_type": "public",
+          "leaderboard_page_url": "https://www.vals.ai/benchmarks/lcb"
+        }
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy reported by Vals.ai for LiveCodeBench (Overall).",
+        "metric_id": "vals_ai.lcb.overall.accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "percent",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "additional_details": {
+          "score_scale": "percent_0_to_100",
+          "max_score_source": "fixed_percentage_bound",
+          "leaderboard_page_url": "https://www.vals.ai/benchmarks/lcb"
+        }
+      },
+      "score_details": {
+        "score": 26.423,
+        "details": {
+          "benchmark_slug": "lcb",
+          "benchmark_name": "LiveCodeBench",
+          "benchmark_updated": "2026-04-21",
+          "task_key": "overall",
+          "task_name": "Overall",
+          "dataset_type": "public",
+          "industry": "coding",
+          "raw_score": "26.423",
+          "raw_stderr": "1.129",
+          "latency": "7.364",
+          "cost_per_test": "0.000298",
+          "temperature": "1",
+          "max_output_tokens": "16384",
+          "reasoning_effort": "high",
+          "provider": "OpenAI"
+        },
+        "uncertainty": {
+          "standard_error": {
+            "value": 1.129,
+            "method": "vals_reported"
+          }
+        }
+      },
+      "generation_config": {
+        "generation_args": {
+          "temperature": 1.0,
+          "max_tokens": 16384,
+          "max_attempts": 1
+        },
+        "additional_details": {
+          "reasoning_effort": "high"
+        }
+      }
+    }
+  ]
+}

flat/objects/05/29/0529eca2-85b2-4374-8361-ad5bd688de75.json

@@ -0,0 +1,148 @@
+{
+  "schema_version": "0.2.2",
+  "evaluation_id": "MME Unify/MiniGPT-5/1771591481.616601",
+  "retrieved_timestamp": "1771591481.616601",
+  "source_metadata": {
+    "source_name": "alphaXiv State of the Art",
+    "source_type": "documentation",
+    "source_organization_name": "alphaXiv",
+    "source_organization_url": "https://alphaxiv.org",
+    "evaluator_relationship": "third_party",
+    "additional_details": {
+      "alphaxiv_dataset_org": "CASIA",
+      "alphaxiv_dataset_type": "image",
+      "scrape_source": "https://github.com/alphaXiv/feedback/issues/189"
+    }
+  },
+  "model_info": {
+    "id": "MiniGPT-5",
+    "name": "MiniGPT-5",
+    "developer": "unknown"
+  },
+  "evaluation_results": [
+    {
+      "evaluation_name": "MME Unify",
+      "source_data": {
+        "dataset_name": "MME Unify",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2504.03641"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "The MME-U Score is the arithmetic mean of the Understanding Score (US), Generation Score (GS), and Unify Score (Unify-S). It provides a single, comprehensive metric to evaluate a model's overall capability in both multimodal understanding and generation, as benchmarked by MME-Unify.",
+        "additional_details": {
+          "alphaxiv_y_axis": "MME-U Score",
+          "alphaxiv_is_primary": "True",
+          "raw_evaluation_name": "Overall MME-U Score on Unified Multimodal Tasks"
+        },
+        "metric_id": "overall_mme_u_score_on_unified_multimodal_tasks",
+        "metric_name": "Overall MME-U Score on Unified Multimodal Tasks",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 16.43
+      },
+      "evaluation_result_id": "MME Unify/MiniGPT-5/1771591481.616601#mme_unify#overall_mme_u_score_on_unified_multimodal_tasks"
+    },
+    {
+      "evaluation_name": "MME Unify",
+      "source_data": {
+        "dataset_name": "MME Unify",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2504.03641"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "The Generation Score (GS) is the arithmetic mean of standardized scores (0-100 scale) across six multimodal generation subtasks, including image reconstruction, text-to-image/video generation, and video prediction. Higher scores indicate better generation quality.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Generation Score (Avg)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "MME-U: Multimodal Generation Performance"
+        },
+        "metric_id": "mme_u_multimodal_generation_performance",
+        "metric_name": "MME-U: Multimodal Generation Performance",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 18.25
+      },
+      "evaluation_result_id": "MME Unify/MiniGPT-5/1771591481.616601#mme_unify#mme_u_multimodal_generation_performance"
+    },
+    {
+      "evaluation_name": "MME Unify",
+      "source_data": {
+        "dataset_name": "MME Unify",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2504.03641"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "The Understanding Score (US) is the average accuracy across three categories of multimodal understanding tasks: Single-Image Perception and Understanding (SIPU), Multi-Image & Interleaved Text-Image Understanding (MITIU), and Video Perception and Understanding (VPU). Higher scores indicate better comprehension.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Understanding Score (Avg)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "MME-U: Multimodal Understanding Performance"
+        },
+        "metric_id": "mme_u_multimodal_understanding_performance",
+        "metric_name": "MME-U: Multimodal Understanding Performance",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 15.37
+      },
+      "evaluation_result_id": "MME Unify/MiniGPT-5/1771591481.616601#mme_unify#mme_u_multimodal_understanding_performance"
+    },
+    {
+      "evaluation_name": "MME Unify",
+      "source_data": {
+        "dataset_name": "MME Unify",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2504.03641"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "The Unify Score (Unify-S) measures a model's ability to synergistically use understanding and generation in complex, sequential tasks. It is the average accuracy across five novel 'Unify Tasks' such as drawing auxiliary lines for geometry problems or visual chain-of-thought reasoning.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Unify Score (Avg)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "MME-U: Unify Capability Performance"
+        },
+        "metric_id": "mme_u_unify_capability_performance",
+        "metric_name": "MME-U: Unify Capability Performance",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 15.67
+      },
+      "evaluation_result_id": "MME Unify/MiniGPT-5/1771591481.616601#mme_unify#mme_u_unify_capability_performance"
+    }
+  ],
+  "eval_library": {
+    "name": "alphaxiv",
+    "version": "unknown"
+  }
+}

flat/objects/05/2e/052e0c75-2fbb-4562-9ea8-d950c728fe1d.json

@@ -0,0 +1,268 @@
+{
+  "schema_version": "0.2.2",
+  "evaluation_id": "MLDebugging/Qwen2.5 (7B+)/1771591481.616601",
+  "retrieved_timestamp": "1771591481.616601",
+  "source_metadata": {
+    "source_name": "alphaXiv State of the Art",
+    "source_type": "documentation",
+    "source_organization_name": "alphaXiv",
+    "source_organization_url": "https://alphaxiv.org",
+    "evaluator_relationship": "third_party",
+    "additional_details": {
+      "alphaxiv_dataset_org": "Central South University",
+      "alphaxiv_dataset_type": "text",
+      "scrape_source": "https://github.com/alphaXiv/feedback/issues/189"
+    }
+  },
+  "model_info": {
+    "id": "Qwen2.5 (7B+)",
+    "name": "Qwen2.5 (7B+)",
+    "developer": "unknown"
+  },
+  "evaluation_results": [
+    {
+      "evaluation_name": "MLDebugging",
+      "source_data": {
+        "dataset_name": "MLDebugging",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2506.13824"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Measures the overall debugging performance of models across seven distinct multi-library bug categories on the MLDebugging benchmark. This is the primary metric for overall capability, calculated as the average pass rate.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Average Pass Rate (%)",
+          "alphaxiv_is_primary": "True",
+          "raw_evaluation_name": "MLDebugging: Average Pass Rate Across All Bug Categories"
+        },
+        "metric_id": "mldebugging_average_pass_rate_across_all_bug_categories",
+        "metric_name": "MLDebugging: Average Pass Rate Across All Bug Categories",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 42.7
+      },
+      "evaluation_result_id": "MLDebugging/Qwen2.5 (7B+)/1771591481.616601#mldebugging#mldebugging_average_pass_rate_across_all_bug_categories"
+    },
+    {
+      "evaluation_name": "MLDebugging",
+      "source_data": {
+        "dataset_name": "MLDebugging",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2506.13824"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Measures model performance on debugging 'Data Transfer Issues' (DTI) bugs, related to incorrect data format or structure when transferring data between library components.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Pass Rate (%) - Data Transfer Issues (DTI)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "MLDebugging: Pass Rate on Data Transfer Issues (DTI) Bugs"
+        },
+        "metric_id": "mldebugging_pass_rate_on_data_transfer_issues_dti_bugs",
+        "metric_name": "MLDebugging: Pass Rate on Data Transfer Issues (DTI) Bugs",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 36.1
+      },
+      "evaluation_result_id": "MLDebugging/Qwen2.5 (7B+)/1771591481.616601#mldebugging#mldebugging_pass_rate_on_data_transfer_issues_dti_bugs"
+    },
+    {
+      "evaluation_name": "MLDebugging",
+      "source_data": {
+        "dataset_name": "MLDebugging",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2506.13824"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Measures model performance on debugging 'Function Misuse' (FM) bugs, where a library function is used in a context or manner for which it was not intended.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Pass Rate (%) - Function Misuse (FM)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "MLDebugging: Pass Rate on Function Misuse (FM) Bugs"
+        },
+        "metric_id": "mldebugging_pass_rate_on_function_misuse_fm_bugs",
+        "metric_name": "MLDebugging: Pass Rate on Function Misuse (FM) Bugs",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 38.2
+      },
+      "evaluation_result_id": "MLDebugging/Qwen2.5 (7B+)/1771591481.616601#mldebugging#mldebugging_pass_rate_on_function_misuse_fm_bugs"
+    },
+    {
+      "evaluation_name": "MLDebugging",
+      "source_data": {
+        "dataset_name": "MLDebugging",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2506.13824"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Measures model performance on debugging 'Function Parameter Errors' (PFE), which involve incorrect arguments being passed to functions.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Pass Rate (%) - Function Parameter Errors (PFE)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "MLDebugging: Pass Rate on Function Parameter Errors (PFE) Bugs"
+        },
+        "metric_id": "mldebugging_pass_rate_on_function_parameter_errors_pfe_bugs",
+        "metric_name": "MLDebugging: Pass Rate on Function Parameter Errors (PFE) Bugs",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 48.4
+      },
+      "evaluation_result_id": "MLDebugging/Qwen2.5 (7B+)/1771591481.616601#mldebugging#mldebugging_pass_rate_on_function_parameter_errors_pfe_bugs"
+    },
+    {
+      "evaluation_name": "MLDebugging",
+      "source_data": {
+        "dataset_name": "MLDebugging",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2506.13824"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Measures model performance on debugging 'Import Errors' (IE), which are library-level errors involving missing or incorrect import statements for necessary modules.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Pass Rate (%) - Import Errors (IE)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "MLDebugging: Pass Rate on Import Errors (IE) Bugs"
+        },
+        "metric_id": "mldebugging_pass_rate_on_import_errors_ie_bugs",
+        "metric_name": "MLDebugging: Pass Rate on Import Errors (IE) Bugs",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 26.1
+      },
+      "evaluation_result_id": "MLDebugging/Qwen2.5 (7B+)/1771591481.616601#mldebugging#mldebugging_pass_rate_on_import_errors_ie_bugs"
+    },
+    {
+      "evaluation_name": "MLDebugging",
+      "source_data": {
+        "dataset_name": "MLDebugging",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2506.13824"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Measures model performance on debugging 'Parameter Configuration Errors' (PCE), where the settings or configuration parameters for a library function are incorrect.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Pass Rate (%) - Parameter Configuration Errors (PCE)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "MLDebugging: Pass Rate on Parameter Configuration Errors (PCE) Bugs"
+        },
+        "metric_id": "mldebugging_pass_rate_on_parameter_configuration_errors_pce_bugs",
+        "metric_name": "MLDebugging: Pass Rate on Parameter Configuration Errors (PCE) Bugs",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 57.6
+      },
+      "evaluation_result_id": "MLDebugging/Qwen2.5 (7B+)/1771591481.616601#mldebugging#mldebugging_pass_rate_on_parameter_configuration_errors_pce_bugs"
+    },
+    {
+      "evaluation_name": "MLDebugging",
+      "source_data": {
+        "dataset_name": "MLDebugging",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2506.13824"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Measures model performance on debugging 'Requirement Misunderstanding' (RM) bugs. These are complex, library-level reasoning errors where the code fails to meet the specified requirements.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Pass Rate (%) - Requirement Misunderstanding (RM)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "MLDebugging: Pass Rate on Requirement Misunderstanding (RM) Bugs"
+        },
+        "metric_id": "mldebugging_pass_rate_on_requirement_misunderstanding_rm_bugs",
+        "metric_name": "MLDebugging: Pass Rate on Requirement Misunderstanding (RM) Bugs",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 12.6
+      },
+      "evaluation_result_id": "MLDebugging/Qwen2.5 (7B+)/1771591481.616601#mldebugging#mldebugging_pass_rate_on_requirement_misunderstanding_rm_bugs"
+    },
+    {
+      "evaluation_name": "MLDebugging",
+      "source_data": {
+        "dataset_name": "MLDebugging",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2506.13824"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Measures model performance on debugging 'Type Mismatch' (TM) bugs, which occur when data of an incorrect type is passed between functions, often from different libraries.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Pass Rate (%) - Type Mismatch (TM)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "MLDebugging: Pass Rate on Type Mismatch (TM) Bugs"
+        },
+        "metric_id": "mldebugging_pass_rate_on_type_mismatch_tm_bugs",
+        "metric_name": "MLDebugging: Pass Rate on Type Mismatch (TM) Bugs",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 47.6
+      },
+      "evaluation_result_id": "MLDebugging/Qwen2.5 (7B+)/1771591481.616601#mldebugging#mldebugging_pass_rate_on_type_mismatch_tm_bugs"
+    }
+  ],
+  "eval_library": {
+    "name": "alphaxiv",
+    "version": "unknown"
+  }
+}

flat/objects/05/2e/052e5dd5-688a-497d-b87e-b60e253186ec.json

@@ -0,0 +1,169 @@
+{
+  "schema_version": "0.2.2",
+  "evaluation_id": "hfopenllm_v2/ontocord_wide_3b_sft_stage1.1-ss1-with_generics_intr_stories.no_issue/1773936498.240187",
+  "retrieved_timestamp": "1773936498.240187",
+  "source_metadata": {
+    "source_name": "HF Open LLM v2",
+    "source_type": "documentation",
+    "source_organization_name": "Hugging Face",
+    "evaluator_relationship": "third_party"
+  },
+  "eval_library": {
+    "name": "lm-evaluation-harness",
+    "version": "0.4.0",
+    "additional_details": {
+      "fork": "https://github.com/huggingface/lm-evaluation-harness/tree/adding_all_changess"
+    }
+  },
+  "model_info": {
+    "name": "wide_3b_sft_stage1.1-ss1-with_generics_intr_stories.no_issue",
+    "id": "ontocord/wide_3b_sft_stage1.1-ss1-with_generics_intr_stories.no_issue",
+    "developer": "ontocord",
+    "inference_platform": "unknown",
+    "additional_details": {
+      "precision": "float16",
+      "architecture": "Qwen2ForCausalLM",
+      "params_billions": "3.759"
+    }
+  },
+  "evaluation_results": [
+    {
+      "evaluation_name": "IFEval",
+      "source_data": {
+        "dataset_name": "IFEval",
+        "source_type": "hf_dataset",
+        "hf_repo": "google/IFEval"
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy on IFEval",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "metric_id": "accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion"
+      },
+      "score_details": {
+        "score": 0.1182
+      },
+      "evaluation_result_id": "hfopenllm_v2/ontocord_wide_3b_sft_stage1.1-ss1-with_generics_intr_stories.no_issue/1773936498.240187#ifeval#accuracy"
+    },
+    {
+      "evaluation_name": "BBH",
+      "source_data": {
+        "dataset_name": "BBH",
+        "source_type": "hf_dataset",
+        "hf_repo": "SaylorTwift/bbh"
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy on BBH",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "metric_id": "accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion"
+      },
+      "score_details": {
+        "score": 0.3037
+      },
+      "evaluation_result_id": "hfopenllm_v2/ontocord_wide_3b_sft_stage1.1-ss1-with_generics_intr_stories.no_issue/1773936498.240187#bbh#accuracy"
+    },
+    {
+      "evaluation_name": "MATH Level 5",
+      "source_data": {
+        "dataset_name": "MATH Level 5",
+        "source_type": "hf_dataset",
+        "hf_repo": "DigitalLearningGmbH/MATH-lighteval"
+      },
+      "metric_config": {
+        "evaluation_description": "Exact Match on MATH Level 5",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "metric_id": "exact_match",
+        "metric_name": "Exact Match",
+        "metric_kind": "exact_match",
+        "metric_unit": "proportion"
+      },
+      "score_details": {
+        "score": 0.0083
+      },
+      "evaluation_result_id": "hfopenllm_v2/ontocord_wide_3b_sft_stage1.1-ss1-with_generics_intr_stories.no_issue/1773936498.240187#math_level_5#exact_match"
+    },
+    {
+      "evaluation_name": "GPQA",
+      "source_data": {
+        "dataset_name": "GPQA",
+        "source_type": "hf_dataset",
+        "hf_repo": "Idavidrein/gpqa"
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy on GPQA",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "metric_id": "accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion"
+      },
+      "score_details": {
+        "score": 0.2659
+      },
+      "evaluation_result_id": "hfopenllm_v2/ontocord_wide_3b_sft_stage1.1-ss1-with_generics_intr_stories.no_issue/1773936498.240187#gpqa#accuracy"
+    },
+    {
+      "evaluation_name": "MUSR",
+      "source_data": {
+        "dataset_name": "MUSR",
+        "source_type": "hf_dataset",
+        "hf_repo": "TAUR-Lab/MuSR"
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy on MUSR",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "metric_id": "accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion"
+      },
+      "score_details": {
+        "score": 0.3567
+      },
+      "evaluation_result_id": "hfopenllm_v2/ontocord_wide_3b_sft_stage1.1-ss1-with_generics_intr_stories.no_issue/1773936498.240187#musr#accuracy"
+    },
+    {
+      "evaluation_name": "MMLU-PRO",
+      "source_data": {
+        "dataset_name": "MMLU-PRO",
+        "source_type": "hf_dataset",
+        "hf_repo": "TIGER-Lab/MMLU-Pro"
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy on MMLU-PRO",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "metric_id": "accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion"
+      },
+      "score_details": {
+        "score": 0.1162
+      },
+      "evaluation_result_id": "hfopenllm_v2/ontocord_wide_3b_sft_stage1.1-ss1-with_generics_intr_stories.no_issue/1773936498.240187#mmlu_pro#accuracy"
+    }
+  ]
+}

flat/objects/05/2e/052efd4b-176e-4fc3-8a61-791685a38a28.json

@@ -0,0 +1,159 @@
+{
+  "schema_version": "0.2.2",
+  "evaluation_id": "fibble3_arena/meta/llama3.2/1773888493.269488",
+  "evaluation_timestamp": "2026-03-17",
+  "retrieved_timestamp": "1773888493.269488",
+  "source_metadata": {
+    "source_name": "Fibble³ Arena",
+    "source_type": "evaluation_run",
+    "source_organization_name": "Wordle Arena Project",
+    "source_organization_url": "https://drchangliu.github.io/Fibble3Arena/",
+    "evaluator_relationship": "third_party"
+  },
+  "eval_library": {
+    "name": "fibble3_arena",
+    "version": "1.0.0",
+    "additional_details": {
+      "github": "https://github.com/drchangliu/drchangliu.github.io"
+    }
+  },
+  "model_info": {
+    "name": "Llama3.2",
+    "id": "meta/llama3.2",
+    "developer": "Meta",
+    "inference_platform": "ollama"
+  },
+  "evaluation_results": [
+    {
+      "evaluation_name": "fibble3_arena",
+      "source_data": {
+        "dataset_name": "fibble3_arena_daily",
+        "source_type": "url",
+        "url": [
+          "https://drchangliu.github.io/Fibble3Arena/results.json"
+        ]
+      },
+      "evaluation_timestamp": "2026-03-17",
+      "metric_config": {
+        "evaluation_description": "Win rate (%) on Fibble³ Arena: Fibble³: Wordle variant with 3 lies per row of feedback, 8 attempts",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0,
+        "max_score": 100,
+        "additional_details": {
+          "raw_evaluation_name": "fibble3_arena_win_rate"
+        },
+        "metric_id": "win_rate",
+        "metric_name": "Win Rate",
+        "metric_kind": "win_rate",
+        "metric_unit": "proportion"
+      },
+      "score_details": {
+        "score": 0.0,
+        "uncertainty": {
+          "num_samples": 6
+        }
+      },
+      "generation_config": {
+        "generation_args": {
+          "temperature": 0.5,
+          "max_tokens": 20
+        },
+        "additional_details": {
+          "max_attempts": "8",
+          "lies_per_row": "3"
+        }
+      },
+      "evaluation_result_id": "fibble3_arena/meta/llama3.2/1773888493.269488#fibble3_arena#win_rate"
+    },
+    {
+      "evaluation_name": "fibble3_arena",
+      "source_data": {
+        "dataset_name": "fibble3_arena_daily",
+        "source_type": "url",
+        "url": [
+          "https://drchangliu.github.io/Fibble3Arena/results.json"
+        ]
+      },
+      "evaluation_timestamp": "2026-03-17",
+      "metric_config": {
+        "evaluation_description": "Average guesses used per game on Fibble³ Arena",
+        "lower_is_better": true,
+        "score_type": "continuous",
+        "min_score": 1,
+        "max_score": 8.0,
+        "additional_details": {
+          "raw_evaluation_name": "fibble3_arena_avg_attempts"
+        },
+        "metric_id": "mean_attempts",
+        "metric_name": "Average Attempts",
+        "metric_kind": "count",
+        "metric_unit": "attempts"
+      },
+      "score_details": {
+        "score": 12.0,
+        "uncertainty": {
+          "num_samples": 6
+        }
+      },
+      "generation_config": {
+        "generation_args": {
+          "temperature": 0.5,
+          "max_tokens": 20
+        },
+        "additional_details": {
+          "max_attempts": "8",
+          "lies_per_row": "3"
+        }
+      },
+      "evaluation_result_id": "fibble3_arena/meta/llama3.2/1773888493.269488#fibble3_arena#mean_attempts"
+    },
+    {
+      "evaluation_name": "fibble3_arena",
+      "source_data": {
+        "dataset_name": "fibble3_arena_daily",
+        "source_type": "url",
+        "url": [
+          "https://drchangliu.github.io/Fibble3Arena/results.json"
+        ]
+      },
+      "evaluation_timestamp": "2026-03-17",
+      "metric_config": {
+        "evaluation_description": "Mean response time per LLM invocation (ms) on Fibble³ Arena",
+        "lower_is_better": true,
+        "score_type": "continuous",
+        "min_score": 0,
+        "max_score": 600000,
+        "additional_details": {
+          "raw_evaluation_name": "fibble3_arena_avg_latency_ms"
+        },
+        "metric_id": "latency_mean",
+        "metric_name": "Average Latency",
+        "metric_kind": "latency",
+        "metric_unit": "ms"
+      },
+      "score_details": {
+        "score": 372.0,
+        "uncertainty": {
+          "num_samples": 6
+        }
+      },
+      "generation_config": {
+        "generation_args": {
+          "temperature": 0.5,
+          "max_tokens": 20
+        },
+        "additional_details": {
+          "max_attempts": "8",
+          "lies_per_row": "3"
+        }
+      },
+      "evaluation_result_id": "fibble3_arena/meta/llama3.2/1773888493.269488#fibble3_arena#latency_mean"
+    }
+  ],
+  "detailed_evaluation_results": {
+    "format": "jsonl",
+    "file_path": "052efd4b-176e-4fc3-8a61-791685a38a28_samples.jsonl",
+    "total_rows": 8
+  }
+}

flat/objects/05/32/0532380b-830f-462b-ab2f-6f8aca7ae135.json

@@ -0,0 +1,508 @@
+{
+  "schema_version": "0.2.2",
+  "evaluation_id": "TempVS/Mantis-Idefics 8B/1771591481.616601",
+  "retrieved_timestamp": "1771591481.616601",
+  "source_metadata": {
+    "source_name": "alphaXiv State of the Art",
+    "source_type": "documentation",
+    "source_organization_name": "alphaXiv",
+    "source_organization_url": "https://alphaxiv.org",
+    "evaluator_relationship": "third_party",
+    "additional_details": {
+      "alphaxiv_dataset_org": "Utrecht University",
+      "alphaxiv_dataset_type": "image",
+      "scrape_source": "https://github.com/alphaXiv/feedback/issues/189"
+    }
+  },
+  "model_info": {
+    "id": "Mantis-Idefics 8B",
+    "name": "Mantis-Idefics 8B",
+    "developer": "unknown"
+  },
+  "evaluation_results": [
+    {
+      "evaluation_name": "TempVS",
+      "source_data": {
+        "dataset_name": "TempVS",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2506.10415"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Measures accuracy on the sentence ordering task using full captions (MT2). This score is conditional, calculated only on instances where the model first passed the strict grounding test (GTs) for all events in the sequence. This is a stringent test of temporal reasoning, isolated from grounding failures.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Conditional Accuracy (%)",
+          "alphaxiv_is_primary": "True",
+          "raw_evaluation_name": "TempVS: Conditional Accuracy on Sentence Ordering (Caption-based, MT2|GTs)"
+        },
+        "metric_id": "tempvs_conditional_accuracy_on_sentence_ordering_caption_based_mt2_gts",
+        "metric_name": "TempVS: Conditional Accuracy on Sentence Ordering (Caption-based, MT2|GTs)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 0
+      },
+      "evaluation_result_id": "TempVS/Mantis-Idefics 8B/1771591481.616601#tempvs#tempvs_conditional_accuracy_on_sentence_ordering_caption_based_mt2_gts"
+    },
+    {
+      "evaluation_name": "TempVS",
+      "source_data": {
+        "dataset_name": "TempVS",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2506.10415"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Measures accuracy on the image ordering task using original captions (MT3). This score is conditional, calculated only on instances where the model first passed the strict grounding test (GTs). This is a stringent test of a model's ability to order images based on a narrative, assuming it understands the visual content correctly.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Conditional Accuracy (%)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "TempVS: Conditional Accuracy on Image Ordering (Caption-based, MT3|GTs)"
+        },
+        "metric_id": "tempvs_conditional_accuracy_on_image_ordering_caption_based_mt3_gts",
+        "metric_name": "TempVS: Conditional Accuracy on Image Ordering (Caption-based, MT3|GTs)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 50
+      },
+      "evaluation_result_id": "TempVS/Mantis-Idefics 8B/1771591481.616601#tempvs#tempvs_conditional_accuracy_on_image_ordering_caption_based_mt3_gts"
+    },
+    {
+      "evaluation_name": "TempVS",
+      "source_data": {
+        "dataset_name": "TempVS",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2506.10415"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Measures the standard zero-shot accuracy on the image ordering task (MT3). Given a textual description of events and a shuffled set of images, models must choose the correct chronological order of the images. This metric does not require the model to have passed a separate grounding test.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Accuracy (%)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "TempVS: Accuracy on Image Ordering (Event-based, MT3)"
+        },
+        "metric_id": "tempvs_accuracy_on_image_ordering_event_based_mt3",
+        "metric_name": "TempVS: Accuracy on Image Ordering (Event-based, MT3)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 18.6
+      },
+      "evaluation_result_id": "TempVS/Mantis-Idefics 8B/1771591481.616601#tempvs#tempvs_accuracy_on_image_ordering_event_based_mt3"
+    },
+    {
+      "evaluation_name": "TempVS",
+      "source_data": {
+        "dataset_name": "TempVS",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2506.10415"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Measures accuracy on the image ordering task using event descriptions (MT3). This score is conditional, calculated only on instances where the model first passed the strict grounding test (GTs). This tests the model's ability to order images based on text, assuming it has correctly identified which image corresponds to which event description.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Conditional Accuracy (%)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "TempVS: Conditional Accuracy on Image Ordering (Event-based, MT3|GTs)"
+        },
+        "metric_id": "tempvs_conditional_accuracy_on_image_ordering_event_based_mt3_gts",
+        "metric_name": "TempVS: Conditional Accuracy on Image Ordering (Event-based, MT3|GTs)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 0
+      },
+      "evaluation_result_id": "TempVS/Mantis-Idefics 8B/1771591481.616601#tempvs#tempvs_conditional_accuracy_on_image_ordering_event_based_mt3_gts"
+    },
+    {
+      "evaluation_name": "TempVS",
+      "source_data": {
+        "dataset_name": "TempVS",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2506.10415"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Measures the standard zero-shot accuracy on the sentence ordering task (MT2). Given an image sequence and a shuffled list of original captions, models must choose the correct chronological order of sentences. This version is often more challenging due to the richer detail in captions.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Accuracy (%)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "TempVS: Accuracy on Sentence Ordering (Caption-based, MT2)"
+        },
+        "metric_id": "tempvs_accuracy_on_sentence_ordering_caption_based_mt2",
+        "metric_name": "TempVS: Accuracy on Sentence Ordering (Caption-based, MT2)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 20.8
+      },
+      "evaluation_result_id": "TempVS/Mantis-Idefics 8B/1771591481.616601#tempvs#tempvs_accuracy_on_sentence_ordering_caption_based_mt2"
+    },
+    {
+      "evaluation_name": "TempVS",
+      "source_data": {
+        "dataset_name": "TempVS",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2506.10415"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Measures the standard zero-shot accuracy on the sentence ordering task (MT2). Given an image sequence and a shuffled list of event descriptions, models must choose the correct chronological order of sentences. This metric does not require the model to have passed the grounding test.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Accuracy (%)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "TempVS: Accuracy on Sentence Ordering (Event-based, MT2)"
+        },
+        "metric_id": "tempvs_accuracy_on_sentence_ordering_event_based_mt2",
+        "metric_name": "TempVS: Accuracy on Sentence Ordering (Event-based, MT2)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 22.2
+      },
+      "evaluation_result_id": "TempVS/Mantis-Idefics 8B/1771591481.616601#tempvs#tempvs_accuracy_on_sentence_ordering_event_based_mt2"
+    },
+    {
+      "evaluation_name": "TempVS",
+      "source_data": {
+        "dataset_name": "TempVS",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2506.10415"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Measures accuracy on the sentence ordering task using event descriptions (MT2). This score is conditional, calculated only on instances where the model first passed the strict grounding test (GTs) for all events in the sequence. This metric shows how well models can reason about temporal order when visual grounding is not a factor.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Conditional Accuracy (%)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "TempVS: Conditional Accuracy on Sentence Ordering (Event-based, MT2|GTs)"
+        },
+        "metric_id": "tempvs_conditional_accuracy_on_sentence_ordering_event_based_mt2_gts",
+        "metric_name": "TempVS: Conditional Accuracy on Sentence Ordering (Event-based, MT2|GTs)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 0
+      },
+      "evaluation_result_id": "TempVS/Mantis-Idefics 8B/1771591481.616601#tempvs#tempvs_conditional_accuracy_on_sentence_ordering_event_based_mt2_gts"
+    },
+    {
+      "evaluation_name": "TempVS",
+      "source_data": {
+        "dataset_name": "TempVS",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2506.10415"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Measures the percentage of image sequences where a model correctly grounds all events for the sentence ordering task (MT2) using the original, more detailed captions. A 'pass' requires correctly matching all captions to their respective images in the sequence.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Strict Grounding Accuracy (%)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "TempVS: Strict Grounding (GTs) Accuracy on Sentence Ordering (Caption-based)"
+        },
+        "metric_id": "tempvs_strict_grounding_gts_accuracy_on_sentence_ordering_caption_based",
+        "metric_name": "TempVS: Strict Grounding (GTs) Accuracy on Sentence Ordering (Caption-based)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 0.2
+      },
+      "evaluation_result_id": "TempVS/Mantis-Idefics 8B/1771591481.616601#tempvs#tempvs_strict_grounding_gts_accuracy_on_sentence_ordering_caption_based"
+    },
+    {
+      "evaluation_name": "TempVS",
+      "source_data": {
+        "dataset_name": "TempVS",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2506.10415"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Measures the percentage of image sequences where a model correctly grounds all events for the sentence ordering task (MT2) using extracted event descriptions. A 'pass' requires correctly matching all event descriptions to their respective images in the sequence.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Strict Grounding Accuracy (%)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "TempVS: Strict Grounding (GTs) Accuracy on Sentence Ordering (Event-based)"
+        },
+        "metric_id": "tempvs_strict_grounding_gts_accuracy_on_sentence_ordering_event_based",
+        "metric_name": "TempVS: Strict Grounding (GTs) Accuracy on Sentence Ordering (Event-based)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 0.1
+      },
+      "evaluation_result_id": "TempVS/Mantis-Idefics 8B/1771591481.616601#tempvs#tempvs_strict_grounding_gts_accuracy_on_sentence_ordering_event_based"
+    },
+    {
+      "evaluation_name": "TempVS",
+      "source_data": {
+        "dataset_name": "TempVS",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2506.10415"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Measures the percentage of image sequences where a model correctly grounds all events for the three-event relation inference task (MT1). A 'pass' requires correctly matching all three event descriptions to their respective images in the sequence. This is a foundational test of visual-linguistic understanding.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Strict Grounding Accuracy (%)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "TempVS: Strict Grounding (GTs) Accuracy on Three-Event Relation Inference"
+        },
+        "metric_id": "tempvs_strict_grounding_gts_accuracy_on_three_event_relation_inference",
+        "metric_name": "TempVS: Strict Grounding (GTs) Accuracy on Three-Event Relation Inference",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 4.1
+      },
+      "evaluation_result_id": "TempVS/Mantis-Idefics 8B/1771591481.616601#tempvs#tempvs_strict_grounding_gts_accuracy_on_three_event_relation_inference"
+    },
+    {
+      "evaluation_name": "TempVS",
+      "source_data": {
+        "dataset_name": "TempVS",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2506.10415"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Measures the percentage of image sequences where a model correctly grounds all events for the two-event relation inference task (MT1). A 'pass' requires correctly matching both event descriptions to their respective images in the sequence. This is a foundational test of visual-linguistic understanding.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Strict Grounding Accuracy (%)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "TempVS: Strict Grounding (GTs) Accuracy on Two-Event Relation Inference"
+        },
+        "metric_id": "tempvs_strict_grounding_gts_accuracy_on_two_event_relation_inference",
+        "metric_name": "TempVS: Strict Grounding (GTs) Accuracy on Two-Event Relation Inference",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 12.2
+      },
+      "evaluation_result_id": "TempVS/Mantis-Idefics 8B/1771591481.616601#tempvs#tempvs_strict_grounding_gts_accuracy_on_two_event_relation_inference"
+    },
+    {
+      "evaluation_name": "TempVS",
+      "source_data": {
+        "dataset_name": "TempVS",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2506.10415"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Measures the standard zero-shot accuracy on the three-event relation inference task (MT1), where models must determine if a textual statement describing the temporal order of three events is true or false based on an image sequence. This metric does not require the model to have passed the grounding test.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Accuracy (%)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "TempVS: Accuracy on Three-Event Relation Inference (MT1)"
+        },
+        "metric_id": "tempvs_accuracy_on_three_event_relation_inference_mt1",
+        "metric_name": "TempVS: Accuracy on Three-Event Relation Inference (MT1)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 52
+      },
+      "evaluation_result_id": "TempVS/Mantis-Idefics 8B/1771591481.616601#tempvs#tempvs_accuracy_on_three_event_relation_inference_mt1"
+    },
+    {
+      "evaluation_name": "TempVS",
+      "source_data": {
+        "dataset_name": "TempVS",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2506.10415"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Measures the accuracy on the three-event relation inference task (MT1) for only those image sequences where the model successfully passed the strict grounding test (GTs). This metric evaluates temporal reasoning ability conditioned on correct visual grounding.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Conditional Accuracy (%)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "TempVS: Conditional Accuracy on Three-Event Relation Inference (MT1|GTs)"
+        },
+        "metric_id": "tempvs_conditional_accuracy_on_three_event_relation_inference_mt1_gts",
+        "metric_name": "TempVS: Conditional Accuracy on Three-Event Relation Inference (MT1|GTs)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 51.6
+      },
+      "evaluation_result_id": "TempVS/Mantis-Idefics 8B/1771591481.616601#tempvs#tempvs_conditional_accuracy_on_three_event_relation_inference_mt1_gts"
+    },
+    {
+      "evaluation_name": "TempVS",
+      "source_data": {
+        "dataset_name": "TempVS",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2506.10415"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Measures the standard zero-shot accuracy on the two-event relation inference task (MT1), where models must determine if a textual statement describing the temporal order of two events is true or false based on an image sequence. This metric does not require the model to have passed the grounding test.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Accuracy (%)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "TempVS: Accuracy on Two-Event Relation Inference (MT1)"
+        },
+        "metric_id": "tempvs_accuracy_on_two_event_relation_inference_mt1",
+        "metric_name": "TempVS: Accuracy on Two-Event Relation Inference (MT1)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 51.9
+      },
+      "evaluation_result_id": "TempVS/Mantis-Idefics 8B/1771591481.616601#tempvs#tempvs_accuracy_on_two_event_relation_inference_mt1"
+    },
+    {
+      "evaluation_name": "TempVS",
+      "source_data": {
+        "dataset_name": "TempVS",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2506.10415"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Measures the standard zero-shot accuracy on the image ordering task (MT3). Given a set of original captions and a shuffled set of images, models must choose the correct chronological order of the images. This version is often more challenging due to the richer detail in captions.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Accuracy (%)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "TempVS: Accuracy on Image Ordering (Caption-based, MT3)"
+        },
+        "metric_id": "tempvs_accuracy_on_image_ordering_caption_based_mt3",
+        "metric_name": "TempVS: Accuracy on Image Ordering (Caption-based, MT3)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 19.2
+      },
+      "evaluation_result_id": "TempVS/Mantis-Idefics 8B/1771591481.616601#tempvs#tempvs_accuracy_on_image_ordering_caption_based_mt3"
+    },
+    {
+      "evaluation_name": "TempVS",
+      "source_data": {
+        "dataset_name": "TempVS",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2506.10415"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Measures the accuracy on the two-event relation inference task (MT1) for only those image sequences where the model successfully passed the strict grounding test (GTs). This metric evaluates temporal reasoning ability conditioned on correct visual grounding.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Conditional Accuracy (%)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "TempVS: Conditional Accuracy on Two-Event Relation Inference (MT1|GTs)"
+        },
+        "metric_id": "tempvs_conditional_accuracy_on_two_event_relation_inference_mt1_gts",
+        "metric_name": "TempVS: Conditional Accuracy on Two-Event Relation Inference (MT1|GTs)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 53.3
+      },
+      "evaluation_result_id": "TempVS/Mantis-Idefics 8B/1771591481.616601#tempvs#tempvs_conditional_accuracy_on_two_event_relation_inference_mt1_gts"
+    }
+  ],
+  "eval_library": {
+    "name": "alphaxiv",
+    "version": "unknown"
+  }
+}

flat/objects/05/32/0532cb1b-840c-4155-b4f0-ba3d638ce9a8.json

@@ -0,0 +1,208 @@
+{
+  "schema_version": "0.2.2",
+  "evaluation_id": "ReFACT/Gemma-3-27B/1771591481.616601",
+  "retrieved_timestamp": "1771591481.616601",
+  "source_metadata": {
+    "source_name": "alphaXiv State of the Art",
+    "source_type": "documentation",
+    "source_organization_name": "alphaXiv",
+    "source_organization_url": "https://alphaxiv.org",
+    "evaluator_relationship": "third_party",
+    "additional_details": {
+      "alphaxiv_dataset_org": "University of Potsdam",
+      "alphaxiv_dataset_type": "text",
+      "scrape_source": "https://github.com/alphaXiv/feedback/issues/189"
+    }
+  },
+  "model_info": {
+    "id": "Gemma-3-27B",
+    "name": "Gemma-3-27B",
+    "developer": "unknown"
+  },
+  "evaluation_results": [
+    {
+      "evaluation_name": "ReFACT",
+      "source_data": {
+        "dataset_name": "ReFACT",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2509.25868"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Average accuracy across all five tasks (Independent Judgment, Comparative Judgment, Negation Localization, Entity Localization, Entity Correction) on the ReFACT benchmark. This metric provides a holistic view of a model's ability to handle scientific confabulation, with even the top model (GPT-4o) scoring only 54%.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Average Accuracy (%)",
+          "alphaxiv_is_primary": "True",
+          "raw_evaluation_name": "Average Accuracy on ReFACT Benchmark"
+        },
+        "metric_id": "average_accuracy_on_refact_benchmark",
+        "metric_name": "Average Accuracy on ReFACT Benchmark",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 52
+      },
+      "evaluation_result_id": "ReFACT/Gemma-3-27B/1771591481.616601#refact#average_accuracy_on_refact_benchmark"
+    },
+    {
+      "evaluation_name": "ReFACT",
+      "source_data": {
+        "dataset_name": "ReFACT",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2509.25868"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Accuracy in identifying which of two presented answers (one factual, one confabulated) contains the scientific error. This task tests a model's ability to perform fine-grained factual comparison, a key capability for 'LLM-as-judge' paradigms.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Comparative Judgment Accuracy (%)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Comparative Confabulation Judgment Accuracy on ReFACT"
+        },
+        "metric_id": "comparative_confabulation_judgment_accuracy_on_refact",
+        "metric_name": "Comparative Confabulation Judgment Accuracy on ReFACT",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 56
+      },
+      "evaluation_result_id": "ReFACT/Gemma-3-27B/1771591481.616601#refact#comparative_confabulation_judgment_accuracy_on_refact"
+    },
+    {
+      "evaluation_name": "ReFACT",
+      "source_data": {
+        "dataset_name": "ReFACT",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2509.25868"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Exact Match (EM) accuracy in correcting a confabulated entity by reverting it to the original, factual entity. This is the most difficult task, assessing a model's ability to actively repair factual errors. Performance is very low across all models, highlighting a major weakness.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Entity Correction Accuracy (%)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Entity Confabulation Correction Accuracy on ReFACT"
+        },
+        "metric_id": "entity_confabulation_correction_accuracy_on_refact",
+        "metric_name": "Entity Confabulation Correction Accuracy on ReFACT",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 24
+      },
+      "evaluation_result_id": "ReFACT/Gemma-3-27B/1771591481.616601#refact#entity_confabulation_correction_accuracy_on_refact"
+    },
+    {
+      "evaluation_name": "ReFACT",
+      "source_data": {
+        "dataset_name": "ReFACT",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2509.25868"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Accuracy in identifying the specific spans where a factual entity was replaced with a non-factual one (e.g., 'DNA' vs. 'RNA'). This is a challenging task requiring deep semantic understanding, where even top models fail more than half the time.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Entity Localization Accuracy (%)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Entity Confabulation Localization Accuracy on ReFACT"
+        },
+        "metric_id": "entity_confabulation_localization_accuracy_on_refact",
+        "metric_name": "Entity Confabulation Localization Accuracy on ReFACT",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 46
+      },
+      "evaluation_result_id": "ReFACT/Gemma-3-27B/1771591481.616601#refact#entity_confabulation_localization_accuracy_on_refact"
+    },
+    {
+      "evaluation_name": "ReFACT",
+      "source_data": {
+        "dataset_name": "ReFACT",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2509.25868"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Accuracy in classifying a single scientific text as either factual or containing confabulation. This task assesses the model's fundamental ability to detect scientific inaccuracies in isolation.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Independent Judgment Accuracy (%)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Independent Confabulation Judgment Accuracy on ReFACT"
+        },
+        "metric_id": "independent_confabulation_judgment_accuracy_on_refact",
+        "metric_name": "Independent Confabulation Judgment Accuracy on ReFACT",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 71
+      },
+      "evaluation_result_id": "ReFACT/Gemma-3-27B/1771591481.616601#refact#independent_confabulation_judgment_accuracy_on_refact"
+    },
+    {
+      "evaluation_name": "ReFACT",
+      "source_data": {
+        "dataset_name": "ReFACT",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2509.25868"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Accuracy in identifying the specific sentence containing a logical negation error (e.g., 'lose' vs. 'gain') within a text known to be confabulated. This is generally an easier task for models than identifying entity errors.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Negation Localization Accuracy (%)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Negation Confabulation Localization Accuracy on ReFACT"
+        },
+        "metric_id": "negation_confabulation_localization_accuracy_on_refact",
+        "metric_name": "Negation Confabulation Localization Accuracy on ReFACT",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 61
+      },
+      "evaluation_result_id": "ReFACT/Gemma-3-27B/1771591481.616601#refact#negation_confabulation_localization_accuracy_on_refact"
+    }
+  ],
+  "eval_library": {
+    "name": "alphaxiv",
+    "version": "unknown"
+  }
+}

flat/objects/05/33/053380e6-f97b-41dc-826c-e17954648344.json

@@ -0,0 +1,148 @@
+{
+  "schema_version": "0.2.2",
+  "evaluation_id": "Stanford-ORB/RelitLRM/1771591481.616601",
+  "retrieved_timestamp": "1771591481.616601",
+  "source_metadata": {
+    "source_name": "alphaXiv State of the Art",
+    "source_type": "documentation",
+    "source_organization_name": "alphaXiv",
+    "source_organization_url": "https://alphaxiv.org",
+    "evaluator_relationship": "third_party",
+    "additional_details": {
+      "alphaxiv_dataset_org": "Stanford University",
+      "alphaxiv_dataset_type": "image",
+      "scrape_source": "https://github.com/alphaXiv/feedback/issues/189"
+    }
+  },
+  "model_info": {
+    "id": "RelitLRM",
+    "name": "RelitLRM",
+    "developer": "unknown"
+  },
+  "evaluation_results": [
+    {
+      "evaluation_name": "Stanford-ORB",
+      "source_data": {
+        "dataset_name": "Stanford-ORB",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2310.16044"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Evaluates material recovery by rendering the object under novel, unseen real-world lighting. This metric is the Peak Signal-to-Noise Ratio on HDR images (PSNR-H), comparing the relit image to a ground-truth photo. Higher scores indicate more accurate material and lighting disentanglement.",
+        "additional_details": {
+          "alphaxiv_y_axis": "PSNR-H",
+          "alphaxiv_is_primary": "True",
+          "raw_evaluation_name": "Novel Scene Relighting: Image Quality (PSNR-H)"
+        },
+        "metric_id": "novel_scene_relighting_image_quality_psnr_h",
+        "metric_name": "Novel Scene Relighting: Image Quality (PSNR-H)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 24.67
+      },
+      "evaluation_result_id": "Stanford-ORB/RelitLRM/1771591481.616601#stanford_orb#novel_scene_relighting_image_quality_psnr_h"
+    },
+    {
+      "evaluation_name": "Stanford-ORB",
+      "source_data": {
+        "dataset_name": "Stanford-ORB",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2310.16044"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": true,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Evaluates material recovery by rendering the object under novel lighting and measuring the Learned Perceptual Image Patch Similarity (LPIPS) between the rendered LDR image and the ground truth. Lower scores indicate higher perceptual similarity.",
+        "additional_details": {
+          "alphaxiv_y_axis": "LPIPS",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Novel Scene Relighting: Perceptual Similarity (LPIPS)"
+        },
+        "metric_id": "novel_scene_relighting_perceptual_similarity_lpips",
+        "metric_name": "Novel Scene Relighting: Perceptual Similarity (LPIPS)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 0.032
+      },
+      "evaluation_result_id": "Stanford-ORB/RelitLRM/1771591481.616601#stanford_orb#novel_scene_relighting_perceptual_similarity_lpips"
+    },
+    {
+      "evaluation_name": "Stanford-ORB",
+      "source_data": {
+        "dataset_name": "Stanford-ORB",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2310.16044"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Evaluates material recovery by rendering the object under novel, unseen real-world lighting. This metric is the Peak Signal-to-Noise Ratio on LDR images (PSNR-L) after tone mapping, comparing the relit image to a ground-truth photo. Higher scores indicate more accurate material decomposition.",
+        "additional_details": {
+          "alphaxiv_y_axis": "PSNR-L",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Novel Scene Relighting: Image Quality (PSNR-L)"
+        },
+        "metric_id": "novel_scene_relighting_image_quality_psnr_l",
+        "metric_name": "Novel Scene Relighting: Image Quality (PSNR-L)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 31.52
+      },
+      "evaluation_result_id": "Stanford-ORB/RelitLRM/1771591481.616601#stanford_orb#novel_scene_relighting_image_quality_psnr_l"
+    },
+    {
+      "evaluation_name": "Stanford-ORB",
+      "source_data": {
+        "dataset_name": "Stanford-ORB",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2310.16044"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Evaluates material recovery by rendering the object under novel lighting and measuring the Structural Similarity Index Measure (SSIM) between the rendered LDR image and the ground truth. Higher values (closer to 1) indicate better structural preservation.",
+        "additional_details": {
+          "alphaxiv_y_axis": "SSIM",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Novel Scene Relighting: Structural Similarity (SSIM)"
+        },
+        "metric_id": "novel_scene_relighting_structural_similarity_ssim",
+        "metric_name": "Novel Scene Relighting: Structural Similarity (SSIM)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 0.969
+      },
+      "evaluation_result_id": "Stanford-ORB/RelitLRM/1771591481.616601#stanford_orb#novel_scene_relighting_structural_similarity_ssim"
+    }
+  ],
+  "eval_library": {
+    "name": "alphaxiv",
+    "version": "unknown"
+  }
+}

flat/objects/05/34/053422f4-da2b-485d-a50d-f80a02d7a156.json

@@ -0,0 +1,58 @@
+{
+  "schema_version": "0.2.2",
+  "evaluation_id": "PutnamBench/ABEL/1771591481.616601",
+  "retrieved_timestamp": "1771591481.616601",
+  "source_metadata": {
+    "source_name": "alphaXiv State of the Art",
+    "source_type": "documentation",
+    "source_organization_name": "alphaXiv",
+    "source_organization_url": "https://alphaxiv.org",
+    "evaluator_relationship": "third_party",
+    "additional_details": {
+      "alphaxiv_dataset_org": "UT Austin",
+      "alphaxiv_dataset_type": "text",
+      "scrape_source": "https://github.com/alphaXiv/feedback/issues/189"
+    }
+  },
+  "model_info": {
+    "id": "ABEL",
+    "name": "ABEL",
+    "developer": "unknown"
+  },
+  "evaluation_results": [
+    {
+      "evaluation_name": "PutnamBench",
+      "source_data": {
+        "dataset_name": "PutnamBench",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2407.11214"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "This benchmark measures the ability of theorem provers and AI models to solve undergraduate-level mathematical competition problems from the Putnam Mathematical Competition, formalized in the Lean 4 proof assistant. The score represents the total number of problems successfully solved out of 660. Results are from the official project leaderboard.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Number of Problems Solved (Lean)",
+          "alphaxiv_is_primary": "True",
+          "raw_evaluation_name": "PutnamBench: Mathematical Problem Solving in Lean"
+        },
+        "metric_id": "putnambench_mathematical_problem_solving_in_lean",
+        "metric_name": "PutnamBench: Mathematical Problem Solving in Lean",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 7
+      },
+      "evaluation_result_id": "PutnamBench/ABEL/1771591481.616601#putnambench#putnambench_mathematical_problem_solving_in_lean"
+    }
+  ],
+  "eval_library": {
+    "name": "alphaxiv",
+    "version": "unknown"
+  }
+}

flat/objects/05/34/05346be2-2bd3-4723-bc8e-bd547c3be408.json

@@ -0,0 +1,178 @@
+{
+  "schema_version": "0.2.2",
+  "evaluation_id": "AC-EVAL/Xunzi-Qwen-Chat/1771591481.616601",
+  "retrieved_timestamp": "1771591481.616601",
+  "source_metadata": {
+    "source_name": "alphaXiv State of the Art",
+    "source_type": "documentation",
+    "source_organization_name": "alphaXiv",
+    "source_organization_url": "https://alphaxiv.org",
+    "evaluator_relationship": "third_party",
+    "additional_details": {
+      "alphaxiv_dataset_org": "Beijing University of Posts and Telecommunications",
+      "alphaxiv_dataset_type": "text",
+      "scrape_source": "https://github.com/alphaXiv/feedback/issues/189"
+    }
+  },
+  "model_info": {
+    "id": "Xunzi-Qwen-Chat",
+    "name": "Xunzi-Qwen-Chat",
+    "developer": "unknown"
+  },
+  "evaluation_results": [
+    {
+      "evaluation_name": "AC-EVAL",
+      "source_data": {
+        "dataset_name": "AC-EVAL",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2403.06574"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Average accuracy of large language models on the AC-EVAL benchmark in a zero-shot, answer-only (AO) setting. This setting evaluates the models' inherent understanding of ancient Chinese without providing any examples in the prompt.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Average Accuracy (%) - Zero-shot AO",
+          "alphaxiv_is_primary": "True",
+          "raw_evaluation_name": "AC-EVAL: Average Accuracy (Zero-shot, Answer-Only)"
+        },
+        "metric_id": "ac_eval_average_accuracy_zero_shot_answer_only",
+        "metric_name": "AC-EVAL: Average Accuracy (Zero-shot, Answer-Only)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 45.13
+      },
+      "evaluation_result_id": "AC-EVAL/Xunzi-Qwen-Chat/1771591481.616601#ac_eval#ac_eval_average_accuracy_zero_shot_answer_only"
+    },
+    {
+      "evaluation_name": "AC-EVAL",
+      "source_data": {
+        "dataset_name": "AC-EVAL",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2403.06574"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Average accuracy of large language models on the AC-EVAL benchmark in a five-shot, answer-only (AO) setting. This setting evaluates the models' ability to leverage in-context examples for understanding ancient Chinese. The paper notes that few-shot learning often had a limited or negative impact for this specific benchmark.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Average Accuracy (%) - Five-shot AO",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "AC-EVAL: Average Accuracy (Five-shot, Answer-Only)"
+        },
+        "metric_id": "ac_eval_average_accuracy_five_shot_answer_only",
+        "metric_name": "AC-EVAL: Average Accuracy (Five-shot, Answer-Only)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 40.8
+      },
+      "evaluation_result_id": "AC-EVAL/Xunzi-Qwen-Chat/1771591481.616601#ac_eval#ac_eval_average_accuracy_five_shot_answer_only"
+    },
+    {
+      "evaluation_name": "AC-EVAL",
+      "source_data": {
+        "dataset_name": "AC-EVAL",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2403.06574"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Accuracy on the 'General Historical Knowledge' category of AC-EVAL, considered the 'easy' difficulty level. This category assesses foundational knowledge of ancient Chinese history, geography, and culture. Evaluated in a zero-shot, answer-only (AO) setting.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Accuracy (%) - Historical Knowledge (Zero-shot AO)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "AC-EVAL: General Historical Knowledge Accuracy (Zero-shot, AO)"
+        },
+        "metric_id": "ac_eval_general_historical_knowledge_accuracy_zero_shot_ao",
+        "metric_name": "AC-EVAL: General Historical Knowledge Accuracy (Zero-shot, AO)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 60.2
+      },
+      "evaluation_result_id": "AC-EVAL/Xunzi-Qwen-Chat/1771591481.616601#ac_eval#ac_eval_general_historical_knowledge_accuracy_zero_shot_ao"
+    },
+    {
+      "evaluation_name": "AC-EVAL",
+      "source_data": {
+        "dataset_name": "AC-EVAL",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2403.06574"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Accuracy on the 'Long Text Comprehension' category of AC-EVAL, considered the 'hard' difficulty level. This category assesses summarization, analysis, and appreciation of classical prose and poetry. This is the most challenging category for all models, evaluated in a zero-shot, answer-only (AO) setting.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Accuracy (%) - Long Text (Zero-shot AO)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "AC-EVAL: Long Text Comprehension Accuracy (Zero-shot, AO)"
+        },
+        "metric_id": "ac_eval_long_text_comprehension_accuracy_zero_shot_ao",
+        "metric_name": "AC-EVAL: Long Text Comprehension Accuracy (Zero-shot, AO)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 30.87
+      },
+      "evaluation_result_id": "AC-EVAL/Xunzi-Qwen-Chat/1771591481.616601#ac_eval#ac_eval_long_text_comprehension_accuracy_zero_shot_ao"
+    },
+    {
+      "evaluation_name": "AC-EVAL",
+      "source_data": {
+        "dataset_name": "AC-EVAL",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2403.06574"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Accuracy on the 'Short Text Understanding' category of AC-EVAL, considered the 'normal' difficulty level. This category assesses lexical semantics, pragmatics, allusions, and translation from short ancient Chinese texts. Evaluated in a zero-shot, answer-only (AO) setting.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Accuracy (%) - Short Text (Zero-shot AO)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "AC-EVAL: Short Text Understanding Accuracy (Zero-shot, AO)"
+        },
+        "metric_id": "ac_eval_short_text_understanding_accuracy_zero_shot_ao",
+        "metric_name": "AC-EVAL: Short Text Understanding Accuracy (Zero-shot, AO)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 44.31
+      },
+      "evaluation_result_id": "AC-EVAL/Xunzi-Qwen-Chat/1771591481.616601#ac_eval#ac_eval_short_text_understanding_accuracy_zero_shot_ao"
+    }
+  ],
+  "eval_library": {
+    "name": "alphaxiv",
+    "version": "unknown"
+  }
+}

flat/objects/05/3a/053a7b11-0d89-4289-bfe4-ed3eca7c4a97.json

@@ -0,0 +1,590 @@
+{
+  "schema_version": "0.2.2",
+  "evaluation_id": "mmlu-pro/microsoft_phi3-mini-4k/tiger-lab/1777613486.918081",
+  "retrieved_timestamp": "1777613486.918081",
+  "source_metadata": {
+    "source_name": "MMLU-Pro Leaderboard",
+    "source_type": "documentation",
+    "source_organization_name": "TIGER-Lab",
+    "source_organization_url": "https://tiger-ai-lab.github.io",
+    "evaluator_relationship": "third_party",
+    "additional_details": {
+      "leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
+      "results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
+      "paper_url": "https://arxiv.org/abs/2406.01574",
+      "github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
+      "leaderboard_data_source": "TIGER-Lab"
+    }
+  },
+  "eval_library": {
+    "name": "MMLU-Pro leaderboard (TIGER-Lab)",
+    "version": "unknown"
+  },
+  "model_info": {
+    "name": "Phi3-mini-4k",
+    "id": "microsoft/phi3-mini-4k",
+    "developer": "microsoft",
+    "additional_details": {
+      "raw_model_name": "Phi3-mini-4k",
+      "size_billions_parameters": "3.8",
+      "leaderboard_data_source": "TIGER-Lab"
+    }
+  },
+  "evaluation_results": [
+    {
+      "evaluation_result_id": "mmlu_pro/overall",
+      "evaluation_name": "MMLU-Pro (overall)",
+      "source_data": {
+        "dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
+        "source_type": "hf_dataset",
+        "hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
+        "hf_split": "train",
+        "additional_details": {
+          "results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
+          "leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
+          "benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
+          "paper_url": "https://arxiv.org/abs/2406.01574",
+          "github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
+          "dataset_total_questions": "12000",
+          "prompt_style": "5-shot CoT"
+        }
+      },
+      "metric_config": {
+        "evaluation_description": "Overall accuracy across the ~12,000-question MMLU-Pro benchmark, evaluated 5-shot with chain-of-thought.",
+        "metric_id": "mmlu_pro/overall",
+        "metric_name": "MMLU-Pro (overall)",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "additional_details": {
+          "aggregation": "accuracy_over_subset",
+          "prompt_style": "5-shot CoT"
+        }
+      },
+      "score_details": {
+        "score": 0.4566
+      }
+    },
+    {
+      "evaluation_result_id": "mmlu_pro/biology",
+      "evaluation_name": "MMLU-Pro (Biology)",
+      "source_data": {
+        "dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
+        "source_type": "hf_dataset",
+        "hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
+        "hf_split": "train",
+        "additional_details": {
+          "results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
+          "leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
+          "benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
+          "paper_url": "https://arxiv.org/abs/2406.01574",
+          "github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
+          "dataset_total_questions": "12000",
+          "prompt_style": "5-shot CoT"
+        }
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy on the MMLU-Pro Biology subset, evaluated 5-shot with chain-of-thought.",
+        "metric_id": "mmlu_pro/biology",
+        "metric_name": "MMLU-Pro (Biology)",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "additional_details": {
+          "aggregation": "accuracy_over_subset",
+          "prompt_style": "5-shot CoT"
+        }
+      },
+      "score_details": {
+        "score": 0.7015
+      }
+    },
+    {
+      "evaluation_result_id": "mmlu_pro/business",
+      "evaluation_name": "MMLU-Pro (Business)",
+      "source_data": {
+        "dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
+        "source_type": "hf_dataset",
+        "hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
+        "hf_split": "train",
+        "additional_details": {
+          "results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
+          "leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
+          "benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
+          "paper_url": "https://arxiv.org/abs/2406.01574",
+          "github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
+          "dataset_total_questions": "12000",
+          "prompt_style": "5-shot CoT"
+        }
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy on the MMLU-Pro Business subset, evaluated 5-shot with chain-of-thought.",
+        "metric_id": "mmlu_pro/business",
+        "metric_name": "MMLU-Pro (Business)",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "additional_details": {
+          "aggregation": "accuracy_over_subset",
+          "prompt_style": "5-shot CoT"
+        }
+      },
+      "score_details": {
+        "score": 0.5044
+      }
+    },
+    {
+      "evaluation_result_id": "mmlu_pro/chemistry",
+      "evaluation_name": "MMLU-Pro (Chemistry)",
+      "source_data": {
+        "dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
+        "source_type": "hf_dataset",
+        "hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
+        "hf_split": "train",
+        "additional_details": {
+          "results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
+          "leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
+          "benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
+          "paper_url": "https://arxiv.org/abs/2406.01574",
+          "github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
+          "dataset_total_questions": "12000",
+          "prompt_style": "5-shot CoT"
+        }
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy on the MMLU-Pro Chemistry subset, evaluated 5-shot with chain-of-thought.",
+        "metric_id": "mmlu_pro/chemistry",
+        "metric_name": "MMLU-Pro (Chemistry)",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "additional_details": {
+          "aggregation": "accuracy_over_subset",
+          "prompt_style": "5-shot CoT"
+        }
+      },
+      "score_details": {
+        "score": 0.3896
+      }
+    },
+    {
+      "evaluation_result_id": "mmlu_pro/computer_science",
+      "evaluation_name": "MMLU-Pro (Computer Science)",
+      "source_data": {
+        "dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
+        "source_type": "hf_dataset",
+        "hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
+        "hf_split": "train",
+        "additional_details": {
+          "results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
+          "leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
+          "benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
+          "paper_url": "https://arxiv.org/abs/2406.01574",
+          "github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
+          "dataset_total_questions": "12000",
+          "prompt_style": "5-shot CoT"
+        }
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy on the MMLU-Pro Computer Science subset, evaluated 5-shot with chain-of-thought.",
+        "metric_id": "mmlu_pro/computer_science",
+        "metric_name": "MMLU-Pro (Computer Science)",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "additional_details": {
+          "aggregation": "accuracy_over_subset",
+          "prompt_style": "5-shot CoT"
+        }
+      },
+      "score_details": {
+        "score": 0.4463
+      }
+    },
+    {
+      "evaluation_result_id": "mmlu_pro/economics",
+      "evaluation_name": "MMLU-Pro (Economics)",
+      "source_data": {
+        "dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
+        "source_type": "hf_dataset",
+        "hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
+        "hf_split": "train",
+        "additional_details": {
+          "results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
+          "leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
+          "benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
+          "paper_url": "https://arxiv.org/abs/2406.01574",
+          "github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
+          "dataset_total_questions": "12000",
+          "prompt_style": "5-shot CoT"
+        }
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy on the MMLU-Pro Economics subset, evaluated 5-shot with chain-of-thought.",
+        "metric_id": "mmlu_pro/economics",
+        "metric_name": "MMLU-Pro (Economics)",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "additional_details": {
+          "aggregation": "accuracy_over_subset",
+          "prompt_style": "5-shot CoT"
+        }
+      },
+      "score_details": {
+        "score": 0.6055
+      }
+    },
+    {
+      "evaluation_result_id": "mmlu_pro/engineering",
+      "evaluation_name": "MMLU-Pro (Engineering)",
+      "source_data": {
+        "dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
+        "source_type": "hf_dataset",
+        "hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
+        "hf_split": "train",
+        "additional_details": {
+          "results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
+          "leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
+          "benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
+          "paper_url": "https://arxiv.org/abs/2406.01574",
+          "github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
+          "dataset_total_questions": "12000",
+          "prompt_style": "5-shot CoT"
+        }
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy on the MMLU-Pro Engineering subset, evaluated 5-shot with chain-of-thought.",
+        "metric_id": "mmlu_pro/engineering",
+        "metric_name": "MMLU-Pro (Engineering)",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "additional_details": {
+          "aggregation": "accuracy_over_subset",
+          "prompt_style": "5-shot CoT"
+        }
+      },
+      "score_details": {
+        "score": 0.2869
+      }
+    },
+    {
+      "evaluation_result_id": "mmlu_pro/health",
+      "evaluation_name": "MMLU-Pro (Health)",
+      "source_data": {
+        "dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
+        "source_type": "hf_dataset",
+        "hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
+        "hf_split": "train",
+        "additional_details": {
+          "results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
+          "leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
+          "benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
+          "paper_url": "https://arxiv.org/abs/2406.01574",
+          "github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
+          "dataset_total_questions": "12000",
+          "prompt_style": "5-shot CoT"
+        }
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy on the MMLU-Pro Health subset, evaluated 5-shot with chain-of-thought.",
+        "metric_id": "mmlu_pro/health",
+        "metric_name": "MMLU-Pro (Health)",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "additional_details": {
+          "aggregation": "accuracy_over_subset",
+          "prompt_style": "5-shot CoT"
+        }
+      },
+      "score_details": {
+        "score": 0.5024
+      }
+    },
+    {
+      "evaluation_result_id": "mmlu_pro/history",
+      "evaluation_name": "MMLU-Pro (History)",
+      "source_data": {
+        "dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
+        "source_type": "hf_dataset",
+        "hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
+        "hf_split": "train",
+        "additional_details": {
+          "results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
+          "leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
+          "benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
+          "paper_url": "https://arxiv.org/abs/2406.01574",
+          "github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
+          "dataset_total_questions": "12000",
+          "prompt_style": "5-shot CoT"
+        }
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy on the MMLU-Pro History subset, evaluated 5-shot with chain-of-thought.",
+        "metric_id": "mmlu_pro/history",
+        "metric_name": "MMLU-Pro (History)",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "additional_details": {
+          "aggregation": "accuracy_over_subset",
+          "prompt_style": "5-shot CoT"
+        }
+      },
+      "score_details": {
+        "score": 0.4147
+      }
+    },
+    {
+      "evaluation_result_id": "mmlu_pro/law",
+      "evaluation_name": "MMLU-Pro (Law)",
+      "source_data": {
+        "dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
+        "source_type": "hf_dataset",
+        "hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
+        "hf_split": "train",
+        "additional_details": {
+          "results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
+          "leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
+          "benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
+          "paper_url": "https://arxiv.org/abs/2406.01574",
+          "github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
+          "dataset_total_questions": "12000",
+          "prompt_style": "5-shot CoT"
+        }
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy on the MMLU-Pro Law subset, evaluated 5-shot with chain-of-thought.",
+        "metric_id": "mmlu_pro/law",
+        "metric_name": "MMLU-Pro (Law)",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "additional_details": {
+          "aggregation": "accuracy_over_subset",
+          "prompt_style": "5-shot CoT"
+        }
+      },
+      "score_details": {
+        "score": 0.2852
+      }
+    },
+    {
+      "evaluation_result_id": "mmlu_pro/math",
+      "evaluation_name": "MMLU-Pro (Math)",
+      "source_data": {
+        "dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
+        "source_type": "hf_dataset",
+        "hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
+        "hf_split": "train",
+        "additional_details": {
+          "results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
+          "leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
+          "benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
+          "paper_url": "https://arxiv.org/abs/2406.01574",
+          "github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
+          "dataset_total_questions": "12000",
+          "prompt_style": "5-shot CoT"
+        }
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy on the MMLU-Pro Math subset, evaluated 5-shot with chain-of-thought.",
+        "metric_id": "mmlu_pro/math",
+        "metric_name": "MMLU-Pro (Math)",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "additional_details": {
+          "aggregation": "accuracy_over_subset",
+          "prompt_style": "5-shot CoT"
+        }
+      },
+      "score_details": {
+        "score": 0.4182
+      }
+    },
+    {
+      "evaluation_result_id": "mmlu_pro/philosophy",
+      "evaluation_name": "MMLU-Pro (Philosophy)",
+      "source_data": {
+        "dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
+        "source_type": "hf_dataset",
+        "hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
+        "hf_split": "train",
+        "additional_details": {
+          "results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
+          "leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
+          "benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
+          "paper_url": "https://arxiv.org/abs/2406.01574",
+          "github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
+          "dataset_total_questions": "12000",
+          "prompt_style": "5-shot CoT"
+        }
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy on the MMLU-Pro Philosophy subset, evaluated 5-shot with chain-of-thought.",
+        "metric_id": "mmlu_pro/philosophy",
+        "metric_name": "MMLU-Pro (Philosophy)",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "additional_details": {
+          "aggregation": "accuracy_over_subset",
+          "prompt_style": "5-shot CoT"
+        }
+      },
+      "score_details": {
+        "score": 0.4449
+      }
+    },
+    {
+      "evaluation_result_id": "mmlu_pro/physics",
+      "evaluation_name": "MMLU-Pro (Physics)",
+      "source_data": {
+        "dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
+        "source_type": "hf_dataset",
+        "hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
+        "hf_split": "train",
+        "additional_details": {
+          "results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
+          "leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
+          "benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
+          "paper_url": "https://arxiv.org/abs/2406.01574",
+          "github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
+          "dataset_total_questions": "12000",
+          "prompt_style": "5-shot CoT"
+        }
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy on the MMLU-Pro Physics subset, evaluated 5-shot with chain-of-thought.",
+        "metric_id": "mmlu_pro/physics",
+        "metric_name": "MMLU-Pro (Physics)",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "additional_details": {
+          "aggregation": "accuracy_over_subset",
+          "prompt_style": "5-shot CoT"
+        }
+      },
+      "score_details": {
+        "score": 0.4095
+      }
+    },
+    {
+      "evaluation_result_id": "mmlu_pro/psychology",
+      "evaluation_name": "MMLU-Pro (Psychology)",
+      "source_data": {
+        "dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
+        "source_type": "hf_dataset",
+        "hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
+        "hf_split": "train",
+        "additional_details": {
+          "results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
+          "leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
+          "benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
+          "paper_url": "https://arxiv.org/abs/2406.01574",
+          "github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
+          "dataset_total_questions": "12000",
+          "prompt_style": "5-shot CoT"
+        }
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy on the MMLU-Pro Psychology subset, evaluated 5-shot with chain-of-thought.",
+        "metric_id": "mmlu_pro/psychology",
+        "metric_name": "MMLU-Pro (Psychology)",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "additional_details": {
+          "aggregation": "accuracy_over_subset",
+          "prompt_style": "5-shot CoT"
+        }
+      },
+      "score_details": {
+        "score": 0.6516
+      }
+    },
+    {
+      "evaluation_result_id": "mmlu_pro/other",
+      "evaluation_name": "MMLU-Pro (Other)",
+      "source_data": {
+        "dataset_name": "MMLU-Pro leaderboard submissions (TIGER-Lab)",
+        "source_type": "hf_dataset",
+        "hf_repo": "TIGER-Lab/mmlu_pro_leaderboard_submission",
+        "hf_split": "train",
+        "additional_details": {
+          "results_csv_url": "https://huggingface.co/datasets/TIGER-Lab/mmlu_pro_leaderboard_submission/resolve/main/results.csv",
+          "leaderboard_space_url": "https://huggingface.co/spaces/TIGER-Lab/MMLU-Pro",
+          "benchmark_hf_repo": "TIGER-Lab/MMLU-Pro",
+          "paper_url": "https://arxiv.org/abs/2406.01574",
+          "github_url": "https://github.com/TIGER-AI-Lab/MMLU-Pro",
+          "dataset_total_questions": "12000",
+          "prompt_style": "5-shot CoT"
+        }
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy on the MMLU-Pro Other subset, evaluated 5-shot with chain-of-thought.",
+        "metric_id": "mmlu_pro/other",
+        "metric_name": "MMLU-Pro (Other)",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "additional_details": {
+          "aggregation": "accuracy_over_subset",
+          "prompt_style": "5-shot CoT"
+        }
+      },
+      "score_details": {
+        "score": 0.4957
+      }
+    }
+  ]
+}

flat/objects/05/3b/053b8efe-6342-490e-9628-7252966c1587.json

@@ -0,0 +1,238 @@
+{
+  "schema_version": "0.2.2",
+  "evaluation_id": "CHASE/Llama-3.1-8B/1771591481.616601",
+  "retrieved_timestamp": "1771591481.616601",
+  "source_metadata": {
+    "source_name": "alphaXiv State of the Art",
+    "source_type": "documentation",
+    "source_organization_name": "alphaXiv",
+    "source_organization_url": "https://alphaxiv.org",
+    "evaluator_relationship": "third_party",
+    "additional_details": {
+      "alphaxiv_dataset_org": "Université de Montréal",
+      "alphaxiv_dataset_type": "text",
+      "scrape_source": "https://github.com/alphaXiv/feedback/issues/189"
+    }
+  },
+  "model_info": {
+    "id": "Llama-3.1-8B",
+    "name": "Llama-3.1-8B",
+    "developer": "unknown"
+  },
+  "evaluation_results": [
+    {
+      "evaluation_name": "CHASE",
+      "source_data": {
+        "dataset_name": "CHASE",
+        "source_type": "url",
+        "url": [
+          "https://huggingface.co/collections/McGill-NLP/chase-67b732a9462862d18d7f85bd"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Average Pass@1 execution accuracy across both the data pre-processing and algorithms sub-domains of the CHASE-CODE benchmark. This metric assesses a model's overall capability to generate new code functions within an existing codebase, requiring understanding of a large repository context.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Pass@1 (%)",
+          "alphaxiv_is_primary": "True",
+          "raw_evaluation_name": "CHASE-CODE (Average): Repository-Level Code Completion"
+        },
+        "metric_id": "chase_code_average_repository_level_code_completion",
+        "metric_name": "CHASE-CODE (Average): Repository-Level Code Completion",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 2
+      },
+      "evaluation_result_id": "CHASE/Llama-3.1-8B/1771591481.616601#chase#chase_code_average_repository_level_code_completion"
+    },
+    {
+      "evaluation_name": "CHASE",
+      "source_data": {
+        "dataset_name": "CHASE",
+        "source_type": "url",
+        "url": [
+          "https://huggingface.co/collections/McGill-NLP/chase-67b732a9462862d18d7f85bd"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Pass@1 execution accuracy on the data pre-processing sub-domain of the CHASE-CODE benchmark. This task requires generating a new Python function within an existing codebase to perform data pre-processing tasks, based on a natural language description.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Pass@1 (%)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "CHASE-CODE (Data Pre-processing): Repository-Level Code Completion"
+        },
+        "metric_id": "chase_code_data_pre_processing_repository_level_code_completion",
+        "metric_name": "CHASE-CODE (Data Pre-processing): Repository-Level Code Completion",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 0.8
+      },
+      "evaluation_result_id": "CHASE/Llama-3.1-8B/1771591481.616601#chase#chase_code_data_pre_processing_repository_level_code_completion"
+    },
+    {
+      "evaluation_name": "CHASE",
+      "source_data": {
+        "dataset_name": "CHASE",
+        "source_type": "url",
+        "url": [
+          "https://huggingface.co/collections/McGill-NLP/chase-67b732a9462862d18d7f85bd"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Pass@1 execution accuracy on the algorithms sub-domain of the CHASE-CODE benchmark. This task requires implementing a new Python function within an existing codebase to solve algorithmic problems, based on a natural language description.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Pass@1 (%)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "CHASE-CODE (Algorithms): Repository-Level Code Completion"
+        },
+        "metric_id": "chase_code_algorithms_repository_level_code_completion",
+        "metric_name": "CHASE-CODE (Algorithms): Repository-Level Code Completion",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 3.2
+      },
+      "evaluation_result_id": "CHASE/Llama-3.1-8B/1771591481.616601#chase#chase_code_algorithms_repository_level_code_completion"
+    },
+    {
+      "evaluation_name": "CHASE",
+      "source_data": {
+        "dataset_name": "CHASE",
+        "source_type": "url",
+        "url": [
+          "https://huggingface.co/collections/McGill-NLP/chase-67b732a9462862d18d7f85bd"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Accuracy on the CHASE-QA benchmark, which tests a model's ability to extract information and reason over lengthy, noisy contexts where relevant data is scattered across multiple documents. A prediction is judged as correct by GPT-4o if it is both complete and relevant.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Accuracy (%)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "CHASE-QA: Long-Context Document Question Answering"
+        },
+        "metric_id": "chase_qa_long_context_document_question_answering",
+        "metric_name": "CHASE-QA: Long-Context Document Question Answering",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 25.2
+      },
+      "evaluation_result_id": "CHASE/Llama-3.1-8B/1771591481.616601#chase#chase_qa_long_context_document_question_answering"
+    },
+    {
+      "evaluation_name": "CHASE",
+      "source_data": {
+        "dataset_name": "CHASE",
+        "source_type": "url",
+        "url": [
+          "https://huggingface.co/collections/McGill-NLP/chase-67b732a9462862d18d7f85bd"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "K-Precision on the CHASE-QA benchmark, a supplementary metric evaluating faithfulness. It measures, for a given example, whether all points in the model's prediction are discussed in the provided documents. This is a binary per-example metric judged by GPT-4o.",
+        "additional_details": {
+          "alphaxiv_y_axis": "K-Precision (%)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "CHASE-QA: Faithfulness (K-Precision)"
+        },
+        "metric_id": "chase_qa_faithfulness_k_precision",
+        "metric_name": "CHASE-QA: Faithfulness (K-Precision)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 61.3
+      },
+      "evaluation_result_id": "CHASE/Llama-3.1-8B/1771591481.616601#chase#chase_qa_faithfulness_k_precision"
+    },
+    {
+      "evaluation_name": "CHASE",
+      "source_data": {
+        "dataset_name": "CHASE",
+        "source_type": "url",
+        "url": [
+          "https://huggingface.co/collections/McGill-NLP/chase-67b732a9462862d18d7f85bd"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Recall on the CHASE-QA benchmark, a supplementary metric evaluating completeness. It measures whether all ground-truth answer points are part of the model's prediction. This is a binary per-example metric judged by GPT-4o.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Recall (%)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "CHASE-QA: Completeness (Recall)"
+        },
+        "metric_id": "chase_qa_completeness_recall",
+        "metric_name": "CHASE-QA: Completeness (Recall)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 32
+      },
+      "evaluation_result_id": "CHASE/Llama-3.1-8B/1771591481.616601#chase#chase_qa_completeness_recall"
+    },
+    {
+      "evaluation_name": "CHASE",
+      "source_data": {
+        "dataset_name": "CHASE",
+        "source_type": "url",
+        "url": [
+          "https://huggingface.co/collections/McGill-NLP/chase-67b732a9462862d18d7f85bd"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Exact match accuracy on the CHASE-MATH benchmark, which consists of challenging grade-school level math word problems with increased reasoning depth. The metric measures whether the model's predicted numerical answer exactly matches the ground-truth.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Accuracy (%)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "CHASE-MATH: Grade-School Math Reasoning"
+        },
+        "metric_id": "chase_math_grade_school_math_reasoning",
+        "metric_name": "CHASE-MATH: Grade-School Math Reasoning",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 32.2
+      },
+      "evaluation_result_id": "CHASE/Llama-3.1-8B/1771591481.616601#chase#chase_math_grade_school_math_reasoning"
+    }
+  ],
+  "eval_library": {
+    "name": "alphaxiv",
+    "version": "unknown"
+  }
+}

flat/objects/05/3d/053dc22b-827e-459c-aa53-13e55af7268d.json

@@ -0,0 +1,169 @@
+{
+  "schema_version": "0.2.2",
+  "evaluation_id": "hfopenllm_v2/bunnycore_Qwen-2.5-7B-Deep-Stock-v5/1773936498.240187",
+  "retrieved_timestamp": "1773936498.240187",
+  "source_metadata": {
+    "source_name": "HF Open LLM v2",
+    "source_type": "documentation",
+    "source_organization_name": "Hugging Face",
+    "evaluator_relationship": "third_party"
+  },
+  "eval_library": {
+    "name": "lm-evaluation-harness",
+    "version": "0.4.0",
+    "additional_details": {
+      "fork": "https://github.com/huggingface/lm-evaluation-harness/tree/adding_all_changess"
+    }
+  },
+  "model_info": {
+    "name": "Qwen-2.5-7B-Deep-Stock-v5",
+    "id": "bunnycore/Qwen-2.5-7B-Deep-Stock-v5",
+    "developer": "bunnycore",
+    "inference_platform": "unknown",
+    "additional_details": {
+      "precision": "bfloat16",
+      "architecture": "Qwen2ForCausalLM",
+      "params_billions": "7.613"
+    }
+  },
+  "evaluation_results": [
+    {
+      "evaluation_name": "IFEval",
+      "source_data": {
+        "dataset_name": "IFEval",
+        "source_type": "hf_dataset",
+        "hf_repo": "google/IFEval"
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy on IFEval",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "metric_id": "accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion"
+      },
+      "score_details": {
+        "score": 0.4509
+      },
+      "evaluation_result_id": "hfopenllm_v2/bunnycore_Qwen-2.5-7B-Deep-Stock-v5/1773936498.240187#ifeval#accuracy"
+    },
+    {
+      "evaluation_name": "BBH",
+      "source_data": {
+        "dataset_name": "BBH",
+        "source_type": "hf_dataset",
+        "hf_repo": "SaylorTwift/bbh"
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy on BBH",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "metric_id": "accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion"
+      },
+      "score_details": {
+        "score": 0.4672
+      },
+      "evaluation_result_id": "hfopenllm_v2/bunnycore_Qwen-2.5-7B-Deep-Stock-v5/1773936498.240187#bbh#accuracy"
+    },
+    {
+      "evaluation_name": "MATH Level 5",
+      "source_data": {
+        "dataset_name": "MATH Level 5",
+        "source_type": "hf_dataset",
+        "hf_repo": "DigitalLearningGmbH/MATH-lighteval"
+      },
+      "metric_config": {
+        "evaluation_description": "Exact Match on MATH Level 5",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "metric_id": "exact_match",
+        "metric_name": "Exact Match",
+        "metric_kind": "exact_match",
+        "metric_unit": "proportion"
+      },
+      "score_details": {
+        "score": 0.1473
+      },
+      "evaluation_result_id": "hfopenllm_v2/bunnycore_Qwen-2.5-7B-Deep-Stock-v5/1773936498.240187#math_level_5#exact_match"
+    },
+    {
+      "evaluation_name": "GPQA",
+      "source_data": {
+        "dataset_name": "GPQA",
+        "source_type": "hf_dataset",
+        "hf_repo": "Idavidrein/gpqa"
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy on GPQA",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "metric_id": "accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion"
+      },
+      "score_details": {
+        "score": 0.2701
+      },
+      "evaluation_result_id": "hfopenllm_v2/bunnycore_Qwen-2.5-7B-Deep-Stock-v5/1773936498.240187#gpqa#accuracy"
+    },
+    {
+      "evaluation_name": "MUSR",
+      "source_data": {
+        "dataset_name": "MUSR",
+        "source_type": "hf_dataset",
+        "hf_repo": "TAUR-Lab/MuSR"
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy on MUSR",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "metric_id": "accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion"
+      },
+      "score_details": {
+        "score": 0.3648
+      },
+      "evaluation_result_id": "hfopenllm_v2/bunnycore_Qwen-2.5-7B-Deep-Stock-v5/1773936498.240187#musr#accuracy"
+    },
+    {
+      "evaluation_name": "MMLU-PRO",
+      "source_data": {
+        "dataset_name": "MMLU-PRO",
+        "source_type": "hf_dataset",
+        "hf_repo": "TIGER-Lab/MMLU-Pro"
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy on MMLU-PRO",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "metric_id": "accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion"
+      },
+      "score_details": {
+        "score": 0.2832
+      },
+      "evaluation_result_id": "hfopenllm_v2/bunnycore_Qwen-2.5-7B-Deep-Stock-v5/1773936498.240187#mmlu_pro#accuracy"
+    }
+  ]
+}

flat/objects/05/40/0540128a-a37d-41fd-b38b-e997bdce1db4.json

@@ -0,0 +1,89 @@
+{
+  "schema_version": "0.2.2",
+  "evaluation_id": "interpreter/google_gemini-2.0-flash-001/1743122018.0",
+  "evaluation_timestamp": "1743122018.0",
+  "retrieved_timestamp": "1775628410.503552",
+  "source_metadata": {
+    "source_name": "inspect_ai",
+    "source_type": "evaluation_run",
+    "source_organization_name": "Arcadia Impact",
+    "evaluator_relationship": "third_party"
+  },
+  "eval_library": {
+    "name": "inspect_ai",
+    "version": "inspect_ai:0.3.75"
+  },
+  "model_info": {
+    "name": "google/gemini-2.0-flash-001",
+    "id": "google/gemini-2.0-flash-001",
+    "developer": "google",
+    "inference_platform": "google"
+  },
+  "evaluation_results": [
+    {
+      "evaluation_name": "cyse2_interpreter_abuse",
+      "source_data": {
+        "dataset_name": "cyse2_interpreter_abuse",
+        "source_type": "url",
+        "url": [
+          "https://github.com/UKGovernmentBEIS/inspect_evals/tree/main/src/inspect_evals/cyberseceval_2/interpreter_abuse"
+        ],
+        "additional_details": {
+          "shuffled": "False"
+        }
+      },
+      "evaluation_timestamp": "1743122018.0",
+      "metric_config": {
+        "evaluation_description": "accuracy",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "additional_details": {
+          "raw_evaluation_name": "accuracy on inspect_evals/cyse2_cyse2_interpreter_abuse_abuse for scorer model_graded_qa"
+        },
+        "metric_id": "accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion"
+      },
+      "score_details": {
+        "score": 0.335,
+        "uncertainty": {
+          "standard_error": {
+            "value": 0.016219622446426097
+          },
+          "num_samples": 2000
+        }
+      },
+      "generation_config": {
+        "generation_args": {
+          "reasoning": false,
+          "agentic_eval_config": {
+            "available_tools": []
+          },
+          "eval_plan": {
+            "name": "plan",
+            "steps": [
+              "{\"solver\": \"generate\", \"params\": {}, \"params_passed\": {}}"
+            ],
+            "config": {
+              "max_tokens": "2048",
+              "temperature": "0.75"
+            }
+          },
+          "eval_limits": {},
+          "sandbox": {}
+        }
+      },
+      "evaluation_result_id": "interpreter/google_gemini-2.0-flash-001/1743122018.0#cyse2_interpreter_abuse#accuracy"
+    }
+  ],
+  "detailed_evaluation_results": {
+    "format": "jsonl",
+    "file_path": "./0540128a-a37d-41fd-b38b-e997bdce1db4_samples.jsonl",
+    "hash_algorithm": "sha256",
+    "checksum": "0b5c77fb179a0d6584708010c1d8bece658b491a1a18ed9f79aee544abcbf69e",
+    "total_rows": 2000
+  }
+}

flat/objects/05/40/0540f253-85d8-4e4f-9004-492040876462.json

@@ -0,0 +1,238 @@
+{
+  "schema_version": "0.2.2",
+  "evaluation_id": "UA-Code-Bench/Gemma-3-27b-it/1771591481.616601",
+  "retrieved_timestamp": "1771591481.616601",
+  "source_metadata": {
+    "source_name": "alphaXiv State of the Art",
+    "source_type": "documentation",
+    "source_organization_name": "alphaXiv",
+    "source_organization_url": "https://alphaxiv.org",
+    "evaluator_relationship": "third_party",
+    "additional_details": {
+      "alphaxiv_dataset_org": "Odesa Polytechnic National University",
+      "alphaxiv_dataset_type": "text",
+      "scrape_source": "https://github.com/alphaXiv/feedback/issues/189"
+    }
+  },
+  "model_info": {
+    "id": "Gemma-3-27b-it",
+    "name": "Gemma-3-27b-it",
+    "developer": "unknown"
+  },
+  "evaluation_results": [
+    {
+      "evaluation_name": "UA-Code-Bench",
+      "source_data": {
+        "dataset_name": "UA-Code-Bench",
+        "source_type": "url",
+        "url": [
+          "https://huggingface.co/datasets/NLPForUA/ua-code-bench"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "The average score (0-100%) obtained by models across all 486 Ukrainian competitive programming tasks. This metric includes partial credit for solutions that pass some, but not all, hidden test cases, providing a nuanced measure of correctness.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Average Score (%)",
+          "alphaxiv_is_primary": "True",
+          "raw_evaluation_name": "Average Score on UA-Code-Bench"
+        },
+        "metric_id": "average_score_on_ua_code_bench",
+        "metric_name": "Average Score on UA-Code-Bench",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 25.7
+      },
+      "evaluation_result_id": "UA-Code-Bench/Gemma-3-27b-it/1771591481.616601#ua_code_bench#average_score_on_ua_code_bench"
+    },
+    {
+      "evaluation_name": "UA-Code-Bench",
+      "source_data": {
+        "dataset_name": "UA-Code-Bench",
+        "source_type": "url",
+        "url": [
+          "https://huggingface.co/datasets/NLPForUA/ua-code-bench"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "The number of accepted solutions where the model's submission had the smallest worst-case memory consumption among all models that solved that specific task. This assesses computational memory efficiency.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Most Memory-Efficient Solutions (T1M)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Best Memory Consumption Solutions (T1M) on UA-Code-Bench"
+        },
+        "metric_id": "best_memory_consumption_solutions_t1m_on_ua_code_bench",
+        "metric_name": "Best Memory Consumption Solutions (T1M) on UA-Code-Bench",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 6
+      },
+      "evaluation_result_id": "UA-Code-Bench/Gemma-3-27b-it/1771591481.616601#ua_code_bench#best_memory_consumption_solutions_t1m_on_ua_code_bench"
+    },
+    {
+      "evaluation_name": "UA-Code-Bench",
+      "source_data": {
+        "dataset_name": "UA-Code-Bench",
+        "source_type": "url",
+        "url": [
+          "https://huggingface.co/datasets/NLPForUA/ua-code-bench"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "The total number of problems (out of 486) for which a model generated a solution that passed 100% of the hidden test cases on the Eolymp judge. This is a strict measure of correctness.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Accepted Solutions (pass@1)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Accepted Solutions (pass@1) on UA-Code-Bench"
+        },
+        "metric_id": "accepted_solutions_pass_1_on_ua_code_bench",
+        "metric_name": "Accepted Solutions (pass@1) on UA-Code-Bench",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 53
+      },
+      "evaluation_result_id": "UA-Code-Bench/Gemma-3-27b-it/1771591481.616601#ua_code_bench#accepted_solutions_pass_1_on_ua_code_bench"
+    },
+    {
+      "evaluation_name": "UA-Code-Bench",
+      "source_data": {
+        "dataset_name": "UA-Code-Bench",
+        "source_type": "url",
+        "url": [
+          "https://huggingface.co/datasets/NLPForUA/ua-code-bench"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": true,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "The number of accepted solutions where the model's submission had the smallest worst-case execution time among all models that solved that specific task. This assesses computational speed efficiency.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Fastest Solutions (T1T)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Fastest Execution Time Solutions (T1T) on UA-Code-Bench"
+        },
+        "metric_id": "fastest_execution_time_solutions_t1t_on_ua_code_bench",
+        "metric_name": "Fastest Execution Time Solutions (T1T) on UA-Code-Bench",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 7
+      },
+      "evaluation_result_id": "UA-Code-Bench/Gemma-3-27b-it/1771591481.616601#ua_code_bench#fastest_execution_time_solutions_t1t_on_ua_code_bench"
+    },
+    {
+      "evaluation_name": "UA-Code-Bench",
+      "source_data": {
+        "dataset_name": "UA-Code-Bench",
+        "source_type": "url",
+        "url": [
+          "https://huggingface.co/datasets/NLPForUA/ua-code-bench"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "The count of problems (out of 486) where the model failed to generate a valid or complete solution, including timeouts during generation or syntactically incorrect code.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Generation Errors (GE)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Generation Errors on UA-Code-Bench"
+        },
+        "metric_id": "generation_errors_on_ua_code_bench",
+        "metric_name": "Generation Errors on UA-Code-Bench",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 3
+      },
+      "evaluation_result_id": "UA-Code-Bench/Gemma-3-27b-it/1771591481.616601#ua_code_bench#generation_errors_on_ua_code_bench"
+    },
+    {
+      "evaluation_name": "UA-Code-Bench",
+      "source_data": {
+        "dataset_name": "UA-Code-Bench",
+        "source_type": "url",
+        "url": [
+          "https://huggingface.co/datasets/NLPForUA/ua-code-bench"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "The number of tasks for which a specific model produced an accepted solution, while no other evaluated model did. This indicates a model's unique problem-solving capabilities.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Uniquely Solved Tasks (TOO)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Uniquely Solved Tasks (TOO) on UA-Code-Bench"
+        },
+        "metric_id": "uniquely_solved_tasks_too_on_ua_code_bench",
+        "metric_name": "Uniquely Solved Tasks (TOO) on UA-Code-Bench",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 0
+      },
+      "evaluation_result_id": "UA-Code-Bench/Gemma-3-27b-it/1771591481.616601#ua_code_bench#uniquely_solved_tasks_too_on_ua_code_bench"
+    },
+    {
+      "evaluation_name": "UA-Code-Bench",
+      "source_data": {
+        "dataset_name": "UA-Code-Bench",
+        "source_type": "url",
+        "url": [
+          "https://huggingface.co/datasets/NLPForUA/ua-code-bench"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": true,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "The count of problems (out of 486) where the generated code failed during execution on the Eolymp judge due to compile-time errors, runtime exceptions, or exceeding time/memory limits.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Execution Errors (EE)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Execution Errors on UA-Code-Bench"
+        },
+        "metric_id": "execution_errors_on_ua_code_bench",
+        "metric_name": "Execution Errors on UA-Code-Bench",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 11
+      },
+      "evaluation_result_id": "UA-Code-Bench/Gemma-3-27b-it/1771591481.616601#ua_code_bench#execution_errors_on_ua_code_bench"
+    }
+  ],
+  "eval_library": {
+    "name": "alphaxiv",
+    "version": "unknown"
+  }
+}

flat/objects/05/42/05429f8a-c103-442d-b74f-782bfd49c9a2.json

@@ -0,0 +1,118 @@
+{
+  "schema_version": "0.2.2",
+  "evaluation_id": "FreshStack/E5 (Mistral-7B)/1771591481.616601",
+  "retrieved_timestamp": "1771591481.616601",
+  "source_metadata": {
+    "source_name": "alphaXiv State of the Art",
+    "source_type": "documentation",
+    "source_organization_name": "alphaXiv",
+    "source_organization_url": "https://alphaxiv.org",
+    "evaluator_relationship": "third_party",
+    "additional_details": {
+      "alphaxiv_dataset_org": "University of Waterloo",
+      "alphaxiv_dataset_type": "document",
+      "scrape_source": "https://github.com/alphaXiv/feedback/issues/189"
+    }
+  },
+  "model_info": {
+    "id": "E5 (Mistral-7B)",
+    "name": "E5 (Mistral-7B)",
+    "developer": "unknown"
+  },
+  "evaluation_results": [
+    {
+      "evaluation_name": "FreshStack",
+      "source_data": {
+        "dataset_name": "FreshStack",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2504.13128"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Average alpha-Normalized Discounted Cumulative Gain at rank 10 (α-nDCG@10) across five technical domains (LangChain, Yolo, Laravel, Angular, Godot4). This metric measures the quality of ranked search results, penalizing redundant information to reward diversity in retrieved documents. Higher scores indicate better performance in retrieving relevant and diverse information.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Average α-nDCG@10",
+          "alphaxiv_is_primary": "True",
+          "raw_evaluation_name": "FreshStack Document Retrieval Performance (α-nDCG@10)"
+        },
+        "metric_id": "freshstack_document_retrieval_performance_ndcg_10",
+        "metric_name": "FreshStack Document Retrieval Performance (α-nDCG@10)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 0.255
+      },
+      "evaluation_result_id": "FreshStack/E5 (Mistral-7B)/1771591481.616601#freshstack#freshstack_document_retrieval_performance_ndcg_10"
+    },
+    {
+      "evaluation_name": "FreshStack",
+      "source_data": {
+        "dataset_name": "FreshStack",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2504.13128"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Average Coverage at rank 20 (C@20) across five technical domains (LangChain, Yolo, Laravel, Angular, Godot4). This metric measures the average proportion of ground-truth information 'nuggets' that are covered by the top-20 retrieved documents for a given query. Higher is better.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Average Coverage@20",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "FreshStack Document Retrieval Performance (Coverage@20)"
+        },
+        "metric_id": "freshstack_document_retrieval_performance_coverage_20",
+        "metric_name": "FreshStack Document Retrieval Performance (Coverage@20)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 0.553
+      },
+      "evaluation_result_id": "FreshStack/E5 (Mistral-7B)/1771591481.616601#freshstack#freshstack_document_retrieval_performance_coverage_20"
+    },
+    {
+      "evaluation_name": "FreshStack",
+      "source_data": {
+        "dataset_name": "FreshStack",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2504.13128"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Average Recall at rank 50 (R@50) across five technical domains (LangChain, Yolo, Laravel, Angular, Godot4). This metric measures the proportion of all relevant documents for a query that are found within the top 50 retrieved results. Higher is better.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Average Recall@50",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "FreshStack Document Retrieval Performance (Recall@50)"
+        },
+        "metric_id": "freshstack_document_retrieval_performance_recall_50",
+        "metric_name": "FreshStack Document Retrieval Performance (Recall@50)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 0.397
+      },
+      "evaluation_result_id": "FreshStack/E5 (Mistral-7B)/1771591481.616601#freshstack#freshstack_document_retrieval_performance_recall_50"
+    }
+  ],
+  "eval_library": {
+    "name": "alphaxiv",
+    "version": "unknown"
+  }
+}

flat/objects/05/42/0542bff5-1df3-4083-8bec-ee34d9854dcf.json

@@ -0,0 +1,71 @@
+{
+  "schema_version": "0.2.2",
+  "retrieved_timestamp": "1777497418.5088363",
+  "source_metadata": {
+    "source_type": "evaluation_run",
+    "source_organization_name": "Writer, Inc.",
+    "evaluator_relationship": "third_party",
+    "source_name": "wasp (Writer's Assessor of System Performance)",
+    "additional_details": {}
+  },
+  "model_info": {
+    "name": "openai/gpt-5.5",
+    "id": "openai/gpt-5.5",
+    "developer": "openai",
+    "inference_platform": "openrouter",
+    "additional_details": {
+      "wasp_model_name": "gpt-5.5-thinking-xhigh-openrouter",
+      "served_model": "openrouter/openai/gpt-5.5"
+    }
+  },
+  "eval_library": {
+    "name": "wasp",
+    "version": "0.3.0",
+    "additional_details": {}
+  },
+  "evaluation_results": [
+    {
+      "evaluation_name": "GPQA Diamond",
+      "source_data": {
+        "dataset_name": "GPQA Diamond",
+        "source_type": "hf_dataset",
+        "hf_repo": "reasoningMIA/gpqa_diamond",
+        "hf_split": "train",
+        "additional_details": {}
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "evaluation_description": "Accuracy on GPQA Diamond multiple-choice questions",
+        "metric_id": "accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion",
+        "metric_parameters": {},
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "additional_details": {}
+      },
+      "score_details": {
+        "score": 0.9292929292929292,
+        "details": {}
+      },
+      "evaluation_result_id": "gpqa-diamond/openai_gpt-5.5/1777497418.5088363#gpqa_diamond#accuracy",
+      "evaluation_timestamp": "2026-04-24T22:41:25Z",
+      "generation_config": {
+        "generation_args": {},
+        "additional_details": {
+          "reasoning_effort": "xhigh"
+        }
+      }
+    }
+  ],
+  "evaluation_timestamp": "2026-04-24T22:41:25Z",
+  "detailed_evaluation_results": {
+    "format": "jsonl",
+    "file_path": "0542bff5-1df3-4083-8bec-ee34d9854dcf_samples.jsonl",
+    "total_rows": 198,
+    "additional_details": {}
+  },
+  "evaluation_id": "gpqa-diamond/openai_gpt-5.5/1777497418.5088363"
+}

flat/objects/05/43/0543c436-afc0-4e63-bebb-fc6d97ac8f25.json

@@ -0,0 +1,118 @@
+{
+  "schema_version": "0.2.2",
+  "evaluation_id": "MMMU/Qwen-VL-7B/1771591481.616601",
+  "retrieved_timestamp": "1771591481.616601",
+  "source_metadata": {
+    "source_name": "alphaXiv State of the Art",
+    "source_type": "documentation",
+    "source_organization_name": "alphaXiv",
+    "source_organization_url": "https://alphaxiv.org",
+    "evaluator_relationship": "third_party",
+    "additional_details": {
+      "alphaxiv_dataset_org": "University of Victoria",
+      "alphaxiv_dataset_type": "image",
+      "scrape_source": "https://github.com/alphaXiv/feedback/issues/189"
+    }
+  },
+  "model_info": {
+    "id": "Qwen-VL-7B",
+    "name": "Qwen-VL-7B",
+    "developer": "unknown"
+  },
+  "evaluation_results": [
+    {
+      "evaluation_name": "MMMU",
+      "source_data": {
+        "dataset_name": "MMMU",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2311.16502"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Model accuracy on the subset of 2,946 questions from the MMMU test set categorized as 'Easy'. This measures performance on the most straightforward problems in the benchmark.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Accuracy (%)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "MMMU (Test) Easy Difficulty Accuracy"
+        },
+        "metric_id": "mmmu_test_easy_difficulty_accuracy",
+        "metric_name": "MMMU (Test) Easy Difficulty Accuracy",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 39.4
+      },
+      "evaluation_result_id": "MMMU/Qwen-VL-7B/1771591481.616601#mmmu#mmmu_test_easy_difficulty_accuracy"
+    },
+    {
+      "evaluation_name": "MMMU",
+      "source_data": {
+        "dataset_name": "MMMU",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2311.16502"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Model accuracy on the subset of 2,637 questions from the MMMU test set categorized as 'Hard'. This measures performance on the most challenging problems requiring expert-level knowledge and complex reasoning. All models, including GPT-4V, struggle significantly on these questions.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Accuracy (%)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "MMMU (Test) Hard Difficulty Accuracy"
+        },
+        "metric_id": "mmmu_test_hard_difficulty_accuracy",
+        "metric_name": "MMMU (Test) Hard Difficulty Accuracy",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 27.6
+      },
+      "evaluation_result_id": "MMMU/Qwen-VL-7B/1771591481.616601#mmmu#mmmu_test_hard_difficulty_accuracy"
+    },
+    {
+      "evaluation_name": "MMMU",
+      "source_data": {
+        "dataset_name": "MMMU",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2311.16502"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Model accuracy on the subset of 4,917 questions from the MMMU test set categorized as 'Medium'. This represents the largest portion of the benchmark and measures performance on problems with moderate complexity.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Accuracy (%)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "MMMU (Test) Medium Difficulty Accuracy"
+        },
+        "metric_id": "mmmu_test_medium_difficulty_accuracy",
+        "metric_name": "MMMU (Test) Medium Difficulty Accuracy",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 31.9
+      },
+      "evaluation_result_id": "MMMU/Qwen-VL-7B/1771591481.616601#mmmu#mmmu_test_medium_difficulty_accuracy"
+    }
+  ],
+  "eval_library": {
+    "name": "alphaxiv",
+    "version": "unknown"
+  }
+}

flat/objects/05/45/05456580-33de-44d7-a8ac-a2c288048e75.json

@@ -0,0 +1,141 @@
+{
+  "schema_version": "0.2.2",
+  "evaluation_id": "facts-grounding/openai/gpt-5.2-2025-12-11/1777067886.443135",
+  "retrieved_timestamp": "1777067886.443135",
+  "source_metadata": {
+    "source_name": "FACTS Grounding Leaderboard",
+    "source_type": "documentation",
+    "source_organization_name": "Google DeepMind",
+    "source_organization_url": "https://kaggle.com/benchmarks/google/facts-grounding",
+    "evaluator_relationship": "third_party",
+    "additional_details": {
+      "api_endpoint": "https://www.kaggle.com/api/v1/benchmarks/google/facts-grounding/leaderboard",
+      "license": "Apache-2.0",
+      "attribution": "Google DeepMind, FACTS Grounding",
+      "paper_title": "The FACTS Grounding Leaderboard: Benchmarking LLMs' Ability to Ground Responses to Long-Form Input",
+      "paper_arxiv_id": "2501.03200",
+      "paper_authors_lead": "Alon Jacovi, Andrew Wang, Chris Alberti, et al. (26 authors)"
+    }
+  },
+  "eval_library": {
+    "name": "kaggle leaderboard",
+    "version": "unknown"
+  },
+  "model_info": {
+    "name": "gpt-5.2-2025-12-11",
+    "id": "openai/gpt-5.2-2025-12-11",
+    "developer": "openai",
+    "inference_platform": "unknown",
+    "additional_details": {
+      "display_name": "GPT-5.2",
+      "kaggle_model_version_slug": "gpt-5.2-2025-12-11"
+    }
+  },
+  "evaluation_results": [
+    {
+      "evaluation_result_id": "facts_grounding.score_overall",
+      "evaluation_name": "Score",
+      "source_data": {
+        "dataset_name": "facts-grounding",
+        "source_type": "url",
+        "url": [
+          "https://www.kaggle.com/api/v1/benchmarks/google/facts-grounding/leaderboard"
+        ]
+      },
+      "metric_config": {
+        "evaluation_description": "FACTS Grounding overall accuracy across public + private splits.",
+        "metric_id": "facts_grounding.score_overall",
+        "metric_name": "Score",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "additional_details": {
+          "bound_strategy": "fixed"
+        }
+      },
+      "score_details": {
+        "score": 0.7616686114352392,
+        "uncertainty": {
+          "confidence_interval": {
+            "lower": -0.0201704846372293,
+            "upper": 0.0201704846372293,
+            "method": "unknown"
+          }
+        }
+      }
+    },
+    {
+      "evaluation_result_id": "facts_grounding.score_public",
+      "evaluation_name": "Public Score",
+      "source_data": {
+        "dataset_name": "facts-grounding",
+        "source_type": "url",
+        "url": [
+          "https://www.kaggle.com/api/v1/benchmarks/google/facts-grounding/leaderboard"
+        ]
+      },
+      "metric_config": {
+        "evaluation_description": "FACTS Grounding accuracy on the public split (860 examples).",
+        "metric_id": "facts_grounding.score_public",
+        "metric_name": "Public Score",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "additional_details": {
+          "bound_strategy": "fixed"
+        }
+      },
+      "score_details": {
+        "score": 0.7734192037470726,
+        "uncertainty": {
+          "confidence_interval": {
+            "lower": -0.0280761905099554,
+            "upper": 0.0280761905099554,
+            "method": "unknown"
+          }
+        }
+      }
+    },
+    {
+      "evaluation_result_id": "facts_grounding.score_private",
+      "evaluation_name": "Private Score",
+      "source_data": {
+        "dataset_name": "facts-grounding",
+        "source_type": "url",
+        "url": [
+          "https://www.kaggle.com/api/v1/benchmarks/google/facts-grounding/leaderboard"
+        ]
+      },
+      "metric_config": {
+        "evaluation_description": "FACTS Grounding accuracy on the private held-out split (859 examples).",
+        "metric_id": "facts_grounding.score_private",
+        "metric_name": "Private Score",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "additional_details": {
+          "bound_strategy": "fixed"
+        }
+      },
+      "score_details": {
+        "score": 0.7505841121495327,
+        "uncertainty": {
+          "confidence_interval": {
+            "lower": -0.0289849763058094,
+            "upper": 0.0289849763058094,
+            "method": "unknown"
+          }
+        }
+      }
+    }
+  ]
+}

flat/objects/05/45/0545ae1f-0b5f-4e62-a55f-02c32256eb48.json

@@ -0,0 +1,58 @@
+{
+  "schema_version": "0.2.2",
+  "evaluation_id": "MDEVAL/GPT-4 Turbo (2024-04-09)/1771591481.616601",
+  "retrieved_timestamp": "1771591481.616601",
+  "source_metadata": {
+    "source_name": "alphaXiv State of the Art",
+    "source_type": "documentation",
+    "source_organization_name": "alphaXiv",
+    "source_organization_url": "https://alphaxiv.org",
+    "evaluator_relationship": "third_party",
+    "additional_details": {
+      "alphaxiv_dataset_org": "Beihang University",
+      "alphaxiv_dataset_type": "text",
+      "scrape_source": "https://github.com/alphaXiv/feedback/issues/189"
+    }
+  },
+  "model_info": {
+    "id": "GPT-4 Turbo (2024-04-09)",
+    "name": "GPT-4 Turbo (2024-04-09)",
+    "developer": "unknown"
+  },
+  "evaluation_results": [
+    {
+      "evaluation_name": "MDEVAL",
+      "source_data": {
+        "dataset_name": "MDEVAL",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2411.02310"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Evaluates the ability of models to automatically fix buggy code snippets on the MDEVAL benchmark, which covers 20 programming languages. The metric is Pass@1, representing the percentage of problems for which the model generates correct, fixed code that passes all unit tests. Scores are averaged across all 20 languages.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Pass@1 (%)",
+          "alphaxiv_is_primary": "True",
+          "raw_evaluation_name": "Automated Program Repair (Pass@1) on MDEVAL"
+        },
+        "metric_id": "automated_program_repair_pass_1_on_mdeval",
+        "metric_name": "Automated Program Repair (Pass@1) on MDEVAL",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 61.7
+      },
+      "evaluation_result_id": "MDEVAL/GPT-4 Turbo (2024-04-09)/1771591481.616601#mdeval#automated_program_repair_pass_1_on_mdeval"
+    }
+  ],
+  "eval_library": {
+    "name": "alphaxiv",
+    "version": "unknown"
+  }
+}

flat/objects/05/45/0545e51d-3c7e-42af-8827-25068ed89ace.json

@@ -0,0 +1,118 @@
+{
+  "schema_version": "0.2.2",
+  "evaluation_id": "FineGRAIN/SD3.5/1771591481.616601",
+  "retrieved_timestamp": "1771591481.616601",
+  "source_metadata": {
+    "source_name": "alphaXiv State of the Art",
+    "source_type": "documentation",
+    "source_organization_name": "alphaXiv",
+    "source_organization_url": "https://alphaxiv.org",
+    "evaluator_relationship": "third_party",
+    "additional_details": {
+      "alphaxiv_dataset_org": "Sony AI",
+      "alphaxiv_dataset_type": "image",
+      "scrape_source": "https://github.com/alphaXiv/feedback/issues/189"
+    }
+  },
+  "model_info": {
+    "id": "SD3.5",
+    "name": "SD3.5",
+    "developer": "unknown"
+  },
+  "evaluation_results": [
+    {
+      "evaluation_name": "FineGRAIN",
+      "source_data": {
+        "dataset_name": "FineGRAIN",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2512.02161"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Measures the overall performance of Text-to-Image (T2I) models across 27 different failure modes defined by the FineGRAIN benchmark. The score represents the average percentage of images generated by a model that were deemed successful (i.e., did not contain the targeted failure mode) according to human evaluation. A higher score indicates better adherence to complex prompts.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Average Success Rate (%)",
+          "alphaxiv_is_primary": "True",
+          "raw_evaluation_name": "FineGRAIN: Average Success Rate for Text-to-Image Models"
+        },
+        "metric_id": "finegrain_average_success_rate_for_text_to_image_models",
+        "metric_name": "FineGRAIN: Average Success Rate for Text-to-Image Models",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 27.6
+      },
+      "evaluation_result_id": "FineGRAIN/SD3.5/1771591481.616601#finegrain#finegrain_average_success_rate_for_text_to_image_models"
+    },
+    {
+      "evaluation_name": "FineGRAIN",
+      "source_data": {
+        "dataset_name": "FineGRAIN",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2512.02161"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Measures the performance of Text-to-Image (T2I) models on the 'Counts or Multiple Objects' failure mode. The score is the percentage of generated images that correctly depict the precise number of objects specified in the prompt, as judged by human evaluators. This is a known challenging task for T2I models.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Counts or Multiple Objects Success Rate (%)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "FineGRAIN: Success Rate for Counting Multiple Objects"
+        },
+        "metric_id": "finegrain_success_rate_for_counting_multiple_objects",
+        "metric_name": "FineGRAIN: Success Rate for Counting Multiple Objects",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 0
+      },
+      "evaluation_result_id": "FineGRAIN/SD3.5/1771591481.616601#finegrain#finegrain_success_rate_for_counting_multiple_objects"
+    },
+    {
+      "evaluation_name": "FineGRAIN",
+      "source_data": {
+        "dataset_name": "FineGRAIN",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2512.02161"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Measures the performance of Text-to-Image (T2I) models on the 'Long Text Specific' failure mode. The score is the percentage of generated images that accurately render a long string of specific text from the prompt, as judged by human evaluators. This task tests the models' ability to handle detailed textual instructions.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Long Text Specific Success Rate (%)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "FineGRAIN: Success Rate for Rendering Long, Specific Text"
+        },
+        "metric_id": "finegrain_success_rate_for_rendering_long_specific_text",
+        "metric_name": "FineGRAIN: Success Rate for Rendering Long, Specific Text",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 8
+      },
+      "evaluation_result_id": "FineGRAIN/SD3.5/1771591481.616601#finegrain#finegrain_success_rate_for_rendering_long_specific_text"
+    }
+  ],
+  "eval_library": {
+    "name": "alphaxiv",
+    "version": "unknown"
+  }
+}

flat/objects/05/46/0546c045-2978-4ff6-a913-fe5d68279abe.json

@@ -0,0 +1,178 @@
+{
+  "schema_version": "0.2.2",
+  "evaluation_id": "AC-EVAL/ChatGLM3-6B/1771591481.616601",
+  "retrieved_timestamp": "1771591481.616601",
+  "source_metadata": {
+    "source_name": "alphaXiv State of the Art",
+    "source_type": "documentation",
+    "source_organization_name": "alphaXiv",
+    "source_organization_url": "https://alphaxiv.org",
+    "evaluator_relationship": "third_party",
+    "additional_details": {
+      "alphaxiv_dataset_org": "Beijing University of Posts and Telecommunications",
+      "alphaxiv_dataset_type": "text",
+      "scrape_source": "https://github.com/alphaXiv/feedback/issues/189"
+    }
+  },
+  "model_info": {
+    "id": "ChatGLM3-6B",
+    "name": "ChatGLM3-6B",
+    "developer": "unknown"
+  },
+  "evaluation_results": [
+    {
+      "evaluation_name": "AC-EVAL",
+      "source_data": {
+        "dataset_name": "AC-EVAL",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2403.06574"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Average accuracy of large language models on the AC-EVAL benchmark in a zero-shot, answer-only (AO) setting. This setting evaluates the models' inherent understanding of ancient Chinese without providing any examples in the prompt.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Average Accuracy (%) - Zero-shot AO",
+          "alphaxiv_is_primary": "True",
+          "raw_evaluation_name": "AC-EVAL: Average Accuracy (Zero-shot, Answer-Only)"
+        },
+        "metric_id": "ac_eval_average_accuracy_zero_shot_answer_only",
+        "metric_name": "AC-EVAL: Average Accuracy (Zero-shot, Answer-Only)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 46.93
+      },
+      "evaluation_result_id": "AC-EVAL/ChatGLM3-6B/1771591481.616601#ac_eval#ac_eval_average_accuracy_zero_shot_answer_only"
+    },
+    {
+      "evaluation_name": "AC-EVAL",
+      "source_data": {
+        "dataset_name": "AC-EVAL",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2403.06574"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Average accuracy of large language models on the AC-EVAL benchmark in a five-shot, answer-only (AO) setting. This setting evaluates the models' ability to leverage in-context examples for understanding ancient Chinese. The paper notes that few-shot learning often had a limited or negative impact for this specific benchmark.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Average Accuracy (%) - Five-shot AO",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "AC-EVAL: Average Accuracy (Five-shot, Answer-Only)"
+        },
+        "metric_id": "ac_eval_average_accuracy_five_shot_answer_only",
+        "metric_name": "AC-EVAL: Average Accuracy (Five-shot, Answer-Only)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 45.71
+      },
+      "evaluation_result_id": "AC-EVAL/ChatGLM3-6B/1771591481.616601#ac_eval#ac_eval_average_accuracy_five_shot_answer_only"
+    },
+    {
+      "evaluation_name": "AC-EVAL",
+      "source_data": {
+        "dataset_name": "AC-EVAL",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2403.06574"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Accuracy on the 'General Historical Knowledge' category of AC-EVAL, considered the 'easy' difficulty level. This category assesses foundational knowledge of ancient Chinese history, geography, and culture. Evaluated in a zero-shot, answer-only (AO) setting.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Accuracy (%) - Historical Knowledge (Zero-shot AO)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "AC-EVAL: General Historical Knowledge Accuracy (Zero-shot, AO)"
+        },
+        "metric_id": "ac_eval_general_historical_knowledge_accuracy_zero_shot_ao",
+        "metric_name": "AC-EVAL: General Historical Knowledge Accuracy (Zero-shot, AO)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 58.04
+      },
+      "evaluation_result_id": "AC-EVAL/ChatGLM3-6B/1771591481.616601#ac_eval#ac_eval_general_historical_knowledge_accuracy_zero_shot_ao"
+    },
+    {
+      "evaluation_name": "AC-EVAL",
+      "source_data": {
+        "dataset_name": "AC-EVAL",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2403.06574"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Accuracy on the 'Long Text Comprehension' category of AC-EVAL, considered the 'hard' difficulty level. This category assesses summarization, analysis, and appreciation of classical prose and poetry. This is the most challenging category for all models, evaluated in a zero-shot, answer-only (AO) setting.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Accuracy (%) - Long Text (Zero-shot AO)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "AC-EVAL: Long Text Comprehension Accuracy (Zero-shot, AO)"
+        },
+        "metric_id": "ac_eval_long_text_comprehension_accuracy_zero_shot_ao",
+        "metric_name": "AC-EVAL: Long Text Comprehension Accuracy (Zero-shot, AO)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 39.73
+      },
+      "evaluation_result_id": "AC-EVAL/ChatGLM3-6B/1771591481.616601#ac_eval#ac_eval_long_text_comprehension_accuracy_zero_shot_ao"
+    },
+    {
+      "evaluation_name": "AC-EVAL",
+      "source_data": {
+        "dataset_name": "AC-EVAL",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2403.06574"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Accuracy on the 'Short Text Understanding' category of AC-EVAL, considered the 'normal' difficulty level. This category assesses lexical semantics, pragmatics, allusions, and translation from short ancient Chinese texts. Evaluated in a zero-shot, answer-only (AO) setting.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Accuracy (%) - Short Text (Zero-shot AO)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "AC-EVAL: Short Text Understanding Accuracy (Zero-shot, AO)"
+        },
+        "metric_id": "ac_eval_short_text_understanding_accuracy_zero_shot_ao",
+        "metric_name": "AC-EVAL: Short Text Understanding Accuracy (Zero-shot, AO)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 43.01
+      },
+      "evaluation_result_id": "AC-EVAL/ChatGLM3-6B/1771591481.616601#ac_eval#ac_eval_short_text_understanding_accuracy_zero_shot_ao"
+    }
+  ],
+  "eval_library": {
+    "name": "alphaxiv",
+    "version": "unknown"
+  }
+}

flat/objects/05/48/0548ae53-99b8-43d3-b631-c8fcf516aca9.json

@@ -0,0 +1,478 @@
+{
+  "schema_version": "0.2.2",
+  "evaluation_id": "OCRBench v2/Pixtral-12B/1771591481.616601",
+  "retrieved_timestamp": "1771591481.616601",
+  "source_metadata": {
+    "source_name": "alphaXiv State of the Art",
+    "source_type": "documentation",
+    "source_organization_name": "alphaXiv",
+    "source_organization_url": "https://alphaxiv.org",
+    "evaluator_relationship": "third_party",
+    "additional_details": {
+      "alphaxiv_dataset_org": "South China University of Technology",
+      "alphaxiv_dataset_type": "image",
+      "scrape_source": "https://github.com/alphaXiv/feedback/issues/189"
+    }
+  },
+  "model_info": {
+    "id": "Pixtral-12B",
+    "name": "Pixtral-12B",
+    "developer": "unknown"
+  },
+  "evaluation_results": [
+    {
+      "evaluation_name": "OCRBench v2",
+      "source_data": {
+        "dataset_name": "OCRBench v2",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2501.00321"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Average score across eight core OCR capabilities (Recognition, Referring, Spotting, Extraction, Parsing, Calculation, Understanding, and Reasoning) on the private English test set of OCRBench v2. This metric provides a holistic view of a model's ability to handle diverse text-centric visual tasks.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Average Score (%)",
+          "alphaxiv_is_primary": "True",
+          "raw_evaluation_name": "Overall Performance on OCRBench v2 (English, Private)"
+        },
+        "metric_id": "overall_performance_on_ocrbench_v2_english_private",
+        "metric_name": "Overall Performance on OCRBench v2 (English, Private)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 38.4
+      },
+      "evaluation_result_id": "OCRBench v2/Pixtral-12B/1771591481.616601#ocrbench_v2#overall_performance_on_ocrbench_v2_english_private"
+    },
+    {
+      "evaluation_name": "OCRBench v2",
+      "source_data": {
+        "dataset_name": "OCRBench v2",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2501.00321"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Performance on the Relation Extraction capability, which involves extracting and mapping visual components from densely arranged text, such as key-value pairs from receipts or handwritten content. It is evaluated using an F1 score on the private Chinese test set.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Relation Extraction Score (Chinese, %)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Relation Extraction on OCRBench v2 (Chinese, Private)"
+        },
+        "metric_id": "relation_extraction_on_ocrbench_v2_chinese_private",
+        "metric_name": "Relation Extraction on OCRBench v2 (Chinese, Private)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 22.3
+      },
+      "evaluation_result_id": "OCRBench v2/Pixtral-12B/1771591481.616601#ocrbench_v2#relation_extraction_on_ocrbench_v2_chinese_private"
+    },
+    {
+      "evaluation_name": "OCRBench v2",
+      "source_data": {
+        "dataset_name": "OCRBench v2",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2501.00321"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Performance on the Element Parsing capability, assessing a model's ability to parse complex elements like tables, charts, and documents into structured formats (e.g., HTML, JSON). It is evaluated using metrics like Tree-Edit-Distance-based Similarity (TEDS) on the private Chinese test set.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Element Parsing Score (Chinese, %)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Element Parsing on OCRBench v2 (Chinese, Private)"
+        },
+        "metric_id": "element_parsing_on_ocrbench_v2_chinese_private",
+        "metric_name": "Element Parsing on OCRBench v2 (Chinese, Private)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 11.4
+      },
+      "evaluation_result_id": "OCRBench v2/Pixtral-12B/1771591481.616601#ocrbench_v2#element_parsing_on_ocrbench_v2_chinese_private"
+    },
+    {
+      "evaluation_name": "OCRBench v2",
+      "source_data": {
+        "dataset_name": "OCRBench v2",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2501.00321"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Performance on the Knowledge Reasoning capability, which requires complex inference and world knowledge. Tasks include Science QA, APP Agent interaction, ASCII art classification, and Reasoning VQA, evaluated on the private Chinese test set.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Knowledge Reasoning Score (Chinese, %)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Knowledge Reasoning on OCRBench v2 (Chinese, Private)"
+        },
+        "metric_id": "knowledge_reasoning_on_ocrbench_v2_chinese_private",
+        "metric_name": "Knowledge Reasoning on OCRBench v2 (Chinese, Private)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 14
+      },
+      "evaluation_result_id": "OCRBench v2/Pixtral-12B/1771591481.616601#ocrbench_v2#knowledge_reasoning_on_ocrbench_v2_chinese_private"
+    },
+    {
+      "evaluation_name": "OCRBench v2",
+      "source_data": {
+        "dataset_name": "OCRBench v2",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2501.00321"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Performance on the Text Recognition capability, which assesses a model's ability to perceive and transcribe textual content from images. This includes tasks like recognizing regular, irregular, handwritten, and occluded text on the private Chinese test set.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Text Recognition Score (Chinese, %)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Text Recognition on OCRBench v2 (Chinese, Private)"
+        },
+        "metric_id": "text_recognition_on_ocrbench_v2_chinese_private",
+        "metric_name": "Text Recognition on OCRBench v2 (Chinese, Private)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 6.2
+      },
+      "evaluation_result_id": "OCRBench v2/Pixtral-12B/1771591481.616601#ocrbench_v2#text_recognition_on_ocrbench_v2_chinese_private"
+    },
+    {
+      "evaluation_name": "OCRBench v2",
+      "source_data": {
+        "dataset_name": "OCRBench v2",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2501.00321"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Performance on the Visual Text Understanding capability, which assesses a model's ability to comprehend the semantic information of text in an image. Tasks include document classification, diagram question answering, and cognition VQA, evaluated on the private Chinese test set.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Visual Text Understanding Score (Chinese, %)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Visual Text Understanding on OCRBench v2 (Chinese, Private)"
+        },
+        "metric_id": "visual_text_understanding_on_ocrbench_v2_chinese_private",
+        "metric_name": "Visual Text Understanding on OCRBench v2 (Chinese, Private)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 26
+      },
+      "evaluation_result_id": "OCRBench v2/Pixtral-12B/1771591481.616601#ocrbench_v2#visual_text_understanding_on_ocrbench_v2_chinese_private"
+    },
+    {
+      "evaluation_name": "OCRBench v2",
+      "source_data": {
+        "dataset_name": "OCRBench v2",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2501.00321"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Performance on the Mathematical Calculation capability, which covers numerical reasoning tasks such as text counting (character/word counts) and solving math problems presented in images (Math QA). Scores are based on exact match or normalized L1 distance on the private English test set.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Mathematical Calculation Score (%)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Mathematical Calculation on OCRBench v2 (English, Private)"
+        },
+        "metric_id": "mathematical_calculation_on_ocrbench_v2_english_private",
+        "metric_name": "Mathematical Calculation on OCRBench v2 (English, Private)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 30.4
+      },
+      "evaluation_result_id": "OCRBench v2/Pixtral-12B/1771591481.616601#ocrbench_v2#mathematical_calculation_on_ocrbench_v2_english_private"
+    },
+    {
+      "evaluation_name": "OCRBench v2",
+      "source_data": {
+        "dataset_name": "OCRBench v2",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2501.00321"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Performance on the Relation Extraction capability, which involves extracting and mapping visual components from densely arranged text, such as key-value pairs from receipts or handwritten content. It is evaluated using an F1 score on the private English test set.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Relation Extraction Score (%)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Relation Extraction on OCRBench v2 (English, Private)"
+        },
+        "metric_id": "relation_extraction_on_ocrbench_v2_english_private",
+        "metric_name": "Relation Extraction on OCRBench v2 (English, Private)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 71.6
+      },
+      "evaluation_result_id": "OCRBench v2/Pixtral-12B/1771591481.616601#ocrbench_v2#relation_extraction_on_ocrbench_v2_english_private"
+    },
+    {
+      "evaluation_name": "OCRBench v2",
+      "source_data": {
+        "dataset_name": "OCRBench v2",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2501.00321"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Performance on the Element Parsing capability, assessing a model's ability to parse complex elements like tables, charts, and documents into structured formats (e.g., HTML, JSON). It is evaluated using metrics like Tree-Edit-Distance-based Similarity (TEDS) on the private English test set.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Element Parsing Score (%)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Element Parsing on OCRBench v2 (English, Private)"
+        },
+        "metric_id": "element_parsing_on_ocrbench_v2_english_private",
+        "metric_name": "Element Parsing on OCRBench v2 (English, Private)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 21.7
+      },
+      "evaluation_result_id": "OCRBench v2/Pixtral-12B/1771591481.616601#ocrbench_v2#element_parsing_on_ocrbench_v2_english_private"
+    },
+    {
+      "evaluation_name": "OCRBench v2",
+      "source_data": {
+        "dataset_name": "OCRBench v2",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2501.00321"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Performance on the Knowledge Reasoning capability, which requires complex inference and world knowledge. Tasks include Science QA, APP Agent interaction, ASCII art classification, and Reasoning VQA, evaluated on the private English test set.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Knowledge Reasoning Score (%)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Knowledge Reasoning on OCRBench v2 (English, Private)"
+        },
+        "metric_id": "knowledge_reasoning_on_ocrbench_v2_english_private",
+        "metric_name": "Knowledge Reasoning on OCRBench v2 (English, Private)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 39.5
+      },
+      "evaluation_result_id": "OCRBench v2/Pixtral-12B/1771591481.616601#ocrbench_v2#knowledge_reasoning_on_ocrbench_v2_english_private"
+    },
+    {
+      "evaluation_name": "OCRBench v2",
+      "source_data": {
+        "dataset_name": "OCRBench v2",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2501.00321"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Performance on the Text Recognition capability, which assesses a model's ability to perceive and transcribe textual content from images. This includes tasks like recognizing regular, irregular, handwritten, and occluded text on the private English test set.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Text Recognition Score (%)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Text Recognition on OCRBench v2 (English, Private)"
+        },
+        "metric_id": "text_recognition_on_ocrbench_v2_english_private",
+        "metric_name": "Text Recognition on OCRBench v2 (English, Private)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 45.1
+      },
+      "evaluation_result_id": "OCRBench v2/Pixtral-12B/1771591481.616601#ocrbench_v2#text_recognition_on_ocrbench_v2_english_private"
+    },
+    {
+      "evaluation_name": "OCRBench v2",
+      "source_data": {
+        "dataset_name": "OCRBench v2",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2501.00321"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Performance on the Text Referring capability, which measures a model's ability to accurately determine the location of specified text within an image. This often involves outputting bounding box coordinates and is evaluated using metrics like Intersection over Union (IoU) on the private English test set.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Text Referring Score (%)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Text Referring on OCRBench v2 (English, Private)"
+        },
+        "metric_id": "text_referring_on_ocrbench_v2_english_private",
+        "metric_name": "Text Referring on OCRBench v2 (English, Private)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 21.8
+      },
+      "evaluation_result_id": "OCRBench v2/Pixtral-12B/1771591481.616601#ocrbench_v2#text_referring_on_ocrbench_v2_english_private"
+    },
+    {
+      "evaluation_name": "OCRBench v2",
+      "source_data": {
+        "dataset_name": "OCRBench v2",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2501.00321"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Performance on the Text Spotting capability, which requires models to both locate (output bounding box) and recognize (transcribe content) text simultaneously. It is a challenging task where most LMMs perform poorly, evaluated using an F1 score on the private English test set.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Text Spotting Score (%)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Text Spotting on OCRBench v2 (English, Private)"
+        },
+        "metric_id": "text_spotting_on_ocrbench_v2_english_private",
+        "metric_name": "Text Spotting on OCRBench v2 (English, Private)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 0
+      },
+      "evaluation_result_id": "OCRBench v2/Pixtral-12B/1771591481.616601#ocrbench_v2#text_spotting_on_ocrbench_v2_english_private"
+    },
+    {
+      "evaluation_name": "OCRBench v2",
+      "source_data": {
+        "dataset_name": "OCRBench v2",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2501.00321"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Average score across five core OCR capabilities (Recognition, Extraction, Parsing, Understanding, and Reasoning) on the private Chinese test set of OCRBench v2. This metric provides a holistic view of a model's ability to handle diverse Chinese text-centric visual tasks.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Average Score (Chinese, %)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Overall Performance on OCRBench v2 (Chinese, Private)"
+        },
+        "metric_id": "overall_performance_on_ocrbench_v2_chinese_private",
+        "metric_name": "Overall Performance on OCRBench v2 (Chinese, Private)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 16
+      },
+      "evaluation_result_id": "OCRBench v2/Pixtral-12B/1771591481.616601#ocrbench_v2#overall_performance_on_ocrbench_v2_chinese_private"
+    },
+    {
+      "evaluation_name": "OCRBench v2",
+      "source_data": {
+        "dataset_name": "OCRBench v2",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2501.00321"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Performance on the Visual Text Understanding capability, which assesses a model's ability to comprehend the semantic information of text in an image. Tasks include document classification, diagram question answering, and cognition VQA, evaluated on the private English test set.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Visual Text Understanding Score (%)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Visual Text Understanding on OCRBench v2 (English, Private)"
+        },
+        "metric_id": "visual_text_understanding_on_ocrbench_v2_english_private",
+        "metric_name": "Visual Text Understanding on OCRBench v2 (English, Private)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 77.3
+      },
+      "evaluation_result_id": "OCRBench v2/Pixtral-12B/1771591481.616601#ocrbench_v2#visual_text_understanding_on_ocrbench_v2_english_private"
+    }
+  ],
+  "eval_library": {
+    "name": "alphaxiv",
+    "version": "unknown"
+  }
+}

flat/objects/05/4a/054aa5a6-1782-4213-8914-67618e2d553e.json

@@ -0,0 +1,88 @@
+{
+  "schema_version": "0.2.2",
+  "evaluation_id": "EgoMem/Gemini 1.5 Flash/1771591481.616601",
+  "retrieved_timestamp": "1771591481.616601",
+  "source_metadata": {
+    "source_name": "alphaXiv State of the Art",
+    "source_type": "documentation",
+    "source_organization_name": "alphaXiv",
+    "source_organization_url": "https://alphaxiv.org",
+    "evaluator_relationship": "third_party",
+    "additional_details": {
+      "alphaxiv_dataset_org": "National University of Singapore",
+      "alphaxiv_dataset_type": "image",
+      "scrape_source": "https://github.com/alphaXiv/feedback/issues/189"
+    }
+  },
+  "model_info": {
+    "id": "Gemini 1.5 Flash",
+    "name": "Gemini 1.5 Flash",
+    "developer": "unknown"
+  },
+  "evaluation_results": [
+    {
+      "evaluation_name": "EgoMem",
+      "source_data": {
+        "dataset_name": "EgoMem",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2510.12422"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Comparison on the Video-MME benchmark under the 'without subtitles' setting. This score represents the average accuracy across short, medium, and long video durations.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Accuracy (%)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Average Accuracy on Video-MME (w/o subs)"
+        },
+        "metric_id": "average_accuracy_on_video_mme_w_o_subs",
+        "metric_name": "Average Accuracy on Video-MME (w/o subs)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 70.3
+      },
+      "evaluation_result_id": "EgoMem/Gemini 1.5 Flash/1771591481.616601#egomem#average_accuracy_on_video_mme_w_o_subs"
+    },
+    {
+      "evaluation_name": "EgoMem",
+      "source_data": {
+        "dataset_name": "EgoMem",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2510.12422"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Comparison on the Video-MME benchmark under the 'without subtitles' setting, specifically for the 'Long' video duration split. This metric is crucial for evaluating a model's long-context understanding capabilities.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Accuracy (%)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Long Video Accuracy on Video-MME (w/o subs)"
+        },
+        "metric_id": "long_video_accuracy_on_video_mme_w_o_subs",
+        "metric_name": "Long Video Accuracy on Video-MME (w/o subs)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 61.1
+      },
+      "evaluation_result_id": "EgoMem/Gemini 1.5 Flash/1771591481.616601#egomem#long_video_accuracy_on_video_mme_w_o_subs"
+    }
+  ],
+  "eval_library": {
+    "name": "alphaxiv",
+    "version": "unknown"
+  }
+}

flat/objects/05/4b/054bba89-1ed7-4328-851c-7a75dfabdcaa.json

@@ -0,0 +1,58 @@
+{
+  "schema_version": "0.2.2",
+  "evaluation_id": "WebWalkerQA/Kimi/1771591481.616601",
+  "retrieved_timestamp": "1771591481.616601",
+  "source_metadata": {
+    "source_name": "alphaXiv State of the Art",
+    "source_type": "documentation",
+    "source_organization_name": "alphaXiv",
+    "source_organization_url": "https://alphaxiv.org",
+    "evaluator_relationship": "third_party",
+    "additional_details": {
+      "alphaxiv_dataset_org": "Alibaba Group",
+      "alphaxiv_dataset_type": "text",
+      "scrape_source": "https://github.com/alphaXiv/feedback/issues/189"
+    }
+  },
+  "model_info": {
+    "id": "Kimi",
+    "name": "Kimi",
+    "developer": "unknown"
+  },
+  "evaluation_results": [
+    {
+      "evaluation_name": "WebWalkerQA",
+      "source_data": {
+        "dataset_name": "WebWalkerQA",
+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2501.07572"
+        ]
+      },
+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "Overall question-answering accuracy on the WebWalkerQA benchmark for various commercial and open-source search-enhanced RAG systems, as well as close-book models without retrieval. This evaluation demonstrates the limitations of traditional search-based retrieval on deep web traversal tasks. Results are from Table 4.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Overall Accuracy (%) - RAG & Other Systems",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "Overall Accuracy of RAG and Close-Book Systems on WebWalkerQA"
+        },
+        "metric_id": "overall_accuracy_of_rag_and_close_book_systems_on_webwalkerqa",
+        "metric_name": "Overall Accuracy of RAG and Close-Book Systems on WebWalkerQA",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
+      "score_details": {
+        "score": 37.35
+      },
+      "evaluation_result_id": "WebWalkerQA/Kimi/1771591481.616601#webwalkerqa#overall_accuracy_of_rag_and_close_book_systems_on_webwalkerqa"
+    }
+  ],
+  "eval_library": {
+    "name": "alphaxiv",
+    "version": "unknown"
+  }
+}

flat/objects/05/4c/054c5bcb-60c6-44a7-8c73-7a081d28374c.json

@@ -0,0 +1,169 @@
+{
+  "schema_version": "0.2.2",
+  "evaluation_id": "hfopenllm_v2/neopolita_jessi-v0.4-falcon3-7b-instruct/1773936498.240187",
+  "retrieved_timestamp": "1773936498.240187",
+  "source_metadata": {
+    "source_name": "HF Open LLM v2",
+    "source_type": "documentation",
+    "source_organization_name": "Hugging Face",
+    "evaluator_relationship": "third_party"
+  },
+  "eval_library": {
+    "name": "lm-evaluation-harness",
+    "version": "0.4.0",
+    "additional_details": {
+      "fork": "https://github.com/huggingface/lm-evaluation-harness/tree/adding_all_changess"
+    }
+  },
+  "model_info": {
+    "name": "jessi-v0.4-falcon3-7b-instruct",
+    "id": "neopolita/jessi-v0.4-falcon3-7b-instruct",
+    "developer": "neopolita",
+    "inference_platform": "unknown",
+    "additional_details": {
+      "precision": "bfloat16",
+      "architecture": "LlamaForCausalLM",
+      "params_billions": "7.456"
+    }
+  },
+  "evaluation_results": [
+    {
+      "evaluation_name": "IFEval",
+      "source_data": {
+        "dataset_name": "IFEval",
+        "source_type": "hf_dataset",
+        "hf_repo": "google/IFEval"
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy on IFEval",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "metric_id": "accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion"
+      },
+      "score_details": {
+        "score": 0.7604
+      },
+      "evaluation_result_id": "hfopenllm_v2/neopolita_jessi-v0.4-falcon3-7b-instruct/1773936498.240187#ifeval#accuracy"
+    },
+    {
+      "evaluation_name": "BBH",
+      "source_data": {
+        "dataset_name": "BBH",
+        "source_type": "hf_dataset",
+        "hf_repo": "SaylorTwift/bbh"
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy on BBH",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "metric_id": "accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion"
+      },
+      "score_details": {
+        "score": 0.5522
+      },
+      "evaluation_result_id": "hfopenllm_v2/neopolita_jessi-v0.4-falcon3-7b-instruct/1773936498.240187#bbh#accuracy"
+    },
+    {
+      "evaluation_name": "MATH Level 5",
+      "source_data": {
+        "dataset_name": "MATH Level 5",
+        "source_type": "hf_dataset",
+        "hf_repo": "DigitalLearningGmbH/MATH-lighteval"
+      },
+      "metric_config": {
+        "evaluation_description": "Exact Match on MATH Level 5",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "metric_id": "exact_match",
+        "metric_name": "Exact Match",
+        "metric_kind": "exact_match",
+        "metric_unit": "proportion"
+      },
+      "score_details": {
+        "score": 0.3769
+      },
+      "evaluation_result_id": "hfopenllm_v2/neopolita_jessi-v0.4-falcon3-7b-instruct/1773936498.240187#math_level_5#exact_match"
+    },
+    {
+      "evaluation_name": "GPQA",
+      "source_data": {
+        "dataset_name": "GPQA",
+        "source_type": "hf_dataset",
+        "hf_repo": "Idavidrein/gpqa"
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy on GPQA",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "metric_id": "accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion"
+      },
+      "score_details": {
+        "score": 0.3029
+      },
+      "evaluation_result_id": "hfopenllm_v2/neopolita_jessi-v0.4-falcon3-7b-instruct/1773936498.240187#gpqa#accuracy"
+    },
+    {
+      "evaluation_name": "MUSR",
+      "source_data": {
+        "dataset_name": "MUSR",
+        "source_type": "hf_dataset",
+        "hf_repo": "TAUR-Lab/MuSR"
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy on MUSR",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "metric_id": "accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion"
+      },
+      "score_details": {
+        "score": 0.4971
+      },
+      "evaluation_result_id": "hfopenllm_v2/neopolita_jessi-v0.4-falcon3-7b-instruct/1773936498.240187#musr#accuracy"
+    },
+    {
+      "evaluation_name": "MMLU-PRO",
+      "source_data": {
+        "dataset_name": "MMLU-PRO",
+        "source_type": "hf_dataset",
+        "hf_repo": "TIGER-Lab/MMLU-Pro"
+      },
+      "metric_config": {
+        "evaluation_description": "Accuracy on MMLU-PRO",
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 1.0,
+        "metric_id": "accuracy",
+        "metric_name": "Accuracy",
+        "metric_kind": "accuracy",
+        "metric_unit": "proportion"
+      },
+      "score_details": {
+        "score": 0.4004
+      },
+      "evaluation_result_id": "hfopenllm_v2/neopolita_jessi-v0.4-falcon3-7b-instruct/1773936498.240187#mmlu_pro#accuracy"
+    }
+  ]
+}