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flat/objects/13/00/1300fea4-bc0d-4bd7-a25f-811f4527352f.json

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flat/objects/13/01/13015a6a-19b0-49b0-b9a0-00b605956203.json

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flat/objects/13/10/131056b5-224c-4899-bb73-2affd6e6f93a.json

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+        "source_type": "url",
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+          "alphaxiv_is_primary": "False",
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+        "metric_id": "facexbench_face_recognition",
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+        "metric_id": "facexbench_face_tools_use",
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+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "FaceXBench: Face Authentication"
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+        "metric_id": "facexbench_face_authentication",
+        "metric_name": "FaceXBench: Face Authentication",
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+}

flat/objects/13/16/131637f1-b93d-4b43-a488-4e9964cf4b2a.json

@@ -0,0 +1,88 @@
+{
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+}

flat/objects/13/19/13199b24-fbc6-498b-8b60-5fd49c83564a.json

@@ -0,0 +1,238 @@
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+    "developer": "unknown"
+  },
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+        "score_type": "continuous",
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+        "evaluation_description": "This metric represents the average score of various text-to-image models across nine visual dimensions (Object, Background, Color, Texture, Lighting, Text, Composition, Pose, Special Effects) on the LongBench-T2I benchmark. The benchmark consists of 500 complex, long-form text prompts. Scores are automatically assigned by the Google Gemini-2.0-Flash multimodal model, with higher scores indicating better adherence to the detailed instructions.",
+        "additional_details": {
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+        },
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+        "metric_name": "LongBench-T2I: Overall Model Performance (Evaluated by Gemini-2.0-Flash)",
+        "metric_kind": "score",
+        "metric_unit": "points"
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+    },
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+        "url": [
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+        "score_type": "continuous",
+        "min_score": 0.0,
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+        "score_type": "continuous",
+        "min_score": 0.0,
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+    },
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+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "This metric assesses how well a model adheres to the specified color palettes, tones, and overall mood described in the prompt. The evaluation is performed by the Gemini-2.0-Flash model on the LongBench-T2I dataset.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Color & Tone Score (Gemini-2.0-Flash Eval)",
+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "LongBench-T2I: Color and Tone Fidelity (Evaluated by Gemini-2.0-Flash)"
+        },
+        "metric_id": "longbench_t2i_color_and_tone_fidelity_evaluated_by_gemini_2_0_flash",
+        "metric_name": "LongBench-T2I: Color and Tone Fidelity (Evaluated by Gemini-2.0-Flash)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
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+        "source_type": "url",
+        "url": [
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+      },
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+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
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+        },
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+        "metric_name": "LongBench-T2I: Composition and Framing Fidelity (Evaluated by Gemini-2.0-Flash)",
+        "metric_kind": "score",
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+    },
+    {
+      "evaluation_name": "LongBench-T2I",
+      "source_data": {
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+        "source_type": "url",
+        "url": [
+          "https://www.alphaxiv.org/abs/2505.24787"
+        ]
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+      "metric_config": {
+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "This metric evaluates a model's ability to correctly generate and depict all specified objects in the prompt, including their attributes and relationships. The evaluation is performed by the Gemini-2.0-Flash model on the LongBench-T2I dataset.",
+        "additional_details": {
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+          "alphaxiv_is_primary": "False",
+          "raw_evaluation_name": "LongBench-T2I: Object Generation Fidelity (Evaluated by Gemini-2.0-Flash)"
+        },
+        "metric_id": "longbench_t2i_object_generation_fidelity_evaluated_by_gemini_2_0_flash",
+        "metric_name": "LongBench-T2I: Object Generation Fidelity (Evaluated by Gemini-2.0-Flash)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
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+    },
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+        "lower_is_better": false,
+        "score_type": "continuous",
+        "min_score": 0.0,
+        "max_score": 100.0,
+        "evaluation_description": "This metric assesses a model's capability to accurately render any specified text, symbols, or runes within the image. This is a known challenging area for text-to-image models. The evaluation is performed by the Gemini-2.0-Flash model on the LongBench-T2I dataset.",
+        "additional_details": {
+          "alphaxiv_y_axis": "Text & Symbol Score (Gemini-2.0-Flash Eval)",
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+          "raw_evaluation_name": "LongBench-T2I: Text and Symbol Generation Fidelity (Evaluated by Gemini-2.0-Flash)"
+        },
+        "metric_id": "longbench_t2i_text_and_symbol_generation_fidelity_evaluated_by_gemini_2_0_flash",
+        "metric_name": "LongBench-T2I: Text and Symbol Generation Fidelity (Evaluated by Gemini-2.0-Flash)",
+        "metric_kind": "score",
+        "metric_unit": "points"
+      },
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+    }
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+}

flat/objects/13/1a/131a06e9-4b67-473d-9f7b-a4730ed8afd0.json

@@ -0,0 +1,388 @@
+{
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+    "source_type": "documentation",
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+        "additional_details": {
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+          "alphaxiv_is_primary": "True",
+          "raw_evaluation_name": "Overall Performance of Multilingual Models on the MILU Benchmark"
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+    },
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flat/objects/13/21/1321431b-edd7-4ccd-9874-864a1277d1d7.json

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flat/objects/13/22/13226669-9b4c-439f-a84f-227a5642ad99.json

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flat/objects/13/24/1324a300-0184-47b2-a9ff-b88c2d4c1620.json

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flat/objects/13/25/1325a5e6-6e30-4112-bfa9-09059dcb40ae.json

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flat/objects/13/25/1325c42e-e48d-47bc-a25d-c37391f79852.json

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flat/objects/13/2b/132b0de7-787b-49b3-88aa-0eb2c85d23d4.json

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flat/objects/13/2b/132b20fc-f16b-4930-b6fb-030d17abfe11.json

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flat/objects/13/2b/132b289c-65f7-4e78-901e-9f69d4f581c9.json

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flat/objects/13/3a/133ac534-c9d7-4d61-8eb1-3e9afb5d057c.json

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flat/objects/13/3b/133b1895-94b9-4386-bdf4-5dc2c0ec0959.json

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flat/objects/13/3b/133bde70-9816-41c8-ac06-a13df413af2b.json

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flat/objects/13/3d/133d7ea6-c3e3-4e45-aaf6-a5e6480e4599.json

@@ -0,0 +1,169 @@
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flat/objects/13/3e/133ed68f-bdf3-4c14-8de5-646a4b58638b.json

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flat/objects/13/45/13452475-5aab-4985-a01e-f45a0aa1ebe4.json

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flat/objects/13/48/1348f84e-9c26-4b71-8b06-5e6eb531bc70.json

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flat/objects/13/4b/134b5850-ef38-4108-8184-36edefd52072.json

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flat/objects/13/51/1351ae23-8278-40ba-9c92-7bff04214ecb.json

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flat/objects/13/54/1354bdc7-f178-4782-9cfa-c953d27ec563.json

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flat/objects/13/5e/135e1e89-c17a-4e0d-8a6c-da18211ac62f.json

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flat/objects/13/5e/135ec0e0-3fca-4223-8847-061f79e50dd5.json

@@ -0,0 +1,58 @@
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flat/objects/13/5f/135f4a08-e6ac-455c-bc01-537f77219d3c.json

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flat/objects/13/5f/135fd2de-5726-4130-9d3e-6da599d7f251.json

@@ -0,0 +1,88 @@
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