Add manifest (metadata.json)

metadata.json

@@ -1,69 +1,69 @@
 {
-  "name": "DynaMath",
-  "release_date": "2024-05-01",
-  "subsets": {
-    "main": {
-      "language": [
-        "en"
-      ],
-      "modalities": [
-        "single_image_start"
-      ],
-      "task_type": "short_answer",
-      "score_type": "llm_extract",
-      "score_protocol": {
-        "reference": "VLMEvalKit@vlmeval/dataset/dynamath.py:58-109 — rule parse of the answer first (JSON 'short answer' / direct parse_answer), auxiliary LLM extraction only on parse failure, then rule compare: float abs(diff)<=0.001, MC letter equality, text containment. Official DynaMath@evaluation/gpt4o/gpt_4o_json_eval.py:98-123 uses the same JSON 'short answer' parse + abs(diff)<=0.001 rule compare. [Re-verified in clone 2026-07-07: DynaMath_auxeval at dynamath.py:58-110 — abs(diff)<=0.001 at :92.]",
-        "note": "Official grading is rule-based over a JSON-formatted answer forced by their inference prompt; the mm-eval prompt (lmms-eval style question+Options trailer) does not force JSON, so LLM extraction fallback (VLMEvalKit protocol) is required. Official float tolerance is ABSOLUTE 0.001 (score_params.numeric_abs_tol), not relative. Official headline metrics are average-case AND worst-case accuracy over 10 generated variants per seed question; the mm-eval copy ships 501 rows (seed variant only), so worst-case accuracy is not reproducible."
-      },
-      "prompt_template": "<image>{{ question }}{% if options %}\nOptions:\n{% for k, v in options.items() %}{{ k }}. {{ v }}{% if not loop.last %}\n{% endif %}{% endfor %}{% endif %}\n",
-      "mapping_from_source": {
-        "media": {
-          "from": "image",
-          "type": "list",
-          "min_items": 1,
-          "max_items": 1
-        },
-        "id": {
-          "from": "id"
-        },
-        "question": {
-          "from": "question"
-        },
-        "answer": {
-          "from": "answer",
-          "optional": true
-        },
-        "options": {
-          "from": "options",
-          "optional": true,
-          "note": "list source values are normalized to {A,B,...} dict"
-        },
-        "extra": {
-          "subject": {
-            "from": "subject"
-          },
-          "level": {
-            "from": "level"
-          },
-          "answer_type": {
-            "from": "answer_type"
-          }
-        },
-        "source": {
-          "format": "json",
-          "url": {
-            "test": "https://github.com/DynaMath/DynaMath/tree/main/dataset"
-          }
-        }
-      },
-      "prompt_template_source": {
-        "origin": "official",
-        "reference": "https://github.com/EvolvingLMMs-Lab/lmms-eval/blob/main/lmms_eval/tasks/dynamath/utils.py (dynamath_doc_to_text — image+question+optional Options:/A./B. trailer)",
-        "notes": "Tier 4: lmms-eval DynaMath canonical evaluation prompt."
-      },
-      "score_params": {
-        "numeric_abs_tol": 0.001
-      }
+ "name": "DynaMath",
+ "release_date": "2024-05-01",
+ "subsets": {
+  "main": {
+   "language": [
+    "en"
+   ],
+   "modalities": [
+    "single_image_start"
+   ],
+   "task_type": "short_answer_qa",
+   "score_type": "llm_extract",
+   "score_protocol": {
+    "reference": "VLMEvalKit@vlmeval/dataset/dynamath.py:58-109 — rule parse of the answer first (JSON 'short answer' / direct parse_answer), auxiliary LLM extraction only on parse failure, then rule compare: float abs(diff)<=0.001, MC letter equality, text containment. Official DynaMath@evaluation/gpt4o/gpt_4o_json_eval.py:98-123 uses the same JSON 'short answer' parse + abs(diff)<=0.001 rule compare. [Re-verified in clone 2026-07-07: DynaMath_auxeval at dynamath.py:58-110 — abs(diff)<=0.001 at :92.]",
+    "note": "Official grading is rule-based over a JSON-formatted answer forced by their inference prompt; the mm-eval prompt (lmms-eval style question+Options trailer) does not force JSON, so LLM extraction fallback (VLMEvalKit protocol) is required. Official float tolerance is ABSOLUTE 0.001 (score_params.numeric_abs_tol), not relative. Official headline metrics are average-case AND worst-case accuracy over 10 generated variants per seed question; the mm-eval copy ships 501 rows (seed variant only), so worst-case accuracy is not reproducible."
+   },
+   "prompt_template": "<image>{{ question }}{% if options %}\nOptions:\n{% for k, v in options.items() %}{{ k }}. {{ v }}{% if not loop.last %}\n{% endif %}{% endfor %}{% endif %}\n",
+   "mapping_from_source": {
+    "media": {
+     "from": "image",
+     "type": "list",
+     "min_items": 1,
+     "max_items": 1
+    },
+    "id": {
+     "from": "id"
+    },
+    "question": {
+     "from": "question"
+    },
+    "answer": {
+     "from": "answer",
+     "optional": true
+    },
+    "options": {
+     "from": "options",
+     "optional": true,
+     "note": "list source values are normalized to {A,B,...} dict"
+    },
+    "extra": {
+     "subject": {
+      "from": "subject"
+     },
+     "level": {
+      "from": "level"
+     },
+     "answer_type": {
+      "from": "answer_type"
+     }
+    },
+    "source": {
+     "format": "json",
+     "url": {
+      "test": "https://github.com/DynaMath/DynaMath/tree/main/dataset"
+     }
     }
+   },
+   "prompt_template_source": {
+    "origin": "official",
+    "reference": "https://github.com/EvolvingLMMs-Lab/lmms-eval/blob/main/lmms_eval/tasks/dynamath/utils.py (dynamath_doc_to_text — image+question+optional Options:/A./B. trailer)",
+    "notes": "Tier 4: lmms-eval DynaMath canonical evaluation prompt."
+   },
+   "score_params": {
+    "numeric_abs_tol": 0.001
+   }
   }
-}
+ }
+}