{
  "name": "DynaMath",
  "release_date": "2024-05-01",
  "subsets": {
    "main": {
      "language": [
        "en"
      ],
      "modalities": [
        "single_image_start"
      ],
      "task_type": "short_answer_qa",
      "score_pipeline": [
        "rule-match",
        "llm-match"
      ],
      "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
      }
    }
  }
}