feat: add mme-cc benchmark task

[Luodian]

Summary

• add a new mme_cc benchmark task under lmms_eval/tasks/mme_cc
• load all 12 MME-CC subtasks from MaxwellWen/MME-CC JSON files and keep task auto-discovery via YAML
• implement prompt/visual/target helpers plus exact-match and answered-rate metrics, and include a task README with a smoke command

Validation

• uv run pre-commit run --files lmms_eval/tasks/mme_cc/mme_cc.yaml lmms_eval/tasks/mme_cc/utils.py lmms_eval/tasks/mme_cc/README.md (pass)
• HF_TOKEN= uv run python -m lmms_eval --model dummy_video_reader --model_args response=yes --tasks mme_cc --limit 8 --batch_size 1 --log_samples --output_path /tmp/mme_cc_smoke
  • score table:
    • mme_cc_answered_rate = 1
    • mme_cc_exact_match = 0
  • output files:
    • /tmp/mme_cc_smoke/20260222_225753_results.json
    • /tmp/mme_cc_smoke/20260222_225753_samples_mme_cc.jsonl
  • JSONL sample check: filtered_resps entries are non-empty (e.g., "yes" for doc_id 0-7)

Smoke Validation (limit=8)

Status: PASS (LMM-299 / mme_cc)

Output Table

Metric	Value
mme_cc_exact_match	0.125
mme_cc_answered_rate	1.000

Sample Output

Sample 1 (doc_id: 0)

• Input: Please list the math section of this image in the following format and swap the scores of deepseek-V3 and GPT-4o. Do not include any content other than this: ↵ ↵ Model name: Score ↵ Model name: Score ↵ Model name: Score ↵ ... ↵ Return only your final answer with no explanation.
• Model Output: Grok-3: 52 ↵ Grok-3 mini: 40 ↵ Gemini-2 Pro: 36 ↵ DeepSeek-V3: 9 ↵ Claude 3.5 Sonnet: 16 ↵ GPT-4o: 39
• Reference: Grok-3: 52 ↵ Grok-3 mini: 40 ↵ Gemini-2 Pro: 36 ↵ GPT-4o: 39 ↵ Claude 3.5 Sonnet: 16 ↵ DeepSeek-V3: 9
• Scores: mme_cc_exact_match = 0.0 (total: 1.0, subtask: Chart_Modification) · mme_cc_answered_rate = 1.0 (total: 1.0)
• Tokens: output=333, reasoning=275

Sample 2 (doc_id: 1)

• Input: Extract the corresponding data of the blue bars from Store 1 to Store 5 in the following format, and exchange the maximum and minimum values of the blue bar data without including any additional content ↵ Store 1: xx ↵ Store 2: xx ↵ ... ↵ Return only your final answer with no explanation.
• Model Output: Store 1: 25 ↵ Store 2: 32 ↵ Store 3: 1 ↵ Store 4: 30 ↵ Store 5: 37
• Reference: Store 1: 25 ↵ Store 2: 32 ↵ Store 3: 1 ↵ Store 4: 30 ↵ Store 5: 37
• Scores: mme_cc_exact_match = 1.0 (total: 1.0, subtask: Chart_Modification) · mme_cc_answered_rate = 1.0 (total: 1.0)
• Tokens: output=351, reasoning=313

Test Params

uv run python -m lmms_eval --model openai_compatible --model_args "model_version=bytedance-seed/seed-1.6-flash" --tasks mme_cc --batch_size 1 --limit 8 --log_samples