feat: add VisFactor benchmark

[Yunnglin]

Summary

• add the VisFactor multimodal benchmark using the ModelScope lmms-lab-encoder/visfactor dataset
• preserve the official interleaved multi-image prompts and category-specific deterministic answer normalization
• reproduce the official two-level metric: logical AND across rows in each (category_id, eval_index) item, then macro-average accuracy across subtests
• add focused scorer/aggregation tests, VLM smoke tests, generated metadata, and bilingual documentation

Research and alignment

• Official repository: CUHK-ARISE/VisFactor (vlmeval/dataset/visfactor.py)
• Paper: arXiv:2502.16435
• Cross-checked against the merged lmms-eval VisFactor task implementation
• Verified ModelScope schema and standard loader support: 3,046 rows, 808 logical items, 20 subtests, and 6,048 images

Validation

• make lint
• pytest tests/benchmark/test_visfactor.py tests/benchmark/test_vlm.py::TestVLMBenchmark::test_visfactor_mock -q
• pytest tests/api/test_benchmark_meta.py -q
• pytest tests/cli/test_all.py::TestRun::test_ci_lite -v -s -p no:warnings
• real API smoke run with qwen-vl-plus, 20 shuffled rows, eval_batch_size=5
• independently re-scored the 20 real predictions with the lmms-eval reference scorer: 0 mismatches; 20/20 outputs followed the JSON answer contract
• compared 9,138 generated prediction cases over all 3,046 rows with the lmms-eval scorer: 0 mismatches

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