CulMind: Benchmarking Multimodal Understanding and Reasoning in Chinese Cultural Heritage
Evaluating Multimodal Large Language Models (MLLMs) in Chinese Cultural Heritage (CCH) requires fine-grained reasoning over visual, textual, stylistic, and historical clues. However, existing CCH benchmarks mainly emphasize final-answer accuracy, while the accuracy and completeness of reasoning processes remain underexplored. To address this gap, we introduce CulMind and CulMind-R: a high-quality benchmark for multimodal CCH covering 50 tasks from collections of more than 100 museums, and a 24-task reasoning subset that adaptively defines task-specific dimensions for reasoning process evaluation. To evaluate reasoning quality, we propose ReaScore, a task-adaptive metric that evaluates rea...
| Authors | Zhangwei Cao, Shuhan Fan, Yuting Wei et al. |
|---|---|
| Primary category | cs.CL |
| Published | 19 June 2026 |
| Last updated at source | 19 June 2026 |
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