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cs.CL2025
Can Multimodal Foundation Models Understand Schematic Diagrams? An Empirical Study on Information-Seeking QA over Scientific Papers
Yilun Zhao, Chengye Wang, Chuhan Li +1
This paper introduces MISS-QA, the first benchmark specifically designed to evaluate the ability of models to interpret schematic diagrams within scientific literature. MISS-QA com…
cs.AI2025★ 1 cited
Advancing AI Research Assistants with Expert-Involved Learning
Tianyu Liu, Simeng Han, Hanchen Wang +27
Large language models (LLMs) and large multimodal models (LMMs) promise to accelerate biomedical discovery, yet their reliability remains unclear. We introduce ARIEL (AI Research A…
cs.CV2025
MMVU: Measuring Expert-Level Multi-Discipline Video Understanding
Yilun Zhao, Lujing Xie, Haowei Zhang +16
We introduce MMVU, a comprehensive expert-level, multi-discipline benchmark for evaluating foundation models in video understanding. MMVU includes 3,000 expert-annotated questions…