SciVer: Evaluating Foundation Models for Multimodal Scientific Claim Verification

Chengye Wang, YIFEI SHEN, Zexi Kuang, Arman Cohan, Yilun Zhao

SciVer: Evaluating Foundation Models for Multimodal Scientific Claim Verification: 12 upvotes on Hugging Face Daily Papers, #8 of 19 papers on 2025-06-19. Day-by-day upvote history.

We introduce SciVer, the first benchmark specifically designed to evaluate the ability of foundation models to verify claims within a multimodal scientific context. SciVer consists of 3,000 expert-annotated examples over 1,113 scientific papers, covering four subsets, each representing a common reasoning type in multimodal scientific claim verification. To enable fine-grained evaluation, each example includes expert-annotated supporting evidence. We assess the performance of 21 state-of-the-art multimodal foundation models, including o4-mini, Gemini-2.5-Flash, Llama-3.2-Vision, and Qwen2.5-VL. Our experiment reveals a substantial performance gap between these models and human experts on SciVer. Through an in-depth analysis of retrieval-augmented generation (RAG), and human-conducted error evaluations, we identify critical limitations in current open-source models, offering key insights to advance models' comprehension and reasoning in multimodal scientific literature tasks.

Paper page on Hugging Face · arXiv

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