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ToolSciVer: Multimodal Scientific Claim Verification with Visual Tool Augmented Reinforcement Learning

arXiv cs.CL Multimodal & Generative Binglin Zhou, Peng Shi, Ryo Kamoi, Nan Zhang, Rui Zhang 2026-07-17

TL;DR - ToolSciVer trains vision-language models to verify scientific claims using specialized tools for extracting evidence from tables, charts, and high-resolution figure regions. It outperforms prompting- and reinforcement-learning-based tool-use baselines across two datasets and five VLMs.

Provides type-aware tools for table focusing, chart parsing, and region zooming.
Uses GRPO with rewards covering correctness, formatting, response length, and efficient, valid tool use.
Converts dense scientific visuals into explicit evidence relevant to each claim.
Evaluated on SciVer and MuSciClaims with Qwen, InternVL, and Gemma model families.

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