ToolSciVer: Multimodal Scientific Claim Verification with Visual Tool Augmented Reinforcement Learning
Multimodal Scientific Claim Verification (MSCV) requires models to verify scientific claims using visually grounded evidence from papers, including figures, tables, charts, and textual context. However, existing methods often fail because they struggle to locate decisive visual evidence, accurately read structured scientific visuals, and integrate multimodal observations into reliable reasoning. We introduce ToolSciVer, the first tool-augmented framework for MSCV to our knowledge. ToolSciVer equips a VLM with three type-aware visual tools, table row/column focus, chart-to-structure parsing, an
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Paper → model → repo connections mined from source citations (Tier-1 exact match).
Why these links exist
Every edge carries a method, confidence, and the source snippet that justified it — so bad links are debuggable.
- FuzzyOverlapping authors or contributors · 62%sgl-project/sglang →
“Shared author/contributor keys: zhou”
- FuzzySimilar title/name (fuzzy) · 59%aymericdamien/TopDeepLearning →
“Fuzzy title match (0.73): “ToolSciVer: Multimodal Scientific Claim Verification with Vi” ≈ “aymericdamien/TopDeepLearning””
- LinkedLinked via arxiv author · 85%Binglin Zhou →
“ToolSciVer: Multimodal Scientific Claim Verification with Visual Tool Augmented Reinforcement Learning”
- LinkedLinked via arxiv author · 85%Yupeng Shi →
“ToolSciVer: Multimodal Scientific Claim Verification with Visual Tool Augmented Reinforcement Learning”
- LinkedLinked via arxiv author · 85%Ryo Kamoi →
“ToolSciVer: Multimodal Scientific Claim Verification with Visual Tool Augmented Reinforcement Learning”
- LinkedLinked via arxiv author · 85%Weinan Zhang →
“ToolSciVer: Multimodal Scientific Claim Verification with Visual Tool Augmented Reinforcement Learning”
- LinkedLinked via arxiv author · 85%Renrui Zhang →
“ToolSciVer: Multimodal Scientific Claim Verification with Visual Tool Augmented Reinforcement Learning”