SciLens: Multi-modal Scientific Claim Verification with Agentic Entailment and Grounding

2026-06-18

AI Summary

Scientific claim verification remains challenging for existing vision-language models due to the intricate interplay of numerical data, comparative reasoning, and contextual information, often embedded in complex tables and charts. To address this, this work proposes SciLens, a novel framework that decomposes claims into empirical and contextual atomic components. It performs modality-specific, fine-grained evidence anchoring for empirical atoms—such as rows, columns, axes, and legends—and conducts holistic verification through atomic-level entailment rules. By unifying the modeling of multimodal structured information, SciLens achieves 79.2% macro F1 and 63.1% pairwise accuracy on the SciClaimEval development set, substantially advancing both the accuracy and interpretability of scientific claim verification.

Abstract

Scientific discovery increasingly relies on automated systems that generate hypotheses, inspect multimodal evidence, and validate claims at scale. Yet scientific claim verification is not well served by asking a vision-language model for a direct binary judgment: claims often combine numerical results, comparisons, scope qualifiers, and explanatory context, while evidence is encoded in tables and figures with distinct grounding structures. We present SciLens, an evidence-conditioned atomic entailment framework for multimodal scientific claim verification. SciLens decomposes each claim into central empirical atoms and background atoms, grounds the central atoms to modality-specific evidence witnesses, and predicts the final label with an atom-level entailment rule. For tables, atoms are grounded to rows, columns, cells, arithmetic relations, and table scope; for figures, they are grounded through panels, axes, legends, visual encodings, categories, trends, ranks, and qualifier checks. This yields a unified validation procedure in which a claim is supported only if every central empirical atom is entailed by the current evidence. On the SciClaimEval development set, SciLens achieves 79.2% macro-F1 and 63.1% pair accuracy, showing that structured agentic validation improves both evidence sensitivity and interpretability.

Problem

• scientific claim verification
• multimodal evidence
• claim entailment
• evidence grounding
• vision-language models

Innovation

• multi-modal claim verification
• atomic entailment
• evidence grounding
• structured agentic validation
• scientific reasoning