SciLens: Multi-modal Scientific Claim Verification with Agentic Entailment and Grounding
For researchers and automated systems needing to verify scientific claims with multimodal evidence, SciLens provides a structured, interpretable approach that outperforms direct vision-language model judgments.
SciLens introduces an evidence-conditioned atomic entailment framework for multimodal scientific claim verification, decomposing claims into atoms and grounding them to tables and figures. It achieves 79.2% macro-F1 and 63.1% pair accuracy on the SciClaimEval development set, demonstrating improved evidence sensitivity and interpretability.
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.