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This research addresses the critical challenge of detecting harm in multimodal models where benign images and captions combine to create unsafe content, framing it as a cross-modal reasoning problem. The authors introduce MuPHI, a diagnostic benchmark revealing that current Vision-Language Models fail to bind evidence into harm mechanisms, leading to poor out-of-distribution transfer. To solve this, they propose CHIL, a learning framework that prioritizes the reasoning chain and counterfactual robustness over simple label prediction, significantly improving generalization capabilities.
Learning to Reason About Implicit Multimodal Harm: From Diagnosis to Generalization
arXiv:2605.29951v2 Announce Type: replace-cross Abstract: A benign image paired with a benign caption can become harmful only in combination, making detection fundamentally a cross-modal reasoning problem. Existing multimodal harm benchmarks evaluate only classification accuracy, leaving unclear whether models identify the cross-modal mechanism that produces harm. We introduce MuPHI (Multimodal Pragmatic Harm Interpretation), a controlled diagnostic benchmark in which harm arises purely from image-text composition, with benign counterfactuals and annotated harm rationales. Using MuPHI, we systematically evaluate where compositional harm reasoning breaks in vision-language models (VLMs). MuPHI reveals that VLMs reliably ground each modality yet fail to bind the evidence into the harm mechanism and their predictions respond weakly to label-determining counterfactual interventions. Moreover, existing harm detection approaches trained on labels substantially improves in-domain (ID) detection but transfers poorly across harm distributions. We therefore propose Compositional Harm Interpretation Learning (CHIL), which rewards evidence identification in each modality, cross-modal binding and verdict consistency in the reasoning chain rather than just predicted label. CHIL improves 81% OOD transfer evaluations by an average of +2.5 macro-F1 and correct counterfactual flip rate by up to +3.8 and +5.4 % points, while also improving overall reasoning quality over label-only optimization. These results suggest that optimizing how a model reasons, not just what it concludes, offers a promising direction for multimodal safety systems that generalize beyond dataset-specific correlations.
Abstract:A benign image paired with a benign caption can become harmful only in combination, making detection fundamentally a cross-modal reasoning problem. Existing multimodal harm benchmarks evaluate only classification accuracy, leaving unclear whether models identify the cross-modal mechanism that produces harm. We introduce MuPHI (Multimodal Pragmatic Harm Interpretation), a controlled diagnostic benchmark in which harm arises purely from image-text composition, with benign counterfactuals and annotated harm rationales. Using MuPHI, we systematically evaluate where compositional harm reasoning breaks in vision-language models (VLMs). MuPHI reveals that VLMs reliably ground each modality yet fail to bind the evidence into the harm mechanism and their predictions respond weakly to label-determining counterfactual interventions. Moreover, existing harm detection approaches trained on labels substantially improves in-domain (ID) detection but transfers poorly across harm distributions. We therefore propose Compositional Harm Interpretation Learning (CHIL), which rewards evidence identification in each modality, cross-modal binding and verdict consistency in the reasoning chain rather than just predicted label. CHIL improves 81% OOD transfer evaluations by an average of +2.5 macro-F1 and correct counterfactual flip rate by up to +3.8 and +5.4 % points, while also improving overall reasoning quality over label-only optimization. These results suggest that optimizing how a model reasons, not just what it concludes, offers a promising direction for multimodal safety systems that generalize beyond dataset-specific correlations.
Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Machine Learning (cs.LG); Multimedia (cs.MM)
arXiv:2605.29951 [cs.AI]
(or arXiv:2605.29951v2 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2605.29951 Focus to learn more arXiv-issued DOI via DataCite
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