MuPHI: Learning Implicit Multimodal Harm Reasoning via Semantically Grounded Reward Optimization

arXiv:2605.29951v1 Announce Type: cross Abstract: Understanding how harm emerges from interaction between otherwise benign image-text pairs requires intent-aware cross-modal reasoning beyond surface-level features. Existing vision-language models (VLMs) excel at literal reasoning over perceptual cues but often fail to derive harmful semantics that rely on implicit, context-dependent reasoning. To evaluate VLMs on compositional harm detection and reasoning, we introduce Multimodal Pragmatic Harm Interpretation (MuPHI), a dataset containing image-text pairs where harm is encoded in subtle multim

Source: arXiv cs.LG — read the full report at the original publisher.

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