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The paper introduces MuPHI, a new dataset for evaluating VLMs on compositional harm detection requiring subtle multimodal reasoning, and MuPHIRM, a training framework that optimizes multi-perspective rewards to improve VLM harm detection and reasoning. MuPHIRM learns joint semantics by optimizing rewards based on both detection accuracy and the quality of generated harm rationales. Experiments show that MuPHIRM significantly improves harm detection and reasoning quality, as well as out-of-distribution robustness, compared to existing methods.
Current vision-language models can be surprisingly blind to subtle, context-dependent harms lurking in image-text pairs, but a new reasoning-augmented training framework can help them see the bigger picture.
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 multimodal cues. MuPHI spans diverse harm categories and includes annotated harm rationales for assessing VLM reasoning chains. To improve both detection and reasoning in VLMs, we propose MuPHIRM, a reasoning-augmented training framework which learns joint semantics by optimizing multi-perspective rewards. MuPHIRM improves both harm detection and reasoning quality of VLMs while demonstrating superior out-of-distribution robustness compared to both trained and inference-time baselines. Our findings suggest that reasoning-oriented reward optimization offers a promising direction towards building multimodal systems that generalize beyond benchmark-specific shortcuts.
