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
The rapid advancement and deployment of multimodal AI necessitate improved safety and ethical guardrails, moving beyond literal interpretation to pragmatic understanding of harm.
This research addresses a critical limitation in current AI models, enabling them to better discern subtle, context-dependent harm in multimodal content, which is crucial for ethical deployment and societal impact.
The introduction of the MuPHI dataset and its focus on implicit harm reasoning will drive the development of more sophisticated, safety-aware multimodal AI systems.
- · AI safety researchers
- · Generative AI developers
- · Social media platforms
- · Content moderation services
- · Malicious actors abusing AI
- · Platforms with weak moderation
- · Oversimplified VLM approaches
Multimodal AI systems will become more adept at identifying and mitigating subtle forms of harmful content.
This improved detection will lead to fewer instances of AI-generated or amplified harmful content reaching users, enhancing platform safety and user trust.
Societal discourse online could become more constructive as AI moderation shifts from blunt keyword filters to contextually aware harm assessment, shaping future public interaction norms.
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Read at arXiv cs.LG