Hi-Guard

Policy-aligned reasoning for trustworthy multimodal moderation.

Hi-Guard addresses fine-grained multimodal moderation by explicitly modeling safety policies as structured reasoning paths and hierarchical label spaces.

The project introduces a coarse-to-fine risk decision process and path-level reinforcement learning with semantic-distance-aware rewards, improving consistency and interpretability for complex multimodal risk samples.

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