Aetheria: A multimodal interpretable content safety framework based on multi-agent debate and collaboration

Multiple AI agents working together catch more risks than single systems: Aetheria uses five specialized agents that debate and collaborate on content decisions, which proves more effective than traditional single-model approaches—particularly for identifying implicit or hidden risks that might be missed by conventional moderation systems.

Content moderation decisions are now explainable and traceable: Unlike black-box moderation systems, Aetheria generates detailed audit reports that show why content was flagged or approved, making the judgment process transparent and allowing humans to understand and verify the reasoning behind safety decisions.

The framework handles multiple types of content simultaneously: By being "multimodal," Aetheria can analyze text, images, video, and other content formats together rather than separately, which better reflects how harmful content actually appears in the real world and improves detection accuracy.

Knowledge-based reasoning improves accuracy: The system uses RAG (Retrieval-Augmented Generation) to ground its decisions in actual knowledge bases, meaning moderators can verify what information the AI relied on when making safety judgments, reducing arbitrary or unexplained decisions.

Performance gains are significant on difficult cases: Testing on the AIR-Bench benchmark shows Aetheria substantially outperforms existing baselines, with particular improvements in catching subtle or context-dependent harms—the types of violations that are hardest for current systems to identify reliably.