MAVIS: Multi-Agent Video Retrieval via Structured Video Understanding

The dominant paradigm in video retrieval relies on embedding-based full-corpus scanning, which suffers from inherent computational inefficiency and the semantic asymmetry between information-dense videos and sparse textual queries.To bridge this gap, we introduce MAVIS, a novel multi-agent framework that rethinks retrieval as cooperative reasoning rather than brute-force search.MAVIS first bridges the granularity mismatch by parsing raw videos into a Structured Semantic Library, enabling explicit attribute-level indexing.During retrieval, a planner decomposes complex user intents into atomic sub-tasks, dispatching specialized agents to independently nominate candidates.Crucially, MAVIS employs a Logic-aware Debate mechanism with a strict veto protocol, where agents collaboratively prune logical mismatches to identify a compact set of "controversial" candidates for fine-grained verification.This agentic workflow effectively bypasses the inefficiency of full-library traversal.Extensive experiments on MSR-VTT, MSVD, and ActivityNet demonstrate that MAVIS achieves competitive performance without task-specific fine-tuning, offering a scalable and interpretable alternative to traditional dual-encoder approaches.