MAVIS: A Multi-Agent Retrieval Framework Based on Structured Video Understanding

MAVIS: A Multi-Agent Retrieval Framework Based on Structured Video Understanding Original Authors: Jie Zhang et al. | Source: arXiv | Publication Date: June 8, 2026 Core Idea: MAVIS transforms video retrieval from brute-force search to intelligent reasoning through parsing videos into a structured semantic library and introducing multi-agent collaborative reasoning with a logic-aware debate mechanism. It achieves scalable and interpretable video retrieval without task-specific fine-tuning.

Background: Fundamental Dilemmas of Video Retrieval

Video retrieval is a core problem in the multimedia field. Facing massive video data, mainstream embedding-based full-library scanning methods have two major flaws:

1. Computational Efficiency Issue: Full scanning of million-scale libraries is costly, and ANN algorithms still have high latency;
2. Semantic Asymmetry Issue: The multi-dimensional information of videos (visual/audio/temporal) does not match the sparse and abstract nature of text queries, leading to loss of fine-grained semantics and low matching accuracy (e.g., difficulty in distinguishing "running in the rain" from "walking in the rain").

MAVIS Core Design and Technical Architecture

Design Philosophy

Three Transformations: Structured Representation (Video → Semantic Library), Task Decomposition (Complex Query → Atomic Subtasks), Collaborative Verification (Agent Debate Filtering).

Three-Layer Architecture

1. Structured Semantic Library: Decompose videos into visual/temporal/semantic attribute indexes, supporting precise matching and interpretability;
2. Planner and Agents: The planner decomposes queries into subtasks, and specialized agents (visual/action/scene/relation) nominate candidates independently;
3. Logic-Aware Debate: A strict veto protocol excludes conflicting candidates, focuses on controversial candidates for fine-grained verification, and optimizes resource allocation.

Experimental Validation: Three Benchmark Tests

Evaluation Benchmarks

MSR-VTT (10k videos + 200k queries), MSVD (1970 videos), ActivityNet (200 activity categories).

Key Results

• No Task-Specific Fine-Tuning: Maintains competitiveness across benchmarks with strong generality;
• Scalability: Complexity is not linear with library size, leading to significant efficiency improvements;
• Interpretability: Results can be traced back to agent decisions and attribute matching.

Technical Advantages and Application Scenarios

Technical Advantages

1. Solves semantic asymmetry: Structured library matches sparse text queries;
2. Avoids full library traversal: Agent collaboration narrows the search space;
3. Handles complex queries: Subtask division improves performance;
4. Robustness: Multi-agent cross-validation reduces mismatches.

Application Scenarios

Short video content management, video surveillance analysis, film production assistance, educational resource retrieval, etc.

Future Directions and Conclusion

Technical Insights

• Structured representation is superior to single embedding;
• Multi-agent collaboration has great potential;
• Fusion of retrieval and reasoning is a trend.

Future Directions

Cross-modal expansion, online learning, knowledge enhancement, real-time processing.

Conclusion

MAVIS achieves a paradigm shift: from brute-force search to intelligent reasoning, from black box to interpretable. It provides an architectural blueprint for future multimedia retrieval and proves that "smarter search" is more valuable than "faster scanning."