Communication and coordination in multi-agent video intelligence

Develop robust communication and coordination mechanisms for multi-agent collaborations in video intelligence (e.g., frameworks such as VideoMultiAgents and PreMind) to resolve the identified open challenges in effectively deploying multi-agent systems for video understanding, editing, and generation workflows.

Background

The paper surveys agent-based approaches for video intelligence and notes that multi-agent collaborations have shown promise across tasks such as reasoning, editing, stylization, and story generation. However, despite performance gains, effective inter-agent communication and coordination remain unresolved, limiting reliability and scalability.

The authors present UniVA as a unified agentic framework and highlight protocols like MCP and modular designs as promising directions, but they explicitly recognize that core issues in multi-agent communication and coordination are still not solved, motivating future research to address these challenges.