TMAS: Multi-Agent Synergy for Test-Time Compute Scaling
TMAS (Test-time Multi-Agent Synergy) is an innovative framework designed to enhance test-time computation for large language models by facilitating structured collaboration among multiple agents. In contrast to current approaches that either loosely coordinate parallel reasoning paths or depend on unreliable past data, TMAS structures inference as a cooperative effort among specialized agents. This arrangement promotes organized information exchange throughout agents, trajectories, and refinement processes. The framework strategically determines which information to keep and reuse, striking a balance between exploration and exploitation. The research can be accessed on arXiv with the identifier 2605.10344.
Key facts
- TMAS stands for Test-time Multi-Agent Synergy.
- It is a framework for scaling test-time compute via multi-agent synergy.
- TMAS organizes inference as a collaborative process among specialized agents.
- It enables structured information flow across agents, trajectories, and refinement iterations.
- Existing methods weakly coordinate parallel reasoning trajectories or rely on noisy historical information.
- TMAS explicitly decides what to retain and reuse.
- The paper is published on arXiv with ID 2605.10344.
- The approach aims to balance exploration and exploitation.
Entities
Institutions
- arXiv