Two Heads are Better Than One: Test-time Scaling of Multi-agent Collaborative Reasoning

Understand

Test-Time Scaling has emerged as a powerful method to extend the reasoning capabilities of Large Language Models.

• However, single-agent TTS faces significant scalability bottlenecks, as excessively long reasoning traces lead to increased inference costs and stability issues caused by context management failures.
• To address these limitations, we propose leveraging Multi-Agent Systems as a structural upgrade to standard TTS.
• By decomposing monolithic reasoning chains into distinct, manageable contexts across multiple agents, MAS offers a more robust framework for scaling reasoning.