Year: 2026
Type: conference-paper
Abstract: Test-Time Scaling has emerged as a powerful method to extend the reasoning capabilities of Large Language Models.However, singleagent 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.We validate this approach by introducing M500, a dataset comprising 500 high-quality multiagent, multi-turn collaborative reasoning traces generated via DeepSeek-R1.Through Supervised Fine-Tuning on M500, we enable opensource models to internalize collaborative reasoning patterns and show improved TTS performance in MAS.Furthermore, we propose an adaptive scaling strategy incorporating a "CEO" agent to dynamically guide the reasoning process and optimize collaboration depth.Extensive experiments within the AgentVerse framework demonstrate that our fine-tuned models, Qwen2.5-32B-MAS and Phi4-14B-MAS, significantly outperform their base counterparts.
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4415158196