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discussion depth

Discussion depth refers to the number of sequential turns, rounds, or iterative communication cycles that participating agents undergo while collaboratively exploring a topic, debating ideas, or solving a problem. In multi-agent artificial intelligence and computational dialogue systems, it quantifies the progression and structural length of message-passing sequences across the participant network. Tracking and configuring discussion depth allows systems to regulate how extensively agents can exchange feedback, synchronize shared context, and refine their reasoning toward consensus, helping developers balance the potential analytical benefits of extended collaborative discourse against computational cost, latency, and the risk of propagating intermediate reasoning errors.

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Rethinking the Bounds of LLM Reasoning: Are Multi-Agent Discussions the Key?

Rethinking the Bounds of LLM Reasoning: Are Multi-Agent Discussions the Key?

Qineng Wang, Zihao Wang, Ying Su, Hanghang Tong, Yangqiu Song

OrganizationsThe Hong Kong University of Science and TechnologyUniversity of Illinois Urbana-ChampaignZhejiang University

Why you should read this

Demonstrates that single language models with standard few-shot prompts match the reasoning performance of complex multi-agent discussion frameworks, revealing that multi-agent systems only provide advantages in zero-shot settings.

Recent progress in LLMs discussion suggests that multi-agent discussion improves the reasoning abilities of LLMs. In this work, we reevaluate this claim through systematic experiments, where we propose a novel group discussion framework to enrich the set of discussion mechanisms. Interestingly, our results show that a single-agent LLM with strong prompts can achieve almost the same performance as the best existing discussion approach on a wide range of reasoning tasks and backbone LLMs. We observe that the multi-agent discussion performs better than a single agent only when there is no demonstration in the prompt. Further study reveals the common interaction mechanisms of LLMs during the discussion.

Added

2026-09-30