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

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arxiv 2402.18272 v1 pith:7DACMQI2 submitted 2024-02-28 cs.CL cs.AI

Qineng Wang , Zihao Wang , Ying Su , Hanghang Tong , Yangqiu Song This is my paper · ORCID
classification cs.CLcs.AI
keywords discussionllmsmulti-agentreasoningmechanismsabilitiesachieveagent
verification ladder T0 review T1 audit T2 compute T3 formal

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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.

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Forward citations

Cited by 12 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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    Authors of economics meta-analyses found a single-pass AI report more useful than two multi-agent debate tools that cost up to thirty times more to run.

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    A state-aware contrastive router that selects the most relevant agent at each step improves multi-agent LLM accuracy by up to 23.8% while using a fraction of the tokens of fixed-pipeline baselines.

  4. CAViAR: Critic-Augmented Video Agentic Reasoning

    cs.CV 2025-09 conditional novelty 6.0 of 10

    CAViAR, an agent-plus-critic system for long video reasoning, improves on direct video LLM inference across LVBench, Neptune, and ActivityNet-RTL.

  5. Stop Overvaluing Multi-Agent Debate -- We Must Rethink Evaluation and Embrace Model Heterogeneity

    cs.CL 2025-02 conditional novelty 6.0 of 10

    Multi-agent debate mostly underperforms simple chain-of-thought baselines when tested broadly, while randomly mixing different models into the debate reliably improves performance.

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    cs.CL 2025-05 conditional novelty 5.0 of 10

    CoMaPOI uses three LLM agents (Profiler, Forecaster, Predictor) with reverse-reasoning fine-tuning to achieve state-of-the-art next-POI prediction on NYC, TKY, and CA.

  7. Swarm Intelligence Enhanced Reasoning: A Density-Driven Framework for LLM-Based Multi-Agent Optimization

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    SIER uses kernel density estimation and Pareto-style selection to preserve diversity in PRM-guided multi-agent reasoning, improving pass@8 and prm@8 on math benchmarks at higher token cost.

  8. Enigme: Generative Text Puzzles for Evaluating Reasoning in Language Models

    cs.AI 2025-05 conditional novelty 5.0 of 10

    The paper introduces enigme, a procedurally generated text-puzzle library for benchmarking reasoning in transformer-decoder language models.

  9. Breaking Event Rumor Detection via Stance-Separated Multi-Agent Debate

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  12. RBF++: Quantifying and Optimizing Reasoning Boundaries across Measurable and Unmeasurable Capabilities for Chain-of-Thought Reasoning

    cs.CL 2025-05 reject novelty 4.0 of 10

    RBF++ models reasoning limits as harmonic-mean boundaries and uses them to explain, predict, and improve chain-of-thought performance across 38 models and 13 tasks.

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