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LLM Harmony: Multi-Agent Communication for Problem Solving

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arxiv 2401.01312 v1 pith:32I3KPSI submitted 2024-01-02 cs.MA

Sumedh Rasal This is my paper · ORCID
classification cs.MA
keywords communicationframeworkagentslanguagelimitationsllmsmodelsmulti-agent
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Large Language Models (LLMs) have revolutionized Natural Language Processing but exhibit limitations, particularly in autonomously addressing novel challenges such as reasoning and problem-solving. Traditional techniques like chain-of-thought prompting necessitate explicit human guidance. This paper introduces a novel multi-agent communication framework, inspired by the CAMEL model, to enhance LLMs' autonomous problem-solving capabilities. The framework employs multiple LLM agents, each with a distinct persona, engaged in role-playing communication, offering a nuanced and adaptable approach to diverse problem scenarios. Extensive experimentation demonstrates the framework's superior performance and adaptability, providing valuable insights into the collaborative potential of multiple agents in overcoming the limitations of individual models.

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Forward citations

Cited by 6 Pith papers

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

  1. LRAgent: Efficient KV Cache Sharing for Multi-LoRA LLM Agents

    cs.LG 2026-02 conditional novelty 7.0 of 10

    KV caches in multi-LoRA agents decompose into a shared base part plus a low-rank adapter part, yielding near-full-sharing efficiency at under 1.5% accuracy loss (HotpotQA, ScienceQA).

  2. BELLE: A Bi-Level Multi-Agent Reasoning Framework for Multi-Hop Question Answering

    cs.CL 2025-05 conditional novelty 6.0 of 10

    A question-type-aware, bi-level multi-agent debate that selects and combines existing QA operators outperforms fixed single-method baselines on four multi-hop benchmarks.

  3. Evaluating LLMs and Prompting Strategies for Automated Hardware Diagnosis from Textual User-Reports

    cs.LG 2025-07 conditional novelty 5.0 of 10

    A benchmark of 29 LLMs and four prompting strategies for classifying faulty computer components from user reports, finding that small models like gemma-2-2b match larger ones.

  4. Large Language Model Based Multi-Agent System Augmented Complex Event Processing Pipeline for Internet of Multimedia Things

    cs.MA 2025-01 conditional novelty 4.0 of 10

    A proof-of-concept that uses AutoGen LLM agents over Kafka to process video queries, with latency and quality measurements across agent counts, video complexity, and resolution.

  5. Enhancing LLM Reasoning with Multi-Path Collaborative Reactive and Reflection agents

    cs.CL 2024-12 conditional novelty 4.0 of 10

    A multi-path, reactive-plus-reflection agent framework improves gpt-3.5-turbo accuracy on MMLU physics, math, and moral reasoning subsets compared with CoT, self-consistency, and self-refine baselines.

  6. Town Hall Debate Prompting: Enhancing Logical Reasoning in LLMs through Multi-Persona Interaction

    cs.CL 2025-01 reject novelty 3.0 of 10

    A single LLM that debates itself via multiple personas and a final vote improves ZebraLogic puzzle accuracy for GPT-4o and Claude 3.5 but not for GPT-4o Mini.

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