ART：自适应响应调优框架——基于多智能体锦标赛的大语言模型响应优化方法 (ART: Adaptive Response Tuning Framework -- A Multi-Agent Tournament-Based Approach to LLM Response Optimization)

Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language understanding and generation. However, single-model responses often exhibit inconsistencies, hallucinations, and varying quality across different query domains. This paper presents ART (Adaptive Response Tuning), a novel framework that employs tournament-style ELO ranking and multi-agent reasoning to systematically optimize LLM outputs. By enabling multiple LLM agents to compete, critique, and collaborate through structured tournament workflows, ART produces consensus responses that outperform individual model outputs. Our framework introduces configurable tournament parameters, dynamic agent selection, and multiple consensus fusion strategies. Experimental evaluations demonstrate significant improvements in response accuracy, coherence, and reliability compared to baseline single-model approaches. The ART framework provides a scalable, production-ready solution for applications requiring high-quality, vetted LLM responses, achieving an 8.4% improvement in overall quality metrics and R^2 values exceeding 0.96 in ELO rating convergence.

翻译：大语言模型（LLMs）在自然语言理解和生成方面展现出卓越的能力。然而，单一模型的响应常表现出不一致性、幻觉现象以及在不同查询领域质量参差不齐的问题。本文提出ART（自适应响应调优），一种采用锦标赛式ELO排名和多智能体推理来系统优化LLM输出的新型框架。通过让多个LLM智能体在结构化锦标赛工作流中竞争、评判与协作，ART生成优于单一模型输出的共识响应。本框架引入了可配置的锦标赛参数、动态智能体选择及多种共识融合策略。实验评估表明，相较于基线单模型方法，该框架在响应准确性、连贯性和可靠性方面均有显著提升。ART框架为需要高质量、可验证LLM响应的应用提供了可扩展、生产就绪的解决方案，在整体质量指标上实现了8.4%的提升，且ELO评分收敛的R^2值超过0.96。