ART: Adaptive Response Tuning Framework -- A Multi-Agent Tournament-Based Approach to LLM Response Optimization

TL;DR

ART is a multi-agent tournament framework that optimizes LLM responses by leveraging competitive reasoning and consensus strategies, significantly enhancing response quality and reliability.

Contribution

The paper introduces a novel multi-agent tournament approach with configurable parameters and consensus fusion, improving LLM output quality over traditional single-model methods.

Findings

8.4% improvement in response quality metrics

ELO rating convergence with R^2 > 0.96

Enhanced coherence and reliability of LLM outputs

Abstract

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.