ART: Adaptive Response Tuning Framework -- A Multi-Agent Tournament-Based Approach to LLM Response Optimization
Omer Jauhar Khan
TL;DR
ART introduces a tournament-based, multi-agent framework that uses extended ELO ranking to optimize LLM responses. By enabling agents to compete, critique, and collaborate, ART produces consensus outputs that outperform single-agent baselines, with production-ready implementation and measurable improvements in overall quality and ELO convergence. The framework is model-agnostic, scalable, and auditable, making it suitable for high-stakes applications where reliability and transparency are essential.
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 R22 values exceeding 0.96 in ELO rating convergence.