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

ART: Adaptive Response Tuning Framework A Multi-Agent Tournament-Based Approach to LLM Response Optimization Omer Jauhar Khan Department of Computer Science National University of Computer and Emerging Sciences (FAST-NUCES) Peshawar, 25000, Pakistan Email: p218055@pwr.nu. edu.pk Abstract—Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language understanding and generation. However, single-model responses often exhibit incon- sistencies, 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, cri- tique, and collaborate through structured tournament workflows, ART produces consensus responses that outperform individual model outputs. Our framework introduces configurable tourna- ment parameters, dynamic agent selection, and multiple con- sensus 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 R² values exceeding 0.96 in ELO rating convergence. Index Terms—Large Language Models, Multi-Agent Systems, ELO Rating, Tournament Selection, Consensus Generation, Re- sponse Optimization, Natural Language Processing, LLM Eval- uation I. INTRODUCTION A. Background and Motivation The proliferation of Large Language Models (LLMs) has revolutionized natural language processing, enabling sophisti- cated applications ranging from conversational agents to code generation and scientific analysis. Models such as GPT-4, Claude, LLaMA, and PaLM demonstrate impressive capabil- ities in understanding context, generating coherent text, and reasoning about complex topics [1], [2]. However, despite these advances, individual LLM responses suffer from several well-documented limitations: 1) Hallucinations: LLMs may generate plausible-sounding but factually incorrect information [3] 2) Inconsistency: Repeated queries may yield different responses of varying quality [4] 3) Bias: Individual models may exhibit systematic biases from their training data [5] 4) Domain Limitations: Models may excel in certain domains while underperforming in others [6] 5) Confidence Calibration: LLMs often struggle to accu- rately assess their own uncertainty [7] These limitations pose significant challenges for appli- cations requiring reliable, accurate, and consistent outputs. Medical diagnosis assistance, legal document analysis, edu- cational content generation, and financial decision support are examples where response quality is critical. B. Problem Statement Given a query Q and a set of n LLM agents A = {a1, a2, ..., an}, each capable of generating a response ri = ai(Q), the problem is to: 1) Evaluate the quality of each response ri across multiple criteria 2) Rank agents based on their response quality using a principled scoring system 3) Select or synthesize an optimal response R∗that max- imizes overall quality 4) Adapt agent rankings over time to reflect cumulative performance C. Contributions This paper makes the following contributions: 1) ART Framework Architecture: A comprehensive multi-agent framework for LLM response optimization featuring modular components for agent management, tournament orchestration, and consensus generation. 2) Tournament-Based ELO Ranking: Application of the ELO rating system to LLM agent evaluation, with exten- sions for multi-agent matches, partial wins, and dynamic K-factor adjustment. 3) Multi-Strategy Consensus Engine: Multiple fusion strategies for synthesizing optimal responses from agent outputs, including weighted voting, contextual aggrega- tion, and hybrid synthesis. 4) Empirical Evaluation: Comprehensive experiments demonstrating the effectiveness of tournament-based op- timization across diverse query types and model config- urations. 5) Production-Ready Implementation: A complete, doc- umented implementation with RESTful API, Docker deployment, and extensive test coverage. D. Paper Organization The remainder of this paper is organized as follows: Sec- tion II reviews related work in multi-agent LLM systems and response optimization. Section III presents the theoretical foundations of the ART framework. Section IV details the arXiv:2512.00617v2 [cs.CL] 24 Dec 2025 system architecture and implementation. Section V describes the experimental methodology and presents results. Section VI discusses implications and limitations. Section VII concludes with future research directions. II. RELATED WORK A. Multi-Agent LLM Systems The use of multiple LLM agents for improved task perfor- mance has gained significant attention. Debate framewor

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