Multi Agent LLM Systems: Breaking Accuracy Barriers with Parallel Processing
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Multi Agent LLM Systems: Breaking Accuracy Barriers with Parallel Processing
Sudheer Peddineni Kalava
Abstract
Multi-Agent Large Language Model (LLM) systems leverage parallel processing and coordination techniques to enhance accuracy, efficiency, and cost-effectiveness. These systems outperform single LLM architectures by distributing tasks across multiple models, enabling faster response times and better resource utilization. This paper explores the design, implementation, and optimization of multi-agent LLM systems, with a focus on parallel processing techniques, performance optimization strategies, and real-world applications. We discuss key architectural considerations, experimental results, current limitations, and future research directions.
Keywords
Multi-Agent Systems, Large Language Models, Parallel Processing, Optimization, Performance Scaling, Artificial Intelligence