Synergistic minds: A collaborative multi-agent framework for integrated AI tool development using diverse large language models
Year: 2025
Type: article
Abstract: This paper introduces an innovative multi-agent framework for integrated AI tool development that unifies diverse large language models (LLMs) into a cohesive system capable of addressing multifaceted tasks. Unlike conventional monolithic AI systems, our approach dynamically decomposes complex queries and routes them to specialized agents, including models fine-tuned for summarization, translation, code generation, and domain-specific analysis, that collaborate through a centralized orchestration layer. This orchestration not only coordinates inter- agent communication via a shared memory module but also integrates user feedback via a reinforcement learning loop for continuous system improvement. A comprehensive case study in research assistance demonstrates that our system outperforms single-model baselines in both quantitative metrics (e.g., ROUGE, BLEU, unit test accuracy) and qualitative user satisfaction. In addition, we discuss technical challenges, scalability issues, and future directions.
Author: Arpan Shaileshbhai Korat
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4413901613