Abstract
Large Language Models (LLMs) have revolutionized artificial intelligence (AI) applications across industries. As organizations increasingly deploy these powerful systems, ensuring their alignment with human values, safety requirements, organizational policies, and contextual regulations remain a significant challenge. Existing alignment methods like Reinforcement Learning from Human Feedback (RLHF) are labor intensive, expensive, time-consuming, prone to human error, and lack standardized frameworks for implementation. To overcome such issues, a system is proposed herein that uses an Adaptive Multi-Agent Orchestration Framework for Contextual LLM Alignment. The proposed system addresses critical limitations in current alignment approaches.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Morandi, Andrea and Gupta, Saurabh, "END-TO-END FRAMEWORK FOR LLM MULTI-AGENT ALIGNMENT THROUGH COLLABORATIVE FEEDBACK ORCHESTRATION", Technical Disclosure Commons, (November 10, 2025)
https://www.tdcommons.org/dpubs_series/8850