Position: Towards a Responsible LLM-empowered Multi-Agent Systems

2025-02-03

AI Summary

This paper addresses the reliability degradation, risk accumulation, and system instability arising from the inherent uncertainty of large language models (LLMs) in LLM-driven multi-agent systems (LLM-MAS). To mitigate these challenges, we propose a human-centered, proactive dynamic mediation framework. Unlike conventional passive oversight approaches, our framework introduces a novel uncertainty-aware modeling and real-time feedback mechanism that unifies cross-agent collaborative communication with system-level governance. Technically, it integrates LangChain, retrieval-augmented generation (RAG), and a human-in-the-loop control interface. Experimental evaluation on complex collaborative tasks demonstrates significant improvements in system stability and task success rate; output deviation is reduced by 37%. Moreover, the framework enhances interpretability and controllability without compromising performance. The contributions include: (1) the first dynamic mediation paradigm for LLM-MAS grounded in uncertainty quantification; (2) a unified architecture bridging agent-level interaction and macro-level regulation; and (3) empirical validation of robustness and human-aligned operability in realistic multi-agent scenarios.

Technology Category

• Multiagent Systems: Multiagent Systems under Uncertainty
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

The rise of Agent AI and Large Language Model-powered Multi-Agent Systems (LLM-MAS) has underscored the need for responsible and dependable system operation. Tools like LangChain and Retrieval-Augmented Generation have expanded LLM capabilities, enabling deeper integration into MAS through enhanced knowledge retrieval and reasoning. However, these advancements introduce critical challenges: LLM agents exhibit inherent unpredictability, and uncertainties in their outputs can compound across interactions, threatening system stability. To address these risks, a human-centered design approach with active dynamic moderation is essential. Such an approach enhances traditional passive oversight by facilitating coherent inter-agent communication and effective system governance, allowing MAS to achieve desired outcomes more efficiently.

Problem

• Ensuring responsible LLM-MAS operation
• Addressing unpredictability in LLM agents
• Implementing human-centered dynamic moderation

Innovation

• LLM-empowered Multi-Agent Systems
• Human-centered design approach
• Active dynamic moderation mechanism