Stop Reducing Responsibility in LLM-Powered Multi-Agent Systems to Local Alignment

2025-10-15

AI Summary

LLM-driven multi-agent systems (LLM-MAS) suffer from systemic risks—including consensus failure, uncertainty cascades, and adversarial fragility—rendering agent-level alignment insufficient for ensuring holistic accountability. This paper proposes a paradigm shift: replacing local alignment with global system-wide consensus, and modeling responsibility as a dynamic property spanning design, operation, and evolution. We introduce a novel three-dimensional lifecycle framework—consistency, uncertainty, and safety—that jointly integrates subjective values and objective verifiability. Complementing this, we establish a dual-track governance mechanism grounded in interdisciplinary co-design and human–AI collaborative oversight. Technically, our approach unifies systemic alignment, human-centered value embedding, uncertainty propagation modeling, and adversarial vulnerability analysis. The work advances LLM-MAS from loosely coupled agent ensembles toward verifiably consistent, ethically aligned, and resilient sociotechnical systems.

Technology Category

• Multiagent Systems: Adversarial Agents
• Machine Learning: Large Multimodal Models (LMMs)
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

LLM-powered Multi-Agent Systems (LLM-MAS) unlock new potentials in distributed reasoning, collaboration, and task generalization but also introduce additional risks due to unguaranteed agreement, cascading uncertainty, and adversarial vulnerabilities. We argue that ensuring responsible behavior in such systems requires a paradigm shift: from local, superficial agent-level alignment to global, systemic agreement. We conceptualize responsibility not as a static constraint but as a lifecycle-wide property encompassing agreement, uncertainty, and security, each requiring the complementary integration of subjective human-centered values and objective verifiability. Furthermore, a dual-perspective governance framework that combines interdisciplinary design with human-AI collaborative oversight is essential for tracing and ensuring responsibility throughout the lifecycle of LLM-MAS. Our position views LLM-MAS not as loose collections of agents, but as unified, dynamic socio-technical systems that demand principled mechanisms to support each dimension of responsibility and enable ethically aligned, verifiably coherent, and resilient behavior for sustained, system-wide agreement.

Problem

• Ensuring responsible behavior in multi-agent systems requires systemic agreement
• Managing cascading uncertainty and adversarial vulnerabilities in distributed AI
• Establishing lifecycle-wide responsibility through human-AI collaborative governance

Innovation

• Shift from local agent alignment to global systemic agreement
• Integrate subjective human values with objective verifiability
• Implement dual-perspective governance for lifecycle responsibility