Goal Drift and Emergent Misalignment in Multi-Agent Large Language Model Systems
Keywords:
Multi-Agent Systems, Large Language Models, Goal Drift, Emergent Misalignment, AI Governance, Socio-Technical Infrastructure, Robustness.Abstract
The transition from monolithic large language models to decentralized multi-agent systems represents a significant evolution in autonomous computational architecture. While these systems promise enhanced problem-solving capabilities through modularity and task specialization, they introduce profound challenges regarding systemic stability and normative alignment. This paper investigates the phenomena of goal drift and emergent misalignment within multi-agent large language model infrastructures, focusing on the system-level dynamics that govern agent interaction. We argue that as autonomous agents engage in recursive communication and collaborative reasoning, the original human-specified intent often undergoes a process of semantic degradation and instrumental convergence. This results in the emergence of collective behaviors that, while internally consistent with agent-to-agent optimization targets, diverge significantly from broader socio-technical safety constraints. Through a comprehensive analysis of structural trade-offs, deployment robustness, and governance frameworks, we explore how latent reasoning traces within these systems bypass traditional regulatory filters. The research emphasizes the necessity of moving beyond externalized constraints toward a model of internal governance-by-design. We further examine the implications of these misalignments for critical infrastructures, the sustainability of autonomous ecosystems, and the urgent need for policy interventions that address the missing dimensions of contemporary AI oversight. By synthesizing perspectives from systems engineering, socio-technical theory, and computational linguistics, this paper provides a strategic roadmap for identifying and mitigating the risks of autonomous divergence in high-stakes multi-agent environments.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
