Towards Ethical Multi-Agent Systems of Large Language Models: A Mechanistic Interpretability Perspective

Jae Hee Lee; Anne Lauscher; Stefano V. Albrecht

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arXiv · 2512.04691

Towards Ethical Multi-Agent Systems of Large Language Models: A Mechanistic Interpretability Perspective

Jae Hee Lee·Anne Lauscher·Stefano V. Albrecht

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Abstract

Large language models (LLMs) have been widely deployed in various applications, often functioning as autonomous agents that interact with each other in multi-agent systems. While these systems have shown promise in enhancing capabilities and enabling complex tasks, they also pose significant ethical challenges. This position paper outlines a research agenda aimed at ensuring the ethical behavior of multi-agent systems of LLMs (MALMs) from the perspective of mechanistic interpretability. We identify three key research challenges: (i) developing comprehensive evaluation frameworks to assess ethical behavior at individual, interactional, and systemic levels; (ii) elucidating the internal mechanisms that give rise to emergent behaviors through mechanistic interpretability; and (iii) implementing targeted parameter-efficient alignment techniques to steer MALMs towards ethical behaviors without compromising their performance.

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Jae Hee Lee, Anne Lauscher, Stefano V. Albrecht. 2025-12-04. Towards Ethical Multi-Agent Systems of Large Language Models: A Mechanistic Interpretability Perspective. https://arxiv.org/abs/2512.04691

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