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

• URL: http://arxiv.org/abs/2512.04691v1
• Date: Thu, 04 Dec 2025 11:41:44 GMT
• Title: Towards Ethical Multi-Agent Systems of Large Language Models: A Mechanistic Interpretability Perspective
• Authors: Jae Hee Lee, Anne Lauscher, Stefano V. Albrecht,
• Abstract summary: 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.<n>This position paper outlines a research agenda aimed at ensuring the ethical behavior of MALMs from the perspective of mechanistic interpretability.
• Score: 33.482090931732735
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Interpreting Agentic Systems: Beyond Model Explanations to System-Level Accountability [0.6745502291821954]
  Agentic systems have transformed how Large Language Models can be leveraged to create autonomous systems with goal-directed behaviors.<n>Current interpretability techniques, developed primarily for static models, show limitations when applied to agentic systems.<n>This paper assesses the suitability and limitations of existing interpretability methods in the context of agentic systems.
  arXiv  Detail & Related papers  (2026-01-23T21:05:32Z)
• Agentic Reasoning for Large Language Models [122.81018455095999]
  Reasoning is a fundamental cognitive process underlying inference, problem-solving, and decision-making.<n>Large language models (LLMs) demonstrate strong reasoning capabilities in closed-world settings, but struggle in open-ended and dynamic environments.<n>Agentic reasoning marks a paradigm shift by reframing LLMs as autonomous agents that plan, act, and learn through continual interaction.
  arXiv  Detail & Related papers  (2026-01-18T18:58:23Z)
• Investigating The Functional Roles of Attention Heads in Vision Language Models: Evidence for Reasoning Modules [76.21320451720764]
  We introduce CogVision, a dataset that decomposes complex multimodal questions into step-by-step subquestions.<n>Using a probing-based methodology, we identify attention heads that specialize in these functions and characterize them as functional heads.<n>Our analysis reveals that these functional heads are universally sparse, vary in number and distribution across functions, and mediate interactions and hierarchical organization.
  arXiv  Detail & Related papers  (2025-12-11T05:42:53Z)
• SelfAI: Building a Self-Training AI System with LLM Agents [79.10991818561907]
  SelfAI is a general multi-agent platform that combines a User Agent for translating high-level research objectives into standardized experimental configurations.<n>An Experiment Manager orchestrates parallel, fault-tolerant training across heterogeneous hardware while maintaining a structured knowledge base for continuous feedback.<n>Across regression, computer vision, scientific computing, medical imaging, and drug discovery benchmarks, SelfAI consistently achieves strong performance and reduces redundant trials.
  arXiv  Detail & Related papers  (2025-11-29T09:18:39Z)
• PerspAct: Enhancing LLM Situated Collaboration Skills through Perspective Taking and Active Vision [2.32300953742759]
  This study evaluates whether explicitly incorporating diverse points of view using the ReAct framework can enhance an LLM's ability to understand and ground the demands of other agents.<n>We introduce active visual exploration across a suite of seven scenarios of increasing perspective-taking complexity.<n>Our results demonstrate that explicit perspective cues, combined with active exploration strategies, significantly improve the model's interpretative accuracy and collaborative effectiveness.
  arXiv  Detail & Related papers  (2025-11-11T10:54:15Z)
• WebDevJudge: Evaluating (M)LLMs as Critiques for Web Development Quality [62.43165871914528]
  We introduce WebDevJudge, a systematic benchmark for assessing LLM-as-a-judge performance in web development.<n>WebDevJudge comprises human preference labels over paired web implementations, annotated with structured and query-grounded rubrics.<n>In-depth analysis indicates this gap stems from fundamental model limitations, including failures in recognizing functional equivalence, verifying task feasibility, and mitigating bias.
  arXiv  Detail & Related papers  (2025-10-21T12:16:04Z)
• A Survey on Agentic Multimodal Large Language Models [84.18778056010629]
  We present a comprehensive survey on Agentic Multimodal Large Language Models (Agentic MLLMs)<n>We explore the emerging paradigm of agentic MLLMs, delineating their conceptual foundations and distinguishing characteristics from conventional MLLM-based agents.<n>To further accelerate research in this area for the community, we compile open-source training frameworks, training and evaluation datasets for developing agentic MLLMs.
  arXiv  Detail & Related papers  (2025-10-13T04:07:01Z)
• Fundamentals of Building Autonomous LLM Agents [64.39018305018904]
  This paper reviews the architecture and implementation methods of agents powered by large language models (LLMs)<n>The research aims to explore patterns to develop "agentic" LLMs that can automate complex tasks and bridge the performance gap with human capabilities.
  arXiv  Detail & Related papers  (2025-10-10T10:32:39Z)
• MAFE: Multi-Agent Fair Environments for Decision-Making Systems [30.91792275900066]
  We introduce the concept of a Multi-Agent Fair Environment (MAFE) and present and analyze three MAFEs that model distinct social systems.<n> Experimental results demonstrate the utility of our MAFEs as testbeds for developing multi-agent fair algorithms.
  arXiv  Detail & Related papers  (2025-02-25T04:03:50Z)
• Reflection-Bench: Evaluating Epistemic Agency in Large Language Models [10.801745760525838]
  Epistemic agency is the ability to flexibly construct, adapt, and monitor beliefs about dynamic environments.<n>We propose Reflection-Bench, a benchmark consisting of seven tasks with long-term relevance and minimization of data leakage.<n>Our findings suggest several promising research directions, including enhancing core cognitive functions, improving cross-functional coordination, and developing adaptive processing mechanisms.
  arXiv  Detail & Related papers  (2024-10-21T17:59:50Z)
• AgentBoard: An Analytical Evaluation Board of Multi-turn LLM Agents [74.16170899755281]
  We introduce AgentBoard, a pioneering comprehensive benchmark and accompanied open-source evaluation framework tailored to analytical evaluation of LLM agents.<n>AgentBoard offers a fine-grained progress rate metric that captures incremental advancements as well as a comprehensive evaluation toolkit.<n>This not only sheds light on the capabilities and limitations of LLM agents but also propels the interpretability of their performance to the forefront.
  arXiv  Detail & Related papers  (2024-01-24T01:51:00Z)
• AntEval: Evaluation of Social Interaction Competencies in LLM-Driven Agents [65.16893197330589]
  Large Language Models (LLMs) have demonstrated their ability to replicate human behaviors across a wide range of scenarios. However, their capability in handling complex, multi-character social interactions has yet to be fully explored. We introduce the Multi-Agent Interaction Evaluation Framework (AntEval), encompassing a novel interaction framework and evaluation methods.
  arXiv  Detail & Related papers  (2024-01-12T11:18:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.