Conformity Dynamics in LLM Multi-Agent Systems: The Roles of Topology and Self–Social Weighting

ACL ARR 2026 January Submission4907 Authors

Keywords: Multi-Agent Systems, Large Language Models, Conformity Dynamics, Network Topology, Misinformation Detection

Abstract:

Large Language Models (LLMs) are increasingly instantiated as interacting agents in multi-agent systems (MAS), where collective decisions emerge through social interaction rather than independent reasoning. A fundamental yet underexplored mechanism in this process is conformity, the tendency of agents to align their judgments with prevailing group opinions. This paper presents a systematic study of how network topology shapes conformity dynamics in LLM-based MAS through a misinformation detection task.
We introduce a confidence-normalized pooling rule that controls the trade-off between self-reliance and social influence, enabling comparisons between two canonical decision paradigms: Centralized Aggregation and Distributed Consensus. Experimental results demonstrate that network topology critically governs both the efficiency and robustness of collective judgments. Centralized structures enable immediate decisions but are sensitive to hub competence and exhibit same-model alignment biases. In contrast, distributed structures promote more robust consensus, while increased network connectivity speeds up convergence but also heightens the risk of wrong-but-sure cascades, in which agents converge on incorrect decisions with high confidence. These findings characterize the conformity dynamics in LLM-based MAS, clarifying how network topology and self–social weighting jointly shape the efficiency, robustness, and failure modes of collective decision-making.
The code and dataset are available at: \href{https://anonymous.4open.science/r/Topology-of-Multi-Agent-Systems-5FF1}{\texttt{Topology-of-Multi-Agent-Systems}}.

Paper Type: Long

Research Area: Computational Social Science, Cultural Analytics, and NLP for Social Good

Research Area Keywords: LLM/AI agents, NLP tools for social analysis, misinformation detection and analysis, robustness

Contribution Types: Model analysis & interpretability, NLP engineering experiment, Data resources, Data analysis

Languages Studied: English

Submission Number: 4907