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

TL;DR

This paper systematically investigates how network topology influences conformity, decision efficiency, and robustness in LLM multi-agent systems, revealing trade-offs between centralized and distributed structures in collective decision-making.

Contribution

It introduces a confidence-normalized pooling rule and provides a comprehensive analysis of how topology affects conformity dynamics in LLM-based MAS.

Findings

Centralized networks enable quick decisions but are sensitive to hub competence.

Distributed networks foster robust consensus but are slower to converge.

Increased connectivity speeds up convergence but risks wrong-but-sure cascades.

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.