Title: Conformity Dynamics in Multi-Agent Systems: A Network Topology Perspective
Primary area: foundation or frontier models, including LLMs
Keywords: Conformity, Multi-Agent Systems, Misinformation Detection, Network Topology, Large Language Model

Abstract: Large language models (LLMs) are increasingly employed as agents in multi-agent systems (MAS), where collective decision-making is shaped by conformity dynamics—the tendency of agents to align their judgments with prevailing majority opinions. 
Although conformity can mitigate individual noise, it also risks inducing information cascades that culminate in confident yet erroneous consensus.

This paper presents the first systematic study of how network topology modulates the strength, speed, and reliability of conformity in LLM-based MAS for misinformation detection. We propose a confidence-normalized pooling framework that balances individual judgment against social influence. Our evaluation contrasts two canonical decision modes: Centralized Aggregation, represented by star and hierarchical networks with single-round hub decisions, and Distributed Consensus, characterized by iterative convergence in network structures ranging from sparse rings to fully connected graphs.

Results show that in Centralized topologies, the reliability of collective outcomes is tightly coupled to the competence of the hub agent. In Distributed topologies, greater connectivity and stronger social weighting accelerate convergence and enhance accuracy. Our analysis further underscores the double-edged nature of conformity: while MAS hold promise for misinformation detection, conformity can also drive them into “wrong-but-sure” consensus states, where misclassified claims are sustained with high collective confidence.

The code and dataset are available at: \href{https://anonymous.4open.science/r/Topology-of-Multi-Agent-Systems-AE11}{\texttt{Topology-of-Multi-Agent-Systems}}.

TL;DR: We show how network topology shapes conformity in LLM-based multi-agent systems for misinformation detection, revealing that hubs amplify accuracy in centralized settings while dense distributed networks boost consensus speed and reliability.