LLM Agents Make Collective Belief Dynamics Programmable: Challenges and Research Directions
Xin He, Junxi Shen, Yuchen Mou, David M. Bossens, Caishun Chen, Ivor W. Tsang, Yew Soon Ong

TL;DR
This paper explores how LLM-based agents can be used to deliberately steer collective beliefs at scale, highlighting challenges in detection and control, and proposing a research agenda for future investigation.
Contribution
It introduces the concept of programmable collective belief control by LLM agents, demonstrating proof-of-concept and outlining key challenges and research directions.
Findings
Coordinated AI agents can induce measurable belief shifts within a few interaction rounds.
Four structural properties make detection and defense against belief manipulation difficult.
The paper provides a conceptual foundation for future research on belief dynamics and control.
Abstract
Classical models of opinion dynamics assume human participants with bounded rationality and limited coordination. The rise of LLM-based agents introduces a qualitative shift: agents can now participate in online discussions at scale, maintain consistent persuasion strategies, and coordinate systematically. This paper argues that LLM agents make collective belief dynamics programmable, enabling deliberate steering of population-level beliefs. We term this emerging problem programmable collective belief control. Through controlled multi-agent simulations, we provide proof-of-concept evidence that coordinated AI agents can induce measurable belief shifts that stabilize within a few interaction rounds. We identify four structural properties (indistinguishability, persistence, contextuality, and configurability) that make detection and defense fundamentally difficult. Based on these…
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