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Bayesian Belief Layer for Controllable Opinion Dynamics in LLM Agents

Bayesian Chronicle Agents separate explicit belief state from generated speech, making persuasion strength configurable and multi-agent opinion changes auditable.

arXiv · Sep 18, 2026

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Source Summary

Bayesian Chronicle Agents represent each stance as a probability and update it once per heard utterance. A single **κ prior-strength parameter** controls stubbornness, producing consensus, persistent disagreement, or committed-minority influence.

Practical Implication

For multi-agent simulations, keep belief state outside the language model and let generation express rather than own that state. Persistent disagreement matched analytical fixed points at **R² 0.93–0.99**, and κ retained **perfect rank-order recovery across four models** after the language round-trip.

Agent-Ready Context

Bayesian Chronicle Agents represent each stance as a probability and update it once per heard utterance. A single **κ prior-strength parameter** controls stubbornness, producing consensus, persistent disagreement, or committed-minority influence.

For multi-agent simulations, keep belief state outside the language model and let generation express rather than own that state. Persistent disagreement matched analytical fixed points at **R² 0.93–0.99**, and κ retained **perfect rank-order recovery across four models** after the language round-trip.

This controls a narrow form of opinion dynamics, not general agent reasoning or memory. The abstract does not show whether the same parameterization holds for richer beliefs, longer interactions, or tool-using agents.

Connected Context · Feed7 Judgment

This turns one class of multi-agent memory into an explicit, inspectable state transition rather than prose remembered by each model. It reinforces the candidates’ broader external-state and harness-control pattern, while sharply narrowing the claim: a scalar prior-strength parameter can govern stylized stance dynamics, but does not establish a general memory format or coordination mechanism for long-horizon, tool-using agents.

huangruiteng/loopxBoth place durable control state in the harness rather than model context; the Bayesian layer supplies a mathematically constrained belief update, while LoopX covers broader goals, evidence, ownership, and gates.Handover of In-Context Learning State Across Session BoundariesThe handover work asks which task-relevant state must survive session boundaries; Bayesian Chronicle Agents provide a concrete narrow case where the preserved state is an explicit stance probability and prior strength.TauricResearch/TradingAgentsTradingAgents demonstrates role-based debate with persistent decisions, while this work makes opinion change during interaction externally controllable, suggesting a more inspectable alternative for belief evolution within such debates.A Taxonomy of Cognitive Capability Gaps in Generative and Agentic AIThe taxonomy separates durable state from other capability gaps; this work implements and validates one tightly scoped form of that state without claiming to solve monitoring, action control, adaptation, or general reasoning.

Context Map

agentresearch#multi-agent#agent-memory

Uncertainty

This controls a narrow form of opinion dynamics, not general agent reasoning or memory. The abstract does not show whether the same parameterization holds for richer beliefs, longer interactions, or tool-using agents.