Belief Engine: Configurable and Inspectable Stance Dynamics in Multi-Agent LLM Deliberation

• URL: http://arxiv.org/abs/2605.15343v1
• Date: Thu, 14 May 2026 19:13:12 GMT
• Title: Belief Engine: Configurable and Inspectable Stance Dynamics in Multi-Agent LLM Deliberation
• Abstract summary: Belief Engine (BE) is an auditable belief-update layer that treats "belief" as an evidential state over a proposition.<n>BE extracts arguments into structured memory and updates stance with a log-odds rule controlled by evidence uptake u and prior anchoring a.<n>On DEBATE, a human deliberation dataset with pre/post opinions, BE best reconstructs participants whose final stance follows extracted evidence.
• Score: 0.299647994399672
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: LLM-based agents are increasingly used to simulate deliberative interactions such as negotiation, conflict resolution, and multi-turn opinion exchange. Yet generated transcripts often do not reveal why an agent's stance changes: movement may reflect evidence uptake, anchoring, role drift, echoing, or changed prompt and retrieval context. We introduce the Belief Engine (BE), an auditable belief-update layer that treats "belief" as an evidential state over a proposition and exposes it as scalar stance. BE extracts arguments into structured memory and updates stance with a log-odds rule controlled by evidence uptake u and prior anchoring a. Across multiple base LLMs, parameter sweeps show that these controls reliably shape stance dynamics while preserving an evidence-level update trail. On DEBATE, a human deliberation dataset with pre/post opinions, BE best reconstructs participants whose final stance follows extracted evidence; stable and evidence-opposed cases instead point to anchoring or factors outside the extracted evidence stream. BE provides configurable infrastructure for studying evidence-grounded deliberation, where openness, commitment, convergence, and disagreement can be tied to explicit update assumptions rather than hidden prompt effects.

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