Belief Engine: Configurable Stance Dynamics for Multi-Agent LLM Deliberation

LLM-based AI agents are increasingly used to simulate deliberative social interactions, including negotiation, conflict resolution, and multi-turn opinion exchanges. In these settings, it is important to know not only what an agent says, but how its stance changes after receiving new information. Current systems often make this difficult: agents may drift from assigned roles, echo an interaction partner, remain fixed despite relevant evidence, or appear to change because the prompt or retrieved context has changed. We introduce the Belief Engine (BE), a framework that uses ``belief'' in a Bayesian modelling sense: a maintained evidential state over a proposition, exposed as scalar stance. BE extracts structured evidence, stores active and archived argument records, and updates the belief state through a Bayesian log-odds rule with two interpretable controls: evidence uptake $u$ and prior anchoring $a$. Across multiple LLM models, sweeps of $u$ and $a$ demonstrate stable control over stance dynamics: higher uptake makes agents more responsive to new evidence, while stronger anchoring preserves the initial stance. On DEBATE, a human deliberation dataset with pre/post opinions, BE is most accurate when participants move in the direction of extracted evidence, showing its strength in evidence-following deliberative contexts; when all participants are evaluated together, gains are more modest because many people remain stable or move for reasons not captured by the extracted evidence stream. BE therefore provides a configurable belief-update layer for evidence-grounded deliberation: LLM agents can be made open-minded, anchored, or evidence-sensitive by construction, while their stance trajectories and memory can be inspected, compared, and calibrated against human opinion change.