AGENTIC-SYSTEMSMay 18, 20261 min read12 views

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

Researchers introduce Belief Engine, an auditable layer for LLM agents that tracks and controls how they update their stances based on evidence and prior anchoring during deliberations.

Computer Science > Artificial Intelligence

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

View PDF HTML (experimental)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.

Current browse context:

Bibliographic and Citation Tools

Code, Data and Media Associated with this Article

Demos

Recommenders and Search Tools

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Source: arXiv cs.AI Recent

More in this category

agentic-systems

Capability from Access Structure, Not Scale: Lower Bounds and Pre-Registered Tests for Hybrid Sequence Models

A new study challenges the assumption that scaling alone guarantees capability convergence in AI models. Instead, researchers demonstrate that true capability depends on 'access structure'—specifically, hybrid architectures combining compressive state channels with scalable verbatim-index channels.

agentic-systems

The Steering Budget: Examples beat Knobs

Generative AI models are traditionally steered using 'knobs' like prompts, but they hit a hard ceiling. A new study reveals that providing concrete examples instead of adjusting prompts unlocks the model's full 'steering budget,' offering far greater reach and expressiveness.

agentic-systems

HG-RAG: Hierarchy-Guided Retrieval-Augmented Generation for Structured Knowledge Graphs

HG-RAG (Hierarchy-Guided Retrieval-Augmented Generation) is a novel framework designed to overcome the limitations of traditional flat RAG systems by traversing hierarchical knowledge graphs, significantly improving multi-hop reasoning and reducing hallucinations in LLMs.

agentic-systems

Enhancing Small Language Models Reasoning through Knowledge Graph Grounding

Researchers have proposed a neuro-symbolic framework that enhances the reasoning capabilities of Small Language Models (SLMs) like Gemma 3 and Llama 3.2 using Knowledge Graphs, achieving up to a 2x performance boost.

agentic-systems

Orchestrating Power Grid Studies with Multi-Agent AI and MCP Servers

This paper explores how Agentic AI and Model Context Protocol (MCP) can support power-grid studies by integrating LLMs with numerical simulation tools under human supervision.

agentic-systems

Human AI Construction of Bayesian Networks for Operational Decision Support -- A Virtual Survey Approach

Researchers propose a new methodology using Large Language Models and AI agents to construct Bayesian Belief Networks, bridging the gap between expert opinion and data-driven learning. The framework is demonstrated through a healthcare decision-making model, revealing key insights into customer behavior.