Belief Updating in LLMs

LLM: Large Language Model

Belief updating in large language models concerns how models form and revise views as new evidence arrives, facts change, or other agents make claims. Researchers test this with controlled multi-turn benchmarks, Bayesian and human comparisons, social-persuasion tasks, and probes of internal representations; agent methods also use explicit belief states, retrieval, fine-tuning, and steering. Models can track some evidence well, yet often show primacy or recency effects, rely on stale context, or shift under social pressure, with results depending on the model and evaluation setup. A central challenge is making models resist unsupported influence without blocking legitimate evidence-based revision, while determining whether changed answers reflect changed internal beliefs or surface compliance.

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97 papers40 papers in the last four weeks, up 82% on the four weeks before. 0.2% of all new papers.

Dynamic Theory of Mind as a Temporal Memory Problem: Evidence from Large Language Models
Mar 15, 2026Thuy Ngoc Nguyen, Duy Nhat Phan, Cleotilde GonzalezTheory of Mind Belief Updating in LLMs
Department of Computer Science, University of Dayton · University of Dayton Research Institute, University of Dayton · Department of Social and Decision Sciences, Carnegie Mellon University
Evaluating LLMs as Human Surrogates in Controlled Experiments
Mar 8, 2026Adnan Hoq, Tim WeningerHuman Behavior Simulation Large Language Model-Based Social Simulation
University of Notre Dame Notre Dame, Indiana, USA
LLMs Remember First, Forget Last: Dual-Process Interference in Large Language Models
Feb 27, 2026Sourav Chattaraj, Kanak RajLLM Reliability Belief Updating in LLMs
Thomson Reuters, Bengaluru, India
Empirical Characterization of Inference-Time Elicited Probability Transformations in Large Language Models
Feb 26, 2026Mike Farmer, Abhinav Kochar, Yugyung LeeLLM Inference Belief Updating in LLMs
Bloch School of Management, Regnier Institute for Entrepreneurship & Innovation, University of Missouri–Kansas City. · Department of Computer Science, School of Science and Engineering, University of Missouri–Kansas City.
Diagnosing Retrieval Bias Under Multiple In-Context Knowledge Updates in Large Language Models
Feb 18, 2026Boyu Qiao, Sean Guo, Xian Yang +4Knowledge Conflicts in Language Models Belief Updating in LLMs
Institute of Information Engineering, Chinese Academy of Sciences · School of Cyber Security, University of Chinese Academy of Sciences · Hong Kong University of Science and Technology +1
Vulnerability of LLMs' Stated Beliefs? LLMs Belief Resistance Check Through Strategic Persuasive Conversation Interventions
Jan 20, 2026Fan Huang, Haewoon Kwak, Jisun AnLLM Evaluation Fine-Tuning
Indiana University Bloomington, United States
DEBATE: A Large-Scale Benchmark for Evaluating Opinion Dynamics in Role-Playing LLM Agents
Oct 29, 2025Yun-Shiuan Chuang, Ruixuan Tu, Chengtao Dai +9Opinion Dynamics Large Language Model-Based Social Simulation
University of Wisconsin–Madison · Google DeepMind · Stanford University

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Dynamic Theory of Mind as a Temporal Memory Problem: Evidence from Large Language Models

Thuy Ngoc Nguyen, Duy Nhat Phan, Cleotilde Gonzalez

Department of Computer Science, University of Dayton · University of Dayton Research Institute, University of Dayton · Department of Social and Decision Sciences, Carnegie Mellon University

Theory of Mind Belief Updating in LLMs Temporal Reasoning in Language Models Cognitive Modeling

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Theory of Mind (ToM) is central to social cognition and human-AI interaction, and Large Language Models (LLMs) have been used to help understand and represent ToM. However, most evaluations treat ToM as a static judgment at a single moment, primarily relying on tests of false beliefs. This overlooks a key dynamic dimension of ToM: the ability to represent, update, and retrieve others' beliefs over time. We investigate dynamic ToM as a temporally extended representational memory problem, asking whether LLMs can track belief trajectories across interactions rather than only inferring current beliefs. We introduce DToM-Track, an evaluation framework to investigate temporal belief reasoning in controlled multiturn conversations, testing the recall of beliefs held prior to an update, the inference of current beliefs, and the detection of belief change. Using LLMs as computational probes, we find a consistent asymmetry: models reliably infer an agent's current belief but struggle to maintain and retrieve prior belief states once updates occur. This pattern persists across LLM model families and scales, and is consistent with recency bias and interference effects well documented in cognitive science. These results suggest that tracking belief trajectories over time poses a distinct challenge beyond classical false-belief reasoning. By framing ToM as a problem of temporal representation and retrieval, this work connects ToM to core cognitive mechanisms of memory and interference and exposes the implications for LLM models of social reasoning in extended human-AI interactions.