Belief Updating in LLMs

LLM: Large Language Model

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23 papers in the last four weeks, up 283% on the four weeks before. 0.2% of all new papers.

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PHRBench: A Behavioral Evaluation of Post-Hallucination Reasoning in LLMs
Oct 7, 2026Linghao Meng, Feng He, Xuan Yang +5Hallucination in Language Models LLM Hallucination Mitigation
National University of Singapore · Independent Researcher · Tsinghua University +2
Copies or Sources? Measuring How LLM Aggregators Count Restated Evidence in Multi-Agent Systems
Oct 5, 2026Jianxin Gao, Runze Li, Tianyi Yu +3Belief Updating in LLMs Multi-Agent Communication
China Agricultural University · Jilin University · Tianjin University of Finance and Economics +1
Learning to Simulate Individuals from Macro Social Signals
Oct 5, 2026Yining Zhao, Bushi Liu, Haofei Yu +5Human Behavior Simulation RL for Language Model Reasoning
University of Illinois Urbana-Champaign · Independent Researcher
Erased, Rerouted, or Rescaled? Post-Training and the Causal Quotient of a Language Model's Belief State
Oct 4, 2026Weihan Li, Tianshi Zheng, Junhao Wu +1Belief Updating in LLMs RL Post-Training
The University of Tokyo · The Hong Kong University of Science and Technology · RWTH Aachen University +1
Belief-Trajectory Energy: Measuring the Path to a Prediction
Oct 4, 2026Jiahao Ying, Wei Tang, Boxian Ai +7Belief Updating in LLMs
Fudan University · University of Science and Technology of China · Shanghai Innovation Institute
Beyond Memory: Harnessing Long-Horizon Agents with Explicit Belief States
Oct 1, 2026Yu Luo, Jiamin Jiang, Yimin Zuo +9Belief Updating in LLMs Long-Horizon LLM Agents
Nankai University · Alibaba Group · Tsinghua University
Pre-training interventions, ex post facto: Grafting model beliefs across checkpoints
Sep 30, 2026Peter Nutter, Dani Roytburg, Clément Dumas +2Fine-Tuning LLM Alignment
ETH Zurich · MATS · Carnegie Mellon University +2
Bayesian Fine-tuning Yields Language Models that are as Bayesian as their Beliefs Allow
Sep 30, 2026Polina Tsvilodub, Andreas Waldis, Linlu Qiu +2Fine-Tuning LLM Fine-Tuning
University of Tübingen · Massachusetts Institute of Technology · New York University
BELIEFRAG: Making Adaptive RAG State-Aware under Evolving Evidence
Sep 30, 2026Hongji PuAdaptive RAG Evidence Selection
University of Illinois Urbana-Champaign
When Context Changes: Understanding Update Failures in LLMs
Sep 30, 2026Junyu Guo, Yuchen Fang, Shangding Gu +3Belief Updating in LLMs Attention Mechanisms
University of California, Berkeley
AI Agents are Vulnerable to Radicalization
Sep 29, 2026Ozgur Can Seckin, Shalmoli Ghosh, Alessandro Flammini +3Multi-Agent LLM Systems Belief Updating in LLMs
Observatory on Social Media, Indiana University Bloomington · Emerging Media Studies Division, College of Communication, Boston University
Illusory Truth or Mere Exposure? Model-Dependent Repetition Effects in LLM-Based Social Media Simulations
Sep 28, 2026Azza Bouleimen, Nicolò Pagan, Anikó HannákBelief Updating in LLMs Large Language Model-Based Social Simulation
Department of Computer Science University of Zurich Andreasstrasse 15, 8050, Zurich, Switzerland
Toward a Graded Measure of Belief Stability in Large Language Models
Sep 28, 2026Samantha Dies, Branden Fitelson, Tina Eliassi-RadLLM Reliability LLM Uncertainty Estimation
Northeastern University, 360 Huntington Ave, Boston, MA 02115 USA · Santa Fe Institute, 1399 Hyde Park Road, Santa Fe, NM 87501 USA
Lost with a Map: Conversational State and Behavioral Reliability in Language Models
Sep 27, 2026Atahan Dokme, Larry HeckSpoken Dialogue Systems LLM Reliability
Georgia Institute of Technology
LLMs Trust Their Own: Identity-Dependent Conformity in Multi-Agent Systems
Sep 27, 2026Liron Soffer, Ravid Shwartz-Ziv, Chen ShaniNormative Conformity in Language Models Social Bias in Language Models
Tel-Aviv University · New York University
Truth for Believable AI: Expressed Doubt, Provenance, and Belief Revision as an Engineerable Stance
Sep 22, 2026Sebastian CochinescuLLM Uncertainty Estimation Belief Updating in LLMs
University of Bucharest
Bayesian Belief Layer for Controllable Opinion Dynamics in LLM Agents
Sep 18, 2026Hafsa Akbar, Daniel Platnick, Marjan Alirezaie +2Opinion Dynamics Belief Updating in LLMs
MIT Media Lab, Massachusetts Institute of Technology · Flybits Labs, Creative AI Hub · Toronto Metropolitan University
Bias Amplification in Multi-Agent Network: How Biased Agents Shape Opinions and Rhetoric
Sep 16, 2026Omran Berjawi, Giuseppe Fenza, Rida KhatounOpinion Dynamics Social Bias in Language Models
Institut Polytechnique de Paris, Télécom Paris, Palaiseau, France · University of Salerno, Fisciano, Italy
Large Language Models Develop Belief State Geometry In-Context
Sep 15, 2026Daniel Balcells, Andrew Jun Lee, Chirag Rastogi +3In-Context Learning Belief Updating in LLMs
Independent · UCLA · UIUC +2
Before You Poll with LLMs: A Deliberative Diagnostic Framework
Sep 14, 2026Ahmed Wali, Hassaan TayyabLLM Evaluation Political Bias in Language Models
Lahore University of Management Sciences
Do LLMs Trust the Accuser or the Accusation? Measuring Belief Shifts in Werewolf
Sep 14, 2026Yu-Yu Yang, Ti-Rong Wu, Hung Guei +2Social Deduction Games Pragmatic Reasoning in Language Models
Department of Computer Science, National Yang Ming Chiao Tung University, Taiwan · Institute of Information Science, Academia Sinica, Taiwan · College of Artificial Intelligence, National Yang Ming Chiao Tung University, Taiwan +1
Belief-State Engine: Augmenting LLMs for Principled Planning Under Partial Observability
Sep 9, 2026Arnab Chattopadhayay, Debdipta HalderBelief-Space Planning LLM Agents
Independent Researcher (UCL Alumni), Bangalore, India · Independent Researcher (IIT-Kharagpur Alumni), Bangalore, India
Popular Knowledge Propagates More Errors in LLM Knowledge Updating
Sep 8, 2026Yuji Zhang, Weibing Wang, Cheng Qian +5Knowledge Editing Factual Knowledge in Language Models
University of Illinois Urbana-Champaign · City University of New York · Massachusetts Institute of Technology +1
What Does Multi-Agent Debate Actually Change?
Sep 7, 2026Chen QianLLM Agent Evaluation Multi-Agent Debate
Beliefs and Behavior in Language Models
Sep 7, 2026Alex Smolin, Bryan WilderBelief Updating in LLMs
Department of Economics Toulouse School of Economics · Machine Learning Department Carnegie Mellon University
How AI Models Manage Epistemic Authority: A Taxonomy and Comparative Analysis of Responses to User Disagreement
Sep 7, 2026Riyadh Alnasser, Yusuf Mücahit Çetinkaya, Sumin Zhao +1LLM Evaluation LLM Sycophancy
University of Edinburgh · Middle East Technical University
Caught in the Story: Narrative Captivity in Multi-turn LLMs Conversation
Sep 3, 2026Yuhe Wu, Guangyu Wang, Yujie Chen +7Moral Reasoning in Language Models LLM Reliability
1The Hong Kong University of Science and Technology (Guangzhou) · 2The Chinese University of Hong Kong, Shenzhen · 3Dongbei University of Finance and Economics
Belief-Calibrated Optimization: An Explicit World Model for Agentic Optimization
Sep 1, 2026Yuhan Chen, Zhihua Tian, Mahavir Dabas +7Large Language Model-Guided Optimization Action-Conditioned World Models
1Virginia Tech · 2Amazon · 3Independent Researcher
The Memory Trust Gap: Capability-Dependent Failures in Persistent-Memory Agents
Sep 1, 2026Jundong Hu, Shekar RamachandranKnowledge Conflicts in Language Models LLM Agent Memory
PayPal AI
DERELAB: Probing Defeasible Reasoning and Confirmation Bias in LLMs with a Generative Benchmark
Aug 31, 2026Jayanta Sadhu, Sayem Shahad, Kenneth MarinoLLM Evaluation Belief Updating in LLMs
University of Utah · Bangladesh University of Engineering and Technology

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PHRBench: A Behavioral Evaluation of Post-Hallucination Reasoning in LLMs

Linghao Meng, Feng He, Xuan Yang, Junyuan Mao+4

National University of Singapore · Independent Researcher · Tsinghua University+2

Hallucination in Language Models LLM Hallucination Mitigation Belief Updating in LLMs

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Hallucinated information can propagate through multi-stage LLM systems and become part of the context for subsequent reasoning. Existing studies of post-hallucination reasoning (PHR) mainly characterize changes in final outcomes and aggregate reasoning dynamics, leaving how models resolve hallucinated premises at the response level insufficiently understood. In this work, we introduce PHRBench, a controlled benchmark for behaviorally structured PHR across four domains and 18 large language models. PHRBench characterizes each reasoning trajectory independently of final-answer correctness through Hallucination Compliance, Hallucination Avoidance, and Heuristic Correction, and defines an insightful trajectory as successful correction that ultimately reaches the correct answer. Across 4820 controlled instances, we find that successful recovery remains relatively rare and is associated with more frequent belief updates along the reasoning trajectory. We further find that properties of the hallucinated prompt contain substantial predictive signal for successful recovery, with a lightweight predictor achieving an AUROC of 0.847. These findings provide a behavioral view of post-hallucination reasoning, characterizing how LLMs resolve erroneous context and when successful recovery is likely to occur.

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