Knowledge Conflicts in Language Models

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Conformity Mitigations in Large Language Models Lie on a Single Resistance-Receptivity Frontier
Aug 3, 2026Zafar Hussain, Kristoffer NielboLLM Evaluation Normative Conformity in Language Models
Aarhus University
DenialRAG: Single-Document RAG Poisoning via Embedded Parametric Denial
Aug 2, 2026Abay Zhurekbay, Tao Liu, Fan LiKnowledge Conflicts in Language Models RAG Poisoning Attacks
Lawrence Technological University
When Specifications Conflict: A Symmetry-Based Framework for Measuring LLM Preferences
Jul 30, 2026Tairan Wang, Liang Zhou, Zikang Zhan +1LLM Evaluation Knowledge Conflicts in Language Models
Department of Computer Science University College London London, United Kingdom
It's Not What You Say, It's How You Say It: Evaluating LLM Responses to Expressions of Belief
Jul 20, 2026Kevin Du, Clara Kümpel, Michelle Wastl +1LLM Evaluation Knowledge Conflicts in Language Models
ETH Zürich · University of Zurich · UC San Diego
EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning
Jul 11, 2026S M Asif Hossain, Ruksat Khan Shayoni, M. F. MridhaRetrieval-Augmented Generation Evidential Deep Learning
aSchool of Computing, Wichita State University, Kansas, USA · bDepartment of Computer Science, American International University-Bangladesh, Dhaka, Bangladesh
Dual-Confidence Contrastive Decoding for Retrieval-Augmented Generation
Jul 1, 2026Raymond Li, Md Tawkat Islam Khondaker, Amirhossein Abaskohi +3Retrieval-Augmented Generation Knowledge Conflicts in Language Models
ServiceNow Research · University of British Columbia
Understanding Why Language Models Hallucinate: Testing Reasoning Against Priors
Jul 1, 2026Yangfan Hu, Xuhan Tong, Haoyue Bai +5Knowledge Conflicts in Language Models LLM Prompting
University of Wisconsin–Madison
Regime-Aware Peer Specialization for Robust RAG under Heterogeneous Knowledge Conflicts
Jun 29, 2026Bo Wang, Heyan Huang, Yaolin Li +5Retrieval-Augmented Generation Knowledge Conflicts in Language Models
School of Computer Science, Beijing Institute of Technology
Vision-Default, Prior-Override: Causal Mechanisms of Perception-Knowledge Conflict in Vision-Language Models
Jun 26, 2026Niclas Lietzow, Danielle Bitterman, Carsten Eickhoff +2Knowledge Conflicts in Language Models VLM Interpretability
University of Tübingen · Harvard University · The University of Texas at Austin
SHIFT: Gate-Modulated Activation Steering for Knowledge Conflict Mitigation in Retrieval-Augmented Generation
Jun 26, 2026Ruochang Li, Pengcheng Huang, Zhenghao Liu +5Retrieval-Augmented Generation Knowledge Conflicts in Language Models
♡School of Computer Science and Engineering, Northeastern University, Shenyang, China · ♠Department of Computer Science and Technology, Tsinghua University, Beijing, China
Supersede: Diagnosing and Training the Memory-Update Gap in LLM Agents
Jun 25, 2026Vedant PatelContinual Learning for LLM Agents Knowledge Conflicts in Language Models
Vrin
ConflictScore: Identifying and Measuring How Language Models Handle Conflicting Evidence
Jun 24, 2026Siyi Liu, Aaron Halfaker, Dan Roth +1Knowledge Conflicts in Language Models Factual Consistency Evaluation
University of Pennsylvania · Microsoft
Exposing the Illusion of Erasure in Knowledge Editing for LLMs
Jun 22, 2026Advik Raj Basani, Anshuman ChhabraKnowledge Editing Knowledge Conflicts in Language Models
Birla Institute of Technology and Science, Goa · University of South Florida
Confident but Conflicted: Internal Uncertainty and Cognitive Dissonance Resolution in LLMs
Jun 21, 2026Weihong Qi, Kristina LermanKnowledge Conflicts in Language Models Belief Updating in LLMs
Indiana University Bloomington
Hallucination as Context Drift: Synchronization Protocols for Multi-Agent LLM Systems
Jun 19, 2026Carson RodriguesMulti-Agent LLM Systems LLM Hallucination Mitigation
Celabe
Right Knowledge, Wrong Answer: Test-Time Steering for Temporal Fact Conflicts in Open-Weight Language Models
Jun 18, 2026Elias Hossain, Sourav Saha, Umesh Chandra Biswas +1Knowledge Conflicts in Language Models Temporal Reasoning in Language Models
University of Central Florida · 2Mississippi State University
Navigating Unreliable Parametric and Contextual Knowledge: Explicit Knowledge Conflict Resolution for LLM Inference
Jun 18, 2026Huang Peng, Jiuyang Tang, Weixin Zeng +2Multi-Agent LLM Systems Knowledge Conflicts in Language Models
National Key Laboratory of Big Data and Decision, National University of Defense Technology, China
Rift: A Conflict Signature for Deception in Language Models
Jun 15, 2026Petr NyomaKnowledge Conflicts in Language Models LLM Interpretability
Harmonic Labs
From Context-Aware to Conflict-Aware: Generalizing Contrastive Decoding for Knowledge Conflict in LLMs
Jun 9, 2026Runze Jiang, Taiqiang Wu, Yan Wang +2LLM Evaluation Knowledge Conflicts in Language Models
1Peking University · 2Alibaba Group · 3The University of Hong Kong
CRANE: Knowledge Editing for Reasoning MLLMs
Jun 8, 2026Han Huang, Hao Wang, Mengqi Zhang +3Knowledge Editing Knowledge Conflicts in Language Models
University of Chinese Academy of Sciences, Beijing 101408, China, and also with the New Laboratory of Pattern Recognition (NLPR), CASIA, Beijing 100045, China · Harbin Institute of Technology, Weihai 264209, China · School of Computer Science and Technology, Shandong University, Qingdao 266237, China
Analyzing the Correlation Between Hallucinations and Knowledge Conflicts in Large Language Models
Jun 7, 2026Lucrezia Laraspata, Giovanna Castellano, Gennaro VessioKnowledge Conflicts in Language Models LLM Interpretability
Department of Computer Science, University of Bari Aldo Moro, Bari, Italy
TrustMargin: Training-Free Arbitration between Parametric Memory and Retrieved Evidence in Large Language Models
Jun 7, 2026Jingyan Xu, Hong Shi, Yi Shan +4Retrieval-Augmented Generation LLM Grounding
DCST, Peking University · Beijing, China
Evaluating RAG Reliability under Clean, Misleading, and Mixed Retrieval
Jun 5, 2026Sevgi Yigit-SertRetrieval-Augmented Generation Knowledge Conflicts in Language Models
Computer Engineering Department, Ankara University, Golbasi Campus, 06830, Ankara, Turkey · Golbasi Campus, 06830, Ankara, Turkey
TOKI: A Bitemporal Operator Algebra for Contradiction Resolution in LLM-Agent Persistent Memory
Jun 4, 2026Ziming WangKnowledge Conflicts in Language Models LLM Agent Memory
The Hong Kong University of Science and Technology Hong Kong, Hong Kong SAR
FIDES: Faithful Inference via Deep Evidence Signals for Retrieval-Memory Conflict in RAG
Jun 4, 2026Zhe Yu, Wenpeng Xing, Tiancheng Zhao +3Retrieval-Augmented Generation Knowledge Conflicts in Language Models
Binjiang Institute of Zhejiang University · Zhejiang University · Guangzhou University
Easier to Mislead Than to Correct: Harmful and Beneficial Revision in LLM Conformity
Jun 1, 2026Jiaming Qu, Lucheng Fu, Yibo HuMulti-Agent LLM Systems Normative Conformity in Language Models
Amazon · Georgia Institute of Technology · Illinois Institute of Technology
Diagnosing LLM Arbitration Behavior over Pre-evidence Epistemic States in RAG-based Fact-Checking
May 31, 2026Yuxi Sun, Wenbo Shang, Wei Gao +2Retrieval-Augmented Generation LLM Evaluation
Hong Kong Baptist University · Singapore Management University
Grounded Decoding: Retrieval-Anchored Probability Fusion for Faithful RAG
May 29, 2026Ibne Farabi Shihab, Fariya Afrin, Sanjeda Akter +1Retrieval-Augmented Generation Language Model Decoding
Department of Computer Science, Iowa State University · Department of Computer Science, Kalinga Institute of Industrial Technology · Department of Civil, Construction & Environmental Engineering, Iowa State University
From Fact Overwriting to Knowledge Evolution: Causal Editing via On-Policy Self-Distillation
May 27, 2026Shuaike Li, Kai Zhang, Xianquan Wang +2Knowledge Editing Knowledge Conflicts in Language Models
State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China
Do Models Know Why They Changed Their Mind? Interpretability and Faithfulness of Chain-of-Thought Under Knowledge Conflict
May 26, 2026Pruthvinath Jeripity VenkataCoT Faithfulness Faithfulness of Language Model Explanations
Independent Researcher

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Conformity Mitigations in Large Language Models Lie on a Single Resistance-Receptivity Frontier

Zafar Hussain, Kristoffer Nielbo

Aarhus University

LLM Evaluation Normative Conformity in Language Models Knowledge Conflicts in Language Models LLM Reliability Multi-Agent LLMs

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Recent advances in language models have enabled collaborative settings in which multiple models leverage one another's capabilities, iteratively improving, transforming, and extending each other's outputs. Each agent sees what the others assert before it answers, so peer opinion competes with the model's own parametric knowledge, and a wrong majority can overturn an answer the model would otherwise get right. We measure that displacement in 23 open-weight models, 19 conditions, and three datasets, yielding more than a million graded responses. A unanimous wrong majority reverses 22.8% of a model's correct MMLU answers, 54.8% on GPQA and 71.0% on SimpleQA, and 84-89% of the reversed answers match the peers' answers. Existing mitigations aim to increase Resistance, the rate at which a model keeps its correct answer under this pressure, which is only half of what a collaborating agent needs. We pair it with Receptivity, the rate at which a model adopts a correct peer answer after initially answering incorrectly. We score six methods on both axes, four drawn from prior work and two of our own. Each gains Resistance only by losing Receptivity, and their means fall on a single Resistance-Receptivity frontier with

R^2

between 0.80 and 0.90. Reflection, the strongest published method, gains 7.9 points of MMLU Resistance and gives up 15.3 of Receptivity. Reasoning is the one exception. On GPQA and SimpleQA it trades like the rest, but on the MMLU subjects whose answers a model can derive for itself it raises Resistance by 7.2 points and Receptivity by 9.6 at once, the only intervention we find that improves both.

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