Knowledge Editing

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6 papers in the last four weeks, up 50% on the four weeks before. 0.1% of all new papers.

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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
Knowledge Editing for Masked Diffusion Language Models
Jun 2, 2026Haewon Park, Yohan JoKnowledge Editing Masked Diffusion Models
Graduate School of Data Science, Seoul National University
HyperPatch: Sequential Knowledge Editing Under n-ary Structural Drift
Jun 2, 2026Yu-Kai Chan, Wen-Sheng Lien, Dong-Ting Yao +4Knowledge Editing Continual Learning for LLMs
National Yang Ming Chiao Tung University Hsinchu, Taiwan
Can Factual Opinions Be Edited (Manipulated) in Large Language Models?
Jun 2, 2026Yuanpu Cao, Ziyi Yin, Fenglong Ma +1Knowledge Editing LLM Security
The Pennsylvania State University
Revisiting Ripple Effects in Knowledge Editing through Pressure-Aware Joint Neighborhood Optimization
Jun 1, 2026Haoben Huang, Shuxin Liu, Ou Wu +1Knowledge Editing Model Editing
Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, China · College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
AnyEdit++: Adaptive Long-Form Knowledge Editing via Bayesian Surprise
May 31, 2026Bowen Tian, Caixue He, Jiemin Wu +4Knowledge Editing Long-Context Modeling
The Hong Kong University of Science and Technology (Guangzhou), Guangzhou 511400, China · Southeast University, Nanjing, China · Knowin AI, Shenzhen, China +2
Do Text Edits Generalize to Visual Generation? Benchmarking Cross-Modal Knowledge Editing in UMMs
May 30, 2026Xin Gao, Cheng Yang, Chufan Shi +1Knowledge Editing Unified Multimodal Models
University of California San Diego · University of Southern California
Towards Localized and Disentangled Knowledge Editing for Multimodal Large Language Models
May 28, 2026Leijiang Gu, Zhen Zeng, Feng Li +2Knowledge Editing Multimodal Large Language Models
1Hefei University of Technology, Hefei, China
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
The Labyrinth and the Thread: Rethinking Regularizations in Sequential Knowledge Editing for Large Language Models
May 26, 2026Zheng Wang, Kaixuan Zhang, Wanfang Chen +2Knowledge Editing Factual Knowledge in Language Models
Bosch Center for Artificial Intelligence (BCAI) · Bosch (China) Investment Ltd. · School of Statistics, East China Normal University.
Iterate Until Retrieved: Factual Nugget Optimization for Discoverable Continual Corrections in Agentic RAG
May 25, 2026Moshe Hazoom, Gal Patel, Alon Talmor +1Knowledge Editing Agentic RAG
Mosaic AI · The Hebrew University of Jerusalem
PALoRA: Projection-Adaptive LoRA for Preserving Reasoning in Large Language Models
May 23, 2026Mustafa Hayri Bilgin, Mariam Barry, Albert Bifet +2Knowledge Editing Continual Learning for LLMs
IT Group, Research, BNP Paribas · LTCI, Télécom Paris, Institut Polytechnique de Paris · AI Institute, University of Waikato +1
Scalable Knowledge Editing for Mixture-of-Experts LLMs via Tensor-Structured Updates
May 15, 2026Roman Maksimov, Vladimir Aletov, Dmitry Bylinkin +3Knowledge Editing Mixture-of-Experts Language Models
1Moscow Independent Research Institute of Artificial Intelligence · 2Basic Research of Artificial Intelligence Laboratory · 3Innopolis University
Merging Methods for Multilingual Knowledge Editing for Large Language Models: An Empirical Odyssey
May 13, 2026Kunil Lee, Ki-Young Shin, Jong-Hyeok Lee +1Knowledge Editing Multilingual Language Models
Department of Computer Science and Engineering, POSTECH · Designovel Co., Ltd. · LLSOLLU +1
More Edits, More Stable: Understanding the Lifelong Normalization in Sequential Model Editing
May 12, 2026Xin Ma, Wei Chen, Qi Liu +4Knowledge Editing Continual Learning
State Key Laboratory of Cognitive Intelligence, University of Science and Technology of China, Anhui, China.
DeepRefine: Agent-Compiled Knowledge Refinement via Reinforcement Learning
May 11, 2026Haoyu Huang, Jiaxin Bai, Shujie Liu +6Knowledge Editing Knowledge Augmentation for Language Models
1HKUST · 2HKBU · 3Microsoft Research Asia
Benchmarking Safety Risks of Knowledge-Intensive Reasoning under Malicious Knowledge Editing
May 11, 2026Qinghua Mao, Xi Lin, Jinze Gu +3Knowledge Editing Knowledge Conflicts in Language Models
School of Computer Science · Shanghai Jiao Tong University
BetaEdit: Null-Space Constrained Sequential Model Editing
May 10, 2026Bingqing Liu, Wei Liu, Yuhua LiKnowledge Editing Model Editing
Huazhong University of Science and Technology, Wuhan, China
EditPropBench: Measuring Factual Edit Propagation in Scientific Manuscripts
May 3, 2026Garvin KruthofKnowledge Editing LLM Evaluation
Technical University of Munich
HoReN: Normalized Hopfield Retrieval for Large-Scale Sequential Model Editing
May 2, 2026Yuan Fang, Yi Xie, Xuming RanKnowledge Editing Memory-Augmented Language Models
1IXL Learning, Inc · 2Technical University of Munich · 3National University of Singapore
From Backward Spreading to Forward Replay: Revisiting Target Construction in LLM Parameter Editing
May 1, 2026Wei Liu, Hongkai Liu, Zhiying Deng +2Knowledge Editing Model Editing
National University of Singapore · Laboratory for Artificial Intelligence and New Forms of Education, National Engineering Research Center for Educational Big Data, Faculty of Artificial Intelligence in Education, Central China Normal University · University of Oxford
Towards Scalable Lifelong Knowledge Editing with Selective Knowledge Suppression
Apr 21, 2026Dahyun Jung, Jaewook Lee, Heuiseok LimKnowledge Editing Continual Learning for LLMs
Department of Computer Science and Engineering, Korea University
Correct When Paired, Wrong When Split: Decoupling and Editing Modality-Specific Neurons in MLLMs
Apr 20, 2026Tingchao Fu, Wenkai Wang, Fanxiao Li +6Knowledge Editing Multimodal Large Language Models
School of Information Science and Engineering, Yunnan University · School of Software, Yunnan University · 3National University of Singapore +1
One Mask to Rule Them All: On Hidden Facts after Editing and How to Find Them
Apr 18, 2026Ali Holmov, Paul Youssef, Nandi Schoots +1Knowledge Editing Transformer Interpretability
Technical University of Munich · Marburg University · University of Oxford
Edit Knowledge, Not Just Facts via Multi-Step Reasoning over Background Stories
Feb 2, 2026Ya Gao, Kalle Kujanpää, Pekka Marttinen +2Knowledge Editing Knowledge Augmentation for Language Models
Aalto University · System 2 AI
Representation Interventions Enable Lifelong Knowledge Memory Control in LLMs
Nov 25, 2025Xuyuan Liu, Shengyu Chen, Xinshuai Dong +6Knowledge Editing Continual Learning for LLMs
Dartmouth College · NEC Laboratories America · Carnegie Mellon University
Can Fine-Tuning Erase Your Edits? On the Fragile Coexistence of Knowledge Editing and Adaptation
Nov 8, 2025Yinjie Cheng, Paul Youssef, Christin Seifert +2Knowledge Editing LLM Fine-Tuning
School of Computer Science University of Sheffield Sheffield, UK · Mathematics and Computer Science Marburg University Marburg, Germany
LMEnt: A Suite for Analyzing Knowledge in Language Models from Pretraining Data to Representations
Sep 3, 2025Daniela Gottesman, Alon Gilae-Dotan, Ido Cohen +4Language Model Pretraining Knowledge Editing
Tel Aviv University · Mila – Quebec AI Institute & McGill University

Jun 8, 2026, cs.CVJ/K move · Enter open · S save

CRANE: Knowledge Editing for Reasoning MLLMs

Han Huang, Hao Wang, Mengqi Zhang, Shu Wu+2

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

Knowledge Editing Knowledge Conflicts in Language Models Multimodal Large Language Models Multimodal CoT Reasoning Model Editing

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The emergence of reasoning multimodal large language models (MLLMs), which generate explicit chain-of-thought (CoT) reasoning before producing answers, has introduced a new challenge for knowledge editing: methods that appear successful under traditional metrics (teacher-forcing accuracy up to 100%) can fail severely when the model's reasoning process is examined (Grounded Success as low as 0%). We identify three failure modes: (1) Structural Collapse, where weight-modifying methods destroy the CoT format; (2) Cognitive Dissonance, where the model's reasoning chain actively rejects the injected edit fact based on visual evidence; and (3) Shallow Internalization, where methods succeed on exact queries but fail on rephrase or multi-hop variants. On reasoning MLLMs, these modes interact: methods that generalize (FT, LoRA) trigger format collapse, while methods without deep modification cannot generalize. To expose these failures, we propose a CoT-aware evaluation protocol and construct ReasonEdit-Bench, with conflict stratification, multi-level probes, and multi-hop portability tests. We propose CRANE, a retrieval-augmented framework that requires no per-edit parameter modification. CRANE combines a modality-aware dual-library retrieval system with a two-phase training strategy: Supervised Fine-Tuning (SFT) for structural initialization, followed by GRPO with a Cognitive Routing Reward that trains the model to arbitrate between visual priors and injected edit facts. On ReasonEdit-Bench, CRANE achieves 96.9% Grounded Success on conflict scenarios and 96.9% intermediate entity usage in multi-hop chains, with 97.6% text-locality and 68.1% image-locality Edit Independence. On the out-of-distribution MMEVOKE benchmark, CRANE reaches 87.0% under gold retrieval.

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