Model Editing

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Model editing studies how to update a trained model’s knowledge or behavior without retraining it from scratch, for tasks such as correcting facts, removing sensitive information, personalizing responses, and improving safety. Common approaches locate influential layers or neurons and apply low-rank or closed-form weight changes, while others use adapters, external memories, routing, or retrieval to store and apply edits. Researchers evaluate whether edits work on paraphrases and related questions, stay local to the target, preserve general capabilities, and remain stable across sequential updates, including in multimodal and generative models. A central challenge is balancing useful generalization with protection against unintended changes, forgetting, and degradation after further fine-tuning.

Latest papers

64 papers9 papers in the last four weeks, against 2 the four weeks before. 0.1% of all new papers.

Diagnosing and Mitigating Evidence Misgrounding in Multimodal Knowledge Editing
Oct 21, 2026Zhiwu Wang, Yichen Wu, Jinqian Yang +4Multimodal Knowledge Editing Knowledge Editing
School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, Shaanxi, China · Harvard Medical School, Boston, MA 02155, U.S. · School of Mathematics and Statistics, Xidian University, Xi’an, Shaanxi, China +2
Generalizable Model Editing Could Be More Than Direct Generalization
Oct 11, 2026Xiaotian Ye, Chunyao Yang, Xiaohan Wang +4Model Editing Language Model Editing
University of Cambridge · NLPR, MAIS, Institute of Automation, Chinese Academy of Sciences · School of Advanced Interdisciplinary Sciences, University of the Chinese Academy of Sciences +3
FORGE: Verification-Gated Behavioral Repair for Generative Language Models
Oct 4, 2026Hsin-Ling Hsu, Min-Yu Chen, Nai-Chia Chen +3Social Bias in Language Models LLM Safety
Department of Management Information Systems National Chengchi University Taipei, Taiwan
Operational Abstractions of Neural Network Concepts via Topological Representations
Oct 4, 2026Mathieu Pont, Christoph GarthDisentangled Representation Learning Model Editing
RPTU Kaiserslautern-Landau RPTU Kaiserslautern-Landau Kaiserslautern, Germany
Repurposing Obsolete Representations for Post-Deployment Adaptation
Oct 1, 2026Daniel Bethell, Charmaine Barker, Simos GerasimouModel Editing Machine Unlearning
Department of Computer Science, University of York, UK · Department of Elect. Eng., and Computer Science and Eng., Cyprus University of Technology, Cyprus
STAGE: Subspace-Targeted Affine Generative Erasure for Text-to-3D Models
Oct 1, 2026Karol Dziekan, Przemysław Spurek, Dawid Malarz3D Asset Generation Text-to-3D Generation
Jagiellonian University · IDEAS Research Institute
Platonic Task Arithmetic
Oct 1, 2026Junghwan Park, Woojin ChoVLM Adaptation Task Arithmetic
TelePIX
LLM Persona Unlearning
Sep 30, 2026Kemou Li, Zhuan Shi, Qizhou Wang +4Model Editing Persona-Conditioned Generation
State Key Laboratory of Internet of Things for Smart City, University of Macau · Mila – Québec AI Institute · McGill University +3
Imprint Reader: From Weight-Update Readout to Behavioral Intervention
Sep 28, 2026Guanxu Chen, Qihao Lin, Jing ShaoLLM Interpretability Neural Network Interpretability
Shanghai Artificial Intelligence Laboratory · Shanghai Jiao Tong University
Open Vocabulary Domain Unlearning
Sep 25, 2026Sumanth Udupa, Mehrtash Harandi, Yadan Luo +1VLM Unlearning Machine Unlearning
The University of Queensland · Monash University
Decoy Direction Optimization: A Post-Hoc Defense Against LLM Abliteration
Sep 14, 2026Aashiq Muhamed, Mona T. Diab, Virginia SmithAdversarial Attacks on LLMs LLM Safety
Carnegie Mellon University
PersonaEdit: Representative Sample Selection for Personalized Model Editing
Aug 28, 2026You-Mei Huang, Chung-Chi Chen, An-Zi YenLLM Personalization Model Editing
Department of Computer Science, National Yang Ming Chiao Tung University, Taiwan · Human-Agent Ally Lab (HAA Lab), National Institute of Informatics (NII), Japan
Beyond Global Editing: Per-Instance Disentangled Subspaces for Training-Free Hallucination Mitigation in LVLMs
Aug 10, 2026Ali Cheraghian, Hamidreza Dastmalchi, Hamed Barzamini +4Large Vision-Language Models Model Editing
Macquarie University, Australia · Australian National University, Australia · York University, Canada +4
PRISM Edit: One Vector for All Temporal Answers
Jul 13, 2026Chen Huang, Qi Zheng, Ruiqin Zheng +2Temporal Reasoning in Language Models Model Editing
Tsinghua University · ByteDance
Evaluating and Understanding Model Editing for Medical Vision Language Models
Jul 6, 2026Guli Zhu, Chenwei Wu, Liyue ShenKnowledge Editing Medical VLMs
EECS, University of Michigan, Ann Arbor, MI 48105, USA
Multimodal Knowledge Edit-Scoped Generalization for Online Recursive MLLM Editing
Jul 2, 2026Siyuan Li, Youyuan Zhang, Ruitong Liu +2Knowledge Editing Multimodal Large Language Models
Harbin Institute of Technology, Shenzhen, China · Peng Cheng Laboratory · Peking University +1
KnowledgeDebugger -- an Exploration Tool for Knowledge Localization and Editing in Transformers
Jul 1, 2026Eric Benz, Lennart Stöpler, Nikolai Bolik +1Knowledge Editing Transformer Interpretability
Heidelberg University
Revocable Learned State via Process Sidecars
Jun 29, 2026John SweeneyLLM Safety Alignment Task Arithmetic
Sideplane AI
Reproducibility Study of "AlphaEdit: Null-Space Constrained Knowledge Editing for Language Models"
Jun 25, 2026Ananth K Suresh, Arya HariharanKnowledge Editing Model Editing
Independent
Orthogonal Representation Editing: Decoupling Semantic Entanglement in Batch Knowledge Editing of LLMs
Jun 21, 2026Wenhao Yu, Zhicong Lu, Bo Lv +4Knowledge Editing Model Editing
School of Computer Science and Technology, Tianjin University · Kexin Technology · University of Chinese Academy of Sciences +2
BIM-Edit: Benchmarking Large Language Models for IFC-Based Building Information Modeling
Jun 18, 2026Bharathi Kannan Nithyanantham, Clemens Kujat, Tobias Sesterhenn +5LLM Evaluation Building Information Modeling
University of Rostock · Clausthal University of Technology
LOKI: Memory-Free Null-Space Constrained Lifelong Knowledge Editing
Jun 18, 2026Masih Eskandar, Miquel Sirera Perelló, Stratis Ioannidis +1Knowledge Editing Continual Learning for LLMs
Department of Electrical and Computer Engineering Northeastern University Boston, MA, 02115
Repeated Shared Access Enables Grokking, but Edit Propagation Depends on an Addressable Memory
Jun 17, 2026Yanan NiuMemory-Augmented Neural Networks Knowledge Editing
Kuaishou Technology
User as Engram: Internalizing Per-User Memory as Local Parametric Edits
Jun 17, 2026Bojie LiKnowledge Editing LLM Personalization
Pine AI
When to Write and When to Suppress: Route-Specialized Dual Adapters for Memory-Assisted Knowledge Editing
Jun 12, 2026Baijia Zhang, Yining HuangKnowledge Editing Factual Knowledge in Language Models
Xinjiang University · Meta Emergence Laboratory
Reinforcement Learning for Neural Model Editing
Jun 11, 2026Shaivi MalikReinforcement Learning Model Editing
TimeROME-DLM: Temporal Causal Tracing and Low-Rank Inference-Time Knowledge Editing for Masked Diffusion Language Models
Jun 11, 2026Zhengtao Yao, Liuyang Song, Hongbo Zhang +4Knowledge Editing Masked Diffusion Models
University of Southern California, USA · Peking University, China · Stevens Institute of Technology, USA +1
LoMC: Localized Multidirectional Correction for Refusal Suppression in Routed Foundation Models
Jun 10, 2026Yan Hong, Kedong Xiu, Wei Li +6Mixture-of-Experts Language Models LLM Refusal Behavior
Ant Group · Zhejiang University · Shanghai Jiao Tong University
Can Editing 1 Neuron Fix Repetition Loops in LLMs?
Jun 9, 2026Aristotelis Lazaridis, Aman Sharma, Dylan Bates +3Model Editing Data Repetition in Language Model Training
Edgerunner AI
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

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Diagnosing and Mitigating Evidence Misgrounding in Multimodal Knowledge Editing

Zhiwu Wang, Yichen Wu, Jinqian Yang, Yizhou Yang+3

School of Mathematics and Statistics, Xi’an Jiaotong University, Xi’an, Shaanxi, China · Harvard Medical School, Boston, MA 02155, U.S. · School of Mathematics and Statistics, Xidian University, Xi’an, Shaanxi, China+2

Multimodal Knowledge Editing Knowledge Editing Model Editing Visual Evidence Sensitivity in VLMs

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Multimodal knowledge editing aims to update visually grounded facts in Multimodal Large Language Models (MLLMs) while preserving unrelated text and visual behaviors. However, standard answer-level metrics can hide evidence misgrounding: an edit may produce the target answer on the original image yet remain sensitive to incidental context rather than the intended visual evidence. To examine this issue in a controlled setting, we construct WaterBird-Edit, an analysis set that decouples bird habitat labels from background context and measures whether edited predictions remain stable when the target evidence is preserved but the background changes. Motivated by this diagnosis, we propose VICE (Visual In-Context Editing), a retrieval-based framework that stores edits in external memory and applies them through visual in-context demonstrations designed to expose task-relevant evidence. Experiments on three standard benchmarks show that VICE achieves strong lifelong editing performance, while results on WaterBird-Edit show that it substantially reduces sensitivity to the controlled background shifts. Code and dataset are available in https://github.com/Wang-Z-W/VICE-main.