The paper introduces GLIME, a lifelong model editing framework that integrates knowledge editing with preference optimization to handle continual updates in large language models. GLIME employs replay-based editing and a gradient constraint to prevent overfitting to target prompts and preserve previously edited knowledge. Experiments demonstrate that GLIME enhances knowledge generalization while maintaining editing performance and overall model capabilities.
By Dahyun Jung, Suhyune Son, Heuiseok Lim
DOW-KE is an anchor‑free knowledge‑editing method that directly optimizes weight updates across multiple layers by backpropagating the final editing objective through the entire model. Unlike traditional locate‑then‑edit pipelines that optimize intermediate activations and then apply local weight updates—leading to a closure gap—DOW-KE ensures that what is optimized is exactly what is deployed, incorporating preservation constraints within the update parameterization. Experiments on two datasets and three models show that DOW-KE achieves the highest overall Score and neighborhood Specificity in five of six evaluated settings.
By Ran Chen, Junbo Zhang, Qianli Zhou, Xinyang Deng, Wen Jiang
The paper introduces RIPPLE, a method for adapting workflow-synthesizing agents through prompt-policy editing without retraining the underlying model. RIPPLE diagnoses failed execution trajectories, maps failures to specific policy segments, and restricts edits to those segments. It then evaluates candidate edits in isolation and replays only those that remain safe after composition, achieving up to a 23.1% improvement in validation success on a synthetic benchmark and positive gains on additional language‑model backbones.
By Manqing Mao, Hong Wang, Samson Koelle, Jie Yuan, Zhuoer Wang, James Feng, Yanjun Lin, Daniel Edmiston, Nikki Lijing Kuang, Zhecheng Sheng, Wei Niu
arXiv:2606. 00570v1 Announce Type: cross Abstract: Parameter-based knowledge editing updates the internal knowledge of large language models (LLMs) via localized weight modifications and has attracted significant attention.
By Wanying Ren, Xin Song, Futing Wang, Guoxiu He, Aixin Sun
arXiv:2608. 11660v1 Announce Type: cross Abstract: Large language models (LLMs) achieve remarkable performance across natural language tasks, yet they are trained on static corpora and their knowledge quickly becomes outdated in a fast-changing world.
By Tianci Liu, Zihan Dong, Tianchun Li, Yi-Chung Chen, Qiming Cao, Xingchen Wang, Shiyang Wang, Zichen Miao, Linjun Zhang, Haoyu Wang, Jing Gao
arXiv:2606. 19679v1 Announce Type: cross Abstract: Lifelong knowledge editing aims to efficiently and sequentially update language models over time, as new knowledge becomes available or when the model makes mistakes, while preserving acceptable performance on past knowledge.
By Masih Eskandar, Miquel Sirera Perell\'o, Stratis Ioannidis, Jennifer Dy
arXiv:2511. 05852v4 Announce Type: replace-cross Abstract: Knowledge editing (KE) offers a lightweight alternative to retraining for updating large language models (LLMs).
By Yinjie Cheng, Paul Youssef, Christin Seifert, J\"org Schl\"otterer, Zhixue Zhao
arXiv:2604. 01029v2 Announce Type: replace-cross Abstract: Multi-LLM revision pipelines, in which a second model reviews and improves a draft produced by a first, are widely assumed to derive their gains from genuine error correction.
By Jingjie Ning, Xueqi Li, Chengyu Yu
arXiv:2407. 00740v2 Announce Type: replace-cross Abstract: As large language models (LLMs) are widely adopted in real-world applications, it has become critical to ensure LLMs satisfy safety constraints, such as non-toxicity and logical consistency, as well as task- and situation-specific constraints.
By Hye Ryung Son, Saehee Eom, Mooho Song, Jay-Yoon Lee
arXiv:2609.00184v1 Announce Type: cross
Abstract: Large language models (LLMs) rely on static pretraining corpora, causing their knowledge to become outdated over time. Existing approaches for evalua...
By Jonathan Zheng, Zirui Shao, Alan Ritter, Wei Xu
arXiv:2607. 01978v1 Announce Type: new Abstract: Online multimodal knowledge editing requires injecting a continual stream of visual-textual corrections into multimodal large language models (MLLMs) with bounded overhead and minimal disruption to unrelated behaviors.
By Siyuan Li, Youyuan Zhang, Ruitong Liu, Junxi Wang, Jing Li
arXiv:2607. 26455v1 Announce Type: cross Abstract: Large language models (LLMs) have demonstrated strong capabilities in knowledge acquisition and reasoning, yet their ability to retain previously acquired knowledge under repeated updates remains insufficiently understood.
By Ruxi Gu, Zhenliang Zhang, Wei Wang