arXiv Computation and Language September 2, 2026 By Shuaiyi Li, Zhisong Zhang, Yang Deng, Chenlong Deng, Tianqing Fang, Hongming Zhang, Haitao Mi, Dong Yu, Wai Lam

InComeS: Integrating Compression and Selection Mechanisms into LLMs for Efficient Model Editing

Read the original on arXiv Computation and Language →

InComeS is a framework that improves large language models (LLMs) for model editing by compressing editing contexts into a key‑value cache associated with a special gist token. It adds cross‑attention modules to dynamically select the most relevant information from these gist pools, allowing efficient handling of multiple edits beyond the LLM’s context window. Experiments on diverse editing benchmarks show that InComeS enhances both effectiveness and efficiency compared to existing methods.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computation and Language.

arXiv AI

Jul 3

Multimodal Knowledge Edit-Scoped Generalization for Online Recursive MLLM Editing

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

llms multimodal benchmarks

arXiv Computation and Language

Aug 31

ContextPilot: Teaching Agents for Proactive Context Management via Fine-grained RL

ContextPilot is a proactive context‑management framework designed to improve long‑horizon agentic reasoning with large language models. It expands the toolset to include planning, long‑term memory, and soft context offloading, and introduces a reinforcement‑learning strategy that focuses on critical editing decisions and assigns action‑level advantages. Experiments on long‑context QA and deep search tasks demonstrate that ContextPilot achieves stronger performance with a more compact working context, outperforming existing baselines across various base models and benchmarks.

By Zhuoshi Pan, Qizhi Pei, Junru Lu, Honglin Lin, H. Vicky Zhao, Di Yin, Xing Sun

llms agents nlp reinforcement-learning benchmarks

arXiv AI

Jun 24

Representation Interventions Enable Lifelong Knowledge Memory Control in LLMs

arXiv:2511. 20892v4 Announce Type: replace Abstract: Large language models (LLMs) often produce incorrect or outdated content after being employed.

By Xuyuan Liu, Shengyu Chen, Xinshuai Dong, Yanchi Liu, Xujiang Zhao, Haoyu Wang, Yujun Yan, Haifeng Chen, Zhengzhang Chen

llms benchmarks

arXiv Machine Learning

Jul 23

LaSEr-Edit: Localized Span-level Error Editing with Energy-based Localization

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

llms rag safety

arXiv AI

Jul 28

Beyond Block Boundaries: Multi-Block Editing for Diffusion Large Language Models

arXiv:2607. 22663v1 Announce Type: new Abstract: Block diffusion has emerged as the dominant paradigm for scaling discrete diffusion language models (dLLMs), because decoding text in fixed-size blocks preserves parallel generation within each block while keeping the quadratic attention cost tractable.

By Xingyu Mou, Zijin Huang, Tianze Zhang, Yuxin Ma, Lanning Wei, Zengfeng Huang, Da Zheng, Lun Du

llms diffusion fine-tuning efficiency benchmarks

arXiv AI

Jun 6

Edit-R2: Context-Aware Reinforcement Learning for Multi-Turn Image Editing

arXiv:2606. 05950v1 Announce Type: new Abstract: Text-guided image editing has advanced rapidly with diffusion models and unified multimodal foundation models.

By Yuxiao Ye, Haoran He, Fangyuan Kong, Xintao Wang, Pengfei Wan, Kun Gai, Ling Pan

diffusion reinforcement-learning multimodal benchmarks

One email a morning, machine-written

Related stories

Multimodal Knowledge Edit-Scoped Generalization for Online Recursive MLLM Editing

ContextPilot: Teaching Agents for Proactive Context Management via Fine-grained RL

Representation Interventions Enable Lifelong Knowledge Memory Control in LLMs

LaSEr-Edit: Localized Span-level Error Editing with Energy-based Localization

Beyond Block Boundaries: Multi-Block Editing for Diffusion Large Language Models

Edit-R2: Context-Aware Reinforcement Learning for Multi-Turn Image Editing