arXiv AI By Ning Liao, Zihao Long, Xiaoxing Wang, Xue Yang, Yaoming Wang, Ziyuan Zhuang, Xunliang Cai, Rongxiang Weng, Junchi Yan

ACE: Pluggable Adaptive Context Elasticizer across Agents

Read the original on arXiv AI →

arXiv:2606. 31564v1 Announce Type: new Abstract: The increasing complexity of agentic tasks has led to rapidly growing trajectory lengths, which poses significant challenges for large language model (LLM) based agents with fixed context windows.

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 AI.

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
arXiv AI
Sep 15

CLEAR: Context Augmentation from Contrastive Learning of Experience via Agentic Reflection

arXiv:2604.07487v2 Announce Type: replace Abstract: Large language model agents rely on effective model context to obtain task-relevant information for decision-making. Many existing context engineer...

By Linbo Liu, Guande Wu, Han Ding, Yawei Wang, Qiang Zhou, Yuzhe Lu, Zhichao Xu, Huan Song, Panpan Xu, Lin Lee Cheong