Long-Context

MemAgent: Reshaping Long-Context LLM with Multi-Conv RL-based Memory Agent
MemAgent introduces a multi-conv RL-based memory agent that enables language models to handle extremely long documents, extending from 8K to 3.5M tokens with minimal performance degradation.
Hongli Yu, Tinghong Chen, Jiangtao Feng, Jiangjie Chen, Weinan Dai, Qiying Yu, Ya-Qin Zhang, Wei-Ying Ma, Jingjing Liu, Mingxuan Wang, Hao Zhou
PDF Cite Project Project
MemAgent: Reshaping Long-Context LLM with Multi-Conv RL-based Memory Agent
SEGMENT+: Long Text Processing with Short-Context Language Models
We introduce SEGMENT+, a framework that enables LMs to efficiently handle extended inputs within limited context windows, improving performance in long-document tasks through structured notes and a filtering module.
Wei Shi, Shuang Li, Kerun Yu, Jinglei Chen, Zujie Liang, Xinhui Wu, Yuxi Qian, Feng Wei, Bo Zheng, Jiaqing Liang, Jiangjie Chen, Yanghua Xiao
PDF Cite Code
SEGMENT+: Long Text Processing with Short-Context Language Models