The paper introduces T$^2$Mem, a Test-Time Memory construction framework that defers memory building to inference, allowing large language models to adaptively retrieve and organize episode-level metadata and raw interactions during reasoning. By extracting lightweight metadata offline and progressively constructing query-specific memory at test time, T$^2$Mem reduces overhead and improves performance on long-horizon tasks. Experiments on LoCoMo and LongMemEval demonstrate consistent gains over strong baselines across various backbone LLMs.
By Zijun Wang, Yifan Ji, Haidong Xin, Zhenghao Liu, Yukun Yan, Zulong Chen, Yu Gu, Ge Yu, Maosong Sun
arXiv:2606. 16707v1 Announce Type: new Abstract: A personalized AI agent needs a user memory: a persistent model of who the user is, built across many conversations and consulted on each new one.
By Bojie Li
TrawMem redefines agent memory systems as Task‑Oriented Role‑Adaptive Workspaces, selecting and organizing memories by their roles in task solving rather than mere relevance. It structures interaction histories into local threads and jointly selects complementary memories to build task‑specific workspaces. Experiments on LoCoMo and MemGallery show consistent macro‑F1 gains of 2.23–3.14 points and improved LLM‑Judge scores with fewer context tokens.
By Mingyu Zhang, Zixu Li, Zhiheng Fu, Zhiwei Chen, Jinhe Bi, Yupeng Hu
arXiv:2608.12990v2 Announce Type: replace
Abstract: Long-horizon LLM agents must preserve information from past interactions to support future tasks. Existing memory systems typically rely on eager c...
By Dongfang Li, Zixuan Liu, Junmai Wang, Jiahe Huang, Fuhao Li, Lin Zhang, Bonian Jia, Baotian Hu, Min Zhang
AtomMem is a long‑term memory system for large language models that stores high‑value atomic facts extracted from long‑form interactions. It organizes these facts into hierarchical event structures and temporal profiles, enabling coherent episodic context and dynamic tracking of user attributes. During retrieval, an associative memory graph connects fragmented memories, and experiments on the LoCoMo benchmark show state‑of‑the‑art performance across reasoning tasks.
By Yanyu Yao, Shangze Li, Zhi Zheng, Hui Zheng, Qi Liu, Tong Xu, Enhong Chen
The paper introduces TriMEM, a multi‑class memory dataset that annotates memory types across varied scenarios, and MemoType, a framework that uses a learned router to adaptively match memories and queries to appropriate retrieval strategies. MemoType’s tailored approach improves retrieval precision, and the authors theoretically show that a single strategy has a fundamental upper bound on expected precision in multi‑class corpora. Experiments on three datasets demonstrate MemoType consistently outperforms existing methods, achieving up to a 16.18% improvement in Recall@1.
By Yi Wen, Derong Xu, Pengyue Jia, Yichao Wang, Yingyi Zhang, Maolin Wang, Junyi Li, Wenlin Zhang, Xiaopeng Li, Yong Liu, Xiangyu Zhao
arXiv:2602.04926v2 Announce Type: replace-cross
Abstract: Retrieval-augmented generation (RAG) has become a standard approach for knowledge-intensive LLM tasks. Complex reasoning remains challenging...
By Ning Wang, Kuanyan Zhu, Daniel Yuehwoon Yee, Yitang Gao, Shiying Huang, Zirun Xu, Sainyam Galhotra
arXiv:2608. 03463v1 Announce Type: new Abstract: Long-term memory is essential for LLM-based agents to sustain interactions and reliably leverage distant history.
By Yuxin Liao, Le Wu, Min Hou, Hao Liu, Han Wu, Zishu Wang
arXiv:2609.40118v1 Announce Type: new
Abstract: As LLM capabilities advance, agents are tackling increasingly complex tasks over longer horizons. Their growing interaction histories make memory compa...
By Jingbo Yang, Kwei-Herng Lai, Xiaowen Wang, Zhaoxuan Tan, Pei Zhou, Mengting Wan, Yaar Harari, Evgeniy Gabrilovich, Shiyu Chang
arXiv:2606.23283v2 Announce Type: replace
Abstract: Memory systems are essential for personalized Large Language Models (LLMs). However, retrieval within these systems primarily relies on semantic si...
By Hongxun Ding, Xiang Yu, Chengbing Wang, Jianfei Xiao, Keqin Bao, Wenjie Wang, Xiangnan He
arXiv:2602. 07885v2 Announce Type: replace Abstract: Long-term memory enables large language model agents to tackle complex tasks through historical interactions.
By Zhenyuan Zhang, Xianzhang Jia, Zhiqin Yang, Zhenbo Song, Wei Xue, Sirui Han, Yike Guo
The paper introduces StructMemEval, a benchmark designed to assess how well large language model (LLM) agents can organize their long‑term memory rather than merely recall facts. It compiles tasks that humans typically solve by structuring knowledge—such as transaction ledgers, to‑do lists, and trees—and evaluates agents on these. Experiments show that simple retrieval‑augmented LLMs struggle with such organization tasks, while memory‑augmented agents perform better when explicitly prompted to structure their memory, yet many modern LLMs still fail to recognize memory structures without prompting.
By Alina Shutova, Alexandra Olenina, Ivan Vinogradov, Anton Sinitsin