EnSIMem is an entity‑structured long‑term memory architecture designed for agents that interact with users over extended periods. It organizes interactions into theme‑coherent episodes and creates dialogue‑grounded index entries of the form [entity][entity type][property:value], preserving source turns, temporal data, and multimodal fields. During online interaction, the agent decomposes requests into evidence requirements, performs entity‑property lookup, and retrieves the necessary evidence to generate responses directly from preserved source material rather than lossy summaries.
By Xuanyu Meng, Xing Fan, Xinyi Fan, Chenlei Guo, Yixuan Xie, Jiawei Han
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:2606. 10677v1 Announce Type: new Abstract: Long-term LLM agents need persistent memory that can track changing facts and provide relevant evidence across sessions.
By Suozhao Ji, Baodong Wu, Zehao Wang, Lei Xia, Qingping Li, Ruisong Wang, Wenbo Ding, Zhenhua Zhu, Boxun Li, Guohao Dai, Yu Wang
arXiv:2607. 29440v1 Announce Type: new Abstract: Long-term multimodal memory must support not only retrieving relevant information but also computing over observations accumulated across interactions.
By Zhoujin Tian, Yao Tian, Hao Zhang, Cheng Chen, Yakun Li, Lei Zhang, Xiaofang Zhou
EdgeMem is a new agent-memory method that preserves original interaction turns and organizes them using complementary content, temporal, and episodic cues via a multi‑anchor hypergraph. It performs lightweight local processing, returning source evidence directly and reserving LLM use only for final answer generation. Experiments on LoCoMo and LongMemEval‑S demonstrate strong retrieval and memory‑grounded question answering, with EdgeMem achieving the highest strict‑judge score among seven systems on LoCoMo while requiring no generative‑LLM calls for construction and retrieval.
By Zeyang Cui, Jiannong Cao, Zhiyuan Wen, Bo Yuan, Junlan Feng, Shengyuan Chen
Large language model agents have shown strong capabilities in generating coherent and contextually appropriate responses, yet robust long-horizon dialogue remains limited by the lack of external memory that is traceable, updatable, and diagnostically transparent. Existing memory-augmented agents often store memories as isolated records or overwritable states, making it difficult to preserve how information originates, evolves, conflicts, or becomes obsolete over time.
Agent Zero Memory is a provenance‑aware long‑term memory system for large language model agents that distills user interactions into three parallel memory structures: an episodic timeline, an associative entity‑event knowledge graph, and a semantic, citation‑locked hierarchical documentary memory. Retrieval is performed via an intent gate, source router, and concurrent searches across the three systems, producing integrated, cited answers that exclude fabrication and require evidence the reader has opened. The system achieves state‑of‑the‑art performance on LongMemEval (95.60%) and LoCoMo (93.60%) while offering a favorable accuracy‑cost‑latency trade‑off across multiple backbone LLMs.
By Ming Wu, Pengyuan Zhu
MemFit is a long‑term memory system designed for conversational agents that stores each dialogue turn verbatim in an append‑only store, enabling near‑instantaneous, LLM‑free insertion. It indexes turns using segment summaries and employs an LLM‑free, multi‑path retrieval strategy that blends lexical and semantic signals with cross‑encoder reranking over caption‑augmented episodes. Experiments on LoCoMo, MemGallery, and LongMemEval‑S demonstrate state‑of‑the‑art performance while drastically reducing memory construction time and cost.
By Mitchell Piehl, Muchao Ye
arXiv:2608.22310v1 Announce Type: new
Abstract: Long-term memory is crucial for personalized responses and long-horizon agent interactions. Existing methods often rely on LLMs to compress or rewrite...
By Yuanhua Lin, Yile Li, Zhiyuan Zhao, Jing Shang, Jian Sun
arXiv:2606. 13177v1 Announce Type: cross Abstract: Large language model (LLM) agents are increasingly expected to operate over long-term interactions, where information from past dialogues must be preserved and recalled to support future tasks.
By Minjae Kim, Jinheon Baek, Soyeong Jeong, Sung Ju Hwang
arXiv:2606. 28349v1 Announce Type: cross Abstract: Long-context reasoning requires models to access, retrieve, and integrate evidence scattered across documents, dialogues, and accumulated interaction histories.
By Zeju Li, Ziyang Zheng, Yizhou Zhou, Qiang Xu
arXiv:2606. 05749v1 Announce Type: cross Abstract: Iterative retrieval-reasoning agents have recently shown promise for multimodal long-document question answering.
By Kaifeng Chen, Hongtao Liu, Qiyao Peng, Jian Yang, Yongqiang Liu, Xiaochen Zhang, Qing Yang