@inproceedings{hu-etal-2026-evermemos,
title = "{E}ver{M}em{OS}: A Self-Organizing Memory Operating System for Structured Long-Horizon Reasoning",
author = "Hu, Chuanrui and
Gao, Xingze and
Zhou, Zuyi and
Xu, Dannong and
Bai, Yi and
Li, Xintong and
Zhang, Hui and
Li, Tong and
Zhang, Chong and
Bing, Lidong and
Deng, Yafeng",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.acl-long.2125/",
pages = "45836--45853",
ISBN = "979-8-89176-390-6",
abstract = "Large Language Models (LLMs) are increasingly deployed as long-term interactive agents, yet their limited context windows make it difficult to sustain coherent behavior over extended interactions. Existing memory systems for LLMs often store isolated records and retrieve fragments, limiting their ability to consolidate evolving experience and resolve conflicts. We introduce EverMemOS, a self-organizing memory operating system that implements an engram-inspired lifecycle for computational memory. First, Episodic Trace Formation converts dialogue streams into MemCells that capture episodic traces, atomic facts, and time-bounded foresight. Second, Semantic Consolidation organizes MemCells into thematic MemScenes, distilling stable semantic structures and updating user profiles. Finally, Reconstructive Recollection performs MemScene-guided agentic retrieval to compose the necessary and sufficient context for downstream reasoning. Experiments on LoCoMo, LongMemEval, and PersonaMem-v2 show that EverMemOS significantly outperforms state-of-the-art methods on memory-augmented reasoning tasks."
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<abstract>Large Language Models (LLMs) are increasingly deployed as long-term interactive agents, yet their limited context windows make it difficult to sustain coherent behavior over extended interactions. Existing memory systems for LLMs often store isolated records and retrieve fragments, limiting their ability to consolidate evolving experience and resolve conflicts. We introduce EverMemOS, a self-organizing memory operating system that implements an engram-inspired lifecycle for computational memory. First, Episodic Trace Formation converts dialogue streams into MemCells that capture episodic traces, atomic facts, and time-bounded foresight. Second, Semantic Consolidation organizes MemCells into thematic MemScenes, distilling stable semantic structures and updating user profiles. Finally, Reconstructive Recollection performs MemScene-guided agentic retrieval to compose the necessary and sufficient context for downstream reasoning. Experiments on LoCoMo, LongMemEval, and PersonaMem-v2 show that EverMemOS significantly outperforms state-of-the-art methods on memory-augmented reasoning tasks.</abstract>
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%0 Conference Proceedings
%T EverMemOS: A Self-Organizing Memory Operating System for Structured Long-Horizon Reasoning
%A Hu, Chuanrui
%A Gao, Xingze
%A Zhou, Zuyi
%A Xu, Dannong
%A Bai, Yi
%A Li, Xintong
%A Zhang, Hui
%A Li, Tong
%A Zhang, Chong
%A Bing, Lidong
%A Deng, Yafeng
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F hu-etal-2026-evermemos
%X Large Language Models (LLMs) are increasingly deployed as long-term interactive agents, yet their limited context windows make it difficult to sustain coherent behavior over extended interactions. Existing memory systems for LLMs often store isolated records and retrieve fragments, limiting their ability to consolidate evolving experience and resolve conflicts. We introduce EverMemOS, a self-organizing memory operating system that implements an engram-inspired lifecycle for computational memory. First, Episodic Trace Formation converts dialogue streams into MemCells that capture episodic traces, atomic facts, and time-bounded foresight. Second, Semantic Consolidation organizes MemCells into thematic MemScenes, distilling stable semantic structures and updating user profiles. Finally, Reconstructive Recollection performs MemScene-guided agentic retrieval to compose the necessary and sufficient context for downstream reasoning. Experiments on LoCoMo, LongMemEval, and PersonaMem-v2 show that EverMemOS significantly outperforms state-of-the-art methods on memory-augmented reasoning tasks.
%U https://aclanthology.org/2026.acl-long.2125/
%P 45836-45853
Markdown (Informal)
[EverMemOS: A Self-Organizing Memory Operating System for Structured Long-Horizon Reasoning](https://aclanthology.org/2026.acl-long.2125/) (Hu et al., ACL 2026)
ACL
- Chuanrui Hu, Xingze Gao, Zuyi Zhou, Dannong Xu, Yi Bai, Xintong Li, Hui Zhang, Tong Li, Chong Zhang, Lidong Bing, and Yafeng Deng. 2026. EverMemOS: A Self-Organizing Memory Operating System for Structured Long-Horizon Reasoning. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 45836–45853, San Diego, California, United States. Association for Computational Linguistics.