@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://preview.aclanthology.org/ingest-acl/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."
}