@inproceedings{su-etal-2026-beyond-dialogue,
    title = "Beyond Dialogue Time: Temporal Semantic Memory for Personalized {LLM} Agents",
    author = "Su, Miao  and
      Guo, Yucan  and
      Hou, Zhongni  and
      Bai, Long  and
      Li, Zixuan  and
      Zhang, Yufei  and
      Yin, Guojun  and
      Lin, Wei  and
      Jin, Xiaolong  and
      Guo, Jiafeng  and
      Cheng, Xueqi",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1496/",
    pages = "29935--29951",
    ISBN = "979-8-89176-395-1",
    abstract = "Memory enables Large Language Model (LLM) agents to perceive, store, and use information from past dialogues, which is essential for personalization. However, existing methods fail to properly model the temporal dimension of memory in two aspects: 1) Temporal inaccuracy: memories are organized by dialogue time rather than their actual occurrence time; 2) Temporal fragmentation: existing methods focus on point-wise memory, losing durative information that captures persistent states and evolving patterns. To address these limitations, we propose Temporal Semantic Memory (TSM), a memory framework that models semantic time for point-wise memory and supports the construction and utilization of durative memory. During memory construction, it first builds a semantic timeline rather than a dialogue one. Then, it consolidates temporally continuous and semantically related information into a durative memory. During memory utilization, it incorporates the query{'}s temporal intent on the semantic timeline, enabling the retrieval of temporally appropriate durative memories and providing time-valid, duration-consistent context to support response generation. Experiments on LongMemEval and LoCoMo show that TSM consistently outperforms existing methods and achieves up to 12.2{\%} absolute improvement in accuracy, demonstrating the effectiveness of the proposed method."
}