@inproceedings{li-etal-2026-locomo,
    title = "Locomo-Plus: Beyond-Factual Cognitive Memory Evaluation Framework for {LLM} Agents",
    author = "Li, Yifei  and
      Guo, Weidong  and
      Zhang, Lingling  and
      Xu, Rongman  and
      Huang, Muye  and
      Liu, Hui  and
      Xu, Lijiao  and
      Xu, Yu  and
      Liu, Jun",
    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.1150/",
    pages = "25085--25100",
    ISBN = "979-8-89176-390-6",
    abstract = "Long-term conversational memory is a core capability for LLM-baseddialogue systems, yet existing benchmarks and evaluation protocolsprimarily focus on surface-level factual recall.In realistic interactions, appropriate responses often depend onimplicit constraints such as user state, goals, or values that are notexplicitly queried later.To evaluate this setting, we introduce \textbf{LoCoMo-Plus}, a benchmarkfor assessing cognitive memory under cue{--}trigger semantic disconnect,where models must retain and apply latent constraints across longconversational contexts.We further show that conventional string-matching metrics and explicittask-type prompting are misaligned with such scenarios, and propose aunified evaluation framework based on constraint consistency.Experiments across diverse backbone models, retrieval-based methods, andmemory systems demonstrate that cognitive memory remains challenging andreveals failures not captured by existing benchmarks.Our code and evaluation framework are publicly available at https://github.com/xjtuleeyf/Locomo-Plus."
}