@inproceedings{banerjee-etal-2026-apex,
    title = "{APEX}-{MEM}: Agentic Semi-Structured Memory with Temporal Reasoning for Long-Term Conversational {AI}",
    author = "Banerjee, Pratyay  and
      Moshtaghi, Masud  and
      Subramanian, Shivashankar  and
      Misra, Amita  and
      Chadha, Ankit",
    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.749/",
    pages = "16470--16489",
    ISBN = "979-8-89176-390-6",
    abstract = {Large language models still struggle with reliable long-term conversational memory: simply enlarging context windows or applying na{\"i}ve retrieval often introduces noise and destabilizes responses. We present APEX-MEM, a conversational memory system that combines three key innovations: (1) a property graph which use domain-agnostic ontology to structure conversations as temporally grounded events in an entity-centric framework, (2) append-only storage that preserves the full temporal evolution of information, and (3) a multi-tool retrieval agent that understands and resolves conflicting or evolving information at query time, producing a compact and contextually relevant memory summary. This retrieval-time resolution preserves the full interaction history while suppressing irrelevant details. APEX-MEM achieves 88.88{\%} accuracy on LOCOMO and 86.2{\%} on LongMemEval, outperforming state-of-the-art session-aware approaches and demonstrating that structured property graphs enable more temporally coherent long-term conversational reasoning.}
}