@inproceedings{liang-etal-2026-vizomem,
    title = "{V}izo{M}em: A Visual-Textual Memory Framework for Efficient Long-Horizon Reasoning",
    author = "Liang, Weijie  and
      Song, Yuanfeng  and
      Chen, Xing  and
      Cao, Caleb Chen  and
      Han, Sirui  and
      Guo, Yike",
    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.365/",
    pages = "7399--7422",
    ISBN = "979-8-89176-395-1",
    abstract = "Agentic systems built upon large language models (LLMs) increasingly depend on long-context modeling to support document understanding, long-term memory recall, and multi-step reasoning. However, extending context windows incurs substantial computational and memory overhead, significantly limiting the scalability and practicality of long-context LLM-based agents. Recent studies suggest that visual representations can serve as an effective medium for compressing and organizing long textual content. Motivated by this insight, we propose VizoMem, a novel visual memory framework for agentic systems. In this framework, textual memories are pre-rendered into structured images and stored as visual notes, enabling compact and persistent memory representations. Moving beyond standard vision-language models like Glyph, we pioneer a specialized retrieval system designed for large-scale visual memory. Our innovation lies in the construction of a dedicated dataset and the development of a highly efficient retrieval model that repurposes foundational vision-language encoders to navigate complex, text-heavy visual environments. Experiments on public datasets demonstrate that our approach significantly reduces token consumption while preserving effective long-term memory recall, highlighting its potential as a scalable alternative to conventional long-context modeling."
}