@inproceedings{wang-etal-2025-longllava,
    title = "{L}ong{LL}a{VA}: Scaling Multi-modal {LLM}s to 1000 Images Efficiently via a Hybrid Architecture",
    author = "Wang, Xidong  and
      Song, Dingjie  and
      Chen, Shunian  and
      Chen, Junying  and
      Cai, Zhenyang  and
      Zhang, Chen  and
      Sun, Lichao  and
      Wang, Benyou",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.1168/",
    doi = "10.18653/v1/2025.findings-emnlp.1168",
    pages = "21419--21436",
    ISBN = "979-8-89176-335-7",
    abstract = "Expanding the long-context capabilities of Multi-modal Large Language Models (MLLMs) is critical for advancing video understanding and high-resolution image analysis. Achieving this requires systematic improvements in model architecture, data construction, and training strategies, particularly to address challenges such as performance degradation with increasing image counts and high computational costs. In this paper, we propose a hybrid architecture that integrates Mamba and Transformer blocks, introduce data construction methods that capture both temporal and spatial dependencies, and employ a progressive training strategy. Our released model, LongLLaVA (Long-Context Large Language and Vision Assistant), demonstrates an effective balance between efficiency and performance. LongLLaVA achieves competitive results across various benchmarks while maintaining high throughput and low memory consumption. Notably, it can process nearly one thousand images on a single A100 80GB GPU, underscoring its potential for a wide range of multi-modal applications."
}