@inproceedings{zhang-etal-2026-progresslm,
    title = "{P}rogress{LM}: Towards Progress Reasoning in Vision-Language Models",
    author = "Zhang, Jianshu  and
      Qian, Chengxuan  and
      Sun, Haosen  and
      Lu, Haoran  and
      Wang, Dingcheng  and
      Xue, Letian  and
      Liu, Han",
    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.516/",
    pages = "11243--11271",
    ISBN = "979-8-89176-390-6",
    abstract = "Estimating task progress requires long-horizon and dynamic reasoning, going beyond static visual perception. Although Vision-Language Models (VLMs) excel at describing what is visible in a single observation, it remains unclear whether they can infer how far a task has progressed from partial information. To study this question, we introduce Progress-Bench, a benchmark with over 3K instances for evaluating progress reasoning from a single observation. We further examine a human-inspired two-stage paradigm that combines episodic retrieval with mental simulation. We instantiate this paradigm through both training-free prompting and a training-based approach using the automatically curated ProgressLM-45K dataset. Experiments on 14 VLMs show that most models struggle with reliable progress estimation, and that training-free reasoning provides only limited and model-dependent benefits. In contrast, the training-based ProgressLM-3B achieves consistent improvements in accuracy, robustness to viewpoint variation, and handling of unanswerable cases, despite its small scale. Additional analyses reveal common failure patterns in existing VLMs and clarify when and why progress reasoning succeeds or fails."
}