@inproceedings{chen-etal-2026-chain,
    title = "Chain-of-Procedure: Hierarchical Visual-Language Reasoning for Procedural {QA}",
    author = "Chen, Guanhua  and
      Yao, Yutong  and
      Sun, Shenghe  and
      Gao, Ci-jun  and
      Liu, Shudong  and
      Chao, Lidia S.  and
      Wan, Feng  and
      Wong, Derek F.",
    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.850/",
    pages = "17207--17224",
    ISBN = "979-8-89176-395-1",
    abstract = "Recent advances in vision-language models (VLMs) have achieved impressive results on standard image-text tasks, yet their potential for visual procedure question answering (VP-QA) remains largely unexplored. VP-QA presents unique challenges where users query next-step actions by uploading images for intermediate states of complex procedures. To systematically evaluate VLMs on this practical task, we propose ProcedureVQA, a novel multimodal benchmark specifically designed for visual procedural reasoning. Through comprehensive analysis, we identify two critical limitations in current VLMs: inadequate cross-modal retrieval of structured procedures given visual states, and misalignment between image sequence granularity and textual step decomposition. To address these issues, we present Chain-of-Procedure (CoP), a hierarchical reasoning framework that first retrieves relevant instructions using visual cues, then performs step refinement through semantic decomposition, and finally generates the next step. Experiments across six VLMs demonstrate CoP{'}s effectiveness, achieving up to 13{\%} absolute improvement over standard baselines."
}