@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://aclanthology.org/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."
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<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.</abstract>
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%0 Conference Proceedings
%T Chain-of-Procedure: Hierarchical Visual-Language Reasoning for Procedural QA
%A Chen, Guanhua
%A Yao, Yutong
%A Sun, Shenghe
%A Gao, Ci-jun
%A Liu, Shudong
%A Chao, Lidia S.
%A Wan, Feng
%A Wong, Derek F.
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F chen-etal-2026-chain
%X 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.
%U https://aclanthology.org/2026.findings-acl.850/
%P 17207-17224
Markdown (Informal)
[Chain-of-Procedure: Hierarchical Visual-Language Reasoning for Procedural QA](https://aclanthology.org/2026.findings-acl.850/) (Chen et al., Findings 2026)
ACL
- Guanhua Chen, Yutong Yao, Shenghe Sun, Ci-jun Gao, Shudong Liu, Lidia S. Chao, Feng Wan, and Derek F. Wong. 2026. Chain-of-Procedure: Hierarchical Visual-Language Reasoning for Procedural QA. In Findings of the Association for Computational Linguistics: ACL 2026, pages 17207–17224, San Diego, California, United States. Association for Computational Linguistics.