@inproceedings{su-etal-2026-comfysearch,
title = "{C}omfy{S}earch: Autonomous Exploration and Reasoning for {C}omfy{UI} Workflows",
author = "Su, Jinwei and
Lan, Qizhen and
Wang, Zeyu and
Xia, Yinghui and
Wen, Hairu and
Duan, Yiqun and
Xiao, Xi and
Jingsong, Yang and
He, Lewei and
Shi, Tianyu",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.37/",
pages = "734--748",
ISBN = "979-8-89176-462-0",
abstract = "AI-generated content has progressed from monolithic models to modular workflows, especially on platforms like ComfyUI, allowing users to customize complex creative pipelines. However, the large number of components in ComfyUI and the difficulty of maintaining long-horizon structural consistency under strict graph constraints frequently lead to low pass rates and workflows of limited quality. To tackle these limitations, we present ComfySearch, an agentic framework that can effectively explore the component space and generate functional ComfyUI pipelines via validation-guided workflow construction. Experiments demonstrate that ComfySearch substantially outperforms existing methods on complex and creative tasks, achieving higher executability (pass) rates, higher solution rates, and stronger generalization."
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<abstract>AI-generated content has progressed from monolithic models to modular workflows, especially on platforms like ComfyUI, allowing users to customize complex creative pipelines. However, the large number of components in ComfyUI and the difficulty of maintaining long-horizon structural consistency under strict graph constraints frequently lead to low pass rates and workflows of limited quality. To tackle these limitations, we present ComfySearch, an agentic framework that can effectively explore the component space and generate functional ComfyUI pipelines via validation-guided workflow construction. Experiments demonstrate that ComfySearch substantially outperforms existing methods on complex and creative tasks, achieving higher executability (pass) rates, higher solution rates, and stronger generalization.</abstract>
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%0 Conference Proceedings
%T ComfySearch: Autonomous Exploration and Reasoning for ComfyUI Workflows
%A Su, Jinwei
%A Lan, Qizhen
%A Wang, Zeyu
%A Xia, Yinghui
%A Wen, Hairu
%A Duan, Yiqun
%A Xiao, Xi
%A Jingsong, Yang
%A He, Lewei
%A Shi, Tianyu
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F su-etal-2026-comfysearch
%X AI-generated content has progressed from monolithic models to modular workflows, especially on platforms like ComfyUI, allowing users to customize complex creative pipelines. However, the large number of components in ComfyUI and the difficulty of maintaining long-horizon structural consistency under strict graph constraints frequently lead to low pass rates and workflows of limited quality. To tackle these limitations, we present ComfySearch, an agentic framework that can effectively explore the component space and generate functional ComfyUI pipelines via validation-guided workflow construction. Experiments demonstrate that ComfySearch substantially outperforms existing methods on complex and creative tasks, achieving higher executability (pass) rates, higher solution rates, and stronger generalization.
%U https://aclanthology.org/2026.findings-emnlp.37/
%P 734-748
Markdown (Informal)
[ComfySearch: Autonomous Exploration and Reasoning for ComfyUI Workflows](https://aclanthology.org/2026.findings-emnlp.37/) (Su et al., Findings 2026)
ACL
- Jinwei Su, Qizhen Lan, Zeyu Wang, Yinghui Xia, Hairu Wen, Yiqun Duan, Xi Xiao, Yang Jingsong, Lewei He, and Tianyu Shi. 2026. ComfySearch: Autonomous Exploration and Reasoning for ComfyUI Workflows. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 734–748, Budapest, Hungary. Association for Computational Linguistics.