@inproceedings{shi-etal-2026-apex,
title = "{APEX}: Academic Poster Editing Agentic Expert",
author = "Shi, Chengxin and
Cai, Qinnan and
Chen, Zeyuan and
Zeng, Long and
Zhao, Yibo and
Yu, Jing and
Yu, Jianxiang and
Li, Xiang",
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.2283/",
pages = "48904--48932",
ISBN = "979-8-89176-462-0",
abstract = "Designing academic posters is a labor-intensive process requiring the precise balance of high-density content and sophisticated layout. While existing paper-to-poster generation methods automate initial drafting, they are typically single-pass and non-interactive, often fail to align with complex, subjective user intent. To bridge this gap, we propose APEX (**A**cademic **P**oster **E**diting agentic e**X**pert), the first agentic framework for interactive academic poster editing, supporting fine-grained control with robust multi-level API-based editing and a review-and-adjustment mechanism. In addition, we introduce APEX-Bench, the first systematic benchmark comprising 514 academic poster editing instructions, categorized by a multi-dimensional taxonomy including operation type, difficulty, and abstraction level, constructed via reference-guided and reference-free strategies to ensure realism and diversity. We further establish a multi-dimensional VLM-as-a-judge evaluation protocol to assess instruction fulfillment, modification scope, and visual consistency harmony. Experimental results demonstrate that APEX significantly outperforms baseline methods. Our implementation is available at https://github.com/Breesiu/APEX."
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<abstract>Designing academic posters is a labor-intensive process requiring the precise balance of high-density content and sophisticated layout. While existing paper-to-poster generation methods automate initial drafting, they are typically single-pass and non-interactive, often fail to align with complex, subjective user intent. To bridge this gap, we propose APEX (**A**cademic **P**oster **E**diting agentic e**X**pert), the first agentic framework for interactive academic poster editing, supporting fine-grained control with robust multi-level API-based editing and a review-and-adjustment mechanism. In addition, we introduce APEX-Bench, the first systematic benchmark comprising 514 academic poster editing instructions, categorized by a multi-dimensional taxonomy including operation type, difficulty, and abstraction level, constructed via reference-guided and reference-free strategies to ensure realism and diversity. We further establish a multi-dimensional VLM-as-a-judge evaluation protocol to assess instruction fulfillment, modification scope, and visual consistency harmony. Experimental results demonstrate that APEX significantly outperforms baseline methods. Our implementation is available at https://github.com/Breesiu/APEX.</abstract>
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%0 Conference Proceedings
%T APEX: Academic Poster Editing Agentic Expert
%A Shi, Chengxin
%A Cai, Qinnan
%A Chen, Zeyuan
%A Zeng, Long
%A Zhao, Yibo
%A Yu, Jing
%A Yu, Jianxiang
%A Li, Xiang
%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 shi-etal-2026-apex
%X Designing academic posters is a labor-intensive process requiring the precise balance of high-density content and sophisticated layout. While existing paper-to-poster generation methods automate initial drafting, they are typically single-pass and non-interactive, often fail to align with complex, subjective user intent. To bridge this gap, we propose APEX (**A**cademic **P**oster **E**diting agentic e**X**pert), the first agentic framework for interactive academic poster editing, supporting fine-grained control with robust multi-level API-based editing and a review-and-adjustment mechanism. In addition, we introduce APEX-Bench, the first systematic benchmark comprising 514 academic poster editing instructions, categorized by a multi-dimensional taxonomy including operation type, difficulty, and abstraction level, constructed via reference-guided and reference-free strategies to ensure realism and diversity. We further establish a multi-dimensional VLM-as-a-judge evaluation protocol to assess instruction fulfillment, modification scope, and visual consistency harmony. Experimental results demonstrate that APEX significantly outperforms baseline methods. Our implementation is available at https://github.com/Breesiu/APEX.
%U https://aclanthology.org/2026.findings-emnlp.2283/
%P 48904-48932
Markdown (Informal)
[APEX: Academic Poster Editing Agentic Expert](https://aclanthology.org/2026.findings-emnlp.2283/) (Shi et al., Findings 2026)
ACL
- Chengxin Shi, Qinnan Cai, Zeyuan Chen, Long Zeng, Yibo Zhao, Jing Yu, Jianxiang Yu, and Xiang Li. 2026. APEX: Academic Poster Editing Agentic Expert. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 48904–48932, Budapest, Hungary. Association for Computational Linguistics.