APEX: Academic Poster Editing Agentic Expert
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| _version_ | 1866911360741277696 |
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| author | Shi, Chengxin Cai, Qinnan Chen, Zeyuan Zeng, Long Zhao, Yibo Yu, Jing Yu, Jianxiang Li, Xiang |
| author_facet | Shi, Chengxin Cai, Qinnan Chen, Zeyuan Zeng, Long Zhao, Yibo Yu, Jing Yu, Jianxiang Li, Xiang |
| contents | 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 (Academic Poster Editing agentic eXpert), 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_04794 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | APEX: Academic Poster Editing Agentic Expert Shi, Chengxin Cai, Qinnan Chen, Zeyuan Zeng, Long Zhao, Yibo Yu, Jing Yu, Jianxiang Li, Xiang Artificial Intelligence 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 (Academic Poster Editing agentic eXpert), 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. |
| title | APEX: Academic Poster Editing Agentic Expert |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2601.04794 |