# APEX: Academic Poster Editing Agentic Expert

arXiv: https://arxiv.org/abs/2601.04794 · HTML: https://mlsanity.com/paper/2601.04794/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in Multimodal and embodied agents, one of 16 directions in LLM agents and tool use.
- Builds on 12 of its 18 references that are in the map.
- First posted on arXiv 8 Jan 2026.

## Placed in

- LLM agents and tool use / [Multimodal and embodied agents](https://mlsanity.com/agents/multimodal-and-embodied-agents/)

## Builds on (most cited first, 12 of 12)

- [MLLM-as-a-Judge: Assessing Multimodal LLM-as-a-Judge with Vision-Language Benchmark](https://mlsanity.com/paper/2402.04788/) (2024)
- [Paper2Poster: Towards Multimodal Poster Automation from Scientific Papers](https://mlsanity.com/paper/2505.21497/) (2025)
- [PosterMaker: Towards High-Quality Product Poster Generation with Accurate Text Rendering](https://mlsanity.com/paper/2504.06632/) (2025)
- [PPTC Benchmark: Evaluating Large Language Models for PowerPoint Task Completion](https://mlsanity.com/paper/2311.01767/) (2023)
- [LayoutFlow: Flow Matching for Layout Generation](https://mlsanity.com/paper/2403.18187/) (2024)
- [Auto-Slides: An Interactive Multi-Agent System for Creating and Customizing Research Presentations](https://mlsanity.com/paper/2509.11062/) (2025)
- [PPTBench: Towards Holistic Evaluation of Large Language Models for PowerPoint Layout and Design Understanding](https://mlsanity.com/paper/2512.02624/) (2025)
- [PPTArena: A Benchmark for PowerPoint Editing](https://mlsanity.com/paper/2512.03042/) (2025)
- [PPTC-R benchmark: Towards Evaluating the Robustness of Large Language Models for PowerPoint Task Completion](https://mlsanity.com/paper/2403.03788/) (2024)
- [PosterForest: Hierarchical Multi-Agent Collaboration for Scientific Poster Generation](https://mlsanity.com/paper/2508.21720/) (2025)
- [LayoutCoT: Unleashing the Deep Reasoning Potential of Large Language Models for Layout Generation](https://mlsanity.com/paper/2504.10829/) (2025)
- [LayoutRectifier: An Optimization-based Post-processing for Graphic Design Layout Generation](https://mlsanity.com/paper/2508.11177/) (2025)

## Built on by (most cited first, 6 of 6)

- [AI for Auto-Research: Roadmap & User Guide](https://mlsanity.com/paper/2605.18661/) (2026)
- [ResearchStudio-Reel: Automate the Last Mile of Research from Paper to Poster, Video, and Blog](https://mlsanity.com/paper/2607.04438/) (2026)
- [PosterMELD: Multi-Agent Paper-to-Poster Generation for Controllable Design Diversity with Editable Print-Ready Outputs](https://mlsanity.com/paper/2608.02218/) (2026)
- [Agentic Visual Generation: From Generative Models to Agentic Control](https://mlsanity.com/paper/2609.06758/) (2026)
- [ConvDeck: Conversational Paper-to-Slide Generation via Stage-Specific User Feedback](https://mlsanity.com/paper/2609.00226/) (2026)
- [SLIDEFORGE: An LLM Agent for Controllable Editing of Slides as Structured Artifacts](https://mlsanity.com/paper/2609.03109/) (2026)

## Abstract (arXiv)

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.