APEX: Academic Poster Editing Agentic Expert

• URL: http://arxiv.org/abs/2601.04794v1
• Date: Thu, 08 Jan 2026 10:21:49 GMT
• Title: APEX: Academic Poster Editing Agentic Expert
• Abstract summary: APEX is the first agentic framework for interactive academic poster editing.<n>APEX supports fine-adjustment control with robust multi-level API-based editing.<n>APEX-Bench is the first systematic benchmark comprising 514 academic poster editing instructions.
• Score: 14.933976965223716
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 (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.

Related papers

• Code as Representation: A Compilable Parsing Paradigm for Academic Documents [50.40789738182074]
  Compilable Academic Document Parsing (CADP) is a paradigm that reconstructs a full page as contextual La plus executable Python.<n>We introduce CADP-Bench, an expert-verified benchmark of full academic pages containing text and multiple SAE types.<n>Results show that even frontier models still struggle to produce high-fidelity executable reconstructions.
  arXiv  Detail & Related papers  (2026-08-18T09:10:43Z)
• APE: Agentic Prompt Enhancer for Image Generation and Editing [58.43341902967105]
  Agentic Prompt Enhancer (APE) is a lightweight framework that post-trains small language models (SLMs) as prompt-enhancement agents.<n>APE supports both single-agent rewriting and role-specialized multi-agent enhancement.<n>Its single-agent instantiation, SAPE, rewrites the prompt in one pass, while its multi-agent instantiation, MAPE, decomposes enhancement into a router--rewriter--composer process.
  arXiv  Detail & Related papers  (2026-05-29T17:59:31Z)
• How Well Do Models Follow Visual Instructions? VIBE: A Systematic Benchmark for Visual Instruction-Driven Image Editing [56.60465182650588]
  We introduce three-level interaction hierarchy that captures deictic grounding, morphological manipulation, and causal reasoning.<n>We propose a robust LMM-as-a-judge evaluation framework with task-specific metrics to enable scalable and fine-grained assessment.<n>We find that proprietary models exhibit early-stage visual instruction-following capabilities and consistently outperform open-source models.
  arXiv  Detail & Related papers  (2026-02-02T09:24:45Z)
• Exploring Structural Complexity in Normative RAG with Graph-based approaches: A case study on the ETSI Standards [3.4765379241536465]
  This paper investigates the efficacy of Graph RAG architectures for building high-performance RAG solutions for normative, standards, and regulatory documents.<n>To help solve this knowledge gap, we propose a specialized RAG methodology tailored to the unique structure and lexical characteristics of standards and regulatory documents.
  arXiv  Detail & Related papers  (2026-01-31T17:00:43Z)
• PaperX: A Unified Framework for Multimodal Academic Presentation Generation with Scholar DAG [22.64989700220684]
  We introduce PaperX, a unified framework that models academic presentation generation as a structural transformation and rendering process.<n>PaperX generates diverse, high quality outputs from a single source.
  arXiv  Detail & Related papers  (2026-01-30T18:27:03Z)
• Improving Alignment Between Human and Machine Codes: An Empirical Assessment of Prompt Engineering for Construct Identification in Psychology [0.0]
  We present an empirical framework for optimizing prompt performance for identifying constructs in texts via prompt engineering.<n>We experimentally evaluate five prompting strategies -codebook-guided empirical prompt selection, automatic prompt engineering, persona prompting, chain-of-thought reasoning, and explanatory prompting.<n>Across three constructs and two models, the classifications most aligned with expert judgments resulted from a few-shot prompt combining codebook-guided empirical prompt selection with automatic prompt engineering.
  arXiv  Detail & Related papers  (2025-12-03T14:07:42Z)
• Multimodal Peer Review Simulation with Actionable To-Do Recommendations for Community-Aware Manuscript Revisions [16.556181117253473]
  We present an interactive web-based system for multimodal, community-aware peer review simulation to enable effective manuscript revisions before paper submission.<n>Our framework integrates textual and visual information through multimodal LLMs, enhances review quality via retrieval-augmented generation (RAG) grounded in web-scale OpenReview data.<n>The system integrates seamlessly into existing academic writing platforms, providing interactive interfaces for real-time feedback and revision tracking.
  arXiv  Detail & Related papers  (2025-11-14T02:29:23Z)
• Paper2Web: Let's Make Your Paper Alive! [51.75896846964824]
  We introduce Paper2Web, a benchmark dataset and framework for assessing academic webpage generation.<n>We present PWAgent, an autonomous pipeline that converts scientific papers into interactive and multimedia-rich academic homepages.
  arXiv  Detail & Related papers  (2025-10-17T17:35:58Z)
• PosterForest: Hierarchical Multi-Agent Collaboration for Scientific Poster Generation [28.02969134846803]
  We introduce the textitPoster Tree, a hierarchical intermediate representation that jointly encodes document structure and visual-textual relationships.<n>Our framework employs a multi-agent collaboration strategy, where agents specializing in content summarization and layout planning iteratively coordinate and provide mutual feedback.
  arXiv  Detail & Related papers  (2025-08-29T15:36:06Z)
• Rethinking Testing for LLM Applications: Characteristics, Challenges, and a Lightweight Interaction Protocol [83.83217247686402]
  Large Language Models (LLMs) have evolved from simple text generators into complex software systems that integrate retrieval augmentation, tool invocation, and multi-turn interactions.<n>Their inherent non-determinism, dynamism, and context dependence pose fundamental challenges for quality assurance.<n>This paper decomposes LLM applications into a three-layer architecture: textbftextitSystem Shell Layer, textbftextitPrompt Orchestration Layer, and textbftextitLLM Inference Core.
  arXiv  Detail & Related papers  (2025-08-28T13:00:28Z)
• Visual Document Understanding and Question Answering: A Multi-Agent Collaboration Framework with Test-Time Scaling [83.78874399606379]
  We propose MACT, a Multi-Agent Collaboration framework with Test-Time scaling.<n>It comprises four distinct small-scale agents, with clearly defined roles and effective collaboration.<n>It shows superior performance with a smaller parameter scale without sacrificing the ability of general and mathematical tasks.
  arXiv  Detail & Related papers  (2025-08-05T12:52:09Z)
• Image Editing As Programs with Diffusion Models [69.05164729625052]
  We introduce Image Editing As Programs (IEAP), a unified image editing framework built upon the Diffusion Transformer (DiT) architecture.<n>IEAP approaches instructional editing through a reductionist lens, decomposing complex editing instructions into sequences of atomic operations.<n>Our framework delivers superior accuracy and semantic fidelity, particularly for complex, multi-step instructions.
  arXiv  Detail & Related papers  (2025-06-04T16:57:24Z)
• P2P: Automated Paper-to-Poster Generation and Fine-Grained Benchmark [27.57464219790922]
  We introduce P2P, the first flexible, LLM-based multi-agent framework that generates high-quality, HTML-rendered academic posters.<n>P2P employs three specialized agents-for visual element processing, content generation, and final poster assembly-each integrated with dedicated checker modules.<n>We establish P2PEval, a comprehensive benchmark featuring 121 paper-poster pairs and a dual evaluation methodology.
  arXiv  Detail & Related papers  (2025-05-21T09:06:05Z)
• Structured Agent Distillation for Large Language Model [56.38279355868093]
  We propose Structured Agent Distillation, a framework that compresses large LLM-based agents into smaller student models.<n>Our method segments trajectories into [REASON] and [ACT] spans, applying segment-specific losses to align each component with the teacher's behavior.<n>Experiments on ALFWorld, HotPotQA-ReAct, and WebShop show that our approach consistently outperforms token-level and imitation learning baselines.
  arXiv  Detail & Related papers  (2025-05-20T02:01:55Z)
• XtraGPT: Context-Aware and Controllable Academic Paper Revision [43.263488839387584]
  We propose a human-AI collaboration framework for academic paper revision centered on criteria-guided intent alignment and context-aware modeling.<n>We instantiate the framework in XtraGPT, the first suite of open-source LLMs for context-aware, instruction-guided writing assistance.
  arXiv  Detail & Related papers  (2025-05-16T15:02:19Z)
• Beyond the Prototype: Divide-and-conquer Proxies for Few-shot Segmentation [63.910211095033596]
  Few-shot segmentation aims to segment unseen-class objects given only a handful of densely labeled samples. We propose a simple yet versatile framework in the spirit of divide-and-conquer. Our proposed approach, named divide-and-conquer proxies (DCP), allows for the development of appropriate and reliable information.
  arXiv  Detail & Related papers  (2022-04-21T06:21:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.