# EfficientPosterGen: Semantic-aware Efficient Poster Generation via Token Compression and Accurate Violation Detection

arXiv: https://arxiv.org/abs/2603.00155 · HTML: https://mlsanity.com/paper/2603.00155/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Placed in Visual token pruning in multimodal models, one of 12 directions in Efficient LLM inference and serving.
- Builds on 29 of its 58 references that are in the map.
- First posted on arXiv 25 Feb 2026.

## Placed in

- Efficient LLM inference and serving / [Visual token pruning in multimodal models](https://mlsanity.com/inference/visual-token-pruning-in-multimodal-models/)

## Builds on (most cited first, 29 of 29)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [The Llama 3 Herd of Models](https://mlsanity.com/paper/2407.21783/) (2024)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness](https://mlsanity.com/paper/2205.14135/) (2022)
- [Lost in the Middle: How Language Models Use Long Contexts](https://mlsanity.com/paper/2307.03172/) (2023)
- [LLaVA-OneVision: Easy Visual Task Transfer](https://mlsanity.com/paper/2408.03326/) (2024)
- [MinerU: An Open-Source Solution for Precise Document Content Extraction](https://mlsanity.com/paper/2409.18839/) (2024)
- [StarVector: Generating Scalable Vector Graphics Code from Images and Text](https://mlsanity.com/paper/2312.11556/) (2023)
- [Empowering LLMs to Understand and Generate Complex Vector Graphics](https://mlsanity.com/paper/2412.11102/) (2024)
- [Paper2Poster: Towards Multimodal Poster Automation from Scientific Papers](https://mlsanity.com/paper/2505.21497/) (2025)
- [PageRank beyond the Web](https://mlsanity.com/paper/1407.5107/) (2014)
- [Glyph: Scaling Context Windows via Visual-Text Compression](https://mlsanity.com/paper/2510.17800/) (2025)
- [DesignBench: A Comprehensive Benchmark for MLLM-based Front-end Code Generation](https://mlsanity.com/paper/2506.06251/) (2025)
- [AutoPoster: A Highly Automatic and Content-aware Design System for Advertising Poster Generation](https://mlsanity.com/paper/2308.01095/) (2023)
- [MMCode: Benchmarking Multimodal Large Language Models for Code Generation with Visually Rich Programming Problems](https://mlsanity.com/paper/2404.09486/) (2024)
- [Chat2SVG: Vector Graphics Generation with Large Language Models and Image Diffusion Models](https://mlsanity.com/paper/2411.16602/) (2024)
- [POSTA: A Go-to Framework for Customized Artistic Poster Generation](https://mlsanity.com/paper/2503.14908/) (2025)
- [SVGEditBench: A Benchmark Dataset for Quantitative Assessment of LLM's SVG Editing Capabilities](https://mlsanity.com/paper/2404.13710/) (2024)
- [SlideCoder: Layout-aware RAG-enhanced Hierarchical Slide Generation from Design](https://mlsanity.com/paper/2506.07964/) (2025)
- [Learning to Generate Posters of Scientific Papers by Probabilistic Graphical Models](https://mlsanity.com/paper/1702.06228/) (2017)
- [P2P: Automated Paper-to-Poster Generation and Fine-Grained Benchmark](https://mlsanity.com/paper/2505.17104/) (2025)
- [PosterVerse: A Full-Workflow Framework for Commercial-Grade Poster Generation with HTML-Based Scalable Typography](https://mlsanity.com/paper/2601.03993/) (2026)
- [Perplexed: Understanding When Large Language Models are Confused](https://mlsanity.com/paper/2404.06634/) (2024)
- [DynEx: Dynamic Code Synthesis with Structured Design Exploration for Accelerated Exploratory Programming](https://mlsanity.com/paper/2410.00400/) (2024)
- [Benchmarking MLLM-based Web Understanding: Reasoning, Robustness and Safety](https://mlsanity.com/paper/2509.21782/) (2025)
- [ComUICoder: Component-based Reusable UI Code Generation for Complex Websites via Semantic Segmentation and Element-wise Feedback](https://mlsanity.com/paper/2602.19276/) (2026)
- [Envisioning Future Interactive Web Development: Editing Webpage with Natural Language](https://mlsanity.com/paper/2510.26516/) (2025)
- [AttnCache: Accelerating Self-Attention Inference for LLM Prefill via Attention Cache](https://mlsanity.com/paper/2510.25979/) (2025)
- [TREAT: A Code LLMs Trustworthiness / Reliability Evaluation and Testing Framework](https://mlsanity.com/paper/2510.17163/) (2025)

## Built on by (most cited first, 9 of 9)

- [Beyond NL2Code: A Structured Survey of Multimodal Code Intelligence](https://mlsanity.com/paper/2606.15932/) (2026)
- [PosterHarness: Turning Scientific Poster Generation into an Auditable Instruction-Following Benchmark](https://mlsanity.com/paper/2607.03006/) (2026)
- [Rubric-Guided Process Reward for Stepwise Model Routing](https://mlsanity.com/paper/2605.29310/) (2026)
- [ResearchStudio-Reel: Automate the Last Mile of Research from Paper to Poster, Video, and Blog](https://mlsanity.com/paper/2607.04438/) (2026)
- [VulTriage: Triple-Path Context Augmentation for LLM-Based Vulnerability Detection](https://mlsanity.com/paper/2605.09461/) (2026)
- [DCVD: Dual-Channel Cross-Modal Fusion for Joint Vulnerability Detection and Localization](https://mlsanity.com/paper/2605.11015/) (2026)
- [CodeShrink: Adaptive Visual Compression for Efficient Multimodal Code Understanding](https://mlsanity.com/paper/2607.29637/) (2026)
- [ConvDeck: Conversational Paper-to-Slide Generation via Stage-Specific User Feedback](https://mlsanity.com/paper/2609.00226/) (2026)
- [From Transient Prompts to Persistent Control: Scientific Poster Generation via Recursive Semantic-Geometric Contracts](https://mlsanity.com/paper/2609.17326/) (2026)

## Abstract (arXiv)

Automated academic poster generation aims to distill lengthy research papers into concise, visually coherent presentations. Existing Multimodal Large Language Models (MLLMs) based approaches, however, suffer from three critical limitations: low information density in full-paper inputs, excessive token consumption, and unreliable layout verification. We present EfficientPosterGen, an end-to-end framework that addresses these challenges through semantic-aware retrieval and token-efficient multimodal generation. EfficientPosterGen introduces three core innovations: (1) Semantic-aware Key Information Retrieval (SKIR), which constructs a semantic contribution graph to model inter-segment relationships and selectively preserves important content; (2) Visual-based Context Compression (VCC), which renders selected text segments into images to shift textual information into the visual modality, significantly reducing token usage while generating poster-ready bullet points; and (3) Agentless Layout Violation Detection (ALVD), a deterministic color-gradient-based algorithm that reliably detects content overflow and spatial sparsity without auxiliary MLLMs. Extensive experiments demonstrate that EfficientPosterGen achieves substantial improvements in token efficiency and layout reliability while maintaining high poster quality, offering a scalable solution for automated academic poster generation. Our code is available at https://github.com/vinsontang1/EfficientPosterGen-Code.