Core Viewpoint - The article introduces PosterAgent, a tool designed to convert academic papers into visually appealing posters, highlighting its efficiency and effectiveness compared to existing methods like GPT-4o [2][18]. Group 1: PosterAgent Overview - PosterAgent can transform a 22-page paper into an editable ".pptx" poster for only $0.0045, significantly reducing token usage by 87% compared to GPT-4o [2][36]. - The tool is built upon the Paper2Poster framework, which establishes the first academic poster evaluation standard, addressing gaps in long-context and multi-modal compression assessments [4][18]. Group 2: Evaluation Metrics - Paper2Poster includes 100 pairs of AI-related papers and their corresponding posters, covering various subfields like computer vision (19%), natural language processing (17%), and reinforcement learning (10%) [20]. - The evaluation metrics focus on four dimensions: visual quality, text coherence, overall assessment, and PaperQuiz, which simulates communication between authors and readers [22][23]. Group 3: PosterAgent Components - The PosterAgent framework consists of three key components: a parser for extracting key content, a planner for organizing text and visuals, and a painter-commenter for generating and refining the poster layout [28][29]. - The system employs a top-down design approach to ensure coherence and alignment of content [25]. Group 4: Performance Comparison - In comparative tests, PosterAgent achieved the highest graphic relevance and visual similarity to human-designed posters, scoring an average of 3.72 when evaluated by a visual language model (VLM) [31][32]. - While GPT-4o-image had the highest visual similarity, it recorded the lowest coherence, indicating that its outputs may appear attractive but lack textual clarity [30][31]. Group 5: Cost Efficiency - PosterAgent demonstrated significant cost efficiency, requiring only 101.1K and 47.6K tokens for different variants, translating to a cost of $0.55 (based on GPT-4o) or $0.0045 (based on Qwen) per poster [36].