PaperBanana: A Multi-Agent Framework for Automated Academic Illustration
PaperBanana is an open-source framework that automates the generation of academic illustrations by using a multi-agent system with visual language models and image generation capabilities.
Why it matters
PaperBanana can significantly streamline the process of creating high-quality illustrations for academic papers, saving researchers valuable time and effort.
Key Points
- 1PaperBanana transforms raw scientific content into high-quality publishable diagrams and charts
- 2It uses a pipeline of 5 specialized agents to handle tasks like reference retrieval, planning, styling, visualization, and iterative refinement
- 3The framework supports conceptual diagrams and data visualizations, and integrates with models from OpenAI, Anthropic, Google Gemini, and other compatible providers
Details
PaperBanana is a reference-driven framework that aims to accelerate the process of creating illustrations for academic papers. It was originally developed within Google Research as PaperVizAgent, and this open-source version continues its evolution with a focus on reliability and diverse use cases. The core of the system is a pipeline of five specialized agents, each with a clear responsibility: the Retriever Agent identifies relevant reference diagrams, the Planner Agent translates the method content and communicative intent into detailed textual descriptions, the Stylist Agent refines the descriptions to meet academic aesthetic standards, the Visualizer Agent transforms the descriptions into actual images using state-of-the-art generative models, and the Critic Agent iteratively refines the results. This structured workflow emulates the collaborative work of a creative team. The framework supports both conceptual diagrams and data visualizations, and can integrate with models from various AI providers like OpenAI, Anthropic, and Google Gemini.
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