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Figure goal

Overview of a multi-agent academic illustration generation framework, showing input optimization, reference retrieval, diagram planning, style refinement, image generation, critic-based revision, and final publication-quality output.

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Paper contextWe propose a multi-agent framework for generating publication-quality academic illustrations from research text. The system takes a paper excerpt, figure caption, and optional user feedback as input. It first performs input optimization using two parallel agents: a Context Enricher that extracts key components, data flows, modules, assumptions, and dependencies from the methodology text, and a Caption Sharpener that rewrites the figure intent into a precise visual specification. A Retriever searches a curated library of academic diagrams, a Planner converts the optimized context into modules and arrows, and a Stylist applies typography and layout rules. A Visualizer then generates the diagram, while a Critic checks faithfulness, readability, concision, and aesthetics before either approving the figure or sending revision instructions back into the loop.

Style

Academic

Advanced(CS / AI · 4:3 · 2K · Nano Banana Pro)Generate Diagram (10 credits)