PaperVizAgent is an autonomous framework designed to generate publication-ready academic illustrations from academic text. The system is described as a way to bridge technical descriptions and visual communication so researchers can create professional-grade figures directly from manuscripts.
According to the source, a researcher provides two inputs: Source context, typically the method sections of a manuscript with technical details of the research, and Communicative intent, a detailed figure caption that describes what the visual should convey.
The framework uses five specialized AI agents: retriever, planner, stylist, visualizer, and critic. The retriever and planner gather references and organize the content. The stylist synthesizes aesthetic guidelines to help the output match academic standards.
The visualizer then renders an image or generates executable python code for statistical plots. After that, the critic evaluates the output against the original text. If inconsistencies are found, the critic provides targeted feedback to the visualizer, triggering iterative refinement.
The source says this multi-agent process is intended to make the final illustration both visually appealing and technically accurate.
Source: research.google.
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