Introducing two AI agents for better figures and peer review

PaperVizAgent: Generating publication-ready figures

PaperVizAgent is an autonomous framework designed to generate publication-ready academic illustrations from academic text. By bridging the gap between technical descriptions and visual communication, PaperVizAgent allows researchers to create professional-grade figures directly from their manuscripts. To initiate the process, a researcher provides two inputs:

Source context: Typically the method sections of a manuscript with technical details of the research.Communicative intent: A detailed figure caption that describes what the visual should convey.

The PaperVizAgent framework orchestrates a collaborative team of five specialized AI agents including: (1) a retriever, (2) a planner, (3) a stylist, (4) a visualizer, and (5) a critic. First, the retriever and planner agents gather references (e.g., existing literature to reference relevant academic figures) and organize the content. Next, the stylist agent synthesizes aesthetic guidelines to ensure the output matches academic standards. The visualizer then renders an image or generates executable python code for statistical plots. Finally, the critic agent evaluates the output against the original text. If inconsistencies are found, the critic provides targeted feedback to the visualizer agent, triggering a loop of iterative refinement.Through iterative refinement, this multi-agent system ensures the final illustration is both visually appealing and technically accurate.