PaperWizAgent: preparing data ready for publication
PaperWizAgent is an autonomous framework designed to produce publication-ready academic illustrations from academic text. Bridging the gap between technical description and visual communication, PaperVizAgent allows researchers to create professional-grade figures directly from their manuscripts. To begin the process, a researcher provides two inputs:
- source context: Methods section of the manuscript, usually with technical details of the research.
- communicative intent: A detailed picture caption that explains what the scene is supposed to convey.
The PaperVizAgent framework organizes a collaborative team of five specialized AI agents that includes: (1) a retriever, (2) a planner, (3) a stylist, (4) a visualizer, and (5) a critic. First, retriever and planner agents gather context (for example, existing literature to reference relevant academic data) 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, starting a loop of iterative refinement. Through iterative refinement, this multi-agent system ensures that the final depiction is both visually appealing and technically accurate.
