Improving the academic workflow: Introducing two AI agents for better figures and peer review

Academic research is evolving at an unprecedented pace driven by the rapid advancements in AI. The academic research workflow is notoriously rigorous, involving far more than just conceptualizing an idea and writing a paper. One hurdle many researchers face is how to effectively visualize their research. While AI can draft text, creating the complex methodology diagrams and precise statistical plots required for top-tier conferences and journals is significantly more difficult. Furthermore, the scientific community relies on the peer review process to maintain the integrity of published research. However, the exponential growth of paper submissions has severely strained this system, leading to reviewer fatigue and inconsistent evaluations. As language models and multi-agent systems become more sophisticated, we see their potential not just as subjects of study, but as active participants in the scientific process itself.

To that end, we introduce two novel agentic frameworks: (i) PaperVizAgent (formally known as PaperBanana), a visualizer agent for drawing academic figures, and (ii) ScholarPeer, a reviewer agent that automatically and rigorously evaluates academic papers, including inlined diagrams). These agents are designed specifically to assist with the academic research lifecycle to empower scientists to focus on innovation rather than administrative overhead. Our evaluations show PaperVizAgent consistently generates expert quality figures that significantly outperform leading baselines (GPT-Image-1.5, Nano-Banana-Pro, Paper2Any) while ScholarPeer delivers highly critical, literature-grounded reviews that beat state-of-the-art automated reviewers.

PaperVizAgent: Generating publication-ready figures

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