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

AI is being positioned as an active participant in academic research, not merely a tool for drafting text. The post introduces PaperVizAgent, which creates publication-ready figures, and ScholarPeer, which produces literature-grounded peer reviews. Both use multi-agent workflows and iterative verification to reduce researchers’ administrative burden while improving visual quality and review rigor.

PaperVizAgent: Generating Publication-Ready Figures

• PaperVizAgent converts manuscript text and a detailed figure caption into academic illustrations.
• It uses five specialized agents:
  • Retriever: Finds relevant literature and reference figures.
  • Planner: Organizes the technical content.
  • Stylist: Develops appropriate visual and aesthetic guidelines.
  • Visualizer: Produces images or executable Python code for statistical plots.
  • Critic: Checks the result against the source text and requests revisions.
• The critic-driven refinement loop is designed to ensure that figures are both technically faithful and visually clear.
• Inputs typically include:
  • The manuscript’s method or technical sections.
  • A communicative-intent description explaining what the figure should convey.

Evaluation Results

• PaperVizAgent was compared with direct prompting, few-shot prompting, GPT-Image-1.5, Nano-Banana-Pro, and Paper2Any.
• Figures were scored from 0 to 100 on:
  • Faithfulness
  • Conciseness
  • Readability
  • Aesthetics
• It achieved an overall score of 60.2, exceeding the human baseline of 50.0 and outperforming the evaluated automated systems.
• Its strongest results were in conciseness and aesthetics, while its statistical plots reached human-competitive quality.

ScholarPeer: Automating Rigorous Peer Review

• ScholarPeer is a search-enabled, context-aware multi-agent system designed to emulate the workflow of a senior academic reviewer.
• Rather than treating review as simple text generation, it combines literature retrieval, adversarial checking, and technical verification.
• Its main components include:
  • A sub-domain historian that builds a current domain narrative from literature.
  • A baseline scout that searches for overlooked datasets, methods, and comparisons.
  • A multi-aspect Q&A engine that tests novelty and technical claims.
  • A review generator that follows conference-specific review guidelines.
• The resulting review includes a summary, strengths, weaknesses, and questions for the authors.

Evaluation Results

• ScholarPeer was evaluated on public datasets against fine-tuned models and other agentic reviewing systems.
• Its active web-search and verification process produced highly critical reviews grounded in existing research.
• Side-by-side evaluations showed strong win rates against competing automated reviewers.
• The system also narrowed the gap between AI-generated reviews and human reviews in terms of realism, diversity, and alignment with expert judgments.

Implications for Academic Research

• The two agents address separate bottlenecks in the publication process:
  • PaperVizAgent improves technical communication through better figures.
  • ScholarPeer helps scale peer review amid growing submission volumes and reviewer fatigue.
• Their multi-agent designs suggest that specialized agents, coordinated through retrieval and iterative critique, may be more effective than a single general-purpose language model.
• The systems are intended to support researchers rather than replace scientific judgment.

Researchers could use PaperVizAgent for early figure prototyping and ScholarPeer for preliminary, literature-informed critique, while retaining human oversight for final scientific and editorial decisions.