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.
Read original(opens in new tab)