Reviewer: A Multi-Agent Workflow Tool for Automated Economic Paper Review

The Reviewer introduced in this article is an automated review tool designed specifically for academic papers in economics. It implements PDF parsing, content analysis, and structured feedback generation through a multi-agent workflow. Developed and maintained by joshunspeakable173, the project is open-sourced on GitHub (link: https://github.com/joshunspeakable173/reviewer). Positioned as an automated academic paper review tool, it aims to address the pain points of traditional review processes and provide efficient assistance to the economics community.

Traditional peer review faces challenges such as long cycles, inconsistent quality, and a scarcity of excellent reviewers. Economic papers, due to their mathematical and structured nature, are even more time-consuming. The development of large language models has brought possibilities for automated auxiliary review. Although they cannot replace human in-depth judgment, they are practical in aspects like initial screening and format checking. Based on this background, the Reviewer project builds a reproducible multi-agent workflow to serve the needs of automated review for economic papers.

Multi-Agent Architecture: Decompose the review process into subtasks, handled by specialized agents—parsing agents extract structured text, prompt agents generate review prompts, quality check agents evaluate extraction quality, repair agents improve document structure, reviewer selection agents match virtual reviewers. The modular design enhances optimizability and interpretability. User-Friendly Design: Zero programming threshold (precompiled packages, no need to configure environment), native Windows support (PowerShell operation), concise process (unzip → place PDF → run command), reducing the usage cost for non-technical users.

Output Organization: Adopts a hierarchical directory structure. Each paper has an independent folder containing subdirectories Parsed (original text), Prompts (prompts), and Reviews (final reviews), facilitating traceability of the entire review process. Review Report Elements: It is speculated to include content summary, methodology evaluation, literature positioning, main advantages, and improvement suggestions. It conforms to the review style of mainstream economic journals, helping authors understand and respond.

• Format Dependence: Assumes the paper follows standard economic formatting. Non-standard layouts may lead to parsing issues, requiring manual preprocessing.
• Network Requirements: Needs to connect to remote services, relies on a stable network, may involve API fees, and data security should be considered for sensitive papers.
• Clear Positioning: The generated reviews are for auxiliary reference only and cannot replace human subjective evaluation of research importance and novelty. Suitable for scenarios like author self-check, initial screening assistance, and learning reference.

Value of Multi-Agent: Modularity (independent development and optimization), interpretability (transparent process), robustness (single component failure does not crash the system), scalability (easy to add new agents), suitable for complex multi-step tasks. Future Directions: Integrate reference databases, support multi-disciplinary standards, interactive interfaces, version control to track modifications. AI is positioned to enhance human capabilities, freeing up scholars' energy for in-depth thinking.

Reviewer provides a practical automated review prototype for the economics community. Through a multi-agent architecture, it simplifies processes and is friendly to non-technical users. Although it has limitations such as format and network issues, it demonstrates the feasibility of AI-assisted academic workflows. We look forward to more similar tools in the future to promote a more efficient and fair academic publishing ecosystem.