# Paper2Agent: Reimagining Research Papers As Interactive and Reliable AI Agents

**Authors:** Jiacheng Miao, Joe R. Davis, Yaohui Zhang, Jonathan K. Pritchard, James Zou  
**arXiv:** [2509.06917](https://arxiv.org/abs/2509.06917)  
**Date:** 2025-09-08  
**Summary page:** https://www.summarizepaper.com/en/paper/2509.06917v2/

## Key points

- Research papers are the primary means of sharing methods, results, and insights in scientific communication.
- New computational methods can be challenging to understand and apply due to technical hurdles.
- AlphaGenome is a powerful framework for genome-scale modeling that requires significant technical expertise to set up and utilize effectively.
- Valuable research outputs are often inaccessible due to technical hurdles.
- A solution is offered by transforming static research papers into dynamic AI agents that serve as knowledgeable research assistants.
- The innovative approach involves automating the conversion process, constructing Model Context Protocol (MCP) servers, and deploying remote MCP servers integrated with AI agents for natural-language interactions.
- This reimagines research papers as interactive and reliable AI agents that accelerate downstream use and foster collaboration within the scientific community.

## Summary

Research papers have long been the primary means of sharing methods, results, and insights in the realm of scientific communication. However, these papers can be difficult to understand and apply due to their passive nature. This is especially true for new computational methods, as technical hurdles often hinder their implementation on new data sets. For instance, AlphaGenome is a powerful framework for genome-scale modeling that requires significant technical expertise to set up and utilize effectively. The complexities involved in installing dependencies, configuring environments, and interpreting inputs and outputs create obstacles for researchers seeking to leverage this valuable resource. This scenario highlights a common challenge faced by researchers – valuable research outputs are often inaccessible due to technical hurdles. To address this issue,  offers a groundbreaking solution by transforming static research papers into dynamic AI agents. These agents serve as knowledgeable research assistants capable of demonstrating, applying, and adapting the methods described in the corresponding paper to new projects. Through automating the conversion process and constructing Model Context Protocol (MCP) servers through iterative testing and validation processes, enables seamless interaction with paper-specific agents for diverse tasks. The workflow involves extracting codebases, setting up automated environments for reproducibility, wrapping core analytical features as MCP tools,and deploying remote MCP servers integrated with AI agents for natural-language interactions. This innovative approach revolutionizes knowledge dissemination by creating interactive AI co-scientists that can reproduce original paper results accurately and execute novel user queries effectively. In essence, reimagines research papers as interactive and reliable AI agents that not only accelerate downstream use but also foster collaboration within the scientific community. By bridging the gap between static publications and dynamic systems through intelligent automation,paves the way for a collaborative ecosystem of AI co-scientists that enhance knowledge dissemination and facilitate impactful discoveries in various fields of research.