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

arXiv: https://arxiv.org/abs/2509.06917 · HTML: https://mlsanity.com/paper/2509.06917/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 34 references that are in the map.
- First posted on arXiv 8 Sep 2025.

## Builds on (most cited first, 10 of 10)

- [ReAct: Synergizing Reasoning and Acting in Language Models](https://mlsanity.com/paper/2210.03629/) (2022)
- [A Survey on LLM-as-a-Judge](https://mlsanity.com/paper/2411.15594/) (2024)
- [Agentless: Demystifying LLM-based Software Engineering Agents](https://mlsanity.com/paper/2407.01489/) (2024)
- [Accelerating scientific discovery with Co-Scientist](https://mlsanity.com/paper/2502.18864/) (2025)
- [Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directions](https://mlsanity.com/paper/2503.23278/) (2025)
- [Paper2Code: Automating Code Generation from Scientific Papers in Machine Learning](https://mlsanity.com/paper/2504.17192/) (2025)
- [Robin: A multi-agent system for automating scientific discovery](https://mlsanity.com/paper/2505.13400/) (2025)
- [CRISPR-GPT for Agentic Automation of Gene-editing Experiments](https://mlsanity.com/paper/2404.18021/) (2024)
- [A large-scale study on research code quality and execution](https://mlsanity.com/paper/2103.12793/) (2021)
- [From Articles to Code: On-Demand Generation of Core Algorithms from Scientific Publications](https://mlsanity.com/paper/2507.22324/) (2025)

## Built on by (most cited first, 23 of 23)

- [DeepScientist: Advancing Frontier-Pushing Scientific Findings Progressively](https://mlsanity.com/paper/2509.26603/) (2025)
- [SKILLFOUNDRY: Building Self-Evolving Agent Skill Libraries from Heterogeneous Scientific Resources](https://mlsanity.com/paper/2604.03964/) (2026)
- [CASCADE: Cumulative Agentic Skill Creation through Autonomous Development and Evolution](https://mlsanity.com/paper/2512.23880/) (2025)
- [Paper2Video: Automatic Video Generation from Scientific Papers](https://mlsanity.com/paper/2510.05096/) (2025)
- [The Last Human-Written Paper: Agent-Native Research Artifacts](https://mlsanity.com/paper/2604.24658/) (2026)
- [AgenticSciML: Collaborative Multi-Agent Systems for Emergent Discovery in Scientific Machine Learning](https://mlsanity.com/paper/2511.07262/) (2025)
- [AI for Auto-Research: Roadmap & User Guide](https://mlsanity.com/paper/2605.18661/) (2026)
- [Towards a Medical AI Scientist](https://mlsanity.com/paper/2603.28589/) (2026)
- [NERFIFY: A Multi-Agent Framework for Turning NeRF Papers into Code](https://mlsanity.com/paper/2603.00805/) (2026)
- [The Agentic Garden of Forking Paths](https://mlsanity.com/paper/2607.01507/) (2026)
- [DeepXiv-SDK: An Agentic Data Interface for Scientific Literature](https://mlsanity.com/paper/2603.00084/) (2026)
- [Inspectable AI for Science: A Research Object Approach to Generative AI Governance](https://mlsanity.com/paper/2604.11261/) (2026)
- [Knows: Agent-Native Structured Research Representations](https://mlsanity.com/paper/2604.17309/) (2026)
- [Agentic Publication Protocol: An Attempt to Modernize Scientific Publication](https://mlsanity.com/paper/2606.27386/) (2026)
- [Paper2Web: Let's Make Your Paper Alive!](https://mlsanity.com/paper/2510.15842/) (2025)
- [What Makes AI Research Replicable? Executable Knowledge Graphs as Scientific Knowledge Representations](https://mlsanity.com/paper/2510.17795/) (2025)
- [Unifying Scientific Communication: Fine-Grained Correspondence Across Scientific Media](https://mlsanity.com/paper/2605.05831/) (2026)
- [SW-A2-Bench: Benchmarking Autonomous Software Agent Generation for Agentic Web](https://mlsanity.com/paper/2604.04226/) (2026)
- [AblateCell: A Reproduce-then-Ablate Agent for Virtual Cell Repositories](https://mlsanity.com/paper/2604.19606/) (2026)
- [FigAct: Turning Scientific Figures into Active Canvases for Explanation](https://mlsanity.com/paper/2609.36190/) (2026)
- [Training Numerical Intelligence via Auto-Diagnosis and Skill Discovery](https://mlsanity.com/paper/2610.03872/) (2026)
- [AgentDiscover: Autonomous Discovery with Minimal Search Scaffolding](https://mlsanity.com/paper/2610.05334/) (2026)
- [PlotGround: Grounding Plot Digitization in Real Scientific Figures and Their Source Data](https://mlsanity.com/paper/2610.06825/) (2026)

## Abstract (arXiv)

We introduce Paper2Agent, an automated framework that converts research papers into AI agents. Paper2Agent transforms research output from passive artifacts into active systems that can accelerate downstream use, adoption, and discovery. Conventional research papers require readers to invest substantial effort to understand and adapt a paper's code, data, and methods to their own work, creating barriers to dissemination and reuse. Paper2Agent addresses this challenge by automatically converting a paper into an AI agent that acts as a knowledgeable research assistant. It systematically analyzes the paper and the associated codebase using multiple agents to construct a Model Context Protocol (MCP) server, then iteratively generates and runs tests to refine and robustify the resulting MCP. These paper MCPs can then be flexibly connected to a chat agent (e.g. Claude Code) to carry out complex scientific queries through natural language while invoking tools and workflows from the original paper. We demonstrate Paper2Agent's effectiveness in creating reliable and capable paper agents through in-depth case studies. Paper2Agent created an agent that leverages AlphaGenome to interpret genomic variants and agents based on ScanPy and TISSUE to carry out single-cell and spatial transcriptomics analyses. We validate that these paper agents can reproduce the original paper's results and can correctly carry out novel user queries. Paper2Agent automatically created AI co-scientist that identified new splicing variant associated with ADHD risk. By turning static papers into dynamic, interactive AI agents, Paper2Agent introduces a new paradigm for knowledge dissemination and a foundation for the collaborative ecosystem of AI co-scientists.