Paper2Code

Create a Paper2Code task, based on the paper: https://arxiv.org/abs/2504.17192
Corresponding open-source project: https://github.com/going-doer/Paper2Code

In the field of machine learning, code implementations for scientific papers are often missing, leading to slow research reproduction and innovation. However, recent Large Language Models (LLMs) have shown excellence in understanding scientific literature and generating high-quality code. To this end, we propose PaperCoder, a multi-agent LLM framework designed to automatically convert machine learning papers into functional code repositories. The PaperCoder process is divided into three stages: the planning stage creates an implementation roadmap, designs the system architecture, identifies file dependencies, and generates configuration files; the analysis stage interprets implementation details; and the generation stage produces modular, dependency-aware code. Our experiments demonstrate the effectiveness of PaperCoder in producing high-quality implementations, particularly excelling in the Paper2CodeBench and PaperBench Code-Dev benchmarks. Furthermore, the framework shows strong execution capabilities across different LLM models, requiring only minor modifications to 0.81% of code lines to run. Despite implementation challenges, PaperCoder’s work is a significant step toward accelerating scientific research progress. Future directions include expanding to other domains where validation does not primarily rely on code and enhancing the ability to handle visual inputs.

After successfully creating the task, obtain the task_id and use it to poll the task query interface; a single task typically takes about 10 minutes.

Price: Based on the pricing of the model called

Create a Paper2Code task, based on the paper: https://arxiv.org/abs/2504.17192
Corresponding open-source project: https://github.com/going-doer/Paper2Code

In the field of machine learning, code implementations for scientific papers are often missing, leading to slow research reproduction and innovation. However, recent Large Language Models (LLMs) have shown excellence in understanding scientific literature and generating high-quality code. To this end, we propose PaperCoder, a multi-agent LLM framework designed to automatically convert machine learning papers into functional code repositories. The PaperCoder process is divided into three stages: the planning stage creates an implementation roadmap, designs the system architecture, identifies file dependencies, and generates configuration files; the analysis stage interprets implementation details; and the generation stage produces modular, dependency-aware code. Our experiments demonstrate the effectiveness of PaperCoder in producing high-quality implementations, particularly excelling in the Paper2CodeBench and PaperBench Code-Dev benchmarks. Furthermore, the framework shows strong execution capabilities across different LLM models, requiring only minor modifications to 0.81% of code lines to run. Despite implementation challenges, PaperCoder’s work is a significant step toward accelerating scientific research progress. Future directions include expanding to other domains where validation does not primarily rely on code and enhancing the ability to handle visual inputs.

After successfully creating the task, obtain the task_id and use it to poll the task query interface; a single task typically takes about 10 minutes.

Price: Based on the pricing of the model called