AI4S又一瓶颈被攻克：两个AI「吵架」，让科研代码部署成功率突破95%

Core Insights - The article discusses the challenges in deploying scientific software, emphasizing that most tools are published but not executable, which limits reproducibility and integration in scientific research [3][6][11] - The emergence of AI for Science (AI4S) highlights the need for tools that can interact seamlessly with AI systems, making the ability to run these tools a fundamental issue [8][9][10] - Deploy-Master is introduced as a solution to streamline the deployment process, focusing on creating a shared infrastructure that ensures tools are executable [12][35][37] Group 1: Challenges in Scientific Software - Scientific software often requires extensive manual effort to compile and run, leading to inefficiencies and reliance on individual expertise [4][5] - The deployment bottleneck persists despite advancements in containerization and cloud computing, affecting the usability of scientific software [7] - The lack of a systematic approach to convert tools into executable formats is identified as a structural barrier to the scalability of AI4S and Agentic Science [11][35] Group 2: Deploy-Master Overview - Deploy-Master is designed as a one-stop automated workflow centered on execution, addressing the entire deployment chain from discovery to execution [12] - The tool employs a multi-stage funnel process to filter and validate scientific tools, reducing an initial pool of 500,000 repositories to 52,550 candidates for automated deployment [15] - A dual model review mechanism is implemented to enhance the success rate of building specifications, achieving over 95% success in generating executable tools [22] Group 3: Deployment Insights - The deployment process is characterized by a long-tail distribution of build times, with most tools completing in around 7 minutes, but some requiring significantly longer due to complexity [25][26] - A diverse language distribution is observed among successfully deployed tools, with Python being the most prevalent, followed by C/C++, R, and Java [27][28] - Failure rates in builds are concentrated in specific areas, primarily due to inconsistencies in build processes and missing dependencies [31][32] Group 4: Future Implications - Deploy-Master's success in creating a large collection of executable tools provides a foundation for community agents and various master agents to operate effectively [35][36] - The methodology established by Deploy-Master can be applied beyond scientific computing to other software ecosystems, emphasizing the importance of a robust execution infrastructure [37]