At 1 AM on April 3, OpenAI unveiled its groundbreaking AI Agent evaluation benchmark, PaperBench. This innovative benchmark assesses an agent's search, integration, and execution capabilities by challenging them to reproduce top-tier papers from the prestigious International Conference on Machine Learning (ICML) 2024. This rigorous process encompasses understanding paper content, coding, conducting experiments, and more. Based on test data shared by OpenAI, current agents derived from renowned large models have yet to match the prowess of top machine learning Ph.D. students, yet they offer significant value in aiding learning and comprehension of research content. PaperBench meticulously evaluates agents' comprehensive automation capabilities, from theory to practice, through detailed task modules and scoring criteria, ensuring fairness and precision throughout the evaluation process.