Jin10 data reported on April 3rd, at 1 AM today, OpenAI released a brand new AI Agent evaluation benchmark - PaperBench. This benchmark primarily assesses agents’ capabilities in search, integration, and execution, requiring the reproduction of top papers from the 2024 International Conference on Machine Learning, including understanding of the paper content, code writing, and experimental execution. According to the test data released by OpenAI, currently, well-known large models’ built agents are still unable to outperform top machine learning PhDs. However, they are very helpful in assisting learning and understanding research content.
This page may contain third-party content, which is provided for information purposes only (not representations/warranties) and should not be considered as an endorsement of its views by Gate, nor as financial or professional advice. See Disclaimer for details.
OpenAI Open Source PaperBench, Redefining Top AI Agent Evaluation
Jin10 data reported on April 3rd, at 1 AM today, OpenAI released a brand new AI Agent evaluation benchmark - PaperBench. This benchmark primarily assesses agents’ capabilities in search, integration, and execution, requiring the reproduction of top papers from the 2024 International Conference on Machine Learning, including understanding of the paper content, code writing, and experimental execution. According to the test data released by OpenAI, currently, well-known large models’ built agents are still unable to outperform top machine learning PhDs. However, they are very helpful in assisting learning and understanding research content.