CORE-Bench: A Benchmark Consisting of 270 Tasks based on 90 Scientific Papers Across Computer Science, Social Science, and Medicine with Python or R Codebases

Practical Solutions and Value of CORE-Bench AI Benchmark **Addressing Computational Reproducibility Challenges** It can be challenging to reproduce scientific research due to software versions, machine differences, and compatibility issues. **Automating Research Reproduction with AI** AI allows for autonomous research, highlighting the importance of replicating existing studies for comparison. **Introducing CORE-Bench Benchmark** CORE-Bench by Princeton University includes 270 tasks from 90 papers, assessing coding, retrieval, and tool skills in Python and R. **Tiered Difficulty Levels** CORE-Bench offers Easy, Medium, and Hard tiers to test agent abilities based on provided information. **Comprehensive Evaluation of Agent Skills** Tasks cover text and image-based outputs, challenging agents to interpret scientific results effectively. **Enhancing Reproducibility with AI Agents** CORE-Bench shows how task-specific AI agents like CORE-Agent can accurately reproduce scientific work. **Catalyzing Research with CORE-Bench** Automating computational reproducibility with CORE-Bench improves agent capabilities and streamlines research processes. For AI adoption and consultation, contact us at hello@itinai.com. Join our community on Twitter, Telegram Channel, and LinkedIn Group for updates. **AI Implementation Guidelines** Learn how AI can enhance operations by identifying automation opportunities, defining KPIs, selecting suitable AI solutions, and implementing them gradually. For insights on leveraging AI, follow us on Telegram or Twitter. Explore AI solutions for sales processes and customer engagement at itinai.com. List of Useful Links: AI Lab in Telegram @itinai – free consultation Twitter – @itinaicom