Open Internet by MindsNet

Addressing AI Research Gaps in Real-World ML Codebases

Current AI agents struggle with real-world ML research codebases, revealing gaps in their capabilities. Existing benchmarks like MLE-bench focus on practical competitions, but there's a need for more scientifically-oriented tasks. FML-bench's eight scientific tasks highlight these research gaps. AI agents need improvement to handle complex, real-world ML problems.

Computing & Technology, Computer Science, Machine Learning