Autonomous Research Engineering overview

Running open-ended ML/AI research experiments end-to-end — reading a problem statement, designing experiments, writing/modifying training or evaluation code, launching jobs, interpreting metrics, and iterating against a held-out scoring function (as exercised by RE-Bench-style evaluations).