代理工具
Iris Drives ML Experiments with a Revisable Knowledge State, Achieving a 64.9% Medal Rate on MLE-Bench in 12 Hours
Rather than repeatedly modifying code along a tree of candidate solutions, Iris first investigates critical unknowns and then organizes evidence across experiments into claims that can be added, updated, or retracted. The paper reports a higher medal rate on the full MLE-Bench in half the time allotted to its direct baseline, but the system’s source code and complete execution traces have not been released.