Current AI agents excel at combining existing techniques but lack the consistency and genuine innovation needed for autonomous research; improving their performance requires better experience management and more stable inference strategies, not just larger models.
This paper evaluates how well AI agents can autonomously improve technical systems through long-horizon experimentation. Rather than just looking at final scores, the researchers developed a framework to analyze what happens during the process—how agents formulate solutions, execute them, and learn from feedback.