Teaching AI to Improve AI: From Experimental Feedback to Meta-Evolution

Talk overview

Large language models are evolving from coding assistants to research agents capable of proposing solutions, running experiments, and iteratively improving results. This talk discusses how execution-based experimentation can be transformed into reusable improvement capabilities, using the AI4AI loop and case studies from NatureBench. It also explores the potential for recursive self-improvement in AI systems.