New AI framework autonomously optimizes training data, architectures and algorithms — outperforming human baselines

AI R&D runs on a cycle of hypothesis, experiment, and analysis — each step demanding substantial manual engineering effort. A new framework from researchers at SII-GAIR aims to close that bottleneck by automating the full optimization loop for training data, model architectures, and learning algorithms.A new framework called ASI-EVOLVE, developed by researchers at the Generative Artificial Intelligence Research Lab (SII-GAIR), aims to solve this bottleneck. Designed as an agentic system for AI-for-AI research, it uses a continuous "learn-design-experiment-analyze" cycle to automate the optimization of the foundational AI stack.In experiments, this self-improvement loop autonomously discovered novel designs that significantly outperformed state-of-the-art human baselines. The system generat