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 fo