Researchers at SII-GAIR unveiled ASI-EVOLVE, an agentic AI-for-AI system that runs a continuous learn-design-experiment-analyze loop to optimize the full foundation-model stack. In tests it created novel linear attention architectures, improved pretraining pipelines, and designed reinforcement learning algorithms—boosting benchmark scores by up to 18-plus points—while reducing the need for constant human intervention.
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