ASI-Evolve: AI Accelerates AI

Can AI accelerate the development of AI itself? While recent agentic systems have shown strong performance on well-scoped tasks with rapid feedback, it remains unclear whether they can tackle the costly, longhorizon, and weakly supervised research loops that drive real AI progress. We present ASI-EVOLVE, an agentic framework for AI-for-AI research that closes this loop through a learn–design–experiment–analyze cycle. ASI-EVOLVE augments standard evolutionary agents with two key components: a cognition base that injects accumulated human priors into each round of exploration, and a dedicated analyzer that distills complex experimental outcomes into reusable insights for future iterations. To our knowledge, ASI- EVOLVE is the first unified framework to demonstrate AI-driven discovery across three central components of AI development: data, architectures, and learning algorithms. In neural architecture design, it discovered 105 SOTA linear attention architectures, with the best discovered model surpassing DeltaNet by +0.97 points, nearly 3× the gain of recent human-designed improvements. In pretraining data curation, the evolved pipeline improves average benchmark performance by +3.96 points, with gains exceeding 18 points on MMLU.

Introduction. Artificial intelligence (AI) advances through many interacting factors; data, model architectures, and learning algorithms are three central research components. Progress in each of these directions depends on repeated cycles of hypothesis generation, implementation, experimentation, and analysis (Ghareeb et al., 2025). In practice, however, these cycles are constrained by multidimensional human bottlenecks (Zhang et al., 2025): the hypothesis space humans can explore in parallel is severely limited (Liu et al., 2025a), experimental workflows demand substantial manual effort and frequent intervention (Feng et al., 2025), and the accumulation of insights across iterations often depends on individual experience and intuition, making knowledge difficult to systematically preserve and transfer (Kosmyna et al., 2025). Together, these constraints fundamentally limit the pace and scale of progress in AI development, raising a central question: can AI accelerate the development of AI itself?

Discussion / Conclusion. In this paper, we presented ASI-EVOLVE, an agentic evolution framework that enables AI to carry out end-to-end autonomous scientific research. Through controlled comparisons against existing evolutionary baselines and systematic ablation studies, we verified that the framework design is effective: equipped with a structured cognition base and a dedicated analyzer, the system achieves rapid cold-start and sustains continuous improvement, reliably reaching SOTA-level results. We further explored whether AI can accelerate its own research pipeline across each stage of the scientific process. The closed learn–design–experiment–analyze loop enables efficient self-improvement, and we demonstrate breakthroughs across three central components of AI development—model architecture, training data, and training algorithms—each posing substantial challenges in terms of implementation complexity, iteration cost, and indirect feedback.