ASI进化：人工智能加速人工智能发展

摘要

人工智能能否加速其自身发展？尽管近期涌现的智能体系统在目标明确且反馈迅速的任务中表现出色，但其能否应对推动真实AI进步所需的高成本、长周期、弱监督的研究闭环仍属未知。我们提出ASI-Evolve——一个面向AI自我研究的智能体框架，通过"学习-设计-实验-分析"的循环机制实现闭环研究。该框架在标准进化智能体基础上融入两大核心组件：注入人类先验知识的认知库为每轮探索提供积累的智慧，以及将复杂实验结果提炼为可复用见解的专用分析器。据我们所知，ASI-Evolve是首个在AI开发三大核心领域（数据、架构、学习算法）均实现AI自主发现的统一框架。在神经架构设计方面，该系统发现了105个顶尖线性注意力架构，其中最优模型以0.97分的优势超越DeltaNet，增益达到近期人工改进成果的近3倍；在预训练数据构建任务中，进化出的数据流水线将基准测试平均性能提升3.96分，MMLU数据集上增益超18分；在强化学习算法设计领域，新发现算法在AMC32、AIME24和OlympiadBench上的表现分别较GRPO算法提升12.5分、11.67分和5.04分。我们进一步通过数学与生物医学实验提供初步证据，表明这种AI自我研究范式可迁移至AI技术栈之外的领域。这些成果共同证明，ASI-Evolve为实现AI在基础研发阶段自我加速迈出重要一步，为闭环AI研究的可行性提供了早期实证。

English

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, long-horizon, 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 3x 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. In reinforcement learning algorithm design, discovered algorithms outperform GRPO by up to +12.5 points on AMC32, +11.67 points on AIME24, and +5.04 points on OlympiadBench. We further provide initial evidence that this AI-for-AI paradigm can transfer beyond the AI stack through experiments in mathematics and biomedicine. Together, these results suggest that ASI-Evolve represents a promising step toward enabling AI to accelerate AI across the foundational stages of development, offering early evidence for the feasibility of closed-loop AI research.

ASI进化：人工智能加速人工智能发展

ASI-Evolve: AI Accelerates AI

摘要

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