Abstract
While AI systems demonstrate exponentially improving capabilities, the pace of AI research itself remains linearly bounded by human cognitive capacity, creating an increasingly severe development bottleneck. We present ASI-Arch, the first demonstration of Artificial Superintelligence for AI research (ASI4AI) in the critical domain of neural architecture discovery--a fully autonomous system that shatters this fundamental constraint by enabling AI to conduct its own architectural innovation. Moving beyond traditional Neural Architecture Search (NAS), which is fundamentally limited to exploring human-defined spaces, we introduce a paradigm shift from automated optimization to automated innovation. ASI-Arch can conduct end-to-end scientific research in the domain of architecture discovery, autonomously hypothesizing novel architectural concepts, implementing them as executable code, training and empirically validating their performance through rigorous experimentation and past experience. ASI-Arch conducted 1,773 autonomous experiments over 20,000 GPU hours, culminating in the discovery of 106 innovative, state-of-the-art (SOTA) linear attention architectures. Like AlphaGo's Move 37 that revealed unexpected strategic insights invisible to human players, our AI-discovered architectures demonstrate emergent design principles that systematically surpass human-designed baselines and illuminate previously unknown pathways for architectural innovation. Crucially, we establish the first empirical scaling law for scientific discovery itself--demonstrating that architectural breakthroughs can be scaled computationally, transforming research progress from a human-limited to a computation-scalable process. We provide comprehensive analysis of the emergent design patterns and autonomous research capabilities that enabled these breakthroughs, establishing a blueprint for self-accelerating AI systems.
Abstract (translated)
尽管人工智能系统的功能呈指数级增长，但其研究本身仍然受到人类认知能力的线性限制，导致发展瓶颈日益严重。我们提出了ASI-Arch，这是第一个用于AI研究（ASI4AI）的人工超级智能演示，在关键领域——神经架构发现中的应用——这是一个完全自主系统，通过使AI能够进行自我创新来打破这一根本约束。 超越传统基于人类定义空间的神经架构搜索（NAS），我们的方法引入了一种从自动化优化向自动化创新转变的范式。ASI-Arch可以在架构发现领域进行全面的科学研究，它能自主提出新颖的概念构想，并将这些想法实现为可执行代码，通过严格的实验和以往的经验进行训练与实证验证其性能。 在17,73个自主实验中，经过20,000小时GPU计算时间的努力，ASI-Arch发现了106种创新的、最先进的线性注意力架构。就像AlphaGo的第37步揭示了人类棋手无法预见的战略洞察一样，我们通过AI发现的架构也展示了系统性的超出人类设计基准的设计原则，并揭示了以前未知的架构创新途径。 尤为重要的是，我们建立了首个关于科学发现本身的实证规模法则——证明了架构突破可以被计算化扩展，从而将研究进步从受制于人的限制转变为可计算扩增的过程。我们对这些突破背后的自出现模式和自主科研能力进行了全面分析，并确立了一个自我加速AI系统的蓝图。
URL
https://arxiv.org/abs/2507.18074
