用无限时间图灵机分析深度学习瓶颈，提出可动态演化的通用状态机。
Infinite Time Turing Machines and their Applications
- 引入无限时间图灵机，拓展计算到超限序数步骤
- 揭示Transformer等架构在可扩展性与效率上的根本局限
- 提出实时演化的查询式计算图，支持模块化与可解释性
本研究通过无限时间图灵机（ITTMs）为深度学习系统建立严格的理论分析基础，将经典计算拓展至超限序数步。利用ITTMs重新审视现代架构如Transformer，揭示其在可扩展性、效率与可解释性方面的根本限制。基于这些洞察，提出从零开始设计的通用状态机（USM），采用动态可查询的计算图实时演化，实现模块化、可解释且资源高效的计算。该框架不仅克服现有模型的低效与僵化，还为构建可扩展、泛化的人工智能系统奠定基础。
原文摘要 · Abstract (English)
This work establishes a rigorous theoretical foundation for analyzing deep learning systems by leveraging Infinite Time Turing Machines (ITTMs), which extend classical computation into transfinite ordinal steps. Using ITTMs, we reinterpret modern architectures like Transformers, revealing fundamental limitations in scalability, efficiency, and interpretability. Building on these insights, we propose the Universal State Machine (USM), a novel computational paradigm designed from first principles. The USM employs a dynamic, queryable computation graph that evolves in real time, enabling modular, interpretable, and resource-efficient computation. This framework not only overcomes the inefficiencies and rigidity of current models but also lays the groundwork for scalable, generalizable artificial intelligence systems.
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