arXiv:2506.23080cs.AI2025-06

AI正经历认知演进,从语言模型迈向可计算的思维系统。

AI's Euclid's Elements Moment: From Language Models to Computable Thought

  • 构建五阶段认知演化框架,类比人类文明的符号革命。
  • 当前进入自省阶段,链式思考与宪法型AI初现端倪。
  • 未来将发展可证明对齐的神经符号系统,实现自我重构。

本文提出一个涵盖五个阶段的认知演化框架,用以理解人工智能的发展轨迹。该框架认为AI的演进类似于人类认知技术的历史进程:从楔形文字、字母表、语法逻辑、数学微积分到形式逻辑系统。这一‘认知几何’模型不仅解释了从专家系统到Transformer的架构变迁,更指明了未来的具体路径。关键在于,这种演进具有反馈性:随着AI能力提升,其自身工具和洞察力会反向重塑底层架构。我们正处于‘元语言时刻’,特征是链式思考提示和宪法型AI的出现。接下来的‘数学符号时刻’和‘形式逻辑系统时刻’将催生可计算的思维算法,通过神经符号架构与程序合成实现,最终达成可证明对齐且可靠的AI,能够重建自身的基础表示。本研究作为三部曲的收官之作,此前已探讨动机(为何)与本质(是什么),本篇聚焦‘如何’,为未来研究提供理论基础,并为初创企业与开发者提供可操作的战略指引。

原文摘要 · Abstract (English)

This paper presents a comprehensive five-stage evolutionary framework for understanding the development of artificial intelligence, arguing that its trajectory mirrors the historical progression of human cognitive technologies. We posit that AI is advancing through distinct epochs, each defined by a revolutionary shift in its capacity for representation and reasoning, analogous to the inventions of cuneiform, the alphabet, grammar and logic, mathematical calculus, and formal logical systems. This "Geometry of Cognition" framework moves beyond mere metaphor to provide a systematic, cross-disciplinary model that not only explains AI's past architectural shifts-from expert systems to Transformers-but also charts a concrete and prescriptive path forward. Crucially, we demonstrate that this evolution is not merely linear but reflexive: as AI advances through these stages, the tools and insights it develops create a feedback loop that fundamentally reshapes its own underlying architecture. We are currently transitioning into a "Metalinguistic Moment," characterized by the emergence of self-reflective capabilities like Chain-of-Thought prompting and Constitutional AI. The subsequent stages, the "Mathematical Symbolism Moment" and the "Formal Logic System Moment," will be defined by the development of a computable calculus of thought, likely through neuro-symbolic architectures and program synthesis, culminating in provably aligned and reliable AI that reconstructs its own foundational representations. This work serves as the methodological capstone to our trilogy, which previously explored the economic drivers ("why") and cognitive nature ("what") of AI. Here, we address the "how," providing a theoretical foundation for future research and offering concrete, actionable strategies for startups and developers aiming to build the next generation of intelligent systems.

认知架构神经符号可证明对齐

Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品;中文卡片由大模型生成,请以原文为准。