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

• URL: http://arxiv.org/abs/2506.23080v2
• Date: Thu, 10 Jul 2025 09:48:50 GMT
• Title: AI's Euclid's Elements Moment: From Language Models to Computable Thought
• Authors: Xinmin Fang, Lingfeng Tao, Zhengxiong Li,
• Abstract summary: This paper presents a comprehensive five-stage evolutionary framework for understanding the development of artificial intelligence.<n>We posit that AI is advancing through distinct epochs, each defined by a revolutionary shift in its capacity for representation and reasoning.
• Score: 2.1142253753427402
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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