AI paper index
From Deterministic Microscopic Causation to the Macroscopic World: Class-Level Self-Reference and Historical Accumulation in Higher-Order Structures
One-line summary
An AI research paper on From Deterministic Microscopic Causation to the Macroscopic World: Class-Level Self-Reference and Historical Accumulation in Higher-Order Structures.
Engineering notes
Engineering notes will be added by the aipentium editorial team.
Chinese explanation / 中文解读
中文解读待补充:本站会优先为大语言模型、生成式AI、ChatGPT相关技术、计算机视觉、深度学习等高价值论文补充中文说明。
Original abstract
This work establishes a unified path from deterministic microscopic causal structures to macroscopic structures, memory, probability, and laws. Its central insight is that microscopic causal structures can compose into higher-order structures capable of class-level self-reference; structures of the same class can then accumulate through the evolution of an always-open reality; and this accumulation simultaneously changes the system’s present causal composition, allowing the past to enter the present, the present to constrain the future, and a macroscopic world with historical direction to emerge. This is no longer merely a philosophical conjecture. Accompanying this work are complete micro-to-macro derivations of 51 laws. Together, they show that the theory can repeatedly enter concrete systems and transform deterministic microscopic models into identifiable and calculable macroscopic phenomena and laws. On this basis, the author maintains that an important part of Hilbert’s sixth problem—how deterministic microscopic causal structures give rise to stable macroscopic structures and laws—has, in substance, been solved. This is not a claim that the entire axiomatization of physics has been completed. It is a declaration that one of its long-closed central paths has become traversable in practice. From microscopic causation to the macroscopic world, and from a single actual evolution to stable macroscopic laws: from this moment onward, the door stands open.Provide this file to ChatGPT in Work mode, then specify a macroscopic phenomenon or law to be derived from the microscopic level to the macroscopic level under this theory. The AI can then return a corresponding derivation by following the framework set out here.
Links and sources
Need this topic turned into a technical roadmap?
aipentium can prepare a custom AI literature review, code map, dataset map, and B2B technology assessment.
Request B2B AI research
Comments