Structural Theory of AI Reasoning — From LLM Unfolding to Structural Localization and Living Reasoning
Year: 2026
Type: article
Abstract: Structural Theory of AI Reasoning develops a structural explanation of AI reasoning that connects the latent and distributed reasoning capabilities of Large Language Models with the explicit localization, traversal, gap bridging, forward extension, validation, persistence, and structural growth mechanisms of Structural Intelligence. The repository distinguishes reasoning answers from reasoning structure growth, interprets LLM reasoning through a Folding–Unfolding framework, introduces the complementary relationship between LLM Unfolding and Structural Intelligence Localization, and develops a Living Reasoning Loop in which validated reasoning outputs can become persistent infrastructure for future reasoning. The framework further explores future AI reasoning architectures based on heterogeneous Per-Node intelligence, Differential Trees, Calling Graphs, TaskGraphs, Action Calling Graphs, CCCs, Structural Runtimes, Runtime Invariants, specialized Unfolding mechanisms, and governed autonomous structural growth. Its central architectural thesis is that future AI reasoning may increasingly emerge from the cooperation of Folded Intelligence, explicit Structural Intelligence, and a reasoning runtime: LLM Unfolding ↔ Structural Localization, followed by reasoning, validation, structural growth, and future reasoning.
Author: Tan, Sizhe
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7204472994
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