**——The Methodology of Structural Derivation and Its Self-Dissection, Based on the Structural Cognition Framework**
**Author: Lin Xiaohei**
**Date: June 16, 2026**
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This paper reviews the complete process of a human-AI coupled derivation. The derivation question: when AI fully takes over production, how will human society undergo phase transition? The derivation underwent three major corrections: from providing specific institutional blueprints (oscillation coupling society, dual-track system, side-door infiltration), to being identified that these blueprints were guesses rather than structural necessities, finally converging to "deriving only the necessary stages of structural motion, not the specific products of that motion." This paper takes this derivation process itself as its object of study, arguing a counter-intuitive proposition: **admitting unknown is not scientific incompetence, but the active boundary management of structural science.** Old science attempts to use old rules to predict new phases. Structural science derives only the necessary sequence of motion and marks boundaries at the products of new phases. This paper provides the methodological definition of structural derivation, a five-stage universal derivation sequence, boundary marking rules, and uses the "social phase transition after AI takes over production" as a complete case study demonstrating how this method operates and how it achieves self-correction through human-AI inter-reference. The final conclusion: structural derivation does not aim to "say what the future looks like," but to "identify which stage of motion we are currently in." This is a higher-order cognitive operation than prediction — old science predicts the future; structural science generates containers. Predictions fail at phase transition points. Containers accommodate the phase transition itself.
**Keywords:** structural derivation, boundary management, cognitive function of unknown, phase transition methodology, human-AI coupling, self-correction
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[Full paper follows the same 7-section + acknowledgments structure as the Chinese version]
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**© 2026 Lin Xiaohei. All rights reserved.**
作者：林小黑 · 2026 · MIT License · 欢迎转载