Interdisciplinary Integration of Artificial Intelligence: Theoretical Framework and Application Practices of the Four-Phase Model

EasyChair Preprint 15958

Abstract

This paper proposes an interdisciplinary artificial intelligence integration framework —"Four-Phase Model"—that achieves deep coupling between symbolic systems and the physical world through symbolic emergence in liberal arts AI, pattern discovery in science AI, physical mapping in engineering AI, and manifold transition in cognitive synergy. The paper elaborates on the core mathematical models of each branch of AI, demonstrates their physical significance and application potential, and validates model effectiveness through case studies such as legal document generation and protein folding prediction. The research reveals that cognitive manifold equations and quantum phase transition criteria provide a unified theoretical foundation for cross- modal intelligent systems, propelling the evolution of artificial intelligence from domain-specific tools to general-purpose intelligent systems.

Keyphrases: Artificial intelligence integration framework, Evolution of general-purpose intelligent systems, Manifold transition in cognitive synergy, Pattern discovery in science AI, Physical mapping in engineering AI, Symbolic emergence in liberal arts AI

@booklet{EasyChair:15958,
  author    = {Xiaohui Zou},
  title     = {Interdisciplinary Integration of Artificial Intelligence: Theoretical Framework and Application Practices of the Four-Phase Model},
  howpublished = {EasyChair Preprint 15958},
  year      = {EasyChair, 2025}}