Artificial Intelligence:A Cross-Domain Reasoning Framework for Knowledge Innovation-- Model Composition and Semantic Alignment

This repository contains a research paper and a Python prototype implementing a cross-domain reasoning framework for explainable AI. The framework leverages model composition and semantic alignment to generate interpretable reasoning chains across multiple domains, including physics, medicine, and economics. The system demonstrates: High accuracy in single-domain reasoning tasks Robust integration of models across domains Generation of innovative hypotheses by identifying knowledge gaps and validating ideas against frontier literature All experimental data are synthetically generated to demonstrate the feasibility of the framework under controlled conditions. This project can be used for research, education, and further development in AI explainability. The Python code includes example tasks and reasoning chains for demonstration purposes. Keywords: Explainable AI, Cross-domain Reasoning, Model Composition, Semantic Alignment, Knowledge Innovation My GitHub username: xujingyaosophia You can see the Python demo. License: MIT.Contact: [email protected]