Artificial Intelligence:A Cross-Domain Reasoning Framework for Knowledge Innovation-- Model Composition and Semantic Alignment
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2025
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| _version_ | 1866901272431427584 |
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| author | Xu, Jingyao |
| author_facet | Xu, Jingyao |
| contents | <p>This repository contains a research paper and a Python prototype implementing a <strong>cross-domain reasoning framework for explainable AI</strong>. The framework leverages <strong>model composition</strong> and <strong>semantic alignment</strong> to generate interpretable reasoning chains across multiple domains, including physics, medicine, and economics.</p> <p>The system demonstrates:</p> <ul> <li> <p>High accuracy in single-domain reasoning tasks</p> </li> <li> <p>Robust integration of models across domains</p> </li> <li> <p>Generation of innovative hypotheses by identifying knowledge gaps and validating ideas against frontier literature</p> </li> </ul> <p>All experimental data are <strong>synthetically generated</strong> to demonstrate the feasibility of the framework under controlled conditions.</p> <p>This project can be used for research, education, and further development in <strong>AI explainability</strong>. The Python code includes example tasks and reasoning chains for demonstration purposes.</p> <p><strong>Keywords:</strong> Explainable AI, Cross-domain Reasoning, Model Composition, Semantic Alignment, Knowledge Innovation</p> <p>My GitHub username: xujingyaosophia</p> <p>You can see the Python demo in my GitHub(still version 1).</p> <p>License: MIT.<br>Contact: sophiaxu069@gmail.com</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17309895 |
| institution | Zenodo |
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| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Artificial Intelligence:A Cross-Domain Reasoning Framework for Knowledge Innovation-- Model Composition and Semantic Alignment Xu, Jingyao <p>This repository contains a research paper and a Python prototype implementing a <strong>cross-domain reasoning framework for explainable AI</strong>. The framework leverages <strong>model composition</strong> and <strong>semantic alignment</strong> to generate interpretable reasoning chains across multiple domains, including physics, medicine, and economics.</p> <p>The system demonstrates:</p> <ul> <li> <p>High accuracy in single-domain reasoning tasks</p> </li> <li> <p>Robust integration of models across domains</p> </li> <li> <p>Generation of innovative hypotheses by identifying knowledge gaps and validating ideas against frontier literature</p> </li> </ul> <p>All experimental data are <strong>synthetically generated</strong> to demonstrate the feasibility of the framework under controlled conditions.</p> <p>This project can be used for research, education, and further development in <strong>AI explainability</strong>. The Python code includes example tasks and reasoning chains for demonstration purposes.</p> <p><strong>Keywords:</strong> Explainable AI, Cross-domain Reasoning, Model Composition, Semantic Alignment, Knowledge Innovation</p> <p>My GitHub username: xujingyaosophia</p> <p>You can see the Python demo in my GitHub(still version 1).</p> <p>License: MIT.<br>Contact: sophiaxu069@gmail.com</p> |
| title | Artificial Intelligence:A Cross-Domain Reasoning Framework for Knowledge Innovation-- Model Composition and Semantic Alignment |
| url | https://doi.org/10.5281/zenodo.17309895 |