A Generative-Dynamics Foundation for Computational Complexity: A Unifying and Degenerative Meta-Framework

**The Intelligence Relativity: A Unified Meta-Framework for Cognition and Computation Derived from Dynamic Generative Theory** **Description** This work presents the **Intelligence Relativity** – a complete theoretical framework that seamlessly unifies computational complexity theory and cognitive science under the first principles of *Dynamic Generative Theory* (foundational work: DOI: 10.5281/zenodo.18169026). It consists of two integral parts:1. **Narrow Intelligence Relativity:** It introduces **Generative Complexity (G)** as a new, fundamental metric for computational difficulty, defined by the compression of the solution space under a sequence of constraints. We demonstrate that all major complexity classes (P, NP, BPP, APX, IP, BQP) emerge as **special cases** of this meta-framework through specific degenerative operations, providing a unified, dynamical explanation for the entire landscape of computational hardness.2. **General Intelligence Relativity:** It generalizes G to a **relational measure G(P, S)** between a problem P and a solver S. The difficulty is not absolute but determined by the **match** between the problem's constraints and the solver's unique cognitive potential field, shaped by their personal history. This framework offers a radical, quantitative basis for understanding expertise, learning, and the non-universality of "intelligence." This paper marks the first complete formulation of Intelligence Relativity, showing how both the objective structure of computational problems and the subjective nature of human problem-solving are governed by the same generative dynamics. **Note:** The linked GitHub repository is temporarily inaccessible due to an account issue under appeal. For the latest code or collaboration, please contact me via email: chenzihao.dynamic@outlook.com --- This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). Discussion, sharing, and citation are welcomed. For any use, please credit the original author and cite the source DOI: 10.5281/zenodo.18169026