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

A Generative-Dynamics Foundation for Computational Complexity: A Unifying and Degenerative Meta-Framework Description This work introduces Generative Complexity – a new paradigm for measuring computational difficulty, derived as a direct and necessary application of Dynamic Generative Theory (see foundational framework: DOI: 10.5281/zenodo.18169026). It argues that the true difficulty of a problem lies not in counting steps within a pre-existing solution space, but in the generative work required for the solution framework to emerge from a potential field under the driving force of constraints (modeled as "correlation potential differences"). Its central contribution is the Degeneration Map: it demonstrates that all major classical and quantum complexity classes (P, NP, BPP, APX, IP, BQP) can be logically recovered as special cases of this more fundamental, dynamical meta-framework. Each class results from applying specific degenerative operations (e.g., stripping determinism, ignoring process, losing directionality, or misinterpreting quantumness) to the full generative dynamics. This provides a unified, causal explanation for the landscape of computational complexity, reorienting the P vs. NP question toward the physical simulability of the generative process itself. 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