Kernelized Magnetic Descent: A Unified Framework for P vs NP with Temporal Division and Error Nucleus

This paper presents an advanced framework for addressing the P vs NP problem by integrating three original hypotheses: Double Negation, n-1 decomposition, and linguistic structure, alongside Temporal Division (neglected vs observed time) and an Error Nucleus. The Kernelized Magnetic Descent algorithm evaluates candidate solutions using a magnetic energy function Phi guiding the search toward the unique correct solution (Phi=0) while rapidly excluding incorrect candidates through the Error Nucleus. This integration allows structured exploration of NP-complete problems and potential polynomial-time convergence for practical instances.