Mirrored Language Structure and Innate Logic of the Human Brain as a Computable Model of the Oracle Turing Machine

TL;DR

This paper introduces a mirrored language structure and logic rules that model an Oracle Turing machine, proposing a biological-inspired computational framework that could solve P vs NP.

Contribution

It presents a novel mirrored language structure and logic rules that enable deterministic computers to simulate Oracle Turing machines, linking biological insights with computational theory.

Findings

MLS has biological and computational significance

Proposes an algorithm for relation learning and recognition

Suggests a pathway to solve P = NP

Abstract

We wish to present a mirrored language structure (MLS) and four logic rules determined by this structure for the model of a computable Oracle Turing machine. MLS has novel features that are of considerable biological and computational significance. It suggests an algorithm of relation learning and recognition (RLR) that enables the deterministic computers to simulate the mechanism of the Oracle Turing machine, or P = NP in a mathematical term.