Scientific Foundation & Citation

This implementation is strictly based on the theoretical framework of Cognitional Mechanics (CM).

• Title: Operationalizing Cognitional Mechanics: A GPU/TPU-Compatible Framework for Non-Commutative Parallel Computation
• Author: T.O.
• Year: 2025
• DOI: 10.5281/zenodo.18071306

Citation

If you use this codebase for academic research or modeling, please cite it as: T.O. (2025). Operationalizing Cognitional Mechanics: A GPU/TPU-Compatible Framework for Non-Commutative Parallel Computation. Zenodo. https://doi.org/10.5281/zenodo.18071306

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Technical Linkage

The scripts provided in this repository (cm_core.py, etc.) are designed to verify the logical infallibility described in the Zenodo record.

• Physical Consistency: Ensures that semantic trajectories do not violate the metrics defined in CM.
• Irreversibility: Implements the non-commutative property as a structural resource for history-preserving computation.

Quantum Computing Was Not Inevitable: Operationalizing Non-Commutative Semantic Computing

Abstract

This repository provides a practical implementation of Cognitional Mechanics (CM), a deterministic computational framework that reclaims non-commutative operator composition from the physical monopoly of quantum mechanics. By treating the order of operations as a standalone structural resource, CM enables high-dimensional semantic processing on classical hardware (CPU/GPU/TPU) without the need for probabilistic collapse or physical quantization.

Core Axioms

• Non-Commutativity as Structure: Information order is the physical state. The sequence of operations defines the semantic trajectory.
• Abolition of Probability: Replaces stochastic wave-function collapse with the Logical Leap, a deterministic convergence regulator.
• Hardware Agnostic: Designed for parallel execution on conventional tensor cores (GPU/TPU) using standard linear algebra.

Key Components

• Semantic Manifold: States are represented as vectors where norm and direction define structural relations rather than probabilities.
• Deterministic Convergence: The C parameter regulates manifold coherence through a fixed re-projection mechanism.

Theoretical Reference

This implementation is based on:

T.O., Operationalizing Cognitional Mechanics: A GPU/TPU-Compatible Framework for Non-Commutative Parallel Computation, Zenodo, 2025. DOI: 10.5281/zenodo.18071306

Usage

Run the reference implementation to verify non-commutative state transitions:

python cm_core.py