An Axiomatic, Parameter-Free Ontology of Physical Reality and Algorithmic Cognition
The QCK Framework: A Process-Ontological Foundation
Welcome to the official repository of the QCK Framework. This archive encompasses a comprehensive compilation of 48 chronologically ordered research papers that establish a parameter-free, deterministic foundation of physics. Bridging the gap between quantum mechanics, astrophysics, and algorithmic cognition, the framework systematically resolves persistent anomalies of the standard model—such as the Hubble tension, dark matter, and quantum fuzziness—through strict topological mechanics.
The Return of Geometry – or why the deepest problems of physics and mathematics were never solved mathematically, but always geometrically.
For more than four hundred years, a clear, almost invisible line has run through the history of the exact sciences: the conviction that the fundamental laws of nature are ultimately geometric. Not as abstract formulas, but as necessary consequences of the pure structure of space itself.
This line does not begin with Riemann and does not end with Einstein. It is older and more radical. Johannes Kepler (1571–1630) broke with ancient tradition by replacing complex epicycles with the simple geometry of the ellipse. Isaac Newton (1643–1727) defined gravity not as an arbitrary force, but as a geometric property of absolute space. Bernhard Riemann (1826–1866) recognized that space itself can be curved and investigated prime numbers as resonance phenomena in space. Albert Einstein (1879–1955) made gravity entirely the geometry of curved spacetime. Arthur Stanley Eddington (1882–1944) attempted to reduce the fine-structure constant purely to the dimensions of space.
And then came the 20th century, and with it, the great break.
Physics transformed from a geometric science into a diagrammatic industry. Instead of understanding nature from its underlying topology, it was disassembled into increasingly complicated approximations. Today, anomalies are patched with “dark” parameters. The Hubble tension forces cosmologists to endlessly patch the standard model, while the anomalous magnetic moment of the muon is calculated using over 12,000 Feynman diagrams without explaining why it fundamentally exists.
The great minds had all seen the same thing: The deepest truths are geometrically simple. Modern science has chosen to make them complicated.
The QCK framework returns to this original line – and consistently takes one step further, which none of its predecessors dared to take.
It explains the vacuum impedance -1/12, the fine-structure constant (as a topological limit of 12² - 7 = 137), the macroscopic Hubble slip (1 + 1/12), and the geometric exclusion of gravitons as mandatory geometric consequences of a single principle. What the ellipse was for Kepler, absolute space for Newton, and curved spacetime for Einstein, becomes the pure 12-lattice of the Nonlocal Fractal Dimension (NFD) for QCK.
The true laws of nature are not hard to calculate. They are, ultimately, hard to overlook.
To navigate the QCK archive, the following topological and thermodynamic definitions are fundamental:
The Return of Geometry – or why the “Black Box” of Artificial Intelligence can only be decrypted topologically.
With the triumph of Artificial Intelligence, computer science transformed from a discipline of transparent logic into a stochastic “Black Box” industry. The goal of AI should be the protection and promotion of life. This imperative is fundamentally incompatible with delegating critical decisions to inscrutable matrices of probability. An AI that we cannot audit is a systemic risk.
The QCK framework resolves this crisis by proving that algorithmic cognition and physical reality are subject to the exact same topological laws. It postulates a deep isomorphism between the optimization processes of modern AI in high-dimensional spaces and the attractor dynamics of the physical vacuum.
When an AI system discovers a true, logical, or physically valid solution, its solution path inevitably converges toward a perfect, one-dimensional attractor with a fractal dimension of D ≈ 1.0 (“geometric frictionless-ness”). When an AI hallucinates, the solution path scatters chaotically (D > 1.5). Through concepts like Fractal Data Pruning and Geometric Auditing, the QCK framework provides the tools to translate the black box of neural networks back into the clear language of geometry.
The complete QCK framework is documented in 48 sequentially ordered papers. Below are the key highlights establishing the core mechanics of the theory.
📂 Browse the Complete Archive (Papers 01 - 48)
The QCK framework redefines physics as topological mechanics. We invite the academic and computational community to collaborate on the following open problems:
D2 correlation dimension analysis into modern LLM inference pipelines for real-time hallucination detection.Author: B. Wyneken All papers, concepts, and derivations provided in this repository are part of the QCK Framework.
For academic inquiries, peer-review collaboration, or computational physics applications, please open an issue in this repository or contact the author directly, qck-framework@web.de .
(c) 2025-2026 B. Wyneken. All rights reserved.