The Eholoko Fluxon Model (EFM) represents a methodological departure from traditional theoretical physics. It is rooted in the conceptual foundations of Dewey B. Larson's Reciprocal System Theory, but it stands on the shoulders of the vast empirical knowledge, datasets, and limiting-case frameworks established by the Standard Model, General Relativity, and ΛCDM cosmology. I hold deep appreciation for the academic community and this work would be entirely impossible without the public datasets and observational catalogs that academia has painstakingly created. Because of EFM’s non-traditional origins, I have chosen to bypass traditional peer-reviewed journals. Instead, I am presenting this work under a fully open-source, reproducible computational standard. I am not a formally trained mathematical physicist, and I do not present these papers as traditional analytical proofs (although some of the papers are framed that way in writing due to my prompting style). My methodology is that of a computational phenomenologist: I formulate conceptual postulates, implement them into GPU-accelerated non-linear field solvers (utilizing AI assistance to architect the JAX and PyTorch code), and test all assumptions by running high-fidelity simulations. The validation of EFM rests on the fact that these dimensionless simulations, when anchored to a single physical constant, output emergent scales—such as the speed of light, the hadron mass spectrum, atomic structure, the mass spectrum of the periodic table—that match observed public data with high concordance. I welcome and invite rigorous skepticism, auditing, and attempts at computational falsification. The entire codebase, simulation checkpoints (upon request as I prefer that researchers validate by conducting their own cosmogenesis simulations), and analysis notebooks are open-source and freely available for any researcher to run, modify, and test. github.com/BecomingPhill/eho…

The Eholoko Fluxon Model (EFM) represents a methodological departure from traditional theoretical physics. It is rooted in the conceptual foundations of Dewey B. Larson's Reciprocal System Theory, but it stands on the shoulders of the vast empirical knowledge, datasets, and limiting-case frameworks established by the Standard Model, General Relativity, and ΛCDM cosmology. I hold deep appreciation for the academic community and this work would be entirely impossible without the public datasets and observational catalogs that academia has painstakingly created. Because of EFM’s non-traditional origins, I have chosen to bypass traditional peer-reviewed journals. Instead, I am presenting this work under a fully open-source, reproducible computational standard. I am not a formally trained mathematical physicist, and I do not present these papers as traditional analytical proofs (although some of the papers are framed that way in writing due to my prompting style). My methodology is that of a computational phenomenologist: I formulate conceptual postulates, implement them into GPU-accelerated non-linear field solvers (utilizing AI assistance to architect the JAX and PyTorch code), and test all assumptions by running high-fidelity simulations. The validation of EFM rests on the fact that these dimensionless simulations, when anchored to a single physical constant, output emergent scales—such as the speed of light, the hadron mass spectrum, atomic structure, the mass spectrum of the periodic table—that match observed public data with high concordance. I welcome and invite rigorous skepticism, auditing, and attempts at computational falsification. The entire codebase, simulation checkpoints (upon request as I prefer that researchers validate by conducting their own cosmogenesis simulations), and analysis notebooks are open-source and freely available for any researcher to run, modify, and test. github.com/BecomingPhill/eho…

Just @antigravity and @GoogleAI forgetting to assign the free quota announced since last week to all African accounts as usual.

Decided that '/molecular imaging' is probably the best name to house this paper under. It's a precursor read for the HiV protease inhibitor paper and opens the door to industrial usage of the solver across many sectors. 'FluxonChem: A First-Principles Continuous Scalar Fluid Thermodynamic Engine for O(N log N ) Computational Chemistry and Applied Pharmacology' github.com/BecomingPhill/eho…

I have added a /applicable sciences folder to the /research folder which will house practical experiments on physical EFM applications. I started with the analysis of the HiV-1 protease using the O(N) solver built off the Fermion paper to see if I could find an inhibitor and if so, there was application for practical novel drug research. I believe there is. github.com/BecomingPhill/eho…

Today I present the Eholoko Fluxon Model Compendium Volume 1: This compendium is generated automatically from the underlying LaTeX source files across the research/ directory on gitHub. It curates exactly 50 core papers out of the broader tracking space. The specific paper selection criteria strictly prioritize: Only the most up-to-date iterations (omitting early drafts and superseded versions). The rigorous subset of research that has been concretely, computationally validated. Papers that collectively construct the contiguous theoretical hierarchy of the EFM across its density states (Ontology, N1, N2, and N3). Note on Ongoing Work: Generating this unified compendium is an active and iterative process. While the core physics and mathematical proofs are strictly intact, cleaning up the combined document structure, unifying cross-references between originally independent papers, and properly formatting the integrated bibliography is an ongoing effort. github.com/BecomingPhill/eho…

With zero empirical parameter fitting and zero machine-learning heuristics, the raw scalar thermodynamics of the EFM NLKG independently capture nearly 60% of the variance in aqueous solubility (R2=0.575), using only mass, specific phase friction, and scalar dipoles extracted from a pure vacuum simulation. The remaining variance is strictly accounted for by the known absence of explicit solvent dielectrics and solid-state crystal lattice energies, which will be integrated into the next iteration of this software. Meanwhile, the O(Nlog⁡N)O(NlogN) Mass Calibration (R2=0.955) proves the spatial topology engine is definitively flawless.

Replacing O(N4)O(N4) Quantum Chemistry with O(Nlog⁡N)O(NlogN) Fluid Thermodynamics. Standard Density Functional Theory (DFT) approaches the "Electron Correlation Problem" by calculating the interaction of every electron against every other electron. For complex molecules like Benzene with delocalized ππ-rings, enforcing the Pauli Exclusion Principle requires factorially exploding matrices that choke supercomputers. The Eholoko Fluxon Model (EFM) proves that Pauli Exclusion is not an abstract algebraic rule; it is simply Kinetic Phase Repulsion in a continuous scalar fluid. In this live solver running on a standard $15/month GPU, we bypassed Slater determinants entirely. We dropped a chaotic scalar fluid over 6 Carbon nuclei. The EFM's Non-Linear Klein-Gordon equations simply radiated the kinetic friction into the vacuum, allowing the fluid to autonomously seek its thermodynamic ground state. In seconds, the fluid natively solved the delocalized aromatic ring. The vacuum mass gap naturally hollowed out the center, and Pauli self-repulsion forced the fluid to distribute perfectly along the continuous perimeter track. We are not programming orbitals. We are computing the physical thermodynamics of the vacuum. The EFM offers a massively scalable, deterministic architecture for computational drug discovery and materials science. Full paper and reproducible PyTorch code available in the EFM Repository.