Deterministic σ-Regularized Benchmarking of the Cekirge Model Against GPT-Transformer Baselines
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| Sprache: | Englisch |
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2025
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| author | CEKIRGE, Huseyin Murat |
| author_facet | CEKIRGE, Huseyin Murat |
| contents | <p>The <strong>Cekirge Method</strong> introduces a deterministic, algebraic paradigm for artificial intelligence—one that replaces iterative optimization with direct analytical resolution.<br>Instead of adjusting parameters through stochastic gradient descent, the method determines the optimal mapping between inputs and targets in a single closed-form computation.<br>Every training run yields identical outcomes, free from the randomness, noise, and instability inherent to conventional neural networks. By enforcing <strong>σ-regularization</strong>, the system guarantees numerical stability, bounded spectral energy, and reproducible behavior across platforms and hardware. This deterministic approach transforms learning from a process of random search into a solvable physical equation. It behaves like a stable mechanical system—elastic, self-damping, and energy-conserving—rather than a stochastic process prone to divergence. Small perturbations in the internal matrices produce proportionally small and predictable variations in loss, confirming that learning unfolds within a confined, thermodynamically bounded energy basin. The Cekirge framework therefore unites computational mathematics with physical law, showing that intelligence can be achieved through <strong>equilibrium rather than iteration</strong><strong>. </strong>Its advantages are multifold: training completes in a single analytical step, energy consumption drops by orders of magnitude, and reproducibility becomes absolute. All internal matrices remain invertible and transparent, making the system fully auditable and interpretable. Because the σ-regularization principle scales naturally to multi-head attention and large-matrix architectures, the method extends beyond toy models to industrial-scale applications. It offers a foundation for <strong>deterministic, energy-efficient, and ecologically responsible AI</strong>, where computation aligns with the fundamental symmetry and conservation principles of physics.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17504600 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Deterministic σ-Regularized Benchmarking of the Cekirge Model Against GPT-Transformer Baselines CEKIRGE, Huseyin Murat AI Deterministic learning Algebraic AI Cekirge Method GPT Benchmarking Energy-efficient Computation <p>The <strong>Cekirge Method</strong> introduces a deterministic, algebraic paradigm for artificial intelligence—one that replaces iterative optimization with direct analytical resolution.<br>Instead of adjusting parameters through stochastic gradient descent, the method determines the optimal mapping between inputs and targets in a single closed-form computation.<br>Every training run yields identical outcomes, free from the randomness, noise, and instability inherent to conventional neural networks. By enforcing <strong>σ-regularization</strong>, the system guarantees numerical stability, bounded spectral energy, and reproducible behavior across platforms and hardware. This deterministic approach transforms learning from a process of random search into a solvable physical equation. It behaves like a stable mechanical system—elastic, self-damping, and energy-conserving—rather than a stochastic process prone to divergence. Small perturbations in the internal matrices produce proportionally small and predictable variations in loss, confirming that learning unfolds within a confined, thermodynamically bounded energy basin. The Cekirge framework therefore unites computational mathematics with physical law, showing that intelligence can be achieved through <strong>equilibrium rather than iteration</strong><strong>. </strong>Its advantages are multifold: training completes in a single analytical step, energy consumption drops by orders of magnitude, and reproducibility becomes absolute. All internal matrices remain invertible and transparent, making the system fully auditable and interpretable. Because the σ-regularization principle scales naturally to multi-head attention and large-matrix architectures, the method extends beyond toy models to industrial-scale applications. It offers a foundation for <strong>deterministic, energy-efficient, and ecologically responsible AI</strong>, where computation aligns with the fundamental symmetry and conservation principles of physics.</p> |
| title | Deterministic σ-Regularized Benchmarking of the Cekirge Model Against GPT-Transformer Baselines |
| topic | AI Deterministic learning Algebraic AI Cekirge Method GPT Benchmarking Energy-efficient Computation |
| url | https://doi.org/10.5281/zenodo.17504600 |