| _version_ | 1866901211996749824 |
|---|---|
| author | Ben-Hur Varriano |
| author_facet | Ben-Hur Varriano |
| contents | <p>The objective evaluation of <strong>Large Language Models (LLMs)</strong> requires resilient methodologies capable of mitigating <strong>stochastic variance and stylistic biases</strong>. This paper formalizes the theoretical underpinnings of the <strong>Sapiens Benchmarking</strong> algorithm, a structured, automated, and scalable framework designed to assess language models via deterministic input-output mappings. We present a rigorous mathematical formalization of its core components, including an advanced topological normalization operator over character monoids, dynamic constraint mapping via language-specific system prompts, and a <strong>multi-stochastic evaluation</strong> metric denoted as the n-shot supremum probability. By defining exact algebraic conditions for syntactic equivalence and incorporating synthetic manifold generators for <strong>contextual length</strong> and <strong>mathematical precision</strong>, <strong>Sapiens Benchmarking</strong> offers a generalized, modular infrastructure suitable for both locally instantiated and API-distributed cognitive architectures. Real-world benchmarking paradigms validate the necessity of strict normalization and structural enforcement in model assessment.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19657379 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Sapiens Benchmarking: A Rigorous Mathematical Framework for Automated and Scalable Language Model Evaluation Ben-Hur Varriano <p>The objective evaluation of <strong>Large Language Models (LLMs)</strong> requires resilient methodologies capable of mitigating <strong>stochastic variance and stylistic biases</strong>. This paper formalizes the theoretical underpinnings of the <strong>Sapiens Benchmarking</strong> algorithm, a structured, automated, and scalable framework designed to assess language models via deterministic input-output mappings. We present a rigorous mathematical formalization of its core components, including an advanced topological normalization operator over character monoids, dynamic constraint mapping via language-specific system prompts, and a <strong>multi-stochastic evaluation</strong> metric denoted as the n-shot supremum probability. By defining exact algebraic conditions for syntactic equivalence and incorporating synthetic manifold generators for <strong>contextual length</strong> and <strong>mathematical precision</strong>, <strong>Sapiens Benchmarking</strong> offers a generalized, modular infrastructure suitable for both locally instantiated and API-distributed cognitive architectures. Real-world benchmarking paradigms validate the necessity of strict normalization and structural enforcement in model assessment.</p> |
| title | Sapiens Benchmarking: A Rigorous Mathematical Framework for Automated and Scalable Language Model Evaluation |
| url | https://doi.org/10.5281/zenodo.19657379 |