A Structural Turbocharger for AI Development: Translation-Free High-Context Reasoning with a Native Zero-State via the MSHD–HSTG Framework
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2025
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| _version_ | 1866901572511858688 |
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| author | Matsuura, Yoshihito |
| author_facet | Matsuura, Yoshihito |
| contents | <p><span>Abstract</span></p> <p><span>Recent AI development has largely relied on translation-centered and optimization-driven</span></p> <p><span>reasoning architectures, in which internal decision processes are implicitly bound to linguistic</span></p> <p><span>representations and forced toward immediate commitment. While effective in low-context</span></p> <p><span>environments, this paradigm introduces structural inefficiencies, premature disambiguation,</span></p> <p><span>and instability under uncertainty.</span></p> <p><span>This work proposes a structural turbocharger framework for AI development, designed to</span></p> <p><span>reconfigure the structure and timing of decision-making without replacing existing models.</span></p> <p><span>The framework is inserted between model inference and output selection, where it constrains</span></p> <p><span>candidate actions through hierarchical dominance (MSHD), preserves explicit and implicit</span></p> <p><span>reasoning branches symmetrically (HSTG), and evaluates semantic proximity using a p-adic</span></p> <p><span>representation.</span></p> <p><span>A central contribution is the introduction of a native zero-state (Z), representing delib-</span></p> <p><span>erate decision suspension. Unlike conventional systems that interpret non-action as failure</span></p> <p><span>or missing data, the zero-state functions as a stable reasoning node, preventing premature</span></p> <p><span>collapse of the decision space while preserving future action paths.</span></p> <p><span>The framework enables translation-free structural reasoning, finite-depth stabilization,</span></p> <p><span>and reduced intermediate branching, thereby improving robustness, explainability, and re-</span></p> <p><span>source efficiency. It is orthogonal to existing architectures and can be attached to current</span></p> <p><span>language models, control systems, and edge AI deployments without retraining.</span></p> <p><span>Rather than positioning Japanese as a target language, this work treats high-context so-</span></p> <p><span>cietal operation as a completion environment in which hierarchical mediation and controlled</span></p> <p><span>non-action have been continuously exercised. We argue that encoding silence, finite-depth</span></p> <p><span>mediation, and decision suspension as first-class structural elements is a necessary step to-</span></p> <p><span>ward the next generation of safe, explainable, and socially compatible AI systems.</span></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18042517 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | A Structural Turbocharger for AI Development: Translation-Free High-Context Reasoning with a Native Zero-State via the MSHD–HSTG Framework Matsuura, Yoshihito AI architecture structural reasoning decision suspension zero-state high-context reasoning translation-free AI MSHD-HSTG AI safety explainable AI <p><span>Abstract</span></p> <p><span>Recent AI development has largely relied on translation-centered and optimization-driven</span></p> <p><span>reasoning architectures, in which internal decision processes are implicitly bound to linguistic</span></p> <p><span>representations and forced toward immediate commitment. While effective in low-context</span></p> <p><span>environments, this paradigm introduces structural inefficiencies, premature disambiguation,</span></p> <p><span>and instability under uncertainty.</span></p> <p><span>This work proposes a structural turbocharger framework for AI development, designed to</span></p> <p><span>reconfigure the structure and timing of decision-making without replacing existing models.</span></p> <p><span>The framework is inserted between model inference and output selection, where it constrains</span></p> <p><span>candidate actions through hierarchical dominance (MSHD), preserves explicit and implicit</span></p> <p><span>reasoning branches symmetrically (HSTG), and evaluates semantic proximity using a p-adic</span></p> <p><span>representation.</span></p> <p><span>A central contribution is the introduction of a native zero-state (Z), representing delib-</span></p> <p><span>erate decision suspension. Unlike conventional systems that interpret non-action as failure</span></p> <p><span>or missing data, the zero-state functions as a stable reasoning node, preventing premature</span></p> <p><span>collapse of the decision space while preserving future action paths.</span></p> <p><span>The framework enables translation-free structural reasoning, finite-depth stabilization,</span></p> <p><span>and reduced intermediate branching, thereby improving robustness, explainability, and re-</span></p> <p><span>source efficiency. It is orthogonal to existing architectures and can be attached to current</span></p> <p><span>language models, control systems, and edge AI deployments without retraining.</span></p> <p><span>Rather than positioning Japanese as a target language, this work treats high-context so-</span></p> <p><span>cietal operation as a completion environment in which hierarchical mediation and controlled</span></p> <p><span>non-action have been continuously exercised. We argue that encoding silence, finite-depth</span></p> <p><span>mediation, and decision suspension as first-class structural elements is a necessary step to-</span></p> <p><span>ward the next generation of safe, explainable, and socially compatible AI systems.</span></p> |
| title | A Structural Turbocharger for AI Development: Translation-Free High-Context Reasoning with a Native Zero-State via the MSHD–HSTG Framework |
| topic | AI architecture structural reasoning decision suspension zero-state high-context reasoning translation-free AI MSHD-HSTG AI safety explainable AI |
| url | https://doi.org/10.5281/zenodo.18042517 |