| _version_ | 1866901892881186816 |
|---|---|
| author | Kanazawa, Ryuki |
| author_facet | Kanazawa, Ryuki |
| contents | <p>Large language models (LLMs) typically respond to prompts such as “act as an expert” or “explain step-by-step,” which modify surface-level behavior but do not alter the underlying structure of reasoning.</p> <p>This study introduces a Time-First generative sequence (Time → Space → Consciousness) as a <strong>structural basis for reasoning</strong>, and examines how LLMs behave when this structure is applied directly. Unlike conventional prompting, this approach provides a <em>generative principle</em> that governs the starting point, direction, and stability of reasoning.</p> <p>Across four different models—ChatGPT, Gemini, Claude, and Perplexity—the following common patterns were observed:</p> <ul> <li> <p>Reasoning converges into a consistent flow: “process → stabilization → evaluation”</p> </li> <li> <p>Higher coherence and reproducibility, with increased depth in structural interpretation</p> </li> <li> <p>Responses shift from surface explanations to causal, structural reinterpretations</p> </li> <li> <p>Significant improvements in originality and extendability compared with baseline prompts</p> </li> </ul> <p>These observations suggest that a Time-First Structural Prompt can influence LLM reasoning at a foundational level. The findings provide preliminary insights toward a new research direction: <strong>Structural Prompting</strong>, which aims to modify the generative principles behind LLM thought processes.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17796407 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Time-First Structural Prompting: A New AI Prompt Framework for Enhancing LLM Reasoning Kanazawa, Ryuki Time-Primary Dimension Time-First Theory Self-Consistency Alignment AI Prompt Stability High-Dimensional Dynamics Temporal Constraint System Emergent Order AI Behavior Modeling Future-State Guidance Theoretical Framework <p>Large language models (LLMs) typically respond to prompts such as “act as an expert” or “explain step-by-step,” which modify surface-level behavior but do not alter the underlying structure of reasoning.</p> <p>This study introduces a Time-First generative sequence (Time → Space → Consciousness) as a <strong>structural basis for reasoning</strong>, and examines how LLMs behave when this structure is applied directly. Unlike conventional prompting, this approach provides a <em>generative principle</em> that governs the starting point, direction, and stability of reasoning.</p> <p>Across four different models—ChatGPT, Gemini, Claude, and Perplexity—the following common patterns were observed:</p> <ul> <li> <p>Reasoning converges into a consistent flow: “process → stabilization → evaluation”</p> </li> <li> <p>Higher coherence and reproducibility, with increased depth in structural interpretation</p> </li> <li> <p>Responses shift from surface explanations to causal, structural reinterpretations</p> </li> <li> <p>Significant improvements in originality and extendability compared with baseline prompts</p> </li> </ul> <p>These observations suggest that a Time-First Structural Prompt can influence LLM reasoning at a foundational level. The findings provide preliminary insights toward a new research direction: <strong>Structural Prompting</strong>, which aims to modify the generative principles behind LLM thought processes.</p> |
| title | Time-First Structural Prompting: A New AI Prompt Framework for Enhancing LLM Reasoning |
| topic | Time-Primary Dimension Time-First Theory Self-Consistency Alignment AI Prompt Stability High-Dimensional Dynamics Temporal Constraint System Emergent Order AI Behavior Modeling Future-State Guidance Theoretical Framework |
| url | https://doi.org/10.5281/zenodo.17796407 |