A Minimal Operational Demonstration of Lifecycle Positioning in AI Systems
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2026
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| _version_ | 1866901736006877184 |
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| author | Paton, Andrew John |
| author_facet | Paton, Andrew John |
| contents | <p>This paper provides a minimal operational demonstration of lifecycle positioning within artificial intelligence systems using the Paton System. A neural network is analysed through admissibility datum stabilisation recursive continuation constraint drift and boundary proximity using observable performance indicators. The results show that AI systems can be located within a structural lifecycle and diagnosed prior to failure. This establishes the Paton System as an operational diagnostic framework rather than a purely descriptive architecture.</p> <p> </p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19112805 |
| institution | Zenodo |
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| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | A Minimal Operational Demonstration of Lifecycle Positioning in AI Systems Paton, Andrew John Admissibility Artificial intelligence Neural networks System diagnostics Constraint drift Lifecycle analysis Paton System <p>This paper provides a minimal operational demonstration of lifecycle positioning within artificial intelligence systems using the Paton System. A neural network is analysed through admissibility datum stabilisation recursive continuation constraint drift and boundary proximity using observable performance indicators. The results show that AI systems can be located within a structural lifecycle and diagnosed prior to failure. This establishes the Paton System as an operational diagnostic framework rather than a purely descriptive architecture.</p> <p> </p> |
| title | A Minimal Operational Demonstration of Lifecycle Positioning in AI Systems |
| topic | Admissibility Artificial intelligence Neural networks System diagnostics Constraint drift Lifecycle analysis Paton System |
| url | https://doi.org/10.5281/zenodo.19112805 |