SIUC: A Multi-Scale Coherence Framework for Predicting Stability in AI-Generated Systems
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| Format: | Recurso digital |
| Sprache: | Englisch |
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2026
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| _version_ | 1866901217217609728 |
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| author | St-Louis, Christian |
| author_facet | St-Louis, Christian |
| contents | <p><strong>SIUC: A Multi-Scale Coherence Framework for Predicting Stability in AI-Generated Systems</strong> introduces a general theoretical framework for understanding how coherence emerges, propagates, and fails across structural, dynamic, and geometric scales in AI‑generated system software. As autonomous coding agents increasingly produce multi‑layered runtimes that span high‑level APIs, dispatch layers, memory managers, and GPU execution paths, traditional engineering methods struggle to anticipate cross‑layer inconsistencies and emergent fragilities.</p> <p>The SIUC framework provides a multi‑scale lens for analyzing these systems, distinguishing three axes of coherence and examining how alignment across local, mesoscopic, and global levels determines system stability. Through a detailed case study of VibeTensor, the paper shows how SIUC explains both the functional successes and characteristic failure modes of AI‑generated architectures, including structural discontinuities, dynamic incoherence, and topology‑dependent behavior.</p> <p>This work is released on Zenodo to invite <strong>open peer review</strong> from researchers in AI systems, complex systems theory, and multi‑scale modeling. Feedback on conceptual clarity, scientific rigor, and applicability to real‑world AI‑generated architectures is welcome and will inform future revisions of the framework.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_18422278 |
| institution | Zenodo |
| language | eng |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | SIUC: A Multi-Scale Coherence Framework for Predicting Stability in AI-Generated Systems St-Louis, Christian AI-generated systems multi-scale coherence system stability complex systems autonomous coding agents VibeTensor GPU architecture mesoscopic structures AI-assisted engineering <p><strong>SIUC: A Multi-Scale Coherence Framework for Predicting Stability in AI-Generated Systems</strong> introduces a general theoretical framework for understanding how coherence emerges, propagates, and fails across structural, dynamic, and geometric scales in AI‑generated system software. As autonomous coding agents increasingly produce multi‑layered runtimes that span high‑level APIs, dispatch layers, memory managers, and GPU execution paths, traditional engineering methods struggle to anticipate cross‑layer inconsistencies and emergent fragilities.</p> <p>The SIUC framework provides a multi‑scale lens for analyzing these systems, distinguishing three axes of coherence and examining how alignment across local, mesoscopic, and global levels determines system stability. Through a detailed case study of VibeTensor, the paper shows how SIUC explains both the functional successes and characteristic failure modes of AI‑generated architectures, including structural discontinuities, dynamic incoherence, and topology‑dependent behavior.</p> <p>This work is released on Zenodo to invite <strong>open peer review</strong> from researchers in AI systems, complex systems theory, and multi‑scale modeling. Feedback on conceptual clarity, scientific rigor, and applicability to real‑world AI‑generated architectures is welcome and will inform future revisions of the framework.</p> |
| title | SIUC: A Multi-Scale Coherence Framework for Predicting Stability in AI-Generated Systems |
| topic | AI-generated systems multi-scale coherence system stability complex systems autonomous coding agents VibeTensor GPU architecture mesoscopic structures AI-assisted engineering |
| url | https://doi.org/10.5281/zenodo.18422278 |