From Attention to Dynamics: A Structural Analysis of Feedback, Coherence, and State in Modern AI Architectures
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| Format: | Recurso digital |
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2026
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| _version_ | 1866901539327574016 |
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| author | Ryder, John F. |
| author_facet | Ryder, John F. |
| contents | <p>This paper argues that current AI interpretability methods are structurally limited by their reliance on scalar probes such as attention weights and activation magnitudes. These tools are incapable of detecting directional, phase-sensitive, and temporally extended coupling dynamics that may govern system behaviour.</p> <p>We introduce the concept of an observable kernel mismatch: systems may exhibit real internal structure that lies outside the detectable class of the measurement tools applied. Drawing on both dynamical systems theory and empirical precedent from astrophysical observation, we show that state space models (SSMs) provide a natural substrate for such dynamics through continuous-time latent state evolution governed by matrix coupling.</p> <p>We propose a set of architectural constraints for dynamically coherent systems and outline testable predictions using dynamical measurement tools such as transfer entropy, Granger causality, and topological analysis.</p> <p>This work reframes AI systems as dynamical systems requiring appropriate measurement frameworks, rather than static function approximators fully characterisable by scalar analysis.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19659575 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | From Attention to Dynamics: A Structural Analysis of Feedback, Coherence, and State in Modern AI Architectures Ryder, John F. AI interpretability; dynamical systems; state space models; transformers; neural architectures; observable kernel; transfer entropy; Granger causality; topological data analysis; machine learning theory <p>This paper argues that current AI interpretability methods are structurally limited by their reliance on scalar probes such as attention weights and activation magnitudes. These tools are incapable of detecting directional, phase-sensitive, and temporally extended coupling dynamics that may govern system behaviour.</p> <p>We introduce the concept of an observable kernel mismatch: systems may exhibit real internal structure that lies outside the detectable class of the measurement tools applied. Drawing on both dynamical systems theory and empirical precedent from astrophysical observation, we show that state space models (SSMs) provide a natural substrate for such dynamics through continuous-time latent state evolution governed by matrix coupling.</p> <p>We propose a set of architectural constraints for dynamically coherent systems and outline testable predictions using dynamical measurement tools such as transfer entropy, Granger causality, and topological analysis.</p> <p>This work reframes AI systems as dynamical systems requiring appropriate measurement frameworks, rather than static function approximators fully characterisable by scalar analysis.</p> |
| title | From Attention to Dynamics: A Structural Analysis of Feedback, Coherence, and State in Modern AI Architectures |
| topic | AI interpretability; dynamical systems; state space models; transformers; neural architectures; observable kernel; transfer entropy; Granger causality; topological data analysis; machine learning theory |
| url | https://doi.org/10.5281/zenodo.19659575 |