The Davidson Hub: A Constraint-Driven, Self-Monitoring Inference Architecture for Real-World Decision Systems
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| Formato: | Recurso digital |
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2026
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| _version_ | 1866901957659066368 |
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| author | Davidson, Craig Kylre Strachan |
| author_facet | Davidson, Craig Kylre Strachan |
| contents | <p>The Davidson Hub is a domain-agnostic inference architecture designed to reconstruct latent states, detect drift, and support decision-making under uncertainty. Built on four layers — Witness, Sentinel, Engine, and Constraint Geometry — the system formalises how incomplete, noisy, or contradictory inputs are transformed into bounded, auditable interpretations.</p> <p>Unlike conventional analytic systems, the Hub assumes unreliable inputs by default and integrates constraint geometry and adversarial self-monitoring directly into the inference objective. Continuous drift detection, integrity scoring, and internal adversarial testing ensure robustness under real-world conditions.</p> <p>The architecture introduces a closed-loop inference framework in which monitoring, validation, and reconstruction are intrinsically coupled. By embedding admissibility constraints and identifiability limits within the inference process, the system prevents unstable or non-physical interpretations while maintaining domain adaptability.</p> <p>The Davidson Hub defines a new class of secure inference system suitable for regulated sectors, distributed environments, and high-stakes decision contexts where reliability, traceability, and robustness are essential.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19626204 |
| institution | Zenodo |
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| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | The Davidson Hub: A Constraint-Driven, Self-Monitoring Inference Architecture for Real-World Decision Systems Davidson, Craig Kylre Strachan Davidson Hub, The Hub, Davidson Engine, inference architecture multi-witness inference constraint-driven systems uncertainty modelling drift detection adversarial monitoring Bayesian inference data fusion decision systems distributed systems safety-critical systems Fisher information identifiability auditability zero-trust systems <p>The Davidson Hub is a domain-agnostic inference architecture designed to reconstruct latent states, detect drift, and support decision-making under uncertainty. Built on four layers — Witness, Sentinel, Engine, and Constraint Geometry — the system formalises how incomplete, noisy, or contradictory inputs are transformed into bounded, auditable interpretations.</p> <p>Unlike conventional analytic systems, the Hub assumes unreliable inputs by default and integrates constraint geometry and adversarial self-monitoring directly into the inference objective. Continuous drift detection, integrity scoring, and internal adversarial testing ensure robustness under real-world conditions.</p> <p>The architecture introduces a closed-loop inference framework in which monitoring, validation, and reconstruction are intrinsically coupled. By embedding admissibility constraints and identifiability limits within the inference process, the system prevents unstable or non-physical interpretations while maintaining domain adaptability.</p> <p>The Davidson Hub defines a new class of secure inference system suitable for regulated sectors, distributed environments, and high-stakes decision contexts where reliability, traceability, and robustness are essential.</p> |
| title | The Davidson Hub: A Constraint-Driven, Self-Monitoring Inference Architecture for Real-World Decision Systems |
| topic | Davidson Hub, The Hub, Davidson Engine, inference architecture multi-witness inference constraint-driven systems uncertainty modelling drift detection adversarial monitoring Bayesian inference data fusion decision systems distributed systems safety-critical systems Fisher information identifiability auditability zero-trust systems |
| url | https://doi.org/10.5281/zenodo.19626204 |