01AI-Augmented Impact Frames: A Constrained, Closed-Loop Architecture for Purpose-Aligned Decision Learning

Programme Context This preprint forms part of a research programme examining decision systems as longitudinal interpretive-learning architectures. The programme develops a coherent theoretical pipeline linking (1) formal decision-learning architecture, (2) translation drift as a structural mechanism of interpretive misalignment, (3) methodological pathways for making translation coherence empirically observable, and (4) design implications for governance in AI- and artefact-mediated environments. Together, the papers treat governance infrastructures as meaning infrastructures and position institutional learning as the maintenance of interpretive coherence over time. Preprint Description AI-Augmented Impact Frames is a formally specified, constrained, closed-loop architecture for studying how organisational decision systems learn over time. The framework treats the decision system—rather than individual decisions or decision-makers—as the primary unit of analysis. The architecture combines: • a formal Operating Spine linking purpose, capabilities, value drivers, strategy, portfolios, and signals• constrained AI-supported interpretation that preserves human decision authority• psychometrically grounded longitudinal measurement using Item Response Theory (IRT) Rather than optimising or automating decisions, the framework is designed to make decision logic explicit, measurable, and falsifiable. AI functions only as interpretive support and is structurally prohibited from recommendations or decision authority. This work defines the architectural and methodological foundation of the Impact Frames research programme. It specifies boundary conditions, failure modes, and falsifiability criteria for empirical evaluation of decision-system learning under conditions of delayed feedback and high uncertainty. Version History Version 1.01 clarified terminology, strengthened falsifiability statements, and improved conceptual definitions without changing the underlying architecture. No empirical datasets are associated with this version. Measurement instruments, code scaffolds, and empirical studies will be released in subsequent linked records. Version 1.03 update: This version restored the intended architectural manuscript file. Version 1.02 contained a mistakenly uploaded file that did not reflect substantive changes to this record. No conceptual, methodological, or structural content was altered relative to Version 1.01. Version 2.00: This release consolidates the manuscript within the full research programme structure. Cross-paper terminology has been harmonised, the unit-of-analysis statement has been standardised across the series, and reference architecture has been aligned. No changes have been made to the formal decision-learning architecture, measurement logic, boundary conditions, or theoretical claims.