The Reference-conditioned Recursive Alignment Framework
Year: 2026
Type: preprint
Abstract: The Reference-Conditioned Recursive Alignment Framework (RCRAF) is proposed as a conditional, falsifiable architecture for systems in which an observed signal, interpretation, or output differs from a reference and repeated processing can shift the representation or output toward that reference. The framework contains exactly two primary theoretical branches. Branch A, the Subjective-Agent Preference-Weighted Recursive Interpretation and Reconstruction branch (PWRIR-A), retains the proposed Discrepancy-Activated Preference-Weighted Recursive Reconstruction Loop (D-PWRRL) and its primary case, the Preference-Reality Discrepancy Recursive Analysis Case (PRDRAC). It addresses a specific hypothesis: a person may consciously intend to remain neutral and may use deliberate reasoning or intellectualization while an automatic, potentially non-conscious preference-congruent pressure changes the relative weight assigned to evidence. This may alter belief, confidence, or interpretation without changing external reality. Branch B, the Mechanical Target-Referenced Recursive Alignment Loop (M-TRAL), concerns non-subjective controllers, optimizers, algorithms, and feedback systems. It examines how specified targets, objective functions, proxy metrics, update rules, and feedback can move outputs toward a configured reference while independent truth, safety, fairness, robustness, or broader constraints may improve, remain unchanged, or worsen. The Target-Proxy Divergence Case (TPDC) is the benchmark case for Branch B. The branches share a structural template but not an ontology, causal explanation, parameter set, or validation route. A common mathematical kernel expresses reference discrepancy, repeated processing, directional update, output reconstruction, and possible feedback. Branch-specific equations, variables, hypotheses, computational tests, rival explanations, measurement requirements, and rejection criteria are specified. The paper distinguishes conceptual definition from mathematical consistency, branch-specific empirical support, cross-domain generality, and universal-law claims. No empirical result for RCRAF itself is asserted. The central contribution is a candidate integrated architecture and a preregisterable research program, not a claim that automatic preference-alignment or target-proxy divergence is already established in every person or system.
Author: Shan, Shamiul Hoque
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7225795797
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