Recursive Information Processing: An Observer-Aware Framework for Adaptive Intelligence and Vision Systems

Contemporary artificial intelligence and perception systems typically model information processing as a linear transformation from inputs to outputs, assuming observer-independent interpretation. In contrast, natural intelligence exhibits adaptive perception in which observation, interpretation, and internal state continuously interact. This paper introduces a Recursive Information Processing Framework (RIPF) that models the observer as an active, evolving component within the information-processing loop. Grounded in information theory, cybernetics, and cognitive science, the framework conceptualizes intelligence as a recursive interaction among incoming information, internal representation, and observer state. Meaning and interpretation emerge through iterative feedback in which perception updates the observer, and the updated observer shapes subsequent interpretation. To demonstrate applicability, a Recursive Vision Framework (RVF) is proposed for adaptive visual systems operating under uncertainty and contextual variability. The architecture enables context-aware interpretation and dynamic inference beyond traditional feed-forward perception pipelines. Comparative analysis positions the framework alongside existing dynamical and predictive approaches while extending them through explicit observer-aware recursion. The proposed model offers a unified perspective linking information theory, machine learning, and cognitive intelligence, suggesting that adaptive intelligence may arise from recursive informational self-reference, with implications for future AI systems, cognitive modeling, and observer-dependent information metrics.