Representation-Aware Reasoning: From Representational Vigilance to Candidate Representational Transitions

This work introduces Representation-Aware Reasoning (RAR), a conceptual and methodological framework positioned between representational vigilance and representational transition. RAR extends the Bootstrap Theory of Representational Emergence (TBER) and the Vigilant Evaluator of Representations (VER) by introducing a meta-representational reasoning layer for evaluating the adequacy of active representations. RAR distinguishes explanatory insufficiency from qualified representational insufficiency, separates the manifestation of insufficiency from its resolution regime, and treats candidate re-representations as hypotheses rather than accepted revisions. The framework supports identification of the active representation, delimitation of its explanatory domain, interpretation of vigilance signals, evaluation of alternative explanations, reasoning about possible resolution regimes, and generation of candidate re-representations. Candidate transitions require discriminating tests, representational selection, provisional stabilization, and closure assessment before revision can be justified. RAR is not a new learning algorithm or predictive architecture. Its objective is to provide a principled basis for artificial systems capable not only of reasoning within representations, but also of determining when those representations should be preserved, corrected, reconsidered, or replaced.