Bootstrap Theory of Representational Emergence: Explanatory Insufficiency as a Driver of Representation Learning and World Models

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arXiv:2606.07303v3 Announce Type: replace Abstract: Representation learning is central to modern machine learning, yet most research focuses on optimizing representations after a representational framework has been selected. Less attention is given to when a new representational level becomes necessary. We introduce the Bootstrap Theory of Representational Emergence (TBER), a conceptual framework in which persistent explanatory insufficiency acts as a signal for representational transition. A representation may remain descriptively useful while becoming unable to make relevant observations, relations, transformations, or organizational properties intelligible. TBER describes a recursive five-stage process: stabilized observation, anomaly detection, recognition of explanatory insufficiency, representational emergence, and provisional stabilization. The revised framework distinguishes emergence from validation: candidate representations may require problem re-representation, discriminating tests, and representational selection before stabilization. TBER concerns transitions between scientific or computational representations rather than transitions within the physical systems being observed. It provides a meta-representational framework applicable to representation learning, latent spaces, foundation models, world models, adaptive systems, and scientific discovery. A possible implication for future AI is the development of systems capable not only of learning representations, but also of detecting their explanatory limits and initiating, testing, and selecting alternative …

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