General Form — Stability Is Not a Property of the World, but a Result of Cognitive Compression

This paper advances a general claim: the apparently stable world is not a prior property of external reality, but a structural result necessarily generated by cognitive systems operating under finite resources while learning and optimizing action across time. Starting from continuous input-streams rather than pre-given objects, entities, or truths, the paper argues that any system facing continuous variation, bounded processing, and action-demand must compress; once compression occurs, different inputs are forced to collapse into reusable internal states, and stability appears. On this basis, the paper distinguishes three layers of stability: Collapse Stability, Pattern Stability, and Object Stability, showing that objects are not the starting point of cognition but a higher-order product of reuse and aggregation. The paper then reverse-derives a minimal cognitive paradigm: compression and reuse are not optional design choices, but the lower structural necessity for learning, prediction, and action under constraint. This framework aims to offer a unified first-principles account relevant to neural representation in neuroscience, stable perception and world modeling in cognitive science, representation learning and generalization in artificial intelligence, and the broader problem of why stable structure appears at all.