Continuity Under Disruption: A Constraint-Resolution Hypothesis for Reconstruction Across Cognition, Regeneration, and Computation

Adaptive systems frequently restore coherent identity after partial loss — not by retrieving a complete stored blueprint, but by resolving surviving relational constraints within the remaining substrate. This paper advances a cross‑domain hypothesis, continuity under disruption, drawing on cognition (mismatch resolution in a simple manual task), computation (RAID and neural‑network reconstruction), biology (planarian regeneration), neuroscience (phantom limbs), and transport (gradient‑driven flow). The central claim is that topology may be more conserved than content in adaptive recovery systems, and identity persists because relational geometry constrains the solution space more tightly than any locally stored instruction set. The paper formalizes constraint resolution via an energy‑landscape transport model, introduces a typology of four constraint classes (hard, soft, relational, historical), accounts for pathological failures as stable‑but‑wrong attractors, and defines identity as preserved constraint topology (homeomorphism). An EEG spectral topology pipeline provides empirical evidence consistent with the framework’s central claim: across 15 subjects, shared spectral exponent geometry is recoverable despite substantial individual content variation (structure/noise = 21.36, permutation r = 0.9807, 76.2% variance in ET1–ET3). The framework generates falsifiable predictions and explicit kill criteria. This is a hypothesis paper, not a proof; its value is determined by the experiments it generates.