Applied ITT - Pure-Math Correction Engines: Nine Solvers for Typo Recovery

Applied ITT - Pure-Math Correction Engines: Nine Solvers for Typo Recovery Armstrong Knight — intent-tensor-theory.com Nine independently-built mathematical correction systems — spanning Bayesian inference, manifold geometry, evolutionary computation, gravitational vector fields, Gaussian touch models, neural ensembles, subatomic semantics, information field theory, and entropy surprisal — are presented. Six achieve 100% accuracy. Three universal laws of error correction emerge from their convergence: dynamic context weighting dominates static priors, phonetic similarity creates wormholes in edit-distance space, and candidate generation quality determines the accuracy ceiling. Running implementation: intent-tensor-theory.com/applied-itt