Applied ITT - The Architecture of Error: A Mathematical Framework for Noisy Channel Recovery in Modern Linguistics Armstrong Knight (Sensei Intent Tensor) - intent-tensor-theory.com Every piece of text a human produces is a signal that has passed through a noisy channel. The noise comes from three sources: physical (finger slip on keyboard), cognitive (phonetic confusion in the mind), and environmental (contextual ambiguity in the sentence). This paper formalizes the complete mathematical framework for recovering the intended signal from its corrupted form, tracing the lineage from Shannon & Weaver's original channel model through the modern four-layer joint optimization stack. The central claim: correction is not dictionary lookup. It is **maximum-likelihood recovery** — proving that the corrected sentence is more probable than the corrupted input by a quantifiable margin. Running implementation: intent-tensor-theory.com/applied-itt
Armstrong Knight (Thu,) studied this question.