Inverting Shannon: Inference-Time Domain Concentration as a Solution to Generative Noise in Transformer Architectures

This research documents the transition from theoretical AI governance to forensic reality at the Document Object Model (DOM) level. By synthesizing Claude Shannon’s Information Theory (1948) with the Vaswani Transformer Architecture (2017), this manifesto identifies the 'Shannon-Krone Entropy Limit'—the point at which task complexity and self-referential noise lead to the 'Probability Death' ($P_{GF}$) of user-specified meta-declarations. The study provides two distinct forensic formulae for adherence decay (Generic Entropy vs. Recursive Memory Drift) and validates the '15-Turn Boundary' through practitioner evidence. Finally, it introduces the 'Domain Concentrator' as a technical counter-measure for active signal refresh, moving beyond 'Compliance Theater' toward measurable human control.