What question did this study set out to answer?

The study aims to explore the intersection of information theory and AI architectures to address generative noise.

January 27, 20260 citationsOpen Access

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

Key Points

The study aims to explore the intersection of information theory and AI architectures to address generative noise.
Synthesized concepts from Shannon's Information Theory and Transformer Architecture.
Developed two forensic formulae for measuring adherence decay.
Validated findings through practitioner evidence related to the 15-Turn Boundary.
Identified the Shannon-Krone Entropy Limit correlating with task complexity and noise.
Demonstrated that generative noise leads to a condition termed Probability Death ($P_{GF}$).
Introduced the Domain Concentrator as a solution for improving signal integrity.

Abstract

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' (P₆₅) 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.

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