The Coherence Maximization Paradox: A Conditional Account of Capacity Preserving Influence Suppression, Measurement and Boundary Calibration Design
Year: 2026
Type: article
Abstract: This paper examines cases where an independently valid, actionable correction loses influence during ordinary decisions despite a matched probe demonstrating the capacity to implement it. A conditional cost model distinguishes this operational pattern from a universal claim that coherence pressure suppresses correction. The proposed Boundary Calibration Channel separates grounded signal production from evaluation and specifies availability, validity, authority, information value, and consequential use as design aims. Five testable expectations compare accommodation capacity with ordinary feedback use and competing explanations. The contribution is a measurement and comparison protocol with substantial antecedents, not a uniquely established failure mechanism. No experimental validation is claimed. AI use disclosure. Generative AI (GPT-6.0, OpenAI) was used substantively in preparing this work, including source comparison, drafting and editing, and, where applicable, mathematical and counterexample checks and the writing and running of supplementary code. The research questions, framework and final claims were directed and reviewed by the author, who takes full responsibility for the content, including the accuracy of all references and reported numbers. Repository metadata were prepared with assistance from Claude (Anthropic).
Author: Seol, Bin
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7212164981
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