Sovereign Syntax in Financial Disclosure: How LLMs Shape Trust in Tokenized Economies

Abstract

Through structural analysis of LLM-generated or LLM-refined whitepapers, this study identifies a recurring pattern in tokenized finance: legitimacy is simulated through formal syntactic depth rather than verifiable disclosure. It introduces the Syntactic Deception Risk Index (SDRI), a quantitative measure of non-referential persuasion derived from syntactic volatility. Grounded in Algorithmic Obedience and The Grammar of Objectivity, the findings show that high-risk disclosures converge on a formal grammar that substitutes substantive content with surface coherence. The concept of sovereign syntax is formalized as the regla compilada (type-0 production) that governs trust independently of source or reference. From this model follow concrete pathways for audit automation, exchange-side filtration, and real-time regulatory screening. SDRI thus exposes how non-human authority embeds in financial language without a traceable epistemic anchor.  

Keywords: Syntactic Bias, Expense Classification, ERP Automation, Fair-Syntax Transformation, Transformer Interpretability, Nominalization, SHAP Analysis, Financial NLP, Classification Error Mitigation, Regulatory Compliance