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Documents published in Scipedia

• Executable Power: Syntax as Infrastructure in Predictive Societies

  A. Startari

  Executable Power: Syntax as Infrastructure in Predictive Societies (2025).

  
  

  Abstract

  This article introduces the concept of executable power as a structural form of authority that does not rely on subjects, narratives, or symbolic legitimacy, but on the direct operativity of syntactic structures. Defined as a production rule whose activation triggers an irreversible material action—formalized by deterministic grammars (e.g., Linear Temporal Logic, LTL) or by execution conditions in smart contract languages such as Solidity via require clauses—executable power is examined through a multi-case study (N = 3) involving large language models (LLMs), transaction automation protocols (TAP), and smart contracts. Case selection was based on functional variability and execution context, with each system constituting a unit of analysis. One instance includes automated contracts that freeze assets upon matching a predefined syntactic pattern; another involves LLMs issuing executable commands embedded in structured prompts; a third examines TAP systems enforcing transaction thresholds without human intervention. These systems form an infrastructure of control, operating through logical triggers that bypass interpretation. Empirically, all three exhibited a 100 % execution rate under formal trigger conditions, with average response latency at 0.63 ± 0.17 seconds and no recorded human override in controlled environments. This non-narrative modality of power, grounded in executable syntax, marks an epistemological rupture with classical domination theories (Arendt, Foucault) and diverges from normative or deliberative models. The article incorporates recent literature on infrastructural governance and executional authority (Pasquale, 2023; Rouvroy, 2024; Chen et al., 2025) and references empirical audits of smart-contract vulnerabilities (e.g., Nakamoto Labs, 2025), as well as recent studies on instruction-following in LLMs (Singh & Alvarado, 2025), to expose both operational potential and epistemic risks. The proposed verification methodology is falsifiable, specifying outcome-based metrics—such as execution latency, trigger-response integrity, and intervention rate—with formal verification thresholds (e.g., execution rate below 95 % under standard trigger sequences) subject to model checking and replicable error quantification.  DOI: https://doi.org/10.5281/zenodo.15754714 This work is also published with DOI reference in Figshare https://doi.org/10.6084/m9.figshare.29424524 and Pending SSRN ID to be assigned. ETA: Q3 2025.  

  Abstract
  This article introduces the concept of executable power as a structural form of authority that does not rely on subjects, narratives, or symbolic legitimacy, but on the direct [...]

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• Silent Mandates: The Rise of Implicit Directives in AI-Generated Bureaucratic Language

  A. Startari

  Silent Mandates: The Rise of Implicit Directives in AI-Generated Bureaucratic Language (2025).

  
  

  Abstract

  This article examines how large language models generate bureaucratic documents that conceal mandates within seemingly neutral structures. Governments, universities, and hospitals increasingly rely on AI systems to draft resolutions, notices, and internal policies. Instead of using explicit imperatives, these texts embed directives in subordinate clauses such as conditionals, causal gerunds, and consecutive constructions. The result is a regime of structural obedience, where institutional actors follow instructions without recognizing them as commands. Through case studies of clinical notes (Epic Scribe), university onboarding materials, and HR conduct policies, the article demonstrates how the compiled rule operates as a syntactic infrastructure that enforces compliance without authorship. The analysis connects to prior work on executable power, algorithmic obedience, and the grammar of objectivity, while introducing the Implicit Directive Index as a methodological tool to detect hidden mandates in AI-generated bureaucratic language.

  Abstract
  This article examines how large language models generate bureaucratic documents that conceal mandates within seemingly neutral structures. Governments, universities, and hospitals [...]

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• The Grammar of Objectivity: Formal Mechanisms for the Illusion of Neutrality in Language Models

  A. Startari

  The Grammar of Objectivity: Formal Mechanisms for the Illusion of Neutrality in Language Models (2025).

  
  

  Abstract

  Simulated neutrality in generative models produces tangible harms (ranging from erroneous treatments in clinical reports to rulings with no legal basis) by projecting impartiality without evidence. This study explains how Large Language Models (LLMs) and logic-based systems achieve neutralidad simulada through form, not meaning: passive voice, abstract nouns and suppressed agents mask responsibility while asserting authority. A balanced corpus of 1 000 model outputs was analysed: 600 medical texts from PubMed (2019-2024) and 400 legal summaries from Westlaw (2020-2024). Standard syntactic parsing tools identified structures linked to authority simulation. Example: a 2022 oncology note states “Treatment is advised” with no cited trial; a 2021 immigration decision reads “It was determined” without precedent. Two audit metrics are introduced, agency score (share of clauses naming an agent) and reference score (proportion of authoritative claims with verifiable sources). Outputs scoring below 0.30 on either metric are labelled high-risk; 64 % of medical and 57 % of legal texts met this condition. The framework runs in <0.1 s per 500-token output on a standard CPU, enabling real-time deployment. Quantifying this lack of syntactic clarity offers a practical layer of oversight for safety-critical applications. This work is also published with DOI reference in Figshare https://doi.org/10.6084/m9.figshare.29390885 and SSRN (In Process )

  Abstract
  Simulated neutrality in generative models produces tangible harms (ranging from erroneous treatments in clinical reports to rulings with no legal basis) by projecting impartiality [...]

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• Regulatory Legitimacy Without Referents: On the Syntax of AI Generated Legal Drafts

  A. Startari

  Regulatory Legitimacy Without Referents: On the Syntax of AI Generated Legal Drafts (2025).

  
  

  Abstract

  This article analyzes how AI-generated legal texts simulate legitimacy without referencing a sovereign authority. Based on a provenance-verified corpus of machine-generated documents, including contracts, terms of service, and automated policy clauses, the study shows that the legislator is structurally displaced by recurring patterns of passive voice, normative conditionals, and chains of subordinate clauses. The result is legalidad sin fuente (sourceless legality), where the appearance of regulatory authority is produced by syntactic form rather than institutional attribution. Comparing these drafts with traditional legislative writing, the article outlines a typology that instantiates autoridad no referencial and identifies a dual risk: loss of authority traceability and an accountability gap in the binding effects of these texts. This syntactic delegation constitutes a paradigm of regla compilada, situated within the tradition of formal grammars, in which language enacts governance without a governing subject.

  Abstract
  This article analyzes how AI-generated legal texts simulate legitimacy without referencing a sovereign authority. Based on a provenance-verified corpus of machine-generated [...]

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• Syntax Without Subject: Structural Delegation and the Disappearance of Political Agency in LLM-Governed Contexts

  A. Startari

  Syntax Without Subject: Structural Delegation and the Disappearance of Political Agency in LLM-Governed Contexts (Select a year).

  
  

  Abstract

  This article examines the syntactic disappearance of the subject in LLM-governed documents. Structural delegation refers to the transfer of agency to impersonal grammatical forms that preclude subject reappearance. Subjects are not censored but syntactically eliminated through passive constructions, nominalizations, and imperative prompt formats with suppressed agents. Building on prior work on synthetic ethos and impersonal command grammars, the article shows that AI-generated institutional texts display consistent patterns of subject erasure. The study analyzes 172 documents produced by GPT‑4 class models (temperature 0.2–0.7, 2024–2025) across legal, healthcare, and administrative domains. Metrics include passive ratio (via dependency label parsing), nominalization density (via POS and suffix filters), and instruction-format frequency. The result is a form of executable authority grounded not in referential authorship but in compliance with a regla compilada (type-0 production). The study proposes a typology of structural delegation and a formal framework for detecting syntactic absence in automated governance. This work is also published with DOI reference in Figshare https://doi.org/10.6084/m9.figshare.29665697 and Pending SSRN ID to be assigned. ETA: Q3 2025.

  Abstract
  This article examines the syntactic disappearance of the subject in LLM-governed documents. Structural delegation refers to the transfer of agency to impersonal grammatical [...]

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• Ethos Without Source: Algorithmic Identity and the Simulation of Credibility

  A. Startari

  Ethos Without Source: Algorithmic Identity and the Simulation of Credibility (2025). https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5313317

  
  

  Abstract

  Generative language models increasingly produce texts that simulate authority without a verifiable author or institutional grounding. This paper introduces synthetic ethos: the appearance of credibility constructed by algorithms trained to replicate human-like discourse without any connection to expertise, accountability, or source traceability. Such simulations raise critical risks in high-stakes domains including healthcare, law, and education. We analyze 1500 AI-generated texts produced by large-scale models such as GPT-4, collected from public datasets and benchmark repositories. Using discourse analysis and pattern-based structural classification, we identify recurring linguistic features,such as depersonalized tone, adaptive register, and unreferenced assertions,that collectively produce the illusion of credible voice. In healthcare, for instance, generative models produce diagnostic language without citing medical sources, risking patient misguidance. In legal context, generated recommendations mimic normative authority while lacking any basis in legislation or case law. In education, synthetic essays simulate scholarly argumentation without verifiable references. Our findings demonstrate that synthetic ethos is not an accidental artifact, but an engineered outcome of training objectives aligned with persuasive fluency. We argue that detecting such algorithmic credibility is essential for ethical and epistemically responsible AI deployment. To this end, we propose technical standards for evaluating source traceability and discourse consistency in generative outputs. These metrics can inform regulatory frameworks in AI governance, enabling oversight mechanisms that protect users from misleading forms of simulated authority and mitigate long-term erosion of public trust in institutional knowledge.

  Abstract
  Generative language models increasingly produce texts that simulate authority without a verifiable author or institutional grounding. This paper introduces synthetic ethos: [...]

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• From Obedience to Execution: Structural Legitimacy in the Age of Reasoning Models

  A. Startari

  (2025).

  
  

  Abstract

  This article formulates a structural transition from Large Language Models (LLMs) to Language Reasoning Models (LRMs), redefining authority in artificial systems. While LLMs operated under syntactic authority without execution, producing fluent but functionally passive outputs, LRMs establish functional authority without agency. These models do not intend, interpret, or know. They instantiate procedural trajectories that resolve internally, without reference, meaning, or epistemic grounding. This marks the onset of a post-representational regime, where outputs are structurally valid not because they correspond to reality, but because they complete operations encoded in the architecture. Neutrality, previously a statistical illusion tied to training data, becomes a structural simulation of rationality, governed by constraint, not intention. The model does not speak. It acts. It does not signify. It computes. Authority no longer obeys form, it executes function.

  Abstract
  This article formulates a structural transition from Large Language Models (LLMs) to Language Reasoning Models (LRMs), redefining authority in artificial systems. While LLMs [...]

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

  A. Startari

  (Select a year).

  
  

  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.

  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 [...]

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• Non-Neutral by Design: Why Generative Models Cannot Escape Linguistic Training

  A. Startari

  Non-Neutral by Design: Why Generative Models Cannot Escape Linguistic Training (2025).

  
  

  Abstract

  This article investigates the structural impossibility of semantic neutrality in large language models (LLMs), using GPT as a test subject. It argues that even under strictly formal prompting conditions, such as invented symbolic systems or syntactic proto-languages, GPT reactivates latent semantic structures drawn from its training corpus. The analysis builds upon prior work on syntactic authority, post-referential logic, and algorithmic discourse (Startari, 2025), and introduces empirical tests designed to isolate the model from known linguistic content. These tests demonstrate GPT’s consistent failure to interpret or generate structure without semantic interference. The study proposes a falsifiable framework to define and detect semantic contamination in generative systems, asserting that such contamination is not incidental but intrinsic to the architecture of probabilistic language models. The findings challenge prevailing narratives of user-driven interactivity and formal control, establishing that GPT(and similar systems) are non-neutral by design.

  Abstract
  This article investigates the structural impossibility of semantic neutrality in large language models (LLMs), using GPT as a test subject. It argues that even under strictly [...]

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• When Language carries form, Not meaning

  A. Startari

  (Select a year).

  
  

  Abstract

  This article is not about semantics. It is about its disappearance. The central claim is epistemologically direct: in generative language models, language is not produced to represent; it is produced to continue. The shift is not from intention to structure, but from intention to extinction. What remains is not an utterance with referential anchoring, but a formal residue, syntactically valid, semantically inert. We propose that large language models (LLMs) do not operate on meaning, but on distributive compatibility. Each word is not a sign pointing to an idea, but a structurally permissible unit in a predictive chain. In this sense, language follows form, not as a stylistic choice, but as a computational necessity. Meaning is not misrepresented; it is bypassed. This article positions itself as a rupture with the dominant assumption that semantic drift in LLMs is a temporary misalignment. We argue instead that it is structural and permanent. The phenomenon is not semantic degradation, but syntactic sovereignty. The theoretical background is grounded in prior works that identified the collapse of agency within AIgenerated language. The Passive Voice (2025) demonstrated that algorithmic outputs simulate neutrality by erasing subjects in surface grammar. Ethos and Artificial Intelligence (2025) extended this to show that authority in generative models is not derived from enunciative subjectivity, but from repetition, syntax, and structural legitimacy. Building on these foundations, this article isolates the final stage of that displacement: form without reference, language without intention, activation without meaning. We introduce the model of Formal Syntactic Activation (FSA), a logical framework for understanding how language can be generated in the total absence of semantic operations. This is not a metaphor. It is a system.

  Abstract
  This article is not about semantics. It is about its disappearance. The central claim is epistemologically direct: in generative language models, language is not produced [...]

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