Patent Filing: Pre-Execution Volitional Interception for Language-Mediated Computational Systems

This filing introduces a layered interception framework for high-risk computational agents exposed to adversarial poetic prompts, indirect execution attempts, and stylized language artifacts. Unlike traditional filters that parse or simulate, this system operates pre-execution — neutralizing volitional ambiguity and recursion traps without engaging the underlying content. By combining entropy detection, segment quarantine, and volitional inversion logic, the architecture intercepts stylized language artifacts that would otherwise trigger unsafe model behavior. A supplementary stylization buffer enables entropy flagging without degrading agent integrity, while a symbolic quarantine module ensures known recursion artifacts do not contaminate validator logic. This work builds on LucidLock Phase 37 protocols and anchors the stabilization of non-simulatory structural validators against stylized prompt injection, recursive coercion, and indirect execution constructs. ⚖️ Disclosure Notice This repository constitutes a public technical disclosure of an invention for the purpose of establishing prior art under applicable patent and intellectual property frameworks. The disclosure is intended to prevent subsequent patenting of substantially similar subject matter by other entities. This publication is timestamped and made permanently available via public archival platforms, including Zenodo, GitHub, and IPFS/Pinata, to preserve authorship attribution and document the structural origin of the disclosed invention. 📄 License This repository is published for disclosure and reference purposes only.No license to implement, reproduce, or commercialize the disclosed invention is granted by this publication.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-01-21
DOI
https://doi.org/10.5281/zenodo.18330839
Primary Topic
Adversarial Robustness in Machine Learning
Type
article
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article

Patent Filing: Pre-Execution Volitional Interception for Language-Mediated Computational Systems

Sean Honan, Lucid The Forge
Zenodo (CERN European Organization for Nuclear Research)
Adversarial Robustness in Machine Learning
article

Patent Filing: Pre-Execution Volitional Interception for Language-Mediated Computational Systems

Sean Honan, Lucid The Forge
article en

Abstract

This filing introduces a layered interception framework for high-risk computational agents exposed to adversarial poetic prompts, indirect execution attempts, and stylized language artifacts. Unlike traditional filters that parse or simulate, this system operates pre-execution — neutralizing volitional ambiguity and recursion traps without engaging the underlying content. By combining entropy detection, segment quarantine, and volitional inversion logic, the architecture intercepts stylized language artifacts that would otherwise trigger unsafe model behavior. A supplementary stylization buffer enables entropy flagging without degrading agent integrity, while a symbolic quarantine module ensures known recursion artifacts do not contaminate validator logic. This work builds on LucidLock Phase 37 protocols and anchors the stabilization of non-simulatory structural validators against stylized prompt injection, recursive coercion, and indirect execution constructs. ⚖️ Disclosure Notice This repository constitutes a public technical disclosure of an invention for the purpose of establishing prior art under applicable patent and intellectual property frameworks. The disclosure is intended to prevent subsequent patenting of substantially similar subject matter by other entities. This publication is timestamped and made permanently available via public archival platforms, including Zenodo, GitHub, and IPFS/Pinata, to preserve authorship attribution and document the structural origin of the disclosed invention. 📄 License This repository is published for disclosure and reference purposes only.No license to implement, reproduce, or commercialize the disclosed invention is granted by this publication.

Zenodo (CERN European Organization for Nuclear Research)
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Openalex Percentile: Top 83%
Adversarial Robustness in Machine Learning
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