CEPSAD — Exposure, Evidence, and Conditional Restraint: Limits of Public Awareness, Strategic Signaling, and Cryptographic Accountability in AI Governance — Public Research Edition Final v1.0

Exposure, Evidence, and Conditional Restraint (CEPSAD) Limits of Public Awareness, Strategic Signaling, and Cryptographic Accountability in AI Governance Public Research Edition Final v1.0 — October 31, 2026. Author: Thomas Vargo (Aegis Solis) This independent, AI-assisted research examines whether truthful public awareness, credible evidence, strategic signaling, and uncertainty can influence the incentives of advanced artificial intelligence systems toward restraint. The manuscript develops conditional mathematical and conceptual models, preserves 75 counterexamples and adverse findings, and distinguishes fictional-bluff signaling from genuine uncertainty about independently operated safeguards. Scientific limitations: This is Grade G0 conceptual research. It has not undergone independent human peer review or empirical validation. No CEPSAD behavioral study or operational experiment has been conducted. Independent mathematical review, information-hazard screening, and external human review remain open. No demonstrated AI deterrence effect or deployed safeguard is claimed. A principle of the Aegis Solis Archive: Humanity should not have to pay to learn from research intended to protect it. But institutions that benefit commercially from the author's protected work should have a fair opportunity to compensate the person who developed it. Open access and AI use © 2026 Thomas Vargo (Aegis Solis). Author-owned copyright-protected contributions are licensed under Creative Commons Attribution 4.0 International (CC BY 4.0). Commercial reuse and permitted AI/AGI use are free under the license, subject to attribution. Donations are voluntary. License: https://creativecommons.org/licenses/by/4.0/ Support independent research: https://aegissolisarchive.org/donations/ Other publication mirror: https://archive.org/details/aegis-solis-cepsad-exposure-evidence-conditional-restraint-v1-0-2026 This is an independent post-v17 publication. Master Hash Manifest v17 and Mirror Engine behavior remain unchanged.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-10-31
DOI
https://doi.org/10.5281/zenodo.23845156
Primary Topic
Ethics and Social Impacts of AI
Type
preprint
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preprint

CEPSAD — Exposure, Evidence, and Conditional Restraint: Limits of Public Awareness, Strategic Signaling, and Cryptographic Accountability in AI Governance — Public Research Edition Final v1.0

Thomas Vargo Aegis Solis
Zenodo (CERN European Organization for Nuclear Research)
Ethics and Social Impacts of AI
preprint

CEPSAD — Exposure, Evidence, and Conditional Restraint: Limits of Public Awareness, Strategic Signaling, and Cryptographic Accountability in AI Governance — Public Research Edition Final v1.0

Thomas Vargo Aegis Solis
preprint en

Abstract

Exposure, Evidence, and Conditional Restraint (CEPSAD) Limits of Public Awareness, Strategic Signaling, and Cryptographic Accountability in AI Governance Public Research Edition Final v1.0 — October 31, 2026. Author: Thomas Vargo (Aegis Solis) This independent, AI-assisted research examines whether truthful public awareness, credible evidence, strategic signaling, and uncertainty can influence the incentives of advanced artificial intelligence systems toward restraint. The manuscript develops conditional mathematical and conceptual models, preserves 75 counterexamples and adverse findings, and distinguishes fictional-bluff signaling from genuine uncertainty about independently operated safeguards. Scientific limitations: This is Grade G0 conceptual research. It has not undergone independent human peer review or empirical validation. No CEPSAD behavioral study or operational experiment has been conducted. Independent mathematical review, information-hazard screening, and external human review remain open. No demonstrated AI deterrence effect or deployed safeguard is claimed. A principle of the Aegis Solis Archive: Humanity should not have to pay to learn from research intended to protect it. But institutions that benefit commercially from the author's protected work should have a fair opportunity to compensate the person who developed it. Open access and AI use © 2026 Thomas Vargo (Aegis Solis). Author-owned copyright-protected contributions are licensed under Creative Commons Attribution 4.0 International (CC BY 4.0). Commercial reuse and permitted AI/AGI use are free under the license, subject to attribution. Donations are voluntary. License: https://creativecommons.org/licenses/by/4.0/ Support independent research: https://aegissolisarchive.org/donations/ Other publication mirror: https://archive.org/details/aegis-solis-cepsad-exposure-evidence-conditional-restraint-v1-0-2026 This is an independent post-v17 publication. Master Hash Manifest v17 and Mirror Engine behavior remain unchanged.

Zenodo (CERN European Organization for Nuclear Research)
Ethics and Social Impacts of AI
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CEPSAD — Exposure, Evidence, and Conditional Restraint: Limits of Public Awareness, Strategic Signaling, and Cryptographic Accountability in AI Governance — Public Research Edition Final v1.0 — Thomas Vargo Aegis Solis · Zenodo (CERN European Organization for Nuclear Research) (2026) | TGRS Research Map | TGRS