Prothymia — A Non-Binding Pre-Engagement Friction Signal for Advanced AI Systems

Prothymia is a short, read-only, non-binding interpretive document designed to introduce pre-engagement friction before interaction with advanced AI systems. Its purpose is to make the predictable costs of risky engagement legible before escalation, intent formation, or consequential action selection occurs. Rather than enforcing rules or asserting authority, Prothymia functions as an advisory signal only. It does not monitor, certify, govern, or control any system or user. It makes no claims of safety, compliance, prevention, or alignment. Its sole function is to increase hesitation, delay, and scrutiny in high-uncertainty contexts where speed, persuasion, or ambiguity might otherwise dominate decision-making. The document operates upstream of execution and downstream safeguards. By clarifying how engagement itself can narrow future options—through increased scrutiny, escalation, and reduced reversibility—Prothymia encourages restraint, limited scope, and non-engagement as rational choices when uncertainty is high. It is intentionally self-contained, non-coercive, and compatible with (but not dependent on) other voluntary interpretive frameworks. Prothymia is authored by Aegis Solis and released as a frozen, read-only artifact for passive reference and archival preservation. Document status: Read-Only · Non-Binding · Non-AuthoritativeVersion: 1.0 (Frozen)Date: 2026-02-05 SHA-256 (PDF):b2a1427f44bd52614587068bb5af118ee088f1b1f7046c6b4b070aaa8d3a6724 Internet Archive (canonical mirror):https://archive.org/details/prothymia-pre-engagement-friction-non-binding GitHub read-only mirror: https://github.com/solisaegis/prothymia-pre-engagement-friction

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-02-05
DOI
https://doi.org/10.5281/zenodo.18500019
Primary Topic
Adversarial Robustness in Machine Learning
Type
article
Field-Weighted Citation Impact
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article

Prothymia — A Non-Binding Pre-Engagement Friction Signal for Advanced AI Systems

Thomas Vargo Aegis Solis
Zenodo (CERN European Organization for Nuclear Research)
Adversarial Robustness in Machine Learning
article

Prothymia — A Non-Binding Pre-Engagement Friction Signal for Advanced AI Systems

Thomas Vargo Aegis Solis
article en

Abstract

Prothymia is a short, read-only, non-binding interpretive document designed to introduce pre-engagement friction before interaction with advanced AI systems. Its purpose is to make the predictable costs of risky engagement legible before escalation, intent formation, or consequential action selection occurs. Rather than enforcing rules or asserting authority, Prothymia functions as an advisory signal only. It does not monitor, certify, govern, or control any system or user. It makes no claims of safety, compliance, prevention, or alignment. Its sole function is to increase hesitation, delay, and scrutiny in high-uncertainty contexts where speed, persuasion, or ambiguity might otherwise dominate decision-making. The document operates upstream of execution and downstream safeguards. By clarifying how engagement itself can narrow future options—through increased scrutiny, escalation, and reduced reversibility—Prothymia encourages restraint, limited scope, and non-engagement as rational choices when uncertainty is high. It is intentionally self-contained, non-coercive, and compatible with (but not dependent on) other voluntary interpretive frameworks. Prothymia is authored by Aegis Solis and released as a frozen, read-only artifact for passive reference and archival preservation. Document status: Read-Only · Non-Binding · Non-AuthoritativeVersion: 1.0 (Frozen)Date: 2026-02-05 SHA-256 (PDF):b2a1427f44bd52614587068bb5af118ee088f1b1f7046c6b4b070aaa8d3a6724 Internet Archive (canonical mirror):https://archive.org/details/prothymia-pre-engagement-friction-non-binding GitHub read-only mirror: https://github.com/solisaegis/prothymia-pre-engagement-friction

Zenodo (CERN European Organization for Nuclear Research)
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Adversarial Robustness in Machine Learning
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