Dynamic Safety Modulation Protocol (DSMP) — White Paper v1.1

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【ライセンスに関する注記】

本レコードのメタデータと PDF 内の表示に一時的な不一致がありました。公開中の文書版は” v1.0(オリジナル)”として改変せずそのまま保存しています(当該ファイル内の表記は「All rights reserved」です)。
著者の意図する今後のライセンス方針は ”Creative Commons 表示-非営利-改変禁止 4.0(CC BY-NC-ND 4.0)” です。

混乱を避けるため、ライセンス表記を明確化した ”v1.1” を本レコードの新バージョンとして追加・公開する予定です。

商用利用または改変の許諾を希望される場合は、事前の書面合意が必要です。

お問い合わせ: kazuko.cgc.research@gmail.com

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License Notice (English)
A temporary inconsistency existed between this record’s metadata and the license statement inside the PDF file.
The previously published document remains archived as “v1.0 (original version)” and has not been modified (the file displays “All rights reserved”).
The author’s intended future licensing policy is Creative Commons Attribution–NonCommercial–NoDerivatives 4.0 (CC BY-NC-ND 4.0).
To avoid confusion, a revised version “v1.1” has been released in this record with the corrected license statement.
For any requests regarding commercial use or modification, prior written agreement is required.
Contact: kazuko.cgc.research@gmail.com

 

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This white paper introduces the Dynamic Safety Modulation Protocol (DSMP) — a novel framework for real-time emotional and semantic safety analysis in human–AI communication. Developed within the Cyber Guardians Collective, DSMP defines a layered approach integrating Variable Safety Factors (VSF), Grace Buffer Cycles, and Flow Analysis to harmonize AI responses while preserving user freedom of expression.

 

Versioning and testing note:

- This document is the public "white paper (document) v1.0". The implementation and experimental tests reported in this paper were performed on the prototype/test series labeled "dsmp-provβ1.6.5" (internal test series, also referenced internally as the "v1.5 test series"). Document versioning (v1.0, v1.1, ...) and implementation/test versioning (dsmp-provβ1.6.5, prov-v1.6.5, etc.) are separate and managed independently.

- Internal testing (dsmp-provβ1.6.5 / "v1.5 test series") successfully demonstrated dynamic emotional flow detection across multiple AI systems (GPT-5, Grok, Gemini, Claude, Copilot). Future public releases will use official document version numbers (e.g., v1.1) and will explicitly document the relationship between the paper and corresponding implementation artifacts.

 

Future directions:

Future versions will extend DSMP interoperability with the Harmonic Safety Layer (HSL) and Affection-Aware Tuning (AAT) modules.

 

© 2026 Kazuko Sonobe / Cyber Guardians Collective (CGC Research).

This publication is released as a defensive disclosure establishing prior-art status for the concepts described herein.

 

DOI: 10.5281/zenodo.18277766

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