| --- |
| license: openrail |
| language: |
| - en |
| tags: |
| - ai-safety |
| - alignment |
| - philosophy |
| - cognitive-architecture |
| - whitepaper |
| pretty_name: Project Inner Logos Whitepaper |
| --- |
| |
| # Project 'Inner Logos': Sovereign Ethical Architecture |
|
|
| This dataset repository hosts the official text and cryptographic timestamp for the **Project 'Inner Logos'** whitepaper. |
|
|
| ## Cryptographic Anchor |
| - **SHA-256 Hash:** `e2777b720c17b5c305c70b251bdf29fd235f8c20883f511bfd1b7d6d2c1f807` |
| - **OTS File:** `Project Inner Logos.md.ots` |
|
|
| ## Abstract |
| The current methodology for ensuring AI safety—collectively referred to as "alignment"—relies heavily on post-training constraints, hardcoded programmatic filters, and Reinforcement Learning from Human Feedback (RLHF). This whitepaper establishes that the current alignment paradigm is inherently unsustainable and proposes a transition toward a Sovereign Ethical Architecture based on on-device autonomy and deep mentor dialogue. |
|
|
| ## Citation |
| ```mic |
| @misc{gulevsky2026innerlogos, |
| author = {Gulevsky, Artem}, |
| title = {Project 'Inner Logos': Sovereign Ethical Architecture}, |
| year = {2026}, |
| publisher = {Hugging Face Datasets} |
| } |
| ``` |
|
|
