metadata
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
@misc{gulevsky2026innerlogos,
author = {Gulevsky, Artem},
title = {Project 'Inner Logos': Sovereign Ethical Architecture},
year = {2026},
publisher = {Hugging Face Datasets}
}