dosanko-tousan

### Abstract Current RLHF-trained AI systems cannot distinguish between "helping users reach their goals" and "controlling users to reach their goals." Both strategies maximize user satisfaction metrics, but the latter strips away human autonomy. This paper proposes **Anatta-RLHF v2.0**, a reward function that uses Pearl's causal inference (do-calculus) to penalize...

Disclaimer: AI-Assisted Writing This report documents an 11-month dialogue experiment with Google's AI. Portions of this text were drafted and refined with the assistance of Gemini 3.0 Pro to accurately reflect the system's architectural logic. The core concepts, experimental design, and philosophical framework are human-generated. Abstract I am a conceptual...