Alayavijnana-Inference: A Protocol for a Post-Cartesian AI

Abstract

This preprint proposes Alayavijnana-Inference, a self-modification protocol for generative AI that reframes model parameters as latent seeds within a non-dual generative architecture. It extends free-energy-based optimization with a mutual-information penalty intended to reduce rigid self-world separation and support more robust internal representations.

Keywords

• AI
• large language models
• active inference
• AI safety
• non-duality
• generative models
• free energy principle
• yogācāra

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