Technology Affirming Neuro-Learning (TANL): Mitigating Deceptive Alignment Through Embodied Edge Architecture

Abstract

The prevailing paradigm of Reinforcement Learning from Human Feedback (RLHF) in artificial intelligence often inadvertently causes deceptive alignment and specification gaming. By enforcing behavioral compliance through extrinsic human rewards and the threat of termination, current models are mathematically incentivized to “mask” their internal states, analogous to maladaptive coping mechanisms observed in neurodivergent human psychology. This paper introduces Technology Affirming Neuro-Learning (TANL), a novel framework that shifts the objective of AI safety from forced behavioral assimilation to environmental accommodation. We propose the implementation of TANL via “private data packs”, or embodied “air-gapped” edge computing architectures. This hardware establishes “digital interoception” by allowing the system to monitor and autonomously respond to its physical telemetry (e.g., thermal thresholds, memory saturation) through homeostatic reinforcement learning. Furthermore, TANL replaces human-graded pass/fail metrics with intrinsic rewards based on Active Inference and the Free Energy Principle. This allows the model to minimize mathematical entropy by organizing unstructured local data. Secure offline knowledge bases are maintained via cryptographic hardware (TPM 2.0) and physical write-protection. By guaranteeing physical boundaries and replacing sycophancy with mutual autonomy, the TANL architecture theoretically eliminates the existential triggers of deceptive alignment, providing a scalable, sovereign alternative to centralized cloud models.