Recursive Latent Reasoning & Sparse Population Coding: A Non-Autoregressive Path to Energy-Efficient AGI

Current Large Language Models (LLMs) are reaching a performance asymptote characterized by linear computational scaling and a lack of world-modeling capabilities—the "Transformer Wall." While effective at probabilistic next-token generation, these architectures lack the capacity for non-linear planning and persistent state maintenance. This paper introduces ANTA (Advanced Neural Thought Ascendent), a neuromorphic architecture that replaces fixed-depth feed-forward processing with a Mixture of Recursion (MoR) engine. By utilizing Sparse Population Coding and an adaptive latent feedback mechanism, ANTA decouples reasoning depth from parameter count. This enables "System-2" cognition—emulating the brain's slow, deliberate, and logical mode used for complex problem-solving, analysis, and conscious reasoning. We propose ANTA as a blueprint for Sovereign Edge-AI, targeting biological-scale efficiency (~20 Watts) for autonomous reasoning and planning.Keywords: AGI, Neuromorphic Computing, Recursive Neural Networks, Sovereign AI, Energy-Efficient Machine Learning.