Uzu013ai | UPDATED |

This paper introduces , a lightweight, high-variance neural architecture designed to operate in zero-shot environments where training data is scarce or non-existent. Unlike traditional Large Language Models (LLMs) that rely on massive parameter counts and probabilistic token prediction, uzu013ai utilizes a Recursive Heuristic Overlay (RHO) to generate outputs based on logical necessity rather than statistical probability. Preliminary testing indicates that uzu013ai offers a 400% increase in inference efficiency compared to industry-standard transformers, though it exhibits higher instability in open-ended generative tasks.

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