Attention-Driven Interrupt Firmware: A Biologically-Grounded Architecture for Artificial Intelligence

Senior Scientist yunguitang@gmail.com Petaluma, CA, USA Current Large Language Models (LLMs) are built on the Universal Approximation Theorem (UAT), which guarantees that neural networks can approximate any continuous function to arbitrary precision. This mathematical foundation is structurally misaligned with formal logic problems, which are discrete and non-continuous by nature. The result is a class of AI systems that mimic the surface appearance of logical reasoning without achieving it — consuming enormous energy in the process. This paper proposes the Attention-Driven Interrupt Firmware (ADIF) architecture, centered on a typed, learnable lookup table that serves simultaneously as the system's routing mechanism, knowledge store, and learning substrate. Every incoming query is first classified as formal logic or subconscious continuous by a weight-free classification gate, then dispatched to the appropriate table category: logic-labeled entries use Reduced Ordered Binary Decision Diagrams for exact canonical matching, while subconscious-labeled entries use fastText semantic vector cosine similarity. Queries with no sufficiently close match trigger the attention path as a last resort. This unified design eliminates the need for a separate routing classifier, makes the routing mechanism explicitly learnable through table growth, and grounds the architecture in the biological principle that the brain's routing of stimuli to appropriate processing levels is itself a form of accumulated structured knowledge. The ADIF architecture provides a principled path toward AI systems that are slimmer, more stable, and more energy-efficient by design.