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

Senior Scientist Petaluma, CA, USA Current Large Language Models (LLMs) are built on the Universal ApproximationTheorem (UAT), which guarantees that neural networks can approximate anycontinuous function to arbitrary precision. This mathematical foundation is structurallymisaligned with formal logic problems, which are discrete and non-continuous bynature. The result is a class of AI systems that mimic the surface appearance of logicalreasoning without achieving it — consuming enormous energy in the process. Thispaper proposes the Attention-Driven Interrupt Firmware (ADIF) architecture, centeredon a typed, learnable lookup table that serves simultaneously as the system's routingmechanism, knowledge store, and learning substrate. Every incoming query iscompared against table entries, each labeled with a route type — formal logic orsubconscious continuous — and dispatched accordingly: closest logic-labeled matchtriggers the formal logic path, closest subconscious-labeled match triggers thelightweight continuous path, and queries with no sufficiently close match trigger theattention path as a last resort. This unified design eliminates the need for a separaterouting classifier, makes the routing mechanism explicitly learnable through tablegrowth, and grounds the architecture in the biological principle that the brain's routingof stimuli to appropriate processing levels is itself a form of accumulated structuredknowledge. We argue that formal logic is not an optional capability to be approximatedby continuous systems, but a structural requirement for artificial intelligence, and thatthe ADIF architecture provides a principled path toward AI systems that are slimmer,more stable, and more energy-efficient by design.