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.