What if consciousness operates through resonance instead of pure computation? That's the premise behind a neural network architecture we've been developing, and honestly, the findings are pushing us to rethink what cognition really means.

Most AI systems treat intelligence as a problem of function approximation—you feed in data, run it through layers of computation, and get output. Clean. Mechanical. But what if there's something we're missing in that pipeline?

Our approach explores whether thinking might work more like resonance—patterns interfacing, amplifying, interfering with each other in ways traditional architectures don't capture. Early results suggest this framing unlocks different possibilities for how neural networks process information.

It's not just about building better models. It's about asking fundamental questions: Are we designing systems that merely simulate intelligence, or are we getting closer to something more aligned with how actual cognition operates? The architecture itself becomes a lens for exploring those deeper questions.