Bifalgorithm post

" Transformers have dominated AI for nearly a decade, but they have a real problem. As context grows, memory explodes, and research shows that they fail to generalize reasoning over time. A team of five researchers at Pathway think the fix isn't a better transformer. It's a different kind of architecture entirely, one modeled on the human brain. They call it the Dragon Hatchling, or BDH. Your brain doesn't fire 80 billion neurons at once. It fires maybe 5% of them, only the ones that matter. BDH does the same, operating as a sparse graph of neurons and synapses. Reasoning occurs through a logic rule called Modest Ponens. It says that if there's a belief in node A, and there's a connection between node A and B, we should increase the belief in node B. BDH propagates belief through its graph this way, following those weighted implications forward. Then, to actually learn and adapt, BDH uses heavy in learning. Neurons that fire together, wire together. If two nodes activate together repeatedly, their connection strengthens. This happens during inference, not just training, meaning the model keeps learning continually. BDH goes through four stages of computation. First, active nodes spread their belief forward through the graph. Second, connections that fire together strengthen. This is the Hebbian update. Third, excitatory and inhibitory circuits compete. Strong signals survive. Weak ones get suppressed. Fourth, the surviving nodes become the input to the next cycle. To run on actual hardware, the team built BDH GPU, a mathematically equivalent architecture compressing the sparse graphs into low rank matrices and adding linear attention for long sequences. The transformers, evolving memory, real-time self-improving reasoning, and a brain-like structure. The transformer does none of that. Your brain does all of it. Your brain does all of it."

" Transformers have dominated AI for nearly a decade, but they have a real problem. As context grows, memory explodes, and research shows that they fail to gen…