I have been building a small step-through demo so I can watch the system read text: the sensor moves, Layer 1 settles, Layer 2 reacts, and I can replay what happened. That demo exposed a timing issue in the Layer 1 -> Layer 2 handoff 😱
Layer 1 needs time to settle on what it is seeing. But I was feeding Layer 2 every little in-between state while Layer 1 was still settling. So Layer 2 was trying to learn from a moving target. That made the earlier Layer 2 results messier than they should have been. Not fake, but not as clean as I originally reported.
The fix was to make Layer 2 wait until Layer 1 has finished its current fixation, then consume the settled output. That feels much closer to the architecture I actually want: look -> settle -> publish upward
The nice part is that the demo is becoming less of a “show people a cool thing” tool and more of a microscope for the system.
With the fix in, Layer 2 is now starting to learn repeated lower-layer SDR paths in the full hierarchy pipeline. It is not language understanding, and there is still scaffolding in the system. But cleaner than before.
This also clarified the bigger direction. I do not want Million Minds to just be a memory system. The goal is for the hierarchy itself to eventually generate language:
read a prompt
settle internally
predict possible next outputs
choose one
feed it back in
continue
That is the path.