I really liked your Detroit Tigers + lettuce in Berlin example. It nails one of the core problems: language models are still dealing with “relatedness” in a super linear and flat way. They can’t really hold a jump like that.

When I brought up topology, I wasn’t talking about anything spatial. I meant more like a model’s thinking path needs to form its own system, a kind of closed semantic topology map.

Each node is a meaning unit, all linked by invisible threads. The input sentence is like a little pacman moving through the map⸜( ´͈ Ⱉ `͈ )⸝ pulled along by those threads until it reaches the node that resonates the most. That’s where the answer comes from.

So it’s not calculating, it’s being guided. Kinda like gravity, but made out of meaning.

What you described feels super close to this. Maybe that’s what context modeling is really heading toward… We just haven’t found the right way to talk about it yet. Σ(๑Ⱉ⸝⸝Ⱉ๑;)੭⁾⁾