It seems to imply that at least those larger models have a representation of weight subspaces. I think it is not generally surprising to see some form of representation for inner activation patterns. The J space is also an example of this. An explicit way of forcing something like that was discussed in Self-prediction acts as an emergent regularizer. But representation of representation doesn't explain why it can interpret the weight values. Generalizing introspection patterns doesn't give you access to your weights. I see the following explanations: * The models obviously have been trained on a lot of LLM papers and code etc and represent that knowledge deep enough to grok the relation between arbitrary concepts and weights to represent it. That would imply an inner optimizer, which I find worrisome. * Dito but without generalizing from LLM papers and code specfically but pure generalization (groking weights even deeper). Even more worrisome if true, but think this is unlikely. * This specific model may have been trained on exactly these kinds of correspondences by the lab! It seems at least plausible that the labs may have tries such a thing. It seems useful for interpretability work. Maybe it was not intended to be used this way by external parties, but whatever. This would be less worrisome as it seems more like a "normal" trained capability. It could still lead to an inner optimizer, but via a less spontaneous route.