But the model probably “knows” how many tokens there are; it’s an extremely salient property of the input
This doesn’t seem that clear to me; what part of training would incentivize the model to develop circuits for exact token-counting? Training a model to adhere to a particular token budget would do some of this, but it seems like it would have relatively light pressure on getting exact estimates right vs guessing things to the nearest few hundred tokens.
One way to test this would be to see if there are SAE features centrally about token counts; my guess would be that these show up in some early layers but are mostly absent in places where the model is doing more sophisticated semantic reasoning about things like introspection prompts. Ofc this might fail to capture the relevant sense of “knowing” etc, but I’d still take it as fairly strong evidence either way.
This doesn’t seem that clear to me; what part of training would incentivize the model to develop circuits for exact token-counting? Training a model to adhere to a particular token budget would do some of this, but it seems like it would have relatively light pressure on getting exact estimates right vs guessing things to the nearest few hundred tokens.
We know from humans that it’s very possible for general intelligences to be blind to pretty major low-level features of their experience; you don’t have introspective access to the fact that there’s a big hole in your visual field or the mottled patterns of blood vessels in front of your eye at all times or the ways your brain distorts your perception of time and retroactively adjusts your memories of the past half-second.
One way to test this would be to see if there are SAE features centrally about token counts; my guess would be that these show up in some early layers but are mostly absent in places where the model is doing more sophisticated semantic reasoning about things like introspection prompts. Ofc this might fail to capture the relevant sense of “knowing” etc, but I’d still take it as fairly strong evidence either way.