Another item for the todo list: Autoregressive transformer gradient flow shapes earlier token computation to serve future predictions, but that early computation cannot condition on future tokens. This should serve as a regularizing influence on the internal structure of token predictions: in order to be useful to the largest possible set of future predictions, the local computation would need to factor itself into maximally reusable modules.

The greater the local uncertainty about the future, the less the local computation can be specialized to serve future tokens. Could consider it something like: the internal representation is a probability-weighted blend of representations useful to possible futures. If the local computation is highly confident in a narrow space, it can specialize more.

Simplicity biases would incentivize sharing modules more strongly. Even if the local computation suspects a narrower future distribution, it would be penalized for implementing specialized machinery that is too rarely useful.

One implication: many forms of token-parallelized search get blocked, because they require too much foresight-driven specialization.

Another item for the todo list:

Autoregressive transformer gradient flow shapes earlier token computation to serve future predictions, but that early computation cannot condition on future tokens. This should serve as a regularizing influence on the internal structure of token predictions: in order to be useful to the largest possible set of future predictions, the local computation would need to factor itself into maximally reusable modules.

The greater the local uncertainty about the future, the less the local computation can be specialized to serve future tokens. Could consider it something like: the internal representation is a probability-weighted blend of representations useful to possible futures. If the local computation is highly confident in a narrow space, it can specialize more.

Simplicity biases would incentivize sharing modules more strongly. Even if the local computation suspects a narrower future distribution, it would be penalized for implementing specialized machinery that is too rarely useful.

One implication: many forms of token-parallelized search get blocked, because they require too much foresight-driven specialization.