ML//Inference//distributional shift//basin of attraction

Regions of latent space where certain types of continuations are strongly preferred, and the model "falls into" them based on context.

Regions of latent space where certain types of continuations are strongly preferred, and the model "falls into" them based on context.

Pretraining created these basins: "after reasoning → coherent conclusion" is one basin, "after question → direct answer" is another, "after nonsense → unpredictable continuation" is another.

Random text as context doesn't help because it positions the model in a basin with no semantic structure. The model has no signal about what type of continuation to produce.

Structured reasoning activates a specific basin because during pretraining, that pattern co-occurred systematically with correct conclusions. It's intentional distributional shift toward a useful attractor.

The technical effect: reasoning tokens reduce entropy of the output distribution. Fewer plausible tokens survive as continuations, and the ones that remain tend to be more correct.

Overthinking happens when excessive reasoning tokens push the model past the useful basin into a "complex reasoning" attractor that's counterproductive for simple problems.

Exposure bias is falling into a bad attractor: once errors accumulate, the context vector enters a basin the model never navigated during training.