Test-time adaptation is a family of methods that adjust a model during evaluation or deployment instead of leaving it completely fixed after training. The adjustment may use the current inputs, feedback or repeated attempts to improve performance on a new distribution or task.
The method can make benchmark progress difficult to attribute to scale alone. A result may reflect the model, the adaptation procedure, additional computation and the way researchers structured the test, so reports should describe the full experimental setup.
