It's not possible to learn anything about other elements when performing binary search, _except_ the only thing there is to learn: if the target is before or after the recently compared element.
If we would guess that there is a bias in the distribution based on recently seen elements, the guess is at least as likely to be wrong as it is to be right. And if we guess incorrectly, in the worst case, the algorithm degrades to a linear scan.
Unless we have prior knowledge. For example: if there is a particular distribution, or if we know we're dealing with integers without any repetition (i.e. each element is strictly greater than the previous one), etc.
If we would guess that there is a bias in the distribution based on recently seen elements, the guess is at least as likely to be wrong as it is to be right. And if we guess incorrectly, in the worst case, the algorithm degrades to a linear scan.
Unless we have prior knowledge. For example: if there is a particular distribution, or if we know we're dealing with integers without any repetition (i.e. each element is strictly greater than the previous one), etc.