The attention mechanism that transformers use to find information in the context is, in its simplest form, O(n^2); for each token position, the model considers whether relevant information has been produced at the position of every other token.
To preserve performance when really long contexts are used, current-generation LLMs use various ways to consider fewer positions in the context; for example, they might only consider the 4096 "most likely" places to matter (de-emphasizing large numbers of "subtle hints" that something isn't correct), or they might have some way of combining multiple tokens worth of information into a single value (losing some fine detail).
Thanks but I don't and I'm not sure why you're jumping to this conclusion.
Selectively gripping and searching the code to pull into context only those parts relevant to the task at hand.
Maybe models would get better in picking up relevant information from large context, but AFAIK it is not the case today.