Great Paper from NVIDIA. We’ve spent the last couple of years treating RAG and long context as two completely separate engineering headaches. One is an external pipeline of bi-encoders and rerankers, the other is an expensive brute-force attention problem. This paper makes a super compelling case that this split is mostly artificial. Instead of bolting on external retrievers, they tap straight into the frozen LLM’s own internal representations to select evidence across both regimes. The wild part is that the backbone stays completely frozen. They only add around 500k trainable parameters. On a massive 3B-token Wikipedia index, this tiny tweak beats traditional dense and hybrid retriever-reranker stacks across the board (HotpotQA recall jumps from 49% to 73% and 2Wiki leaps from 31% to 60%). But it also solves the needle-in-a-haystack problem from the other direction. On long-context benchmarks, using that same internal mechanism to aggressively prune distractors before generation pushes NoLiMA accuracy at 128k context from basically flatlining at 1% up to nearly 25%. Because it strips out the fluff early, you get a massive drop in FLOPs and time-to-first-token past 32k tokens instead of watching your latency explode. Great work by Edan Kinderman and the team. Read the full paper here: arxiv.org/pdf/2610.08463