This week
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Updated through the day. Every headline links straight to the source. The two lines underneath are ours.
This solves a real operational problem: RAG systems that run separate rankers waste GPU residency and recompute context. By using the generator as its own ranker through cached prefill, you cut memory footprint and latency. For anyone running RAG at scale, this is worth benchmarking against your current setup.
The two-stage architecture wins decisively, and this matters for evidence synthesis at scale. If you're building systematic review tools or compliance search, this is the retrieval pattern to adopt. The cost curve means you still need to think about depth limits, but the signal quality justifies the extra stage.
The real story is that flat vector retrieval breaks on recurring entities across services. They built an ontology layer to organize knowledge before retrieval, which is the right architecture for enterprise agents touching multiple systems. If you're wiring agents into complex internal software, this shows why you need structure, not just embeddings. The method is engineering-heavy but the pattern is actionable.
The finding that matters: generative engines retrieve pages using model-rewritten sub-queries, not the original user query, and those retrieved results dictate citations far more than traditional SEO signals do. For publishers, optimizing for traditional search engines misses what actually matters. For builders of generative engines, this highlights how internal retrieval and query rewriting shape the web's information ecosystem. Content matching question phrasing helps, but it's secondary.