I’m struggling to understand how this approach is novel.

How is this different from nearest-neighbor within traditional vector search, used by RAG systems? What am I missing?

1. Compute input embeddings 2. Concurrently compute distance-to/logits-of runtime-specified (sic cached) label embeddings 3. Return greatest/closest label

I do appreciate the Kahneman reference.