Do You Need Embeddings? A Decision Guide
Signals that lexical search over curated metadata will carry you, signals you genuinely want vectors, and a fifty-query gold-set test to settle it on your own corpus before paying for infra.
4 min read
Blog
Notes from the forest
Engineering notes on navigation, ingestion, agent memory and honest benchmarking, written by the people who ran the experiments.
Where the Latency Goes
17.5 s p95 for iterative RAG, 8.4 s for the navigator, same 12B model. A dissection of both loops, and why variance measured in cheap hops instead of full retrieve-and-read rounds collapses the tail.
3 min read
A Three-Hop Question, Worked Example
One question, three documents, traced twice: watch top-k retrieval dead-end on a three-hop question, then watch the same corpus answer it when an agent walks it node by node.
4 min read
Navigate, Don't Retrieve: What a Hunt Actually Looks Like
Three moves, one worked hunt through a small company's corpus, and why the same 12B model goes from 0/11 to 11/11 when it walks a forest instead of reading a top-k paste.
4 min read
Why multi-hop questions break classic RAG (and what to do about it)
Top-k retrieval is a single hop by construction. When an answer needs three, the bottleneck stops being the model and becomes the shape of your corpus.
4 min read
Want the long version?
The paper carries the full architecture, the benchmark tables and the findings that failed their criteria.