by Cheng Li, Jialu Liu, Sean Kulinski, Arnav Singhvi, Wen Sun, Michael Bendersky and Cindy Wang
Effective enterprise data agents require search that is both accurate and fast. Earlier this year we released Instructed-Retriever-1, a retrieval model that can incorporate enterprise data schemas and custom instructions while using parallel test-time scaling to improve retrieval accuracy with low latency. This single-step search approach works well for a large share of user requests. However, more complex, multi-hop questions can still benefit from sequential search, where the model iteratively gathers evidence and refines its queries over multiple steps – at the cost of additional latency.
That's why we're now introducing Adaptive Instructed-Retriever, which combines the speed of parallel retrieval with the performance of sequential search while maintaining strict cost and latency guarantees. The goal is simple: spend additional search steps only when they are useful. This is the same retrieval-efficiency problem faced by Genie Code, Databricks' data agent: it must find the right tables, notebooks, dashboards, and documents in a large, changing workspace without wasting turns on brute-force exploration. Adaptive Instructed-Retriever is designed for this retrieval layer, returning quickly when the evidence is clear and using sequential search only when a harder request requires it. As we show in this post, the trained model matches the quality of leading third-party models at 2x lower latency and improves substantially over single-step search on our retrieval benchmarks.
