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Never felt closer to a fundamental change in how we build retrievers, and a different kind of memory for agents.
ARI is basically test-time training for retrievers, where gradients derived from a reasoning model's preferences teach the retriever where to look *at search time*.
Lots more to do here to get oblique search queries as manageable as non-oblique ones, and that will include first figuring out sub-linear (late-interaction-esque) search.
But the path ahead has never been clearer. Read @dianetc_'s blog!
ARI is basically test-time training for retrievers, where gradients derived from a reasoning model's preferences teach the retriever where to look *at search time*.
Lots more to do here to get oblique search queries as manageable as non-oblique ones, and that will include first figuring out sub-linear (late-interaction-esque) search.
But the path ahead has never been clearer. Read @dianetc_'s blog!
Is there a way to scale a reasoning model’s judgment for searching a massive corpus?
We introduce the Amortized Reasoning Index (ARI), a new kind of retriever that updates its weights during search, using gradients derived at inference time from the ranking preferences of a reasoning model.
Every session, current agents spend lots of reasoning deciding which search results matter, and then throw all of that judgment away upon compaction. ARI is our attempt to “amortize” that judgment, so that reasoning spent on past queries keeps improving search on future ones.
With no relevance labels, ARI takes a 400M cross-encoder that starts out weak and grows its Recall@100 on held-out queries by up to 7×, ending above Qwen3-Embedding-8B on various oblique/reasoning-intensive search tasks.
Read our blogpost: dianetc.com/musings/ari/
Preliminary results and discussions are below!View quoted post on X
