Diane

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!

I wrote a short blog on my experience testing Jev on reasoning-intensive regression problems. The results are within expectations (Jev is focused on classification after all), but the model has great attributes and is wicked fast! Read about it here: dianetc.com/musings/jev/

Awesome blog! And they evaluated on the congress ToT split of OBLIQ-Bench

Last fall, I spent a while trying to get a better understanding of the information retrieval (IR) landscape. This mostly involved a lot of research conversations, some half-baked ideas, and, for a while, not much tangible research progress.

With OBLIQ-Bench being a few months old, I wanted to write a short blog post about what led to its creation, what it says, and what I think should come next!

check it out: dianetc.github.io/musings/obliqu…

We set out to build a better retriever, so we looked for the hardest IR benchmarks.

For each, we asked how much headroom remained by running oracle reranking with a frontier LLM. Most had little room left!

So we built OBLIQ-Bench to study much harder search queries than before.