This is a cool idea. It would be interesting to see how sensitive the results are to the strength of the LLM used for query augmentation. They used GPT-5 in their experiments, and generating all those tokens would presumably add a lot of latency in your query pipeline.

Another baseline that would be interesting to see is just pure LLM reranking--is Jev/the query augmentation adding anything? Presumably the idea is that Jev is cheaper, but even with a costly LLM you might get a lot of cache hits as long as the query (the variable portion) goes last. Even setting caching aside, almost the entire request is prefill plus a very small amount of generation for the judgment itself. You may end up generating fewer tokens just using the LLM as a judge directly than you need to generate the rubric for Jev. OTOH if queries can be normalized or are naturally repetitive you may be able to cache the rubric...