Is re-ranking the top 1000 really the best choice for every query? Optimal Re-Ranking Depth ❓ Does the best depth vary by query? 💡 Yes. The optimal re-ranking depth can be defined per query as the shallowest depth that reaches its best effectiveness, and for some queries deeper re-ranking actually hurts. On MS MARCO, an oracle choosing the depth improves DCG@10 by over 7% while cutting the average depth about 5x. ❓ Can we predict it in advance? 💡 Eleven existing query performance prediction (QPP) methods were mostly poor at it. A promising alternative is "IR-DCG@10": the DCG@10 of the first-stage ranking, computed from LLM relevance judgments. ❓ How much can we save? 💡 With BM25 and Mono-T5, average depth dropped from 511 to 331 (about 3x) at the same effectiveness as full re-ranking; the best QPP baseline gave about 2x. With SPLADE the optimal depth is already around 20, and IR-DCG@10 gives about a 15x reduction. ❓ Any caveats? 💡 Judgments come from a single LLM (GPT-4o-mini) over shallow top-10 pools, the authors call the results preliminary, and a gap to the oracle remains. For deployment, I'd want to know the cost of needing LLM judgments at prediction time. #InformationRetrieval# #Reranking#