[2603.14635] Compute Allocation for Reasoning-Intensive Retrieval Agents

Computer Science > Information Retrieval arXiv:2603.14635 (cs) [Submitted on 15 Mar 2026 (v1), last revised 21 Mar 2026 (this version, v2)] Title:Compute Allocation for Reasoning-Intensive Retrieval Agents Authors:Sreeja Apparaju, Nilesh Gupta View a PDF of the paper titled Compute Allocation for Reasoning-Intensive Retrieval Agents, by Sreeja Apparaju and Nilesh Gupta View PDF HTML (experimental) Abstract:As agents operate over long horizons, their memory stores grow continuously, making retrieval critical to accessing relevant information. Many agent queries require reasoning-intensive retrieval, where the connection between query and relevant documents is implicit and requires inference to bridge. LLM-augmented pipelines address this through query expansion and candidate re-ranking, but introduce significant inference costs. We study computation allocation in reasoning-intensive retrieval pipelines using the BRIGHT benchmark and Gemini 2.5 model family. We vary model capacity, inference-time thinking, and re-ranking depth across query expansion and re-ranking stages. We find that re-ranking benefits substantially from stronger models (+7.5 NDCG@10) and deeper candidate pools (+21% from $k$=10 to 100), while query expansion shows diminishing returns beyond lightweight models (+1.1 NDCG@10 from weak to strong). Inference-time thinking provides minimal improvement at either stage. These results suggest that compute should be concentrated on re-ranking rather than distribut...