IRAH: Hierarchical Agentic Tree Search for Retrieval Pipeline Optimization
Optimizing Information Retrieval (IR) pipelines for Retrieval-Augmented Generation (RAG) requires jointly selecting structural components and numerical hyperparameters under limited evaluation budgets. We introduce the Information Retrieval Agentic Harness (IRAH), which combines Language Agent Tree Search for IR (LATS-IR) with offline meta-learned initialization. LATS-IR performs UCT-guided hierarchical search, first exploring macro-structural pipeline choices and then refining numerical parameters, with natural-language reflections to guide subsequent mutations and provide inspectable search traces. We evaluate IRAH on three in-distribution benchmarks and one out of-distribution benchmar...
| Authors | Maria Ana Cardei, Aniket Singh, Amir Moghadam et al. |
|---|---|
| Primary category | cs.IR |
| Published | 16 October 2026 |
| Last updated at source | 16 October 2026 |
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