Test-Time Strategies for More Efficient and Accurate Agentic RAG
Retrieval-Augmented Generation (RAG) systems face challenges with complex, multihop questions, and agentic frameworks such as Search-R1 (Jin et al., 2025), which operates iteratively, have been proposed to address these complexities. However, such approaches can introduce inefficiencies, including repetitive retrieval of previously processed information and challenges in contextualizing retrieved results effectively within the current generation prompt. Such issues can lead to unnecessary retrieval turns, suboptimal reasoning, inaccurate answers, and increased token consumption.
  In this paper, we investigate test-time modifications to the Search-R1 pipeline to mitigate these identified shortcomings. Specifically, we explore the integration of two components and their combination: a contextualization module to better integrate relevant information from retrieved documents into reasoning, and a de-duplication module that replaces previously retrieved documents with the next most relevant ones. We evaluate our approaches using the HotpotQA (Yang et al., 2018) and the Natural Questions (Kwiatkowski et al., 2019) datasets, reporting the exact match (EM) score, an LLM-as-a-Judge assessment of answer correctness, and the average number of turns.
  Our best-performing variant, utilizing GPT-4.1-mini for contextualization, achieves a 5.6% increase in EM score and reduces the number of turns by 10.5% compared to the Search-R1 baseline, demonstrating improved answer accuracy and retrieval efficiency.
69ba42315d2b9c28ea09f24f
Brian Zhang
69ba42315d2b9c28ea09f250
Deepti Guntur
admin_assigned
2026-03-18T10:17:18.700Z
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/avatars/a6c3ea420263b45168728702e48955ee.svg
Deepti Guntur
deeptiguntur
user
deeptiguntur
69ba42315d2b9c28ea09f251
Zhiyang Zuo
69ba42315d2b9c28ea09f252
Abhinav Sharma
69ba42315d2b9c28ea09f253
Shreyas Chaudhari
69ba42315d2b9c28ea09f254
Wenlong Zhao
69ba42315d2b9c28ea09f255
Franck Dernoncourt
claimed_verified
2026-03-18T08:35:47.856Z
62c5947524171688a9feb992
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Franck Dernoncourt
Franck-Dernoncourt
user
Franck-Dernoncourt
69ba42315d2b9c28ea09f256
Puneet Mathur
69ba42315d2b9c28ea09f257
Ryan Rossi
69ba42315d2b9c28ea09f258
Nedim Lipka
Test-Time Strategies for More Efficient and Accurate Agentic RAG
Retrieval-Augmented Generation (RAG) systems face challenges with complex, multihop questions, and agentic frameworks such as Search-R1 (Jin et al., 2025), which operates iteratively, have been proposed to address these complexities. However, such approaches can introduce inefficiencies, including repetitive retrieval of previously processed information and challenges in contextualizing retrieved results effectively within the current generation prompt. Such issues can lead to unnecessary retrieval turns, suboptimal reasoning, inaccurate answers, and increased token consumption.
  In this paper, we investigate test-time modifications to the Search-R1 pipeline to mitigate these identified shortcomings. Specifically, we explore the integration of two components and their combination: a contextualization module to better integrate relevant information from retrieved documents into reasoning, and a de-duplication module that replaces previously retrieved documents with the next most relevant ones. We evaluate our approaches using the HotpotQA (Yang et al., 2018) and the Natural Questions (Kwiatkowski et al., 2019) datasets, reporting the exact match (EM) score, an LLM-as-a-Judge assessment of answer correctness, and the average number of turns.
  Our best-performing variant, utilizing GPT-4.1-mini for contextualization, achieves a 5.6% increase in EM score and reduces the number of turns by 10.5% compared to the Search-R1 baseline, demonstrating improved answer accuracy and retrieval efficiency.
Test-time modifications to the Search-R1 pipeline improve retrieval efficiency and answer accuracy through contextualization and de-duplication modules.
Retrieval-Augmented Generation agentic frameworks Search-R1 multihop questions contextualization module de-duplication module HotpotQA Natural Questions exact match score LLM-as-a-Judge