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    <abstract>RL-trained search agents interleave retrieval with reasoning to answer knowledge-intensive questions, achieving strong end-to-end accuracy on multi-hop QA benchmarks. Yet end-to-end accuracy conflates two distinct abilities: searching for relevant documents and reading what they contain. Existing evaluations do not measure whether the agent reads the documents it retrieves. We design a controlled evaluation that force-injects gold-bearing documents into each agent’s retrieval pipeline, isolating reading from retrieval. Across five RL search agents on three multi-hop QA benchmarks, up to 46.2% of samples are answered incorrectly even when the gold answer is literally present in the retrieved documents—retrieval succeeds but reading fails. We term this phenomenon Retrieval Myopia (RM). The agent’s interaction format is itself a substantial cause: simply re-presenting the same documents to the same model in a plain QA format, with no weight updates, recovers up to 48.9% of RM cases. Guided by this diagnosis, we propose CWFT (Confidence-Weighted Format Transfer), a training-free pipeline that anchors the agent’s prediction against k confidence-weighted clean-format re-reads. CWFT consistently improves accuracy across all evaluated system–dataset configurations on three multi-hop QA benchmarks.</abstract>
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%0 Conference Proceedings
%T Retrieval Myopia: Why RL Search Agents Fail to Read What They Find
%A Woo, Hyeondong
%A Han, Jaeho
%A Pham, Trung X.
%A Kim, Junyeong
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F woo-etal-2026-retrieval
%X RL-trained search agents interleave retrieval with reasoning to answer knowledge-intensive questions, achieving strong end-to-end accuracy on multi-hop QA benchmarks. Yet end-to-end accuracy conflates two distinct abilities: searching for relevant documents and reading what they contain. Existing evaluations do not measure whether the agent reads the documents it retrieves. We design a controlled evaluation that force-injects gold-bearing documents into each agent’s retrieval pipeline, isolating reading from retrieval. Across five RL search agents on three multi-hop QA benchmarks, up to 46.2% of samples are answered incorrectly even when the gold answer is literally present in the retrieved documents—retrieval succeeds but reading fails. We term this phenomenon Retrieval Myopia (RM). The agent’s interaction format is itself a substantial cause: simply re-presenting the same documents to the same model in a plain QA format, with no weight updates, recovers up to 48.9% of RM cases. Guided by this diagnosis, we propose CWFT (Confidence-Weighted Format Transfer), a training-free pipeline that anchors the agent’s prediction against k confidence-weighted clean-format re-reads. CWFT consistently improves accuracy across all evaluated system–dataset configurations on three multi-hop QA benchmarks.
%U https://aclanthology.org/2026.findings-emnlp.1713/
%P 36608-36622

Markdown (Informal)

[Retrieval Myopia: Why RL Search Agents Fail to Read What They Find](https://aclanthology.org/2026.findings-emnlp.1713/) (Woo et al., Findings 2026)

• Retrieval Myopia: Why RL Search Agents Fail to Read What They Find (Woo et al., Findings 2026)

ACL

• Hyeondong Woo, Jaeho Han, Trung X. Pham, and Junyeong Kim. 2026. Retrieval Myopia: Why RL Search Agents Fail to Read What They Find. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 36608–36622, Budapest, Hungary. Association for Computational Linguistics.