Over-Searching in Search-Augmented Large Language Models

• URL: http://arxiv.org/abs/2601.05503v1
• Date: Fri, 09 Jan 2026 03:24:46 GMT
• Title: Over-Searching in Search-Augmented Large Language Models
• Authors: Roy Xie, Deepak Gopinath, David Qiu, Dong Lin, Haitian Sun, Saloni Potdar, Bhuwan Dhingra,
• Abstract summary: Search-augmented large language models (LLMs) excel at knowledge-intensive tasks by integrating external retrieval.<n>Over-searching leads to computational inefficiency and hallucinations by incorporating irrelevant context.<n>Our finding shows: (i) search generally improves answer accuracy on answerable queries but harms abstention on unanswerable ones; (ii) over-searching is more pronounced in complex reasoning models and deep research systems; and (iii) the composition of retrieved evidence is crucial, as the presence of negative evidence improves abstention.
• Score: 22.821710825732563
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Search-augmented large language models (LLMs) excel at knowledge-intensive tasks by integrating external retrieval. However, they often over-search -- unnecessarily invoking search tool even when it does not improve response quality, which leads to computational inefficiency and hallucinations by incorporating irrelevant context. In this work, we conduct a systematic evaluation of over-searching across multiple dimensions, including query types, model categories, retrieval conditions, and multi-turn conversations. Our finding shows: (i) search generally improves answer accuracy on answerable queries but harms abstention on unanswerable ones; (ii) over-searching is more pronounced in complex reasoning models and deep research systems, is exacerbated by noisy retrieval, and compounds across turns in multi-turn conversations; and (iii) the composition of retrieved evidence is crucial, as the presence of negative evidence improves abstention. To quantify over-searching, we introduce Tokens Per Correctness (TPC), an evaluation metric that captures the performance-cost trade-off for search-augmented LLMs. Lastly, we investigate mitigation approaches at both the query and retrieval levels and release the OverSearchQA to foster continued research into efficient search-augmented LLMs.

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