ScholarQuest: A Taxonomy-Guided Benchmark for Agentic Academic Paper Search in Open Literature Environments

• URL: http://arxiv.org/abs/2606.20235v1
• Date: Thu, 18 Jun 2026 13:47:20 GMT
• Title: ScholarQuest: A Taxonomy-Guided Benchmark for Agentic Academic Paper Search in Open Literature Environments
• Abstract summary: We propose ScholarQuest, a large-scale, taxonomy-guided benchmark for agentic academic paper search.<n>It is constructed from over 1,000 computer science topics and four representative research intents, including method-oriented, setting-anchored, comparison-based, and scope-controlled queries.<n> Benchmarking results show that agentic methods outperform single-shot retrieval baselines, yet the best-performing agent achieves only 0.314 Recall@100 and 0.355 Recall@All, indicating substantial room for improvement.
• Score: 44.35044790171568
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Academic paper search is a core step in scientific research, and LLM-based search agents are emerging as a promising paradigm for iterative, intent-driven literature exploration. However, existing benchmarks are insufficient for systematically evaluating agentic academic search under realistic open literature environments. We propose ScholarQuest, a large-scale, taxonomy-guided benchmark for agentic academic paper search. ScholarQuest is constructed from over 1,000 computer science topics and four representative research intents, including method-oriented, setting-anchored, comparison-based, and scope-controlled queries. It further provides scalable answer construction and a shared retrieval backend ScholarBase for reproducible evaluation. Benchmarking results show that agentic methods outperform single-shot retrieval baselines, yet the best-performing agent only achieves 0.314 Recall@100 and 0.355 Recall@All, indicating substantial room for improvement. In addition, analyses of search efficiency, intent-level robustness, and failure cases further highlight the benchmark's ability to provide multi-dimensional evaluation signals for academic paper search agents.

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