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

2026-06-18 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Information Retrieval · Depth: Expert, quick

Summary

ScholarQuest is a new, large-scale, taxonomy-guided benchmark designed to evaluate LLM-based search agents for academic paper search in realistic open literature environments. Addressing the limitations of existing benchmarks, ScholarQuest is built from over 1,000 computer science topics and incorporates four distinct research intents: method-oriented, setting-anchored, comparison-based, and scope-controlled queries. It features scalable answer construction and a shared retrieval backend called ScholarBase, ensuring reproducible evaluations. Initial benchmarking reveals that agentic methods surpass single-shot retrieval baselines, yet the top-performing agent only achieves 0.314 Recall@100 and 0.355 Recall@All, indicating substantial potential for further development in this area. The benchmark also facilitates multi-dimensional evaluation through analyses of search efficiency, intent-level robustness, and failure cases.

Key takeaway

For Machine Learning Engineers developing LLM-based search agents, recognize that current agentic methods, while better than baselines, still have substantial performance gaps, achieving only 0.314 Recall@100. You should utilize benchmarks like ScholarQuest to systematically evaluate your agents across diverse research intents and identify specific failure cases, guiding targeted improvements in robustness and efficiency. This will help you build more effective and reliable academic search tools.

Key insights

ScholarQuest benchmark reveals LLM-based search agents significantly underperform in academic literature exploration, despite outperforming baselines.

Principles

• Agentic search outperforms single-shot retrieval.
• Taxonomy-guided benchmarks improve evaluation.
• Multi-dimensional analysis reveals agent weaknesses.

Method

ScholarQuest is constructed from 1,000+ computer science topics and four research intents. It enables reproducible evaluation of agentic search via its ScholarBase retrieval backend.

In practice

• Benchmark new LLM agents with ScholarQuest.
• Develop agents robust to diverse search intents.
• Analyze agent efficiency and failure modes.

Topics

• LLM Agents
• Academic Search
• Benchmarking
• Information Retrieval
• ScholarQuest
• Computer Science

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.