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

A New Yardstick for AI-Driven Literature Search

A team of researchers has released ScholarQuest, a taxonomy-guided benchmark designed to evaluate how well LLM-based agents perform academic paper search in open literature environments. The work, published on arXiv, addresses a growing gap: while AI-powered search agents are increasingly used for literature review, existing benchmarks fail to capture the complexity of real-world academic exploration. ScholarQuest introduces a structured taxonomy of search tasks—ranging from simple fact retrieval to multi-step, iterative discovery—and tests agents against a live, uncurated corpus of papers.

Why This Matters

The academic search problem is fundamentally different from web search or QA benchmarks. A researcher seeking "adversarial attacks on graph neural networks" may need to refine queries, follow citation chains, and synthesize findings across subfields. Current benchmarks like HotpotQA or FEVER are static and curated, missing the dynamic, open-ended nature of real literature exploration. ScholarQuest’s taxonomy approach provides a more granular evaluation: it distinguishes between tasks that require simple keyword matching, those needing multi-hop reasoning, and those demanding iterative query refinement based on partial results.

For the AI industry, this benchmark fills a crucial void. As LLM-based agents move from chatbot demos to production research tools—used by scientists, analysts, and engineers—their reliability in open environments becomes critical. A search agent that performs well on curated datasets may fail spectacularly when faced with the noise, ambiguity, and scale of live academic databases like arXiv or PubMed. ScholarQuest’s open literature setting forces agents to contend with real-world challenges: incomplete metadata, evolving terminology, and the need to navigate paywalls or access restrictions.

Implications for AI Practitioners

For developers building research assistants or literature review tools, ScholarQuest offers a more realistic testing ground. The benchmark’s taxonomy can guide feature prioritization: an agent that excels at simple retrieval but fails at iterative search may need better memory or query planning capabilities. Practitioners should also note that the benchmark likely penalizes agents that rely solely on semantic similarity without understanding the structure of academic knowledge—citation networks, field-specific jargon, and temporal relevance.

However, the benchmark’s open literature design introduces a reproducibility challenge. Unlike static datasets, live environments change over time—papers are added, removed, or updated. Evaluations may not be directly comparable across runs, and agents that exploit specific database quirks may not generalize. Practitioners should treat ScholarQuest as a diagnostic tool rather than a leaderboard, using it to identify failure modes in their agents’ search strategies.

Key Takeaways

• ScholarQuest introduces a taxonomy-driven benchmark that evaluates LLM search agents across a spectrum of academic search tasks, from simple retrieval to complex iterative exploration.
• The benchmark’s use of live, uncurated literature environments provides a more realistic assessment than static datasets, but introduces reproducibility challenges.
• For AI practitioners, ScholarQuest highlights the need for agents to handle ambiguity, refine queries iteratively, and navigate the structural complexity of academic knowledge.
• The work underscores that current agentic search systems may be overfit to curated benchmarks and require more robust evaluation in open, noisy environments.