Enterprise technology teams increasingly rely on AI agents to automate complex research workflows, from literature reviews to supply chain intelligence. But how well do these agents actually perform on realistic, open-ended search tasks? A new benchmark called ScholarQuest, detailed in a paper on arXiv, provides a systematic answer.
The Problem: Evaluating Agentic Search in Open Literature
Existing benchmarks for academic paper search often fail to capture the iterative, intent-driven nature of real research. According to the ScholarQuest paper, published on arXiv by researchers including Pan, Tingyue, Cheng, Mingyue, Wang, Daoyu, Zhou, Yitong, Ouyang, Jie, Liu, Qi, and Enhong, current evaluation frameworks are insufficient for systematically testing LLM-based search agents in open literature environments.
ScholarQuest Benchmark Design
ScholarQuest is a large-scale, taxonomy-guided benchmark constructed from over 1,000 computer science topics and four representative research intents: method-oriented, setting-anchored, comparison-based, and scope-controlled queries. The benchmark provides scalable answer construction and a shared retrieval backend called ScholarBase for reproducible evaluation. This design allows researchers to measure agent performance across diverse query types and difficulty levels.
| Metric | Best Agent Performance |
|---|---|
| Recall@100 | 0.314 |
| Recall@All | 0.355 |
Key Findings and Performance
Benchmarking results reported in the paper show that agentic methods outperform single-shot retrieval baselines. However, the best-performing agent only achieved 0.314 Recall@100 and 0.355 Recall@All, indicating substantial room for improvement. The paper also includes analyses of search efficiency, intent-level robustness, and failure cases, highlighting the benchmark's ability to provide multi-dimensional evaluation signals for academic paper search agents.
Implications for Enterprise AI Search
While ScholarQuest focuses on academic literature, its findings have direct relevance for enterprise search applications in domains like supply chain and logistics. AI agents tasked with retrieving technical documents, regulatory updates, or trade compliance information face similar challenges: iterative refinement, multi-intent queries, and the need for high recall. The modest recall rates achieved by top agents underscore that current LLM-based search systems are far from reliable for mission-critical enterprise tasks. For CTOs and technology leaders, this benchmark provides a realistic baseline for evaluating agentic search capabilities and highlights the need for continued investment in retrieval infrastructure and agent orchestration.