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arxiv: 2606.20235 · v1 · pith:DHR2WSOPnew · submitted 2026-06-18 · 💻 cs.IR · cs.AI

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

Tingyue Pan , Mingyue Cheng , Daoyu Wang , Yitong Zhou , Jie Ouyang , Qi Liu , Enhong Chen This is my paper

Pith reviewed 2026-06-26 15:37 UTC · model grok-4.3

classification 💻 cs.IR cs.AI
keywords academic paper searchagentic searchLLM agentsinformation retrieval benchmarktaxonomy-guided queriesrecall evaluationopen literature search
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The pith

A new benchmark for academic paper search shows agentic LLM methods beat simple retrieval but still miss most relevant papers.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces ScholarQuest, a benchmark built from over 1,000 computer science topics and four query intents to test how well LLM-based agents can iteratively search open literature. It supplies scalable ground-truth answers and a shared retrieval backend so different agents can be compared on the same footing. Benchmark runs find that agentic approaches improve on one-shot retrieval, yet the strongest agent reaches only 0.314 recall at 100 results and 0.355 recall overall. Readers who care about research efficiency would see this as evidence that current agents still leave large portions of the literature undiscovered and that better iterative strategies are needed.

Core claim

ScholarQuest demonstrates that agentic academic paper search outperforms single-shot baselines across method-oriented, setting-anchored, comparison-based, and scope-controlled intents, but the highest recall remains below 0.36 even when all retrieved papers are considered, and further analyses of efficiency, intent robustness, and failure modes expose consistent shortcomings in existing agents.

What carries the argument

Taxonomy-guided query construction from more than 1,000 CS topics combined with four representative intents and the shared ScholarBase retrieval backend for reproducible answer construction and evaluation.

If this is right

  • Agentic iteration improves recall over single-shot retrieval on the tested intents.
  • Even the best current agents leave most relevant papers unretrieved at practical cutoffs.
  • The benchmark supplies separate signals on search efficiency, intent-level performance, and common failure patterns.
  • A shared backend allows future agents to be evaluated without rebuilding the retrieval index.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The low recall ceiling suggests that advances in agent planning or better integration of external knowledge could produce measurable gains on this benchmark.
  • Extending the same construction method beyond computer science topics would test whether the performance gap generalizes to other disciplines.
  • Failure-case analysis in the benchmark could guide targeted improvements such as better handling of comparison queries.

Load-bearing premise

Queries built from the taxonomy and four intents accurately reflect the real search needs of researchers working in open literature.

What would settle it

A direct comparison of recall scores when the same agents are run on a fresh set of queries written by practicing researchers rather than the taxonomy-derived ones.

Figures

Figures reproduced from arXiv: 2606.20235 by Daoyu Wang, Enhong Chen, Jie Ouyang, Mingyue Cheng, Qi Liu, Tingyue Pan, Yitong Zhou.

Figure 1
Figure 1. Figure 1: Motivation of ScholarQuest. Agentic paper [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Dataset distribution of ScholarQuest. Side [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Cases of the four research-intent types in ScholarQuest. Each quadrant presents a representative specific [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Efficiency and process statistics of paper [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Recall@All density distributions of agentic [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 7
Figure 7. Figure 7: Representative common zero-recall cases across query types. Each case shows a query where all three [PITH_FULL_IMAGE:figures/full_fig_p008_7.png] view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 0 minor

Summary. The paper introduces ScholarQuest, a large-scale benchmark for agentic academic paper search constructed from over 1,000 computer science topics and four representative intents (method-oriented, setting-anchored, comparison-based, scope-controlled). It supplies scalable answer construction and a shared retrieval backend (ScholarBase) for reproducible evaluation. Benchmarking shows agentic methods outperform single-shot retrieval baselines, but the best agent reaches only 0.314 Recall@100 and 0.355 Recall@All; additional analyses cover search efficiency, intent-level robustness, and failure cases.

Significance. If the query distribution is representative of real open-literature academic search, the benchmark and shared backend would constitute a useful standardized resource for evaluating iterative agentic retrieval, with the reported performance gap and multi-dimensional analyses providing concrete signals for future work. The reproducibility infrastructure is a clear strength.

major comments (2)
  1. [§3 (Benchmark Construction)] §3 (Benchmark Construction, as described in abstract): The taxonomy-guided queries are generated by sampling CS topics and instantiating four fixed intent templates, yet no user study, comparison against real query logs (e.g., Semantic Scholar or arXiv), or coverage analysis of the long tail of researcher needs is reported. This assumption is load-bearing for the central claims that agentic methods outperform baselines and that substantial room for improvement exists, because the headline recall figures are only meaningful if ScholarQuest accurately proxies realistic search distributions.
  2. [Evaluation section] Evaluation section (abstract and methods): The abstract reports concrete recall numbers and outperformance but provides insufficient detail on how ground-truth answer labels are constructed, verified for accuracy, or protected against bias in the shared ScholarBase backend. Without these specifics, it is not possible to confirm that the evaluation supports the stated claims.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments. We address each major point below with clarifications and proposed revisions.

read point-by-point responses

  1. Referee: [§3 (Benchmark Construction)] §3 (Benchmark Construction, as described in abstract): The taxonomy-guided queries are generated by sampling CS topics and instantiating four fixed intent templates, yet no user study, comparison against real query logs (e.g., Semantic Scholar or arXiv), or coverage analysis of the long tail of researcher needs is reported. This assumption is load-bearing for the central claims that agentic methods outperform baselines and that substantial room for improvement exists, because the headline recall figures are only meaningful if ScholarQuest accurately proxies realistic search distributions.

    Authors: We agree that direct empirical validation against real query logs would strengthen claims of representativeness. The four intents were selected after reviewing common academic search patterns described in prior IR literature on scholarly search behaviors. Access to proprietary logs from Semantic Scholar or arXiv is not available to us, precluding direct comparison. In revision we will add a dedicated subsection in §3 that (a) justifies the intent templates with citations to existing taxonomies of research questions, (b) reports coverage statistics over the sampled CS topics, and (c) explicitly discusses the lack of a user study as a limitation of the current benchmark design. revision: partial

  2. Referee: [Evaluation section] Evaluation section (abstract and methods): The abstract reports concrete recall numbers and outperformance but provides insufficient detail on how ground-truth answer labels are constructed, verified for accuracy, or protected against bias in the shared ScholarBase backend. Without these specifics, it is not possible to confirm that the evaluation supports the stated claims.

    Authors: We apologize for the brevity in the current description. The revised manuscript will expand the Answer Construction and ScholarBase subsections to detail: the taxonomy-driven procedure for identifying candidate papers, the verification protocol (including sample manual review and cross-validation across retrieval methods), and safeguards against bias (fixed public backend snapshot, no overlap with any training data). These additions will make the evaluation pipeline fully reproducible and transparent. revision: yes

Circularity Check

0 steps flagged

No circularity: benchmark construction and evaluation are self-contained.

full rationale

The paper constructs ScholarQuest by sampling from over 1,000 external CS topics and instantiating four fixed intent templates, then evaluates agents against single-shot baselines on a shared external backend (ScholarBase). No equations, fitted parameters, or predictions reduce to the paper's own inputs by construction. No self-citations are invoked as load-bearing uniqueness theorems or ansatzes. The outperformance claim and room-for-improvement conclusion rest on direct empirical comparisons within the newly defined benchmark rather than any self-referential derivation.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 2 invented entities

The central contribution is the new benchmark itself. It rests on the domain assumption that the chosen topics and intents represent real academic search needs, with no free parameters or invented physical entities.

axioms (1)
  • domain assumption The four research intents (method-oriented, setting-anchored, comparison-based, scope-controlled) represent key aspects of academic paper search.
    Invoked to guide query construction and multi-dimensional evaluation.
invented entities (2)
  • ScholarQuest benchmark no independent evidence
    purpose: To systematically evaluate agentic academic paper search
    Newly proposed construction from taxonomy and intents.
  • ScholarBase no independent evidence
    purpose: Shared retrieval backend for reproducible evaluation
    Introduced to support the benchmark.

pith-pipeline@v0.9.1-grok · 5720 in / 1328 out tokens · 23853 ms · 2026-06-26T15:37:49.520127+00:00 · methodology

discussion (0)

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Reference graph

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