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ScholarEval: Research Idea Evaluation Grounded in Literature

Hanane Nour Moussa, Patrick Queiroz Da Silva, Daniel Adu-Ampratwum, Alyson East, Zitong Lu, Nikki Puccetti, Mingyi Xue, Huan Sun, Bodhisattwa Prasad Majumder, Sachin Kumar

arXiv:2510.16234v2cs.AIcs.CLcs.LG

TL;DR

Comprehensive research-idea evaluation remains underexplored, despite the need for detailed, actionable feedback. ScholarEval uses literature retrieval to assess ideas’ soundness and contribution; it achieves higher rubric coverage and evaluation quality than baselines, with users preferring it as a useful tool.

  • Problem

    Existing research-idea evaluation is often limited to prediction, a single dimension, one discipline, or sparse feedback rather than comprehensive, actionable assessment.

    Source: SCHOLAREVAL · p. 2

  • Method

    ScholarEval is a retrieval-augmented, multi-stage pipeline that evaluates research ideas for soundness and contribution using relevant literature.

    Source: §2 · p. 2

  • Results

    ScholarEval achieves higher coverage of human-review points and higher-quality evaluations than baselines, and users prefer it as a useful idea-evaluation tool.

    Source: §7 · p. 9

  • Takeaways & Limitations

    The study indicates ScholarEval’s potential to augment research ideation in AI and human workflows.

    Source: §7 · p. 9

    Human-review coverage is not a definitive measure of overall quality, and evaluation is limited to four disciplines.

    Source: Limitations · p. 9

Abstract

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As AI tools become increasingly common for research ideation, robust evaluation is critical to ensure the validity and usefulness of generated ideas. We introduce ScholarEval, a retrieval augmented evaluation framework that assesses research ideas based on two fundamental criteria: soundness - the empirical validity of proposed methods based on existing literature, and contribution - the degree of advancement made by the idea across different dimensions relative to prior research. To evaluate ScholarEval, we introduce ScholarIdeas, the first expert-annotated dataset of multi-domain research ideas and reviews, comprised of 117 ideas across four disciplines: artificial intelligence, neuroscience, biochemistry, and ecology. Our evaluation shows that ScholarEval achieves significantly higher coverage of points mentioned in the human expert annotated rubrics in ScholarIdeas compared to all baselines. Furthermore, ScholarEval is consistently preferred over our strongest baseline o4-mini-deep-research, a reasoning and search-enabled agentic system by OpenAI, in terms of evaluation actionability, depth, and evidence support. Our large-scale user study also shows that ScholarEval significantly outperforms deep research in literature engagement, idea refinement, and usefulness. We openly release our code, dataset, and ScholarEval tool for the community to use and build on.

1 Introduction

Research ideation is described as both critical and challenging, with a research project’s success fundamentally hinging on it.

1 Introduction

  • Research ideation is a critical and challenging step in scientific research.The passage frames project success as fundamentally hinging on ideation.Source: §1 · p. 1
  • The paper’s code and data are listed at its GitHub repository.The passage provides the ScholarEval repository address.Source: §1 · p. 1

SCHOLAREVAL

ScholarEval evaluates research ideas against existing literature for soundness and contribution, while ScholarIdeas and the study assess its coverage and usefulness. The reported results favor ScholarEval over baselines on rubric coverage and evaluation quality.

SCHOLAREVAL

  • ScholarEval assesses whether proposed methods are empirically supported and how an idea advances prior work across contribution dimensions.Its pipeline retrieves related literature and synthesizes detailed feedback supported by citations.Source: SCHOLAREVAL · p. 2
  • ScholarIdeas contains 117 expert-validated research ideas across four disciplines and 1,076 detailed review rubrics.The disciplines are artificial intelligence, biochemistry, neuroscience, and ecology.Source: SCHOLAREVAL · p. 2
  • ScholarEval achieves higher coverage of expert-annotated review rubrics and higher-quality evaluations than baselines.The introduction reports over 20% relative improvement in rubric coverage over OpenAI Deep Research and user preference for evidence support, depth, and actionability.Source: SCHOLAREVAL · p. 2
  • An 18-expert study comprising 46 evaluations found ScholarEval significantly outperformed deep research on literature engagement, idea refinement, and usefulness.The reported comparison also includes citation use, feedback validity, and relevant evaluation aspects.Source: SCHOLAREVAL · p. 2
  • The paper contributes a multifaceted evaluation framework for long-form research-idea reviews using automatic metrics and expert assessment.The stated contributions also include the ScholarEval tool and user interface.Source: SCHOLAREVAL · p. 2

2 ScholarEval

ScholarEval is a retrieval-augmented, multi-stage pipeline that produces literature-grounded evaluations of research ideas. Its soundness and contribution modules gather and compare relevant evidence to assess methods and novelty.

2 ScholarEval

  • ScholarEval frames evaluation as finding relevant papers and synthesizing their findings into citation-supported assessments of soundness and contribution.The overview identifies soundness and contribution as its two main modules.Source: §2 · p. 2

2.1 Soundness Evaluation

  • The soundness module extracts proposed methods, searches related literature for evidence of their effectiveness, and synthesizes support, contradictions, and improvement suggestions.It retrieves methods, results, and abstracts from related papers to inform method-level assessments.Source: Figure 2 · p. 3, §2.1 · pp. 3–4

2.2 Contribution Evaluation

  • The contribution module extracts idea-specific dimensions, discovers related papers, compares contributions along each dimension, and synthesizes strengths, weaknesses, and suggestions.Its literature search uses abstracts to gather a broad set of related papers for comparison.Source: §2.2 · p. 4

3 ScholarIdeas: A dataset for research idea evaluation

ScholarIdeas addresses the shortage of multi-domain, expert-reviewed research-idea evaluations with a curated dataset and a multi-faceted evaluation protocol.

3.1 Data Curation and Annotation

  • A semi-automatic pipeline retrospectively extracts ideas and review rubrics, then six experts validate fidelity, scope, consistency, and dimension assignments.The extraction process keeps background and methodology sections while dropping sections about execution and results.Source: §3 · p. 4, §3.1 · p. 5

3 ScholarIdeas: A dataset for research idea evaluation

  • ScholarIdeas provides 117 validated idea-review pairs balanced across artificial intelligence, neuroscience, biochemistry, and ecology, with 1,076 review rubrics.The dataset uses existing human-written ideas with expert reviews as a proxy for AI-generated ideas.Source: §3 · p. 4, §3.1 · p. 5, Figure 3 · p. 5

3.2 Evaluation Protocol

  • The evaluation criteria call for identifying strengths and weaknesses, grounding claims in literature, engaging with cited work, and offering actionable suggestions.The framework assesses ScholarEval and baselines against those criteria.Source: §3.2 · p. 5
  • Coverage scores how well evaluations address reference rubrics, using 1–5 judgments averaged across all 1,076 rubrics.The protocol also measures clearly invalid references and uses pairwise LLM judgments of evidence support, actionability, and depth.Source: §3.2 · p. 6

4 Experiments and Results

ScholarEval is evaluated against strong LLM and deep-research baselines, with results covering rubric coverage, reference validity, evaluation quality, and component ablations.

4.1 Experimental Details

  • Baselines include frontier LLMs and two deep-research systems, with prompts aligned to ScholarEval's output format and depth.The deep-research systems are OpenAI Deep Research and DR-Tulu.Source: §4.1 · p. 6

4.2 Results and Analysis

  • ScholarEvalGPT-4.1 and ScholarEvalClaude significantly improve Coverage over all baselines overall and in every discipline, while all variants surpass their backbones.ScholarEval also covers more expert-annotated major points, not only minor rubric items.Source: §4.2 · pp. 6–7
  • 0% Reference Inv. for ScholarEval variants and DR-Tulu, versus 1% for OpenAI Deep Research; baseline rates are lower bounds.A manual audit found up to 80% of Llama-3.3-70B citations hallucinated in certain evaluations.Source: Table 2 · p. 7, §4.2 · p. 7
  • ScholarEvalClaude is consistently better than OpenAI Deep Research and DR-Tulu in evidence-based evaluation, actionable suggestions, and depth.The comparison covers evidence support, suggestions for improving ideas, and engagement with cited literature.Source: Table 3 · p. 7, §4.2 · p. 7
  • Removing methods and results extraction, paper augmentation, or pairwise comparison degrades ScholarEval's Coverage on ScholarIdeas-AI.The authors attribute the drops to reduced literature context, including prior-work results, relevant papers, and granular comparisons.Source: Table 4 · p. 7, §4.2 · p. 7

5 Expert User Study

A blinded expert study compared ScholarEval with OpenAI Deep Research, finding preferences for ScholarEval across six evaluation dimensions.

Design

  • Eighteen experts completed 46 blinded evaluations comparing ScholarEval with OpenAI Deep Research.Participants were PhD students or more senior, had published at least one paper in their field, and completed 46 evaluations in total.Source: §5 · p. 7

Evaluation

  • The study measured citations, faithfulness, usefulness, focus, literature engagement, and suggestions for refining ideas.The dimensions assess such factors as literature depth, feedback alignment with idea-specific nuances, and the feasibility of improvement suggestions.Source: §5 · p. 8

Results and analysis

  • Experts significantly preferred ScholarEval across all six dimensions, reporting 1.5 more useful Citations, 1.2 higher LitEngage, and a 1.2 increase in Refine.The study also reports advantages in Faithful, Focus, and Useful.Source: §5 · p. 8

6 Related Work

Prior work spans research ideation and literature-grounded assistance, while literature-grounded research idea evaluation remains underexplored. ScholarEval combines literature retrieval, soundness assessment, and dense feedback for idea refinement.

Literature Grounded Systems for Research

Research Ideation

Research Idea Evaluation

  • Prior evaluation systems use preference-induced review rubrics, novelty classifications, idea scoring, novelty evaluation, or pairwise outcome prediction, while ScholarEval grounds evaluation in recent literature and provides dense, actionable feedback (Radensky et al., 2025; Afzal et al., 2025; Wen et al., 2025).ScholarEval also assesses method validity, since novelty alone is not sufficient for successful research execution (Si et al., 2025a).Source: §6 · p. 8

7 Conclusion

ScholarEval evaluates research ideas for soundness and contribution using scholarly literature, and ScholarIdeas pairs multidisciplinary ideas with expert-annotated review rubrics. Experiments report higher rubric coverage and evaluation quality than baselines, alongside user-study preference for ScholarEval as an idea-evaluation tool.

7 Conclusion

  • ScholarEval evaluates research ideas for soundness and contribution grounded in scholarly literature, and experiments report higher coverage of human-review points and higher-quality evaluations than baselines.The user study found strong preference for ScholarEval as a useful idea-evaluation tool, indicating potential to augment ideation in AI and human workflows.Source: §7 · p. 9

Limitations

ScholarEval depends on retrieved literature, and its evaluations may misjudge methods not yet established in that literature; evaluation also takes time and can be costly. The dataset and assessments have scope limits, including subjectivity, open-form evaluation challenges, and a four-discipline sample.

Limitations of ScholarEval

  • ScholarEval may misjudge a method’s effectiveness when it has not yet been proven in existing literature, and an evaluation can take around 12 minutes and cost up to $3 depending on the model backbone.The authors note most ideas have some precedents in literature and say the cost is on par with other literature-search workflows.Source: Limitations · p. 9

Limitations of ScholarIdeas and automatic evaluation

  • Human reviews are not definitive ground truth for subjective idea evaluation, and open-form responses and some evaluation facets remain challenging to assess or automate.Coverage of human reviews can signal comprehensiveness but is not a definitive measure of overall quality.Source: Limitations · p. 9
  • Results are limited to ScholarIdeas’ four disciplines and may not generalize elsewhere; some user-study measures, including Focus, are subjective and may reflect stylistic factors rather than improved quality.The dataset spans artificial intelligence, neuroscience, biochemistry, and ecology.Source: Limitations · p. 9

Limitations of Expert User Study

  • Research idea quality varies because participants submitted unique ideas to each system, and the expert sample’s reported 7.2/10 AI-use score may inflate overall scores.The authors state this does not affect the relative improvement over o4-mini-deep-research, citing evidence that the study was adequately blinded.Source: Limitations · p. 9

Ethical Considerations

The dataset’s source papers are licensed for reuse with attribution.

Ethical Considerations

  • ScholarIdeas uses papers from OpenReview and eLife licensed under Creative Commons Attribution, permitting reuse with attribution.The full list of source papers is provided in Appendix D.Source: Ethical Considerations · p. 9

Ethics Statement

This section documents ScholarEval’s implementation, interface, and prompts for soundness review and contribution analysis, alongside the procedures and source papers used to construct ScholarIdeas. It also describes the dataset’s licensing and validation through domain-matched expert review and checks of idea fidelity and rubric labels.

Ethics Statement

  • ScholarIdeas uses OpenReview and eLife papers licensed under Creative Commons Attribution (CC BY), which permits reuse with attribution.The full list of source papers is provided in Appendix D.Source: Ethics Statement · p. 9

Appendix

  • The appendix covers reproducibility, ScholarEval prompts and curation, evaluation metrics, experimental setup, and output examples.Source: Appendix · p. 12

B ScholarEval details and prompts

B.1.1 Additional Implementation Details

  • The soundness pipeline retrieves relevant snippets and referenced papers, extracts methods and results sections, and presents a TL;DR alongside the expandable evaluation.It uses Semantic Scholar and GROBID; citation checking ensures citation-worthy claims have relevant citations and formats the bibliography.Source: §B.1.1 · p. 13

B.1.2 Prompts

  • Soundness prompts extract and rank nonredundant methods, generate contextual literature-search queries, and synthesize related evidence, outcomes, conditions, contradictions, and suggested actions.The meta-review ties evidence and actions to the research idea and orders them by strength or importance.Source: §B.1.2 · pp. 13–14

B.2.1 Additional Implementation Details

  • For contribution analysis, the system searches Semantic Scholar for relevant papers, then samples 25 before pairwise comparisons to reduce latency, computation, and synthesis context length.A final citation check verifies attribution and bibliography formatting.Source: §B.2.1 · p. 15

B.2.2 Prompts

  • Contribution prompts extract faithful, nonredundant statements under comparable high-level dimensions and generate focused queries for each contribution.Retrieved papers are scored from 0 to 5 for similarity between their contributions and those of the research idea.Source: §B.2.2 · pp. 15–16
  • Contribution prompts provide similarity-score examples 0 (“No Similar Contributions”), 1 (“Minimally Similar Contributions”), 2 (“Somewhat Similar Contributions”), and 3 (“Moderately Similar Contributions”), compare idea novelty with the paper abstract overall, and assess novelty and impact by dimension.Source: §B.2.2 · pp. 16–18
  • For each provided dimension, the comparison states whether the idea or paper presents something absent from the other and assigns a numeric score: 1, 0, or -1.A score of 1 favors the idea's novelty; 0 indicates a tie, insufficient evidence, or no paper coverage of the dimension; -1 favors the paper or indicates substantial replication. The guidance also requests cited strengths and weaknesses and actionable suggestions addressing novelty and impact.Source: §B.2.2 · pp. 17–18

C ScholarEval interface

  • The interface lets users enter or upload an idea, optionally set a literature-search cutoff date, and generate either a Soundness or Contribution review.The cutoff date is useful when evaluating an idea that is already published.Source: C ScholarEval interface · p. 18

D.1 Paper List

  • The manuscript provides a linked list of the 117 papers used to construct ScholarIdeas.Entries link to OpenReview or eLife.Source: §D.1 · p. 18

D.2.1 Details

  • Claude 4 Sonnet extracts each dataset paper’s research idea and review rubrics in two stages: verbatim assessment excerpts, then de-duplicated standalone statements classified by type, axis, and severity.The full extraction prompts are provided in section D.2.2.Source: §D.2.1 · p. 18

D.2.2 Prompts

  • The research-idea extraction prompt frames the idea at proposal stage, covering the problem, proposed methods, and planned experiments while excluding results and conclusions.It also asks for a concise, first-person account of the authors’ planned work, without details that could only have been known after experimentation.Source: §D.2.2 · pp. 18–19
  • The prompts extract only review criticism about the idea’s problem, method, and planned experiments, then de-duplicate and contextualize statements and label each by type, axis, and severity.The output is a list of clear, non-redundant evaluation statements.Source: §D.2.2 · pp. 20–21

D.3.1 Details

  • Six experts across computer science, biochemistry, neuroscience, and ecology were trained, matched to ideas in their specialties, and told to skip ideas outside their expertise.The experts comprised Ph.D. students, postdoctoral researchers, and professors.Source: §D.3.1 · p. 21

D.3.2 Validation Instructions

  • Validation checks that ideas reflect the pre-experiment ideation stage and that review statements concern only the idea and have correct type, axis, and severity labels.Annotators make necessary edits to the research idea or review summary when these checks fail.Source: §D.3.2 · p. 22

D.3.3 Validation Results

This section describes edits made during validation, the rubric-based coverage metric, and the conservative procedure for measuring invalid references.

D.3.3 Validation Results

  • Common validation edits addressed missing research-idea results, severity-classification errors, and rubrics about paper results or presentation materials.Table 6 reports the distribution of edits across research ideas and review rubrics, but the supplied text gives no cell values.Source: §D.3.3 · p. 22, Table 6 · p. 22

E.1 Coverage Metric

  • Coverage scores are Prometheus-Eval judgments with a GPT-4 backbone, averaged across ScholarIdeas rubrics that check whether reviews express reference statements.Paraphrases count when they preserve the reference statement’s meaning.Source: §E.1 · p. 22

E.2 Reference Invalidity

  • Reference invalidity is a lower bound: only links returning 404, 410, or 5xx are counted, and ScholarEval has no such invalid references.The authors note that 403 and 200 responses can mask unresolved references, so baseline invalidity is higher than reported.Source: §E.2 · p. 23

E.3 LLM Metrics

This section reports human preferences for ScholarEval and agreement between human and LLM judgments, alongside baseline-prompt controls and retrieval settings. It also describes limits on papers used in recommendations, reference augmentation, and pairwise comparisons, including the option to restrict retrieval to material published before an idea’s source paper.

E.3 LLM Metrics

  • Human and LLM labels showed high percent agreement across metrics and moderate-to-substantial agreement by Gwet’s AC1 in 18 retained report pairs.Six Ph.D. students rated 33 report pairs; equally split cases were discarded, leaving 18 pairs for agreement analysis.Source: §E.3 · p. 23, Table 7 · p. 24
  • Human preferences substantially favored ScholarEval over OpenAI Deep Research across evidence support, actionability, and depth, corroborating the LLM-judge results.The prose describes results from the full sample of 33 report pairs; Table 8’s caption states a sample of 34 pairs.Source: §E.3 · p. 23, Table 8 · p. 24

F.1 Baselines

  • Baseline prompts requested same-format reports; retrieval-augmented baselines were restricted to literature published before each idea’s source-paper cutoff date to reduce target-paper retrieval.The authors note this cutoff reduces, but does not eliminate, target-paper retrieval.Source: §F.1 · pp. 23–24
  • Baseline soundness prompts evaluated each proposed method using related-work support and contradictions, while contribution prompts compared idea novelty and impact across relevant dimensions.Prompts also requested strengths, weaknesses, and suggestions; contribution dimensions were limited to those reflected in the idea and not redundant.Source: §F.1 · pp. 24–25

F.2 ScholarEval

  • ScholarEval used one snippet-search query per extracted method, up to 20 snippets per query, three paper-search queries per contribution statement, and up to 20 papers per query.These settings were chosen to balance system performance, latency, and context-window limits.Source: §F.2 · p. 26
  • ScholarEval uses up to 8 recommended papers, 10 references-augmented papers, and 25 papers sampled from the final list for pairwise comparison, with retrieval optionally restricted to publications before the source paper’s date.The cutoff is the publication date of the paper from which the research idea was extracted.Source: §F.2 · p. 26

F.3 Ablations

The appendix reports additional coverage analyses and specifies three component ablations, each using the setup and parameters from subsection F.2.

F.3 Ablations

  • The ablations remove methods-and-results extraction, paper augmentation, or pairwise comparison, with each variant forwarding a reduced paper set or summary for review.Without extraction, snippets go directly to soundness-review synthesis; without augmentation, only seed papers proceed; without pairwise comparison, 25 papers are sampled for contribution review.Source: §F.3 · p. 26

G.1 Additional Results and Significance Analysis

  • With o4-mini as its backbone, ScholarEval still achieves higher ScholarIdeas-AI rubric coverage than the comparison systems.This evaluation tests ScholarEval using the underlying model of o4-mini-deep-research.Source: §G.1 · p. 26
  • Additional analyses compare coverage significance overall and by discipline, and report coverage by system type, axis, and severity.The appendix also reports latency and cost for ScholarEval and baseline systems.Source: §G.1 · p. 26
  • The appendix also evaluates Idea Novelty Checker and reports latency and cost for ScholarEval and baseline systems.Idea Novelty Checker is described as outputting novelty verdicts with short rationales.Source: §G.1 · p. 26

G.2 Manual Audit

This section documents the manual audit and supporting materials, including the expert-study setup and statistical model, examples of ScholarEval reviews, and cited works featured in output examples across several disciplines.

G.2 Manual Audit

  • Up to 90% of inspected Llama-3.3-70B references, 50% for Claude 4 Sonnet, and 22% in a GPT-4osearch-preview report were invalid.The audit notes that retrieval did not eliminate invalid links.Source: §G.2 · p. 26
  • Claude 4 Sonnet, GPT-4osearch-preview, and o4-mini-deep-research also misattributed cited papers, including work by Bakalarski et al. (2016) and Brotherton and Balskus (2013).Claude linked a paper by Gong et al. to Bakalarski et al. (2016); GPT-4osearch-preview attributed a publication by Brotherton and Balskus (2013) to Kazane et al.; o4-mini-deep-research misattributed CyCIF and CODEX.Source: §G.2 · pp. 26–27

H.1 Setup and Materials

  • Experts evaluated ideas in a semi-random system order, while a system-guess check showed no significant association with assignments (r = −0.12, p = 0.44).Recruitment used X and international graduate department mailing lists; experts received $25 per idea and a $10 bonus for written feedback.Source: §H.1 · pp. 27–28

H.2 Statistical Methods

  • The user study used a Linear Mixed-Effects Model to account for repeated ideas per participant and variation in rater tendencies and idea quality.The model includes fixed effects for ScholarEval versus deep research and random effects for ideas within and across participants.Source: §H.2 · p. 28

I ScholarEval output examples

  • The neuroscience examples show ScholarEval reviews pairing literature-backed method validation with methodological caveats and concrete suggestions, alongside contribution reviews that assess novelty and limitations.The method review discusses within-hand minus across-hand classification and proposes complementary analyses and validation; the dimension review notes both the design’s novelty and overlap with existing cross-modal plasticity research.Source: I ScholarEval output examples · pp. 31–34
  • The expert-study interface displays soundness and contribution reviews in markdown, with method-level evaluations available in a collapsible text box.Figure 6 shows an example from a user who permitted sharing their output.Source: Figure 6 · p. 36
  • The examples include paper listings for neuroscience, biochemistry, and ecology, alongside additional research-paper entries.The supplied lists include titles such as “Modeled grid cells aligned by a flexible attractor” and “Fitness drivers of division of labor in vertebrates.”Source: I ScholarEval output examples · pp. 36–38
  • Works [3]–[8] address bird abundance and occupancy, plant phenology, chimpanzee tool use, beech bud set, gorilla aggression, and beetle diversity.These entries span macroecology, plant responses, animal behavior, and cropping systems.Source: I ScholarEval output examples · p. 38
  • Works [9]–[13] cover caterpillar olfaction, ecosystem productivity, prediction intervals, pesticide-induced resurgence, and genetic diversity.The topics include ecological interactions and biologically meaningful effects.Source: I ScholarEval output examples · p. 38
  • Works [14]–[19] concern moth responses to plant sounds, crow affiliation, plankton transcriptomics, insect biodiversity sensors, microbial communities, and seasonal polyphenism.The listed methods and systems range from microscopy and transcriptomics to optical sensing and synthetic communities.Source: I ScholarEval output examples · p. 38
  • Work [20] examines bird migration in response to Asian monsoons associated with Qinghai–Tibet Plateau uplift.Source: I ScholarEval output examples · p. 38
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