Scientific Reproducibility

Overview

The field asks whether findings can be recovered from reported methods and artifacts, and how measurement, preprocessing, statistical analysis, software, and evaluation choices affect that recovery. Common approaches include sharing versioned data, code, and environments; preregistration and provenance records; standardized workflows and checklists; independent reruns; and benchmarks that test coding or language-model agents end to end. Audits across fields reveal missing artifacts, underspecified procedures, hidden analytic flexibility, and sensitivity to datasets, configurations, or software; agents can reduce reproduction effort but still struggle with tacit assumptions and validating scientific meaning rather than merely producing runnable code. The central challenge is turning transparency into faithful, durable verification: open artifacts and automation help, but neither guarantees comparable results nor establishes that a claim is sound.

Latest papers

CardsList

108 papers13 papers in the last four weeks, level with the four weeks before. 0.1% of all new papers.

  1. IoT-Zoo: A Container-Based Framework for Heterogeneous IoT Device Profiles and Reproducible Traffic Capture

    Apr 7, 2026Vagner E. Quincozes, Diego Kreutz, Silvio E. QuincozesInternet of ThingsScientific Reproducibility

  2. RESCORE: LLM-Driven Simulation Recovery in Control Systems Research Papers

    Apr 6, 2026Vineet Bhat, Shiqing Wei, Ali Umut Kaypak +3Scientific Code GenerationScientific Reproducibility

  3. How Open Must Language Models be to Enable Reliable Scientific Inference?

    Mar 27, 2026James A. Michaelov, Catherine Arnett, Tyler A. Chang +7AI-Assisted Scientific ResearchLLM Reliability

  4. Code Sharing In Prediction Model Research: A Scoping Review

    Mar 16, 2026Thomas Sounack, Raffaele Giancotti, Catherine A. Gao +8Scientific ReproducibilityML Reproducibility

  5. Supporting Workflow Reproducibility by Linking Bioinformatics Tools across Papers and Executable Code

    Mar 9, 2026Clémence Sebe, Olivier Ferret, Aurélie Névéol +3Entity ResolutionScientific Reproducibility

  6. Supporting Artifact Evaluation with LLMs: A Study with Published Security Research Papers

    Mar 6, 2026David Heye, Karl Kindermann, Robin Decker +5Scientific ReproducibilityAutomated Evaluation

  7. Benchmark of Benchmarks: Unpacking Influence and Code Repository Quality in LLM Safety Benchmarks

    Mar 3, 2026Junjie Chu, Xinyue Shen, Ye Leng +3Benchmark ReproducibilityLLM Safety Benchmarks

  8. What Papers Don't Tell You: Recovering Tacit Knowledge for Automated Paper Reproduction

    Mar 2, 2026Lehui Li, Ruining Wang, Haochen Song +8Scientific Code GenerationScientific Reproducibility

  9. Agentic Scientific Simulation: Execution-Grounded Model Construction and Reconstruction

    Feb 27, 2026Knut-Andreas Lie, Olav Møyner, Elling Svee +1Physics-Based SimulationScientific Code Generation

  10. Criteria-first, semantics-later: reproducible structure discovery in image-based sciences

    Feb 17, 2026Jan BumbergerScientific ReproducibilityAI for Science

  11. ReplicatorBench: Benchmarking LLM Agents for Replicability in Social and Behavioral Sciences

    Feb 11, 2026Bang Nguyen, Dominik Soós, Qian Ma +8LLM Agent EvaluationAI Agent Benchmarks

  12. RegCheck: A tool for structured comparisons between study registrations and papers

    Jan 19, 2026Jamie Cummins, Beth Clarke, Ian Hussey +1Scientific Reproducibility

  13. Standardization of Post-Publication Code Verification is Possible with the Support of the Community

    Jan 12, 2026Susana Lopez-Moreno, Sangil Kim, Eric Dolores-CuencaScientific ReproducibilityML Reproducibility

  14. Can synthetic data reproduce real-world findings in epidemiology? A replication study using adversarial random forests

    Aug 19, 2025Jan Kapar, Kathrin Günther, Lori Ann Vallis +27Synthetic Tabular Data GenerationSynthetic Data Evaluation