GRN · German Reproducibility Network

Reproducibility in Machine Learning-based Research: Overview, Barriers and Drivers

The concept of reproducibility can have different interpretations across various research fields and even within the same field [39]. To avoid confusion, we first specify our terms, broadly defining reproducibility and then further categorizing it into various types and degrees. The first distinction comes from Goodman et al. [42], who specify a fundamental division between whether we (i) mean reproducible in principle (termed “methods” reproducibility) due to sufficient description/sharing of methodologies, materials, etc., or (ii) whether results/conclusions actually prove to be reproducible when experiments or analyses are re-done. In the second category, they distinguish “results” and “inferential” reproducibility, depending on whether the analyses or inferences to broader conclusions are reproduced.

In an era where research evaluation methods are evolving, the Research Contribution Claim Network makes trustworthy tracking of non-traditional research output easy!

In this whitepaper, Patrick Hochstenbach (Ghent University Library), Thomas van Himbergen (SURF), Laurents Sesink (SURF) and Herbert Van de Sompel (DANS) introduce the...

Unreproducible Research is Reproducible

The apparent contradiction in the title is a wordplay on the different meanings attributed to the word reproducible across different scientific fields. What we imply is that unreproducible findings can be built upon reproducible methods. Without denying the importance of facilitating the reproduction of methods, we deem important to reassert that reproduction of findings is a fundamental step of the scientific inquiry. We argue that the commendable quest towards easy deterministic reproducibility of methods and numerical results should not have us forget the even more important necessity of ensuring the reproducibility of empirical findings and conclusions by properly accounting for essential sources of variations. We provide experiments to exemplify the brittleness of current common practice in the evaluation of models in the field of deep learning, showing that even if the results could be reproduced, a slightly different experiment would not support the findings. We hope to help clarify the distinction between exploratory and empirical research in the field of deep learning and believe more energy should be devoted to proper empirical research in our community. This work is an attempt to promote the use of more rigorous and diversified methodologies. It is not an attempt to impose a new methodology and it is not a critique on the nature of exploratory research.

ReproAI: the plugin for reproducible replication packages

An author-facing AI plugin that helps you prepare a cleaner, reproducible replication package before submission.

Keynote: Reproducibility and replicability of computer simulations | Canal U

Since the early days of the reproducibility crisis, much progress has been made in understanding and improving computational reproducibility and replicability (R and R)...

A Novel Kuhnian Ontology for Epistemic Classification of STM Scholarly Articles

Despite rapid gains in scale, research evaluation still relies on opaque, lagging proxies. To serve the scientific community, we pursue transparency: reproducible, auditable epistemic classification useful for funding and policy. Here we formalize KGX3 as a scenario-based model for mapping Kuhnian stages from research papers, prove determinism of the classification pipeline, and define the epistemic manifold that yields paradigm maps. We report validation across recent corpora, operational complexity at global scale, and governance that preserves interpretability while protecting core IP. The system delivers early, actionable signals of drift, crisis, and shift unavailable to citation metrics or citations-anchored NLP. KGX3 is the latest iteration of a deterministic epistemic engine developed since 2019—originating as Soph.io (2019–2020), advanced as iKuhn (2020–2024), and field-tested through Preprint Watch.

Glimmer · Reproducible AI science

Glimmer turns a research project into a navigable knowledge graph you can explore, run, verify, and extend — reproducibly.

Can we measure trust in scientific publications? - LSE Impact

Jonathon Alexis Coates outlines how a constellation of static and dynamic indicators could provide a means for assessing the trustworthiness of published research

hijohnnylin/neuronpedia

open source interpretability platform 🧠

Establishing trust in automated reasoning - MetaROR

Since its beginnings in the 1940s, automated reasoning by computers has become a tool of ever growing importance in scientific research. So far, the rules underlying automated reasoning have mainly been formulated by humans, in the form of program source code. Rules derived from large amounts of data, via machine learning techniques, are a complementary approach currently under intense development. The question of why we should trust these systems, and the results obtained with their help, has been discussed by early practitioners of computational science, but was later forgotten. The present work focuses on independent reviewing, an important source of trust in science, and identifies the characteristics of automated reasoning systems that affect their reviewability. It also discusses possible steps towards increasing reviewability and trustworthiness via a combination of technical and social measures.

The Pravda Network

DFRLab’s groundbreaking investigations, in collaboration with CheckFirst, uncover how the Russian Pravda network leverages cross-platform, multilingual influence operations and manipulates Wikipedia, large language models, and X to amplify pro-Kremlin narratives.

The AI Chemist: To be trustworthy, LLMs need to show their work

Good scientists reveal how they do their experiments and report their results; so should any machine-driven research

bretthollenbeck/fake-reviews-data

Large set of Amazon reviews labeled fake or real

RealFactBench: A Benchmark for Evaluating Large Language Models in Real-World Fact-Checking

Large Language Models (LLMs) hold significant potential for advancing fact-checking by leveraging their capabilities in reasoning, evidence retrieval, and explanation generation. However, existing benchmarks fail to comprehensively evaluate LLMs and Multimodal Large Language Models (MLLMs) in realistic misinformation scenarios. To bridge this gap, we introduce RealFactBench, a comprehensive benchmark designed to assess the fact-checking capabilities of LLMs and MLLMs across diverse real-world tasks, including Knowledge Validation, Rumor Detection, and Event Verification. RealFactBench consists of 6K high-quality claims drawn from authoritative sources, encompassing multimodal content and diverse domains. Our evaluation framework further introduces the Unknown Rate (UnR) metric, enabling a more nuanced assessment of models' ability to handle uncertainty and balance between over-conservatism and over-confidence. Extensive experiments on 7 representative LLMs and 4 MLLMs reveal their limitations in real-world fact-checking and offer valuable insights for further research. RealFactBench is publicly available at https://github.com/kalendsyang/RealFactBench.git.

RealFactBench: A Benchmark for Evaluating Large Language Models in Real-World Fact-Checking

Large Language Models (LLMs) hold significant potential for advancing fact-checking by leveraging their capabilities in reasoning, evidence retrieval, and explanation generation. However, existing benchmarks fail to comprehensively evaluate LLMs and Multimodal Large Language Models (MLLMs) in realistic misinformation scenarios. To bridge this gap, we introduce RealFactBench, a comprehensive benchmark designed to assess the fact-checking capabilities of LLMs and MLLMs across diverse real-world tasks, including Knowledge Validation, Rumor Detection, and Event Verification. RealFactBench consists of 6K high-quality claims drawn from authoritative sources, encompassing multimodal content and diverse domains. Our evaluation framework further introduces the Unknown Rate (UnR) metric, enabling a more nuanced assessment of models' ability to handle uncertainty and balance between over-conservatism and over-confidence. Extensive experiments on 7 representative LLMs and 4 MLLMs reveal their limitations in real-world fact-checking and offer valuable insights for further research. RealFactBench is publicly available at https://github.com/kalendsyang/RealFactBench.git.