We are happy to announce the following papers have been accepted at the workshop:
AI Copilots for Reproducibility in Science: A Case Study
Adrien Bibal, Steven Minton, Deborah Khider, Yolanda Gil
Link: https://arxiv.org/pdf/2506.20130
AI-Generated Code Is Not Reproducible (Yet): An Empirical Study of Dependency Gaps in LLM-Based Coding Agents
Bhanu Prakash Vangala, Ali Adibifar, Tanu Malik, Ashish Gehani
Link: https://www.arxiv.org/pdf/2512.22387
Automated Reproducibility Has a Problem Statement Problem
Thijs Snelleman, Peter Lundestad Lawrence, Holger H. Hoos, Odd Erik Gundersen
Link: https://arxiv.org/pdf/2601.04226
CAIBench: A Meta-Benchmark for Reproducible Labor-Relevant Agentic Cybersecurity Tasks
María Sanz-Gómez, Víctor Mayoral-Vilches, Francesco Balassone, Luis Javier Navarrete Lozano, Cristobal Ricardo Jesús Veas Chavez, Maite del Mundo de Torres
Link: https://arxiv.org/pdf/2510.24317
Exploration of Reproducible Generated Image Detection
Yihang Duan
Link: https://arxiv.org/pdf/2512.21562
Image Tiling for High-Resolution Reasoning: Balancing Local Detail with Global Context
Anatole Jacquin de Margerie, Alexis Roger
Link: https://arxiv.org/pdf/2512.11167
open-sci-ref-0.01: open and reproducible reference baselines for language model and dataset comparison
Marianna Nezhurina, Jörg K.H. Franke, Taishi Nakamura, Timur Carstensen, Niccolò Ajroldi, Ville Komulainen, David Salinas, Jenia Jitsev
Link: https://arxiv.org/pdf/2509.09009
Learning to be Reproducible: Custom Loss Design for Robust Neural Networks
Waqas Ahmed, Sheeba Samuel, Kevin Coakley, Birgitta Koenig-Ries, Odd Erik Gundersen
Link: https://arxiv.org/pdf/2601.00578
Measuring Stability Beyond Accuracy in Small Open-Source Medical Large Language Models for Pediatric Endocrinology
Vanessa D’Amario, Randy Daniel, Alessandro Zanetti, Dhruv Edamadaka, Nitya Alaparthy, Joshua Tarkoff
Link: https://www.arxiv.org/pdf/2601.11567
Simpler Methods Work Better for L1 Penalized Logistic Models and Large Datasets
Edward Raff, James Holt
Link: TBA