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arXiv · 2605.29786

Croissant Tasks: A Metadata Format for Reproducible Machine Learning Evaluations

Omar Benjelloun·Leonardo Martins Bianco·Isabelle Guyon·Thanh Gia Hieu Khuong·Jonathan Lebensold·Sebastian Lobentanzer·Luis Oala·Benedictus Kent Rachmat·Ihsan Ullah·Peyman Vahidi·Joaquin Vanschoren
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Abstract

Reproducibility is fundamental to the scientific method, yet remains a critical challenge in machine learning. Contributing factors include underspecified execution details and brittle software environments. Human-centric remedies, such as checklists and manual verification, help but require intensive effort and fail to scale. To address this, we introduce Croissant Tasks: a declarative, machine-actionable metadata format that abstracts low-level implementation details into high-level specifications. This format enables conceptual reproducibility: verifying claims via independent, agent-generated implementations rather than brittle source code replication. We contribute: (1) the Croissant Tasks specification, formally decoupling task problem from solution; (2) an automated LLM pipeline that retrofits existing benchmarks into this format; and (3) empirical validation showing autonomous agents can ingest these specifications to generate functional, accurate reproduction pipelines from scratch. We envision this format as a new foundation for automated and conceptual reproducibility in machine learning.

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Omar Benjelloun, Leonardo Martins Bianco, Isabelle Guyon, Thanh Gia Hieu Khuong, Jonathan Lebensold, Sebastian Lobentanzer, Luis Oala, Benedictus Kent Rachmat, Ihsan Ullah, Peyman Vahidi, Joaquin Vanschoren. 2026-05-28. Croissant Tasks: A Metadata Format for Reproducible Machine Learning Evaluations. https://arxiv.org/abs/2605.29786

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