Bounded AutoResearch for a Tiny Reproducible Machine-Learning Task

Catalog Row120

Citation KeyFriedman2026BoundedAutoResearchTinyReproducible120

Paper FolderAvailable

DOI10.5281/zenodo.20417016

Platform availability

✅ Zenodo
✅ GitHub
⬜ arXiv
⬜ OSF
⬜ HuggingFace
⬜ Software Heritage
⬜ PyPI
✅ Full documentation

Overview

Extracted from the local paper documentation when available.

This paper presents Deterministic bounded AutoResearch for a small MNIST neural-network task, a public template exemplar that turns an AutoResearch loop into ordinary reproducible research infrastructure. The case study is intentionally small but concrete: 2000 training and 500 test images from MNIST handwritten digit database are evaluated by the bounded small MNIST neural-network classification...

autoresearchreproducible researchmachine learning benchmarkartifact readinesshuman reviewlocal artifact integrity

Use Notes

Concise findings and methods pulled from README/SKILL documentation.

Findings / Concepts

This paper presents Deterministic bounded AutoResearch for a small MNIST neural-network task, a public template exemplar that turns an AutoResearch loop into ordinary reproducible research infrastruct
The case study is intentionally small but concrete: 2000 training and 500 test images from MNIST handwritten digit database are evaluated by the bounded small MNIST neural-network classification loop.

Methods / Techniques

Software pipeline design
Data-driven analysis

Citation

Plain-text citation for quick reuse.

Friedman, Daniel Ari. 2026. Bounded AutoResearch for a Tiny Reproducible Machine-Learning Task. Zenodo. DOI: 10.5281/zenodo.20417016. URL: https://doi.org/10.5281/zenodo.20417016.

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