Bounded AutoResearch for a Tiny Reproducible Machine-Learning Task

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 loop. The run evaluates
4 of 5 proposed candidates,
including Tiny patch-attention classifier, selects
exp-mlp-tanh-64 (MLP,
50890 parameters), and improves test_accuracy from
82.6% to 89.4%
(6.8% absolute change). The validated diagnostic layer reports
macro F1 89.4%, bootstrap accuracy interval
86.4% to 92.0%, Brier score 0.161,
negative log likelihood 0.361, top-2 accuracy
95.6%, and exact McNemar p-value 0.000.
The same pipeline writes proposal, candidate, run, review, benchmark, evidence,
figure, confusion-matrix, statistical-summary, probability-quality, and
security-integrity artifacts from declared output contracts; uses
0 LLM calls at USD 0.00 cost; and records
7 configured stages, 6 supported
local-artifact claims, and 78 required artifacts.
The local security attestation status is passed,
with 0 checksum mismatch(es). The final
readiness status is passed, with review gates deferred to a
human rather than self-approved by the generated run.

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Associated artifacts
GitHub release: v0.3.2 (https://github.com/docxology/template_autoresearch_project/releases/tag/v0.3.2)
DOI: https://doi.org/10.5281/zenodo.20417016
Zenodo: https://zenodo.org/records/20417016
PDF SHA-256: e07b62850a1995935283d37a45c21d71fa7c4e69cdcc451c5a1ea8aee6d0c94a

Overview source: Curated paper metadata.