{
  "title": "Bounded AutoResearch for a Tiny Reproducible Machine-Learning Task",
  "version": "0.3.2",
  "doi": "10.5281/zenodo.20417016",
  "doi_url": "https://doi.org/10.5281/zenodo.20417016",
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  "publication_date": "2026",
  "resource_type": {
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  "creators": [
    {
      "name": "Daniel Ari Friedman",
      "affiliation": "Active Inference Institute",
      "orcid": "0000-0001-6232-9096"
    }
  ],
  "description": "This paper presents Deterministic bounded AutoResearch for a small MNIST neural-network task, a public template exemplar that\nturns an AutoResearch loop into ordinary reproducible research infrastructure.\nThe case study is intentionally small but concrete: 2000 training\nand 500 test images from MNIST handwritten digit database are evaluated by the\nbounded small MNIST neural-network classification loop. The run evaluates\n4 of 5 proposed candidates,\nincluding Tiny patch-attention classifier, selects\nexp-mlp-tanh-64 (MLP,\n50890 parameters), and improves test_accuracy from\n82.6% to 89.4%\n(6.8% absolute change). The validated diagnostic layer reports\nmacro F1 89.4%, bootstrap accuracy interval\n86.4% to 92.0%, Brier score 0.161,\nnegative log likelihood 0.361, top-2 accuracy\n95.6%, and exact McNemar p-value 0.000.\nThe same pipeline writes proposal, candidate, run, review, benchmark, evidence,\nfigure, confusion-matrix, statistical-summary, probability-quality, and\nsecurity-integrity artifacts from declared output contracts; uses\n0 LLM calls at USD 0.00 cost; and records\n7 configured stages, 6 supported\nlocal-artifact claims, and 78 required artifacts.\nThe local security attestation status is passed,\nwith 0 checksum mismatch(es). The final\nreadiness status is passed, with review gates deferred to a\nhuman rather than self-approved by the generated run.\n\n---\nAssociated artifacts\nGitHub release: v0.3.2 (https://github.com/docxology/template_autoresearch_project/releases/tag/v0.3.2)\nDOI: https://doi.org/10.5281/zenodo.20417016\nZenodo: https://zenodo.org/records/20417016\nPDF SHA-256: e07b62850a1995935283d37a45c21d71fa7c4e69cdcc451c5a1ea8aee6d0c94a",
  "keywords": [
    "autoresearch",
    "reproducible research",
    "machine learning benchmark",
    "artifact readiness",
    "human review",
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  "pdf_sha256": "e07b62850a1995935283d37a45c21d71fa7c4e69cdcc451c5a1ea8aee6d0c94a",
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  "checked_at": "2026-07-10T19:08:24Z",
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  "domain": "Computational",
  "type": "Paper",
  "methods": [
    {
      "name": "Software pipeline design",
      "description": "Applied software pipeline design approach"
    },
    {
      "name": "Data-driven analysis",
      "description": "Applied data-driven analysis approach"
    }
  ],
  "key_findings": [
    "This paper presents Deterministic bounded AutoResearch for a small MNIST neural-network task, a public template exemplar that\nturns an AutoResearch loop into ordinary reproducible research infrastruct",
    "The case study is intentionally small but concrete: 2000 training\nand 500 test images from MNIST handwritten digit database are evaluated by the\nbounded small MNIST neural-network classification loop."
  ],
  "related_papers": [
    "2023_NSFReporting",
    "2023_NaturalAIBased",
    "2025_AuBI"
  ],
  "related_software": [
    "docxology/template_autoresearch_project"
  ]
}