Bounded AutoResearch for a Tiny Reproducible Machine-Learning Task

Abstract 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, ev