Curating Same-Family Neural Networks for LLM-Guided Model Improvement: A Controlled Case Study

Abstract:Neural-network repositories contain executable models, recipes, input transformations, and measured accuracies. We study whether one same-family experiment can be curated as prompt guidance for LLM-based improvement of a low-performing target under equal generation and evaluation budgets. TuneNNGen extends NNGPT with a source-guided route and compares it with target-only generation on one CIFAR-10 target, two fixed source-selection rules, three code LLMs, and an additional SVHN target. Under the historical one-epoch search protocol, best-of-budget accuracy on the available evaluation split rises from 23.98% to 50.49% on CIFAR-10 and from 22.54% to 78.80% on SVHN. Selected five-epoch, three-seed means on train-derived validation splits retain gains of 40.94 points on CIFAR-10, 18.83 on Imagenette, and 7.27 on CIFAR-100. Direct-copy and negative-control analyses show that gains depend on source-target compatibility and LLM adaptation; stored source accuracy alone does not predict transferability.

Machine Learning (cs.LG); Computer Vision and Pattern Recognition (cs.CV)

arXiv:2607.05704 [cs.LG]

(or arXiv:2607.05704v2 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.05704 Focus to learn more arXiv-issued DOI via DataCite

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