LLMasTool improves neural architecture search by evolving code-mined hierarchical trees with diversity-guided Bayesian planning and targeted LLM assistance, reporting gains of 0.69, 1.83, and 2.68 points on CIFAR-10, CIFAR-100, and ImageNet16-120.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 2verdicts
UNVERDICTED 2roles
background 1polarities
background 1representative citing papers
H3D-MarNet suppresses metal artifacts in kVCT via wavelet preprocessing and transforms to MVCT using a dual-path CNN-transformer architecture with attention fusion, reporting 28.14 dB PSNR and 0.717 SSIM on affected slices.
citing papers explorer
-
LLM as a Tool, Not an Agent: Code-Mined Tree Transformations for Neural Architecture Search
LLMasTool improves neural architecture search by evolving code-mined hierarchical trees with diversity-guided Bayesian planning and targeted LLM assistance, reporting gains of 0.69, 1.83, and 2.68 points on CIFAR-10, CIFAR-100, and ImageNet16-120.
-
H3D-MarNet: Wavelet-Guided Dual-Path Learning for Metal Artifact Suppression and CT Modality Transformation for Radiotherapy Workflows
H3D-MarNet suppresses metal artifacts in kVCT via wavelet preprocessing and transforms to MVCT using a dual-path CNN-transformer architecture with attention fusion, reporting 28.14 dB PSNR and 0.717 SSIM on affected slices.