To address the challenge of scarce computational resources in genomic modeling, we introduce GERM, a genomic foundation model optimized for accessibility and adaptability. GERM improves upon models like DNABERT-2 by eliminating outliers that hinder low-rank adaptation and post-training quantization, enhancing both efficiency and robustness. We replace the vanilla attention layer with an outlier-free mechanism inspired by associative memory models. By removing outliers during both pre-training and fine-tuning, this approach accelerates adaptation, reduces computational costs, and enhances quantization robustness within acceptable loss margins. Additionally, we propose GERM-T, a strategy that employs small-step continual learning within the outlier-free framework, leveraging original checkpoints to avoid retraining from scratch. Empirically, GERM improves fine-tuning performance by 37.98% and quantization by 64.34% over the baseline model. It also reduces average kurtosis by 92.14% and maximum infinity norm by 82.77%. Compared to leading methods, GERM consistently delivers superior performance, offering a practical solution for genomic modeling in resource-constrained settings.

Conventional computer vision pipelines typically treat low-level enhancement and high-level semantic tasks as isolated processes, focusing on optimizing enhancement for perceptual quality rather than computational utility, neglecting semantic task requirements. To bridge this gap, this paper proposes an integrated joint optimization architecture that aligns the objectives of enhancement tasks with the practical needs of semantic tasks. Specifically, the architecture ensures that medical image segmentation (the semantic task) benefits directly from super-resolution pre-processing (the enhancement task). This integrated architecture fundamentally differs from conventional sequential frameworks by enabling joint training of super-resolution and segmentation networks. Guided by its own content reconstruction loss and semantic loss transferred from segmentation, the super-resolution network prioritizes semantically significant regions for segmentation-driven reconstruction. Comprehensive comparative and ablation studies demonstrate that the network, trained jointly, markedly enhances segmentation performance in low-resolution images, even outperforming those directly from referenced high-resolution images. The code is available at https://github.com/kldys/JOANet.