Fine-tuning large pre-trained foundation models often yields excellent downstream performance but is prohibitively expensive when updating all parameters. Parameter-efficient fine-tuning (PEFT) methods such as LoRA alleviate this by introducing lightweight update modules, yet they commonly rely on weight-agnostic linear approximations, limiting their expressiveness. In this work, we propose PEANuT, a novel PEFT framework that introduces weight-aware neural tweakers, compact neural modules that generate task-adaptive updates conditioned on frozen pre-trained weights. PEANuT provides a flexible yet efficient way to capture complex update patterns without full model tuning. We theoretically show that PEANuT achieves equivalent or greater expressivity than existing linear PEFT methods with comparable or fewer parameters. Extensive experiments across four benchmarks with over twenty datasets demonstrate that PEANuT consistently outperforms strong baselines in both NLP and vision tasks, while maintaining low computational overhead.

Inverse lithography technology (ILT) is an advanced resolution enhancement technique that achieves mask optimization at the pixel level. However, application of ILT is hindered by time-intensive physical simulation. Herein, we propose an efficient ILT algorithm leveraging a deep learning model. A novel loss function is constructed to guide the model training in a self-supervised manner, eliminating the requirement of labelled data that might be nontrivial to acquire. The trained model outputs final mask patterns without further ILT optimization. Sub-resolution assist features (SRAFs) are generated automatically, the complexity of which can be adjusted during the training process to control mask manufacturability. The model was trained and validated on ICCAD-2013 CAD contest dataset. Better pattern fidelity and up to 12,000 times speedup are observed compared to other SOTA models. The trained model also shows good generalization ability to geometrically-different design patterns from another dataset, via a few-shot learning approach.