Finds documents with both search terms in any word order, permitting "n" words as a maximum distance between them. Best choose between 15 and 30 (e.g. NEAR(recruit, professionals, 20)). Finds documents with the search term in word versions or composites. The asterisk * marks whether you wish them BEFORE, BEHIND, or BEFORE and BEHIND the search term (e.g. lightweight*, *lightweight, *lightweight*). Lightweight neural networks are increasingly used for image classification in scenarios where memory, computation, and energy resources are limited. However, reducing model size often results in a noticeable loss of robustness and generalization, as compact architectures struggle to learn fine-grained structural cues such as edges, textures, and high-frequency patterns. This limitation is especially evident in domains like medical imaging, microscopy, and remote sensing, where subtle spatial variations play an important role in discrimination. In this work, we introduce LiFGANet, a lightweight frequency and gradient aware network designed to mitigate this problem by incorporating explicit inductive biases into feature learning. Instead of increasing model capacity, LiFGANet emphasizes structurally informative representations through an efficient frequency gradient interaction mechanism, combined with sparse feature refinement and multi-stage feature aggregation. The proposed design remains compact, with approximately 1.13M parameters, and introduces negligible inference overhead. We evaluate LiFGANet on PneumoniaMNIST, BloodMNIST, FashionMNIST, and EuroSAT. The results show consistent and well-balanced performance across datasets surpassing state-of-the-art lightweight models with similar or larger parameter budgets. Ablation studies further demonstrate the various advantages of frequency guidance and gradient modulation. The code and implementation details are available on GitHub. Show AI generated summary Abstract Dive into the groundbreaking LiFGANet architecture, a lightweight neural network designed to revolutionize image classification in resource-constrained environments. This article explores how LiFGANet tackles the fundamental limitations of compact models by embedding physics-inspired inductive biases that explicitly guide learning toward structurally meaningful features. Discover how