The article titled "SEAL - A Symmetry EncourAging Loss for High Energy Physics" explores the integration of physical symmetries into machine learning models to enhance their robustness and interpretability. It defines SEAL as a method designed to encourage symmetry within model training, aiming to improve performance in high energy physics applications (F1). The primary purpose of SEAL is to incorporate known physical symmetries into learning algorithms, thereby aligning model behavior with fundamental scientific principles (F2). This approach offers benefits such as increased model reliability and clearer insights into learned representations (F3). However, the article also acknowledges significant challenges in implementing these symmetry-respecting models, particularly when dealing with the complexities of real-world experimental data (F4). Overall, the discussion highlights the potential of symmetry-aware machine learning while emphasizing the practical difficulties that remain in this emerging area.