The field of sign language processing is moving towards more accurate and robust recognition and generation systems. Recent developments have focused on improving the realism and naturalness of generated sign language videos, as well as enhancing the recognition accuracy of complex multimodal gestures. The use of deep learning-based approaches, such as attention-based ensemble networks and transformer architectures, has shown significant promise in addressing the challenges of sign language recognition and generation. Notably, the integration of graph-based methods with transformer architectures has demonstrated superior performance in gloss-free translation tasks. Furthermore, the development of standardized evaluation metrics and datasets has enabled more meaningful comparisons across different systems and will facilitate future research in this area. Noteworthy papers include: SLRTP2025 Sign Language Production Challenge, which introduced a standardized evaluation network for sign language production tasks. FusionEnsemble-Net achieved state-of-the-art results in multimodal sign language recognition with an attention-based ensemble of spatiotemporal networks. A Signer-Invariant Conformer and Multi-Scale Fusion Transformer established a new standard for continuous sign language recognition benchmarks. Generation of Indian Sign Language Letters, Numbers, and Words proposed a GAN variant that combines Progressive Growing of GAN and Self-Attention GAN to generate high-quality sign language images. Continuous Bangla Sign Language Translation integrated graph-based methods with transformer architecture to achieve state-of-the-art results in gloss-free translation tasks.