The field of 3D Gaussian Splatting is experiencing significant growth, with a focus on developing innovative methods for compact representation, compression, and segmentation. Researchers are exploring new approaches to reduce the substantial data size associated with 3DGS, enabling its widespread adoption. Noteworthy papers in this area include Smol-GS, which achieves state-of-the-art compression on standard benchmarks, and Feed-Forward 3D Gaussian Splatting Compression with Long-Context Modeling, which proposes a novel framework for modeling long-range spatial dependencies. Additionally, Binary-Gaussian and Content-Aware Texturing for Gaussian Splatting demonstrate promising results in segmentation and texture representation. Gaussian Entropy Fields is also a notable contribution, driving adaptive sparsity in 3D Gaussian optimization and achieving competitive geometric precision on benchmarks.