The field of large language models is moving towards more efficient compression and optimization techniques to reduce computational resources and improve deployment in constrained environments. Researchers are exploring innovative methods such as lossless text compression, meta-networks, and post-training quantization to achieve significant data reduction and model compression. Notably, techniques like layer-wise high-impact parameter ratio optimization and adaptive layer-wise transformations are being developed to improve quantization performance and reduce accuracy loss. Additionally, unified quantization frameworks for new neural architectures like Kolmogorov Arnold Networks are being proposed to enable efficient deployment in resource-constrained environments. Some papers are particularly noteworthy, including Llamazip, which introduces a novel lossless text compression algorithm, and PocketLLM, which achieves superior compression performance via meta-networks. CafeQ is also notable for its calibration-free quantization approach, and ROOT introduces a robust orthogonalized optimizer for large language model training. SUPN proposes shallow universal polynomial networks for efficient function approximation. Overall, these advancements are driving the field towards more efficient and effective large language models.