The fields of RISC-V architecture, electronic design automation, compute-in-memory architectures, and large language models are experiencing significant advancements. Researchers are exploring new designs and implementations to improve efficiency, flexibility, and performance in these areas. Notable papers include the introduction of programmable cache coherence engines, carbon-aware architectures, and novel compilation techniques. Additionally, there is a growing interest in adapting RISC-V architectures for extreme edge applications and leveraging large language models to automate energy-aware refactoring of parallel scientific codes. The development of integrated frameworks for systematic design and evaluation of digital CIM architectures and the creation of novel architectures, such as the Mixture of Experts framework, are also noteworthy. Furthermore, techniques like quantization, pruning, and knowledge distillation are being explored to reduce the memory and computational requirements of large language models. Overall, these advancements have the potential to significantly impact the fields of computing and AI, enabling more efficient, effective, and scalable models and systems. Key areas of focus include efficient deployment, compression techniques, and innovative optimization methods, with a strong emphasis on generalization, robustness, and real-world applicability.