Recent advancements in AI research have focused on optimizing large language models (LLMs) for more efficient and effective performance. OpenAI's o1 model and Google's AlphaProof are notable examples that emphasize optimizing test-time compute rather than merely increasing model parameters. NVIDIA researchers are exploring the upcycling of LLMs into sparse mixture-of-experts, while Stanford University, Together AI, California Institute of Technology, and MIT have introduced LoLCATS (Low-rank Linear Conversion via Attention Transfer), a method for efficient LLM linearization. These innovations aim to enhance AI reasoning and performance without the need for extensive pre-training. Additionally, OpenAI's o1 model, benchmarked on Codeforces paired with AlphaCodium, showed a significant boost in performance. Predictions suggest that within 1.5 years, models like GPT-5 and Claude 4 will further advance AI capabilities. LoLCATS has also demonstrated a 20+ point improvement in performance.