The field of large language models is moving towards more efficient and effective reasoning techniques. Recent developments have focused on reducing the computational cost of test-time optimization, with methods such as amortized latent steering and token-level routing showing promising results. These approaches aim to improve the accuracy and efficiency of large language models, making them more viable for production deployment. Notably, the use of mixture-of-experts architectures and co-designed reasoning workflows has also shown potential in achieving state-of-the-art performance while reducing computational costs. Furthermore, research has highlighted the importance of considering system-level metrics, such as latency and cost-per-token, when evaluating the performance of large language models.

Noteworthy papers include: Amortized Latent Steering, which achieves a 2-5x speedup over iterative methods while matching or surpassing greedy Chain-of-Thought baselines. PiMoE, which integrates computational capabilities into neural networks, enabling iterative alternation within a single chain of thought and achieving significant improvements in response latency and GPU energy consumption. LongCat-Flash-Thinking, which presents an efficient 560-billion-parameter open-source Mixture-of-Experts reasoning model that achieves state-of-the-art performance on complex reasoning tasks. One Filters All, which introduces a general filtering framework that leverages large language models for state estimation and outperforms state-of-the-art learning-based approaches. Energy Use of AI Inference, which estimates the per-query energy of large-scale LLM systems and quantifies achievable efficiency gains at the model, serving platform, and hardware levels.