The field of large language models is witnessing significant developments in reasoning and problem-solving capabilities. Recent research has focused on improving the transparency and performance of these models in complex tasks. Notably, there is a growing trend towards formulating reasoning as a structured process, such as a Markov decision process, to enable more principled and efficient exploration of the solution space. This has led to advancements in areas like chain-of-thought reasoning, multi-step inference, and reflective self-correction. Furthermore, researchers are exploring the integration of external knowledge and the use of retrieval-augmented mechanisms to enhance the accuracy and diversity of generated responses. Another area of focus is the development of more sophisticated memory systems, allowing models to accumulate knowledge over time and reason about past, present, and possible future states. Overall, these innovations are poised to significantly improve the capabilities of large language models in a wide range of applications. Noteworthy papers include: CTRLS, which introduces a framework for chain-of-thought reasoning via latent state-transitions, enabling more efficient and effective exploration of the reasoning space. EduFlow, which presents an end-to-end framework for educational scientific reasoning, incorporating a process-aware reward model and a domain-adapted search framework to enhance reasoning consistency and coherence.