The field of artificial intelligence is witnessing a significant shift towards autonomous reasoning and decision making. Recent developments have focused on enabling large language models (LLMs) to interact with external tools and environments, facilitating dynamic and multi-step reasoning. This has led to improved performance in complex tasks, such as mathematical reasoning and problem-solving. Furthermore, researchers have explored the use of reinforcement learning to fine-tune LLMs for specific tasks, resulting in more robust and generalizable models. Another area of research has centered on developing interpretable and explainable models, with a focus on emergent language and vision-language models. These advancements have the potential to revolutionize various fields, including quantum chemistry, bioinformatics, and computational fluid dynamics, by providing more accessible and automated tools for experts and non-experts alike. Notable papers in this area include: Agentic Reasoning and Tool Integration for LLMs via Reinforcement Learning, which introduces a unified framework for LLMs to interact with external tools and environments. DYSTIL: Dynamic Strategy Induction with Large Language Models for Reinforcement Learning, which proposes a novel strategy-based reinforcement learning framework integrated with LLMs. Foam-Agent: Towards Automated Intelligent CFD Workflows, which presents a multi-agent framework for automating complex OpenFOAM-based CFD simulation workflows from natural language inputs.