The field of scientific discovery is undergoing a significant transformation with the emergence of autonomous systems, which are accelerating discovery across various domains. These systems, often referred to as agents, are being integrated with human scientists, natural language, computer language and code, and physics to create a flexible and versatile framework for scientific inquiry. The use of large language models (LLMs) and artificial intelligence (AI) is enabling the development of autonomous agents that can assist in designing, executing, and analyzing experiments, leading to more efficient exploration of scientific space and accelerated innovation. Noteworthy papers in this area include: Autonomous Agents for Scientific Discovery, which presents a vision for LLM-based scientific agents and their role in transforming the scientific discovery lifecycle. Rise of the Robochemist, which introduces the concept of the robochemist, a new paradigm where autonomous systems assist in designing, executing, and analyzing experiments in chemistry. Spec-Driven AI for Science, which presents ARIA, a spec-driven framework for automated and interpretable data analysis. ToPolyAgent, which introduces a multi-agent AI framework for performing coarse-grained molecular dynamics simulations of topological polymers. LabOS, which represents the first AI co-scientist that unites computational reasoning with physical experimentation through multimodal perception, self-evolving agents, and Extended-Reality(XR)-enabled human-AI collaboration.