The field of artificial intelligence and autonomous scientific discovery is rapidly advancing, with a focus on developing more general-purpose AI systems that can navigate the scientific workflow independently. Recent research has explored the use of large language models, multimodal systems, and integrated research platforms to enable AI systems to generate hypotheses, design experiments, and analyze results. Another area of focus is on developing more efficient and effective algorithms for tasks such as convex hull computation and candidate positioning in multi-issue elections. Additionally, there is a growing interest in understanding the structure of experience and the emergence of goal-directed behavior in cognitive agents. Noteworthy papers in this area include 'Virtuous Machines: Towards Artificial General Science', which demonstrates the capability of AI scientific discovery pipelines to conduct non-trivial research with theoretical reasoning and methodological rigour comparable to experienced researchers, and 'From AI for Science to Agentic Science: A Survey on Autonomous Scientific Discovery', which provides a comprehensive framework for understanding the evolution of AI for Science and the development of agentic AI systems.