The field of autonomous learning and agent development is witnessing significant growth, with a focus on enabling agents to learn and adapt in complex, dynamic environments. Researchers are exploring innovative approaches to address the challenges of sparse rewards, limited exploration, and ineffective knowledge utilization. One notable direction is the development of intrinsic motivation mechanisms, such as artificial curiosity and self-awareness, which allow agents to autonomously generate goals and explore their surroundings. Another area of research is the creation of more efficient and effective learning architectures, including state-driven workflows and modular agents. These advancements have the potential to significantly improve the performance and autonomy of agents in various applications, including Earth observation and geospatial analysis. Noteworthy papers in this area include SynWorld, which proposes a framework for agents to synthesize possible scenarios and refine their action knowledge, and Geo-OLM, which enables sustainable Earth observation studies with cost-efficient open language models. Additionally, the paper on Agentic Knowledgeable Self-awareness presents a novel paradigm for LLM-based agents to autonomously regulate knowledge utilization, and the paper on An Information-Geometric Approach to Artificial Curiosity provides a geometric framework for designing intrinsic rewards.