The field of large language models (LLMs) is moving towards developing more autonomous and adaptable systems. Researchers are focusing on creating agents that can learn from their own experiences, refine their problem-solving strategies, and improve their performance over time. This is being achieved through the development of frameworks that enable self-improvement, such as those that utilize experience-driven lifecycles, self-awareness training, and implicit meta-reinforcement learning. Additionally, there is a growing interest in developing distributed routing systems that can effectively balance performance and expense. Noteworthy papers include: Adaptive Minds, which empowers agents to dynamically select the most relevant tools for a given task, and PolySkill, which enables agents to learn generalizable skills through polymorphic abstraction. Other notable works include EvolveR, which introduces a self-evolving framework for LLM agents, and DiSRouter, which proposes a distributed self-routing paradigm for LLM selections.