The field of large language models (LLMs) is rapidly advancing, with a focus on autonomous learning and planning. Recent developments have led to the creation of modular pipelines that can continuously update an LLM's knowledge with minimal human intervention. These pipelines enable long-term continual learning, allowing LLMs to adapt to rapidly evolving domains and improve their performance over time. Another key area of research is the development of multi-agent frameworks that can handle complex tasks such as index recommendation and planning. These frameworks have been shown to outperform traditional methods and achieve state-of-the-art results. Furthermore, researchers are exploring new learning paradigms that eliminate the need for fine-tuning underlying LLMs, enabling low-cost continual adaptation and real-time learning. Noteworthy papers include ALAS, which presents a modular pipeline for autonomous learning, and MAAdvisor, which proposes a zero-shot LLM-based index advisor with a multi-agent framework. Additionally, AgentFly introduces a novel learning paradigm for adaptive LLM agents that eliminates the need for fine-tuning, and Learn to Memorize proposes an adaptive memory framework for optimizing LLM-based agents. These advancements have the potential to significantly improve the performance and efficiency of LLMs, enabling them to adapt to complex and dynamic environments.