The field of Large Language Models (LLMs) is rapidly evolving, with a growing focus on context-aware and personalized models. Recent research has highlighted the importance of effective memory management, context compression, and retrieval-augmented learning in improving the performance of LLMs. Notably, studies have shown that incorporating multi-dimensional contexts, such as sensory perceptions and user preferences, can significantly enhance the proactive capabilities of LLM agents. Furthermore, the development of novel frameworks and benchmarks, such as PARSEC and ContextAgentBench, has facilitated the evaluation and improvement of LLMs in complex tasks like object rearrangement and conversational coherence. Noteworthy papers include PARSEC, which introduces a novel benchmark for learning user organizational preferences from observed scene context, and SoLoPO, which proposes a framework for unlocking long-context capabilities in LLMs via short-to-long preference optimization. Additionally, MemoryEQA and DSMentor demonstrate the effectiveness of memory-centric approaches in embodied question answering and data science tasks, respectively.