The integration of advanced AI technologies with multimodal data processing and agent-based modeling has seen significant progress across various research areas. A common thread among these developments is the use of large language models (LLMs) to enhance the autonomy, decision-making, and interaction capabilities of AI systems. In the realm of autonomous agents, frameworks like Desire-driven Autonomy and LMAgent are leveraging intrinsic motivations and multimodal interactions to simulate human-like behaviors more accurately. This trend is also evident in traffic modeling, where tokenized multi-agent policies and closed-loop fine-tuning strategies are improving simulation accuracy. The application of AI and microservices for real-time performance optimization in industries such as travel reservation underscores the transformative potential of these technologies. Additionally, agent-based modeling is providing new insights into speculative behaviors in token markets, shedding light on market dynamics. In robotics and human-robot interaction, the development of multimodal benchmarks and the use of LLMs for task planning and motion control are enhancing the versatility and adaptability of robotic systems. Human motion generation has also benefited from the integration of LLMs and vision models, enabling detailed text-guided motion editing and self-correction. Lastly, advancements in dialogue systems are focusing on enhancing the complexity and naturalness of conversations through multi-party dialogue generation and the integration of audio elements, leading to more immersive and versatile human-machine communication. Overall, the synergy between LLMs, multimodal data, and agent-based modeling is driving the field towards more sophisticated and realistic simulations and interactions, with broad implications for both academic research and practical applications.