The field of artificial intelligence is witnessing significant developments in the area of autonomous agents and multi-agent collaboration. Researchers are exploring innovative approaches to enable agents to learn, reason, and interact with each other and their environment more effectively. One notable direction is the integration of large language models (LLMs) with reinforcement learning and multi-agent systems, allowing agents to develop more sophisticated reasoning and problem-solving capabilities. Another area of focus is the development of frameworks that facilitate collaborative belief modeling, intent inference, and adaptive communication among agents, leading to more efficient and human-like collaboration. These advancements have the potential to transform various applications, including travel planning, team coordination, and real-world problem-solving. Noteworthy papers in this area include: DeepTravel, which proposes an end-to-end agentic reinforcement learning framework for autonomous travel planning agents. CoBel-World, which introduces a novel framework for collaborative belief modeling and intent inference in multi-agent systems. ATLAS, which presents a constraints-aware multi-agent collaboration framework for real-world travel planning. Interactive Learning for LLM Reasoning, which investigates the potential of multi-agent interaction to enhance LLMs' independent problem-solving ability.