The field of agentic AI is rapidly evolving, with a focus on developing more generalist and autonomous systems. Recent developments have led to the creation of sophisticated mobile agent systems, incorporating multimodal foundation models, retrieval-augmented generation, and cloud-device collaboration. Notable papers include MobiAgent, AppCopilot, KG-RAG, and VehicleWorld, which have achieved state-of-the-art performance in real-world mobile scenarios and introduced comprehensive environments for intelligent vehicle interaction.
The field of graphical user interface (GUI) agents is also advancing, with a focus on multi-turn reinforcement learning, page graph-based approaches, and active perception capabilities. Researchers are exploring new methodologies to improve the scalability, stability, and generalization of GUI agents, enabling them to perform complex tasks and interact with diverse environments. Notable advancements include the development of frameworks that integrate page graphs, retrieval-augmented generation, and self-evolving preference optimization.
The field of agentic AI research is moving towards the development of more generalist and autonomous systems, with a focus on creating systems that can reason, search, and use tools in a more flexible and dynamic way. Noteworthy papers include Universal Deep Research, InfoSeek, ArcMemo, and WebExplorer, which have introduced generalist agentic systems, scalable frameworks for synthesizing complex Deep Research tasks, and methods for abstract reasoning composition with lifelong LLM memory.
The field of multimodal learning and agentic reinforcement learning is rapidly evolving, with a focus on developing models that can effectively interact with and utilize various tools and environments. Recent research has explored the use of reinforcement learning to enhance the capabilities of large language models, including their ability to reason and make decisions in complex, dynamic worlds. Notable papers include LLaVA-Critic-R1, VerlTool, and ReVPT, which have demonstrated the potential of critic models as competitive policy models and provided unified and modular frameworks for agentic reinforcement learning with tool use.
Overall, these advances have the potential to enable the development of more scalable, general-purpose AI agents that can effectively interact with and utilize various tools and environments. The use of multi-agent systems is becoming increasingly popular, as they can automate entire workflows and generate coherent visual narratives. Furthermore, agentic AI is being applied to compliance-critical domains, such as Anti-Money Laundering, to produce high-quality and regulatorily compliant reports.
