The field of agentic AI research is moving towards the development of more generalist and autonomous systems. Recent work has focused on creating systems that can reason, search, and use tools in a more flexible and dynamic way. This includes the development of new reasoning layers, such as CoreThink, and the integration of large language models with reinforcement learning. The use of external memory and abstract reasoning has also shown promise in improving the performance of these systems. Noteworthy papers include Universal Deep Research, which introduces a generalist agentic system that enables users to create custom research strategies, and InfoSeek, which provides a scalable framework for synthesizing complex Deep Research tasks. Additionally, ArcMemo presents a method for abstract reasoning composition with lifelong LLM memory, and WebExplorer introduces a systematic data generation approach for training long-horizon web agents. These advancements have the potential to unlock new applications and improve the overall performance of agentic AI systems.
Advancements in Agentic AI Research
Sources
Universal Deep Research: Bring Your Own Model and Strategy
Open Data Synthesis For Deep Research
CoreThink: A Symbolic Reasoning Layer to reason over Long Horizon Tasks with LLMs
ArcMemo: Abstract Reasoning Composition with Lifelong LLM Memory
Large Language Model Integration with Reinforcement Learning to Augment Decision-Making in Autonomous Cyber Operations
SFR-DeepResearch: Towards Effective Reinforcement Learning for Autonomously Reasoning Single Agents
Scaling up Multi-Turn Off-Policy RL and Multi-Agent Tree Search for LLM Step-Provers
WebExplorer: Explore and Evolve for Training Long-Horizon Web Agents
Reinforcement Learning Foundations for Deep Research Systems: A Survey