Large reasoning models, agentic memory systems, and multi-modal reinforcement learning. Focus on enabling LLM agents to build, manage, and retrieve long-term memories for complex sequential tasks. Key interests include RL-based training for reasoning, retrieval-augmented reasoning chains, and visual document understanding.