Research Focus
Visual Memory Systems for AI Agents
I study AI agents that can organize useful memories, understand visual evidence, and improve through post-training. These three directions share one goal: building agents that remember what matters and reason reliably before they act.
01 · Memory Systems
How can agents organize and reuse experience?
CLAG (ACL 2026 Findings) augments AI agents with self-organizing memory, structuring past experiences for more focused retrieval and reuse.
02 · Visual Understanding
How can agents reason from what they see?
SCICON (Preprint, 2026) uses contrastive decoding to ground answers in visual evidence without additional training. Breaking Failure Cascades (Preprint, 2026) extends this direction to medical multimodal reasoning.
03 · Post-Training for Agents
How can agents learn to plan and use tools?
Distilling Expert-level Planning (ICML Workshop 2026) uses reinforcement learning to improve tool use for real-world diabetes prescribing. In Breaking Failure Cascades (Preprint, 2026), step-aware reinforcement learning is tailored to multimodal reasoning failures.