Jian Guo

LAGO Policy achieves smooth, collision-free manipulation by seamlessly integrating trajectory optimization with advanced diffusion techniques, setting a new standard for real-world robotic execution.

Bayesian-Agent transforms how LLM agents evolve skills, achieving up to 100% success on complex benchmarks through a novel posterior-guided optimization approach.

Current image quality metrics struggle to articulate *why* one high-quality image is better than another, but this challenge shows MLLMs are closing the gap by providing expert-level explanations.

Uncover the hidden dynamics of your RL agent with a new visualization framework that reveals how TD errors sculpt the optimization landscape and drive policy updates.

Visualizing the critic's loss landscape reveals distinct characteristics linked to stable vs. unstable learning in online RL, offering a new window into algorithm dynamics.

Visualizing the loss landscape of off-policy RL critics reveals distinct geometric patterns linked to convergence and divergence, offering a new diagnostic tool for understanding optimization dynamics.

Achieve state-of-the-art dynamic graph anomaly detection with limited labels by learning a robust decision boundary around normal data, outperforming methods that overfit to scarce anomalies.