Yichen Zhang

Skill Optimizers trained through execution feedback can outperform traditional models by over 9 points, revealing a critical gap in agent learning methodologies.

A one-pass inference method for TD learning achieves pivotal confidence regions without the need for complex covariance estimation, revolutionizing memory efficiency in policy evaluation.

Bidirectional temporal alignment boosts climate data super-resolution, achieving superior performance by capturing implicit temporal correlations often ignored in existing models.

A new structured representation for manipulation tasks reveals critical labeling anomalies that traditional methods miss, enhancing both readability and verification.

A unified evaluation framework for portrait composition could revolutionize how AI interprets and generates artistic images.

Current AI agents struggle with long-horizon professional tasks, achieving only 30% success in complex GUI workflows, revealing critical gaps in their capabilities.

By injecting basic physics, this method achieves up to 9% accuracy gains in human activity recognition, proving that inductive biases still matter for real-world sensor data.

By decoupling patch details from semantics, Cheers achieves state-of-the-art multimodal performance at 20% of the training cost of comparable models.