Ze-Yuan Chen

A quadtree visual tokenizer slashes token count for autoregressive image generation by adaptively focusing representational power on intricate image regions.

Web agents can now achieve state-of-the-art performance and robust test-time scaling by learning to self-verify their actions, bypassing the need for costly external judges during deployment.

Coding agents can now achieve up to 15% higher task completion rates by treating feedback as a persistent note that's followed until resolved, not just a one-off evaluation.

This work studies open-vocabulary task-oriented dexterous grasp generation, where a robot must infer functional intent from free-form language, ground it in multi-view visual observations and object geometry, and generate an executable high-degree-of-freedom grasp.

Evolving the harness of LLM agents can yield a 17-point performance boost without compromising their ability to generalize across tasks.