Yu-Tong Dai

Machine learning agents discover new exact values for notoriously difficult-to-compute link invariants like slice genus and unknotting number.

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.

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

Shifting the focus from visual perception to direct program state access can boost agent success rates while slashing costs by nearly 9x.