Yibo Wang

State-of-the-art deep research agents fail to achieve even 65% accuracy on realistic life and productivity tasks, revealing a critical gap between current capabilities and real-world utility.

A single algorithm now achieves optimal convergence rates for stochastic non-convex optimization across average smoothness, standard smoothness, and noiseless settings, adapting dynamically without knowing any smoothness constants.

Current spatio-temporal video grounding models falter on complex queries, with performance dropping sharply when faced with compositional challenges.

Self-improving search agents thrive when feedback and policy evolution are intertwined, leading to sustained performance gains and reduced hallucinations.