We have 2 of 27 papers
We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.
Not the right person? Other researchers publish under this name.
Findings reveal sustained, dependency-consistent evidence integration, rather than isolated fact retrieval, as a key bottleneck for current deep research agents.
PAVXploreRL is a reinforcement learning framework built on a pretrained latent world model that explicitly optimizes PAV objectives through reward-driven training, and jointly leverages ID trajectories and noise-driven OOD action exploration, without paired video supervision.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
