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Flow policies have become a standard policy class for learning robot behaviors from demonstrations, but reinforcement learning is still critical for improving pre-trained flow policies or learning the

Chung Min Kim, Brent Yi, David McAllister

Conformal prediction is a popular tool for uncertainty quantification that outputs prediction sets with finite-sample coverage guarantees. While prediction set size is commonly used as a heuristic mea

Kevin Zhang, Stephen Bates

Existing methods for 4D hand-object reconstruction often rely on costly per-sequence optimization, while generative approaches typically synthesize interactions from random noise, which can lead to un

Shiqi Li, Sean Cho, Yijie Li

Scientific research often begins by synthesizing ideas from a set of related papers to identify gaps and formulate new directions. However, training language models to perform this form of literature-

Ziyu Chen, Yilun Zhao, Jiashuo Sun

World models offer a data-driven alternative to traditional simulators for robotics, with applications spanning policy evaluation, improvement, and planning. All of these uses depend on faithful 3D ge

Jai Bardhan, Josef Sivic, Vladimir Petrik

Large language models (LLMs) have become increasingly capable problem solvers, but being able to solve a problem is not the same as being able to teach it. Existing approaches to training LLMs as teac

Weixian Xu, Yanzhe Zhang, Zora Zhiruo Wang

Large language models (LLMs) can solve many narrow tasks, but querying them separately for millions of related instances can be prohibitively expensive. Can LLM agents autonomously create cheaper solu

Ankit Sonthalia, Haritz Puerto, Alexander Rubinstein

Web agents complete user requests by reading and acting on pages that third parties write, so an instruction planted on a page can redirect the agent away from the user's goal. The agent cannot simply

Sarim Hashmi, Mukul Ranjan, Kshitij Mishra

We develop the first feedback design for rapid stabilization of the Kuramoto--Sivashinsky equation with a spatially varying anti-diffusion coefficient. For constant coefficients, the single-input Fred

Luke Bhan, Miroslav Krstic, Yuanyuan Shi

Self-improving policies continually expose new failure patterns, changing what their judges must be able to verify. However, current fixed judges constrain both optimization feedback and the discovery

Zewei Zhou, Rachel Luo, Yulong Cao

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