1 citations · 1 across the 7 of their papers we have counts for
9 papers
Seq-Flow: Efficient Probabilistic Forecasting with Self-Rollout Error Control
Yinan Huang, Shitij Govil, Bo Dai +1
Many scientific forecasting tasks require updating a distribution over future trajectories as new observations arrive. Conventional diffusion and flow models generate each forecast…
EcoVLA: Energy-Efficient Device-Edge Co-Inference for Vision-Language-Action Models under Real-Time Constraints
Ao Zhou, Bo Dai, Le Yu +5
Vision-Language-Action (VLA) models have emerged as a promising foundation for Embodied AI, but their high inference cost poses significant challenges for deployment in robotic sys…
Imagine a City: CityGenAgent for Procedural 3D City Generation
Zishan Liu, Zecong Tang, RuoCheng Wu +6
The automated generation of interactive 3D cities is a critical challenge with broad applications in autonomous driving, virtual reality, and embodied intelligence. While recent ad…
Accelerated Sequential Flow Matching: A Bayesian Filtering Perspective
Yinan Huang, Hans Hao-Hsun Hsu, Junran Wang +2
Sequential probabilistic inference from streaming observations requires modeling distributions over future trajectories as new observations arrive. Although diffusion and flow-matc…
PALUM: Part-based Attention Learning for Unified Motion Retargeting
Siqi Liu, Maoyu Wang, Bo Dai +1
Retargeting motion between characters with different skeleton structures is a fundamental challenge in computer animation. When source and target characters have vastly different b…
CustomX: Unified Character, Action, and Scene Customization in Video World Models
Yitong Wang, Fangyun Wei, Hongyang Zhang +2
Recent advances in world models have greatly enhanced interactive environment simulation. Existing methods mainly fall into two categories: (1) static world generation models, whic…
RLinf: Flexible and Efficient Large-scale Reinforcement Learning via Macro-to-Micro Flow Transformation
Chao Yu, Yuanqing Wang, Zhen Guo +26
Reinforcement learning (RL) has demonstrated immense potential in advancing artificial general intelligence, agentic intelligence, and embodied intelligence. However, the inherent…
Spectral Bellman Method: Unifying Representation and Exploration in RL
Ofir Nabati, Bo Dai, Shie Mannor +1
Representation learning is critical to the empirical and theoretical success of reinforcement learning. However, many existing methods are induced from model-learning aspects, misa…
Inference-Aware Fine-Tuning for Best-of-N Sampling in Large Language Models
Yinlam Chow, Guy Tennenholtz, Izzeddin Gur +7
Recent studies have indicated that effectively utilizing inference-time compute is crucial for attaining better performance from large language models (LLMs). In this work, we prop…