most citedLLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery

9 citations · 21 across the 9 of their papers we have counts for

collaborators

10 papers

cs.CV2026

Better Call CineCrew: Consistent Ultra-Long Narrative-to-Film Generation

Jiaben Chen, Sixun Dong, Qinhong Zhou +4

Long-form narrative-to-film generation requires shot-level controllability and cross-clip consistency in both visual identity and character behavior-requirements that remain diffic…

cs.CV2024

Compositional Physical Reasoning of Objects and Events from Videos

Zhenfang Chen, Shilong Dong, Kexin Yi +5

Understanding and reasoning about objects' physical properties in the natural world is a fundamental challenge in artificial intelligence. While some properties like colors and sha…

cs.CV2024

SOK-Bench: A Situated Video Reasoning Benchmark with Aligned Open-World Knowledge

Andong Wang, Bo Wu, Sunli Chen +5

Learning commonsense reasoning from visual contexts and scenes in real-world is a crucial step toward advanced artificial intelligence. However, existing video reasoning benchmarks…

cs.LG2024★ 9 cited

LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery

Pingchuan Ma, Tsun-Hsuan Wang, Minghao Guo +5

Large Language Models have recently gained significant attention in scientific discovery for their extensive knowledge and advanced reasoning capabilities. However, they encounter…

cs.CV2024★ 1 cited

Physically Compatible 3D Object Modeling from a Single Image

Minghao Guo, Bohan Wang, Pingchuan Ma +6

We present a computational framework that transforms single images into 3D physical objects. The visual geometry of a physical object in an image is determined by three orthogonal…

cs.RO2024★ 4 cited

RoboDreamer: Learning Compositional World Models for Robot Imagination

Siyuan Zhou, Yilun Du, Jiaben Chen +3

Text-to-video models have demonstrated substantial potential in robotic decision-making, enabling the imagination of realistic plans of future actions as well as accurate environme…

cs.CV2024★ 2 cited

COMBO: Compositional World Models for Embodied Multi-Agent Cooperation

Hongxin Zhang, Zeyuan Wang, Qiushi Lyu +7

In this paper, we investigate the problem of embodied multi-agent cooperation, where decentralized agents must cooperate given only egocentric views of the world. To effectively pl…

cs.CV2024

ContPhy: Continuum Physical Concept Learning and Reasoning from Videos

Zhicheng Zheng, Xin Yan, Zhenfang Chen +4

We introduce the Continuum Physical Dataset (ContPhy), a novel benchmark for assessing machine physical commonsense. ContPhy complements existing physical reasoning benchmarks by e…

cs.CV2024★ 2 cited

HAZARD Challenge: Embodied Decision Making in Dynamically Changing Environments

Qinhong Zhou, Sunli Chen, Yisong Wang +6

Recent advances in high-fidelity virtual environments serve as one of the major driving forces for building intelligent embodied agents to perceive, reason and interact with the ph…

cs.LG2024★ 3 cited

Improving Reinforcement Learning from Human Feedback with Efficient Reward Model Ensemble

Shun Zhang, Zhenfang Chen, Sunli Chen +3

Reinforcement Learning from Human Feedback (RLHF) is a widely adopted approach for aligning large language models with human values. However, RLHF relies on a reward model that is…