5 papers
ExeWorldBench: Benchmarking Code-Based Executable 3D World Modeling
Xinge Peng, Zhen Fang, Ruibing Wang +8
Coding agents are increasingly evolving beyond reproducing what a world looks like toward constructing executable worlds that capture how a world works. This shift calls for execut…
Reward as An Agent for Embodied World Models
Pu Li, Zhigang Lin, Qiang Wu +3
While RL has become a promising tool for refining world models, existing methods largely rely on conservative rollouts near the training distribution, limiting exploration, behavio…
Text-Driven 3D Indoor Scene Synthesis in Non-Manhattan Environments
Xianhui Meng, Zirui Song, Yuchen Zhang +10
Large Language Models (LLMs) have demonstrated remarkable capabilities in 3D indoor synthesis for Manhattan environments. However, existing methods often fail to capture plausible…
PRPO: Aligning Process Reward with Outcome Reward in Policy Optimization
Ruiyi Ding, Yongxuan Lv, Xianhui Meng +4
Policy optimization for large language models often suffers from sparse reward signals in multi-step reasoning tasks. Critic-free methods like GRPO assign a single normalized outco…
You Only Click Once: Single Point Weakly Supervised 3D Instance Segmentation for Autonomous Driving
Guangfeng Jiang, Jun Liu, Yongxuan Lv +5
Outdoor LiDAR point cloud 3D instance segmentation is a crucial task in autonomous driving. However, it requires laborious human efforts to annotate the point cloud for training a…