Reliable long-horizon prediction remains a central challenge for embodied world models. Although recent generative models can synthesize visually plausible action-conditioned futures, their latent dynamics are typically learned through unconstrained transition modules, which may gradually deviate from coherent physical...
Ke-Zhou Chen, Yu-Wei Zhan, Hao-Ran Liang et al.· 0 citations
A large-scale benchmark suite for open-world aerial object-goal search, with 3 times as many scenes and 18.7 times as many task instances as the largest existing benchmark for this task, and a unified evaluation framework with a unified evaluation framework.
Tong-Tong Feng, Xin Wang, Hao-Ran Hou et al.· 0 citations
This work proposes MoD-VLLM, a novel Modularized Dynamic-Granularity Video LLM framework for multi-event long video understanding, which unifies temporal grounding and semantic understanding iteratively and self-reflectively and proposes a dynamic-granularity reinforcement learning strategy, allowing MoD-VLLM to learn...