4 papers
Relax Within, Balance Across: Geometry-Guided Load Balancing for Vision-Language Mixture-of-Experts
Ziang Wu, Peng Jin, Qishen Yin +4
Vision-language MoE batches contain different numbers of image and text tokens. Image resolution, image count, tiling, and prompt length all change this token mix. We call the stan…
ChronoPhyBench: Do MLLMs Truly Understand the World or Merely Exploit Language Priors?
Bin Zhu, Yanhao Jia, Kexin Zhao +12
Recent advancements in Multimodal Large Language Models (MLLMs) have demonstrated remarkable proficiency in open-world reasoning and understanding. However, a critical ambiguity pe…
BEAM: Binary Expert Activation Masking for Dynamic Routing in MoE
Juntong Wu, Jialiang Cheng, Qishen Yin +5
Mixture-of-Experts (MoE) architectures enhance the efficiency of large language models by activating only a subset of experts per token. However, standard MoE employs a fixed Top-K…
UltraShape 1.0: High-Fidelity 3D Shape Generation via Scalable Geometric Refinement
Tanghui Jia, Dongyu Yan, Dehao Hao +11
In this report, we introduce UltraShape 1.0, a scalable 3D diffusion framework for high-fidelity 3D geometry generation. The proposed approach adopts a two-stage generation pipelin…