2 citations · 3 across the 13 of their papers we have counts for
6 papers · 1 filter
Intern-Decision: Efficient Multimodal Decision Making with Autoregressive Masked Language Modeling
Tong Zhu, Zhe Xu, Zefeng He +7
Interactive agents repeatedly choose among a finite set of actions, labels, or ordinal outcomes. Generating a free-form response for each choice adds latency and complicates the in…
LatentLoop: Scaling Looped MoE Models with Asymmetric Compute Allocation
Zefeng He, Yunkai Chai, Xiaoyang Chen +5
Looped Mixture-of-Experts (MoE) models combine parameter reuse with sparse computation to improve performance under matched budgets. Prior work demonstrates that different loop vis…
Unify-Agent: A Unified Multimodal Agent for World-Grounded Image Synthesis
Shuang Chen, Quanxin Shou, Hangting Chen +16
Unified multimodal models provide a natural and promising architecture for understanding diverse and complex real-world knowledge while generating high-quality images. However, the…
GEMS: Agent-Native Multimodal Generation with Memory and Skills
Zefeng He, Siyuan Huang, Xiaoye Qu +4
Recent multimodal generation models have achieved remarkable progress on general-purpose generation tasks, yet continue to struggle with complex instructions and specialized downst…
ViviDoc: Generating Interactive Documents through Human-Agent Collaboration
Yinghao Tang, Yupeng Xie, Yingchaojie Feng +4
Interactive documents help readers engage with complex ideas through dynamic visualization, interactive animations, and exploratory interfaces. However, creating such documents rem…
ExFusion: Efficient Transformer Training via Multi-Experts Fusion
Jiacheng Ruan, Daize Dong, Xiaoye Qu +5
Mixture-of-Experts (MoE) models substantially improve performance by increasing the capacity of dense architectures. However, directly training MoE models requires considerable com…