6 papers
The Expressivity Boundary of Probabilistic Circuits: A Comparison with Large Language Models
Zhiyu Zhao, Xuejie Liu, Muhan Zhang +1
Probabilistic Circuits (PCs) are deep generative models that support exact and efficient probabilistic inference. Yet in autoregressive language modeling, PCs still lag behind Tran…
Lookahead Path Likelihood Optimization for Diffusion LLMs
Xuejie Liu, Yap Vit Chun, Yitao Liang +1
Diffusion Large Language Models (dLLMs) support arbitrary-order generation, yet their inference performance critically depends on the unmasking order. Existing strategies rely on h…
Plug-and-Play Context Feature Reuse for Efficient Masked Generation
Xuejie Liu, Anji Liu, Guy Van den Broeck +1
Masked generative models (MGMs) have emerged as a powerful framework for image synthesis, combining parallel decoding with strong bidirectional context modeling. However, generatin…
Tractable Transformers for Flexible Conditional Generation
Anji Liu, Xuejie Liu, Dayuan Zhao +3
Non-autoregressive (NAR) generative models are valuable because they can handle diverse conditional generation tasks in a more principled way than their autoregressive (AR) counter…
OmniJARVIS: Unified Vision-Language-Action Tokenization Enables Open-World Instruction Following Agents
Zihao Wang, Shaofei Cai, Zhancun Mu +7
This paper presents OmniJARVIS, a novel Vision-Language-Action (VLA) model for open-world instruction-following agents in Minecraft. Compared to prior works that either emit textua…
A Tractable Inference Perspective of Offline RL
Xuejie Liu, Anji Liu, Guy Van den Broeck +1
A popular paradigm for offline Reinforcement Learning (RL) tasks is to first fit the offline trajectories to a sequence model, and then prompt the model for actions that lead to hi…