8 papers · 1 filter
Adversarial Gradient Forcing for Robust Few-Step Autoregressive Video Generation
Chenxi Xia, Yixuan Xu, Shan Liu +7
Autoregressive video generation models generate videos sequentially, with each new block conditioned on previously generated blocks. By training on the model's own rollouts, Self F…
ARS: Agentic Reward System for Robot Learning
Sheng Hu, Weiyi Lu, Lingbing Zeng +5
Progress reward modeling is the problem of estimating how a robot's behavior changes task progress over time. Reliable estimation requires distinguishing meaningful state changes f…
Pre-VLA: Preemptive Runtime Verification for Reliable Vision-Language-Action and World-Model Rollouts
Zhen Sun, Yongjian Guo, Haoran Sun +6
While large vision-language-action (VLA) models and generative world models (WM) have advanced long-horizon embodied intelligence, their practical deployment remains challenged by…
Linear-DPO: Linear Direct Preference Optimization for Diffusion and Flow-Matching Generative Models
Kesong Li, Yixuan Xu, Kuo-kun Tseng +3
Direct Preference Optimization (DPO) is successful for alignment in LLMs but still faces challenges in text-to-image generation. Existing studies are confined to denoising diffusio…
What Limits Vision-and-Language Navigation ?
Yunheng Wang, Yuetong Fang, Taowen Wang +9
Vision-and-Language Navigation (VLN) is a cornerstone of embodied intelligence. However, current agents often suffer from significant performance degradation when transitioning fro…
Qwen-Image-2.0 Technical Report
Bing Zhao, Chenfei Wu, Deqing Li +72
We present Qwen-Image-2.0, an omni-capable image generation foundation model that unifies high-fidelity generation and precise image editing within a single framework. Despite rece…
LegoDiffusion: Micro-Serving Text-to-Image Diffusion Workflows
Lingyun Yang, Suyi Li, Tianyu Feng +10
Text-to-image generation executes a diffusion workflow comprising multiple models centered on a base diffusion model. Existing serving systems treat each workflow as an opaque mono…
Shiva-DiT: Residual-Based Differentiable Top- Selection for Efficient Diffusion Transformers
Jiaji Zhang, Hailiang Zhao, Jiaju Wu +3
Diffusion Transformers (DiTs) are costly at high resolution because self-attention scales quadratically with token sequence length. Existing pruning methods do not jointly provide…