activity
20242026
most citedIdentify Critical Nodes in Complex Network with Large Language Models

2 citations · 2 across the 3 of their papers we have counts for

collaborators

7 papers

cs.AI2026

Humanize: Judgement Engineering for Agentic Coding

Sihao Liu, Ligeng Zhu, Zijian Zhang +6

Agentic coding makes code generation cheap, but reliable completion remains difficult: the agent that writes the code is a weak judge of whether it is done. We present Humanize, a…

cs.CV2026

VC-Attention: Value Smoothing and Softmax Casting for Low-bit Attention

Xingyang Li, Dongyun Zou, Shining Zhang +8

Diffusion Transformers deliver state-of-the-art video generation, but their long spatiotemporal sequences make attention the dominant deployment cost, and a deployable low-bit kern…

cs.CV2026

JetViT: Efficient High-Resolution Vision Transformer with Post-Training Attention Search

Dongyun Zou, Zhuoyang Zhang, Junyu Chen +8

We introduce JetViT, a novel family of hybrid-architecture Vision Transformer (ViT) models that match the accuracy of state-of-the-art full-attention vision foundation models while…

cs.CV2025

DC-VideoGen: Efficient Video Generation with Deep Compression Video Autoencoder

Junyu Chen, Wenkun He, Yuchao Gu +12

We introduce DC-VideoGen, a post-training acceleration framework for efficient video generation. DC-VideoGen can be applied to any pre-trained video diffusion model, improving effi…

cs.CV2025

DC-Gen: Post-Training Diffusion Acceleration with Deeply Compressed Latent Space

Wenkun He, Yuchao Gu, Junyu Chen +11

Existing text-to-image diffusion models excel at generating high-quality images, but face significant efficiency challenges when scaled to high resolutions, like 4K image generatio…

cs.CV2025

DC-AE 1.5: Accelerating Diffusion Model Convergence with Structured Latent Space

Junyu Chen, Dongyun Zou, Wenkun He +4

We present DC-AE 1.5, a new family of deep compression autoencoders for high-resolution diffusion models. Increasing the autoencoder's latent channel number is a highly effective a…

cs.SI2024★ 2 cited

Identify Critical Nodes in Complex Network with Large Language Models

Jinzhu Mao, Dongyun Zou, Li Sheng +4

Identifying critical nodes in networks is a classical decision-making task, and many methods struggle to strike a balance between adaptability and utility. Therefore, we propose an…