1 citations · 3 across the 11 of their papers we have counts for
11 papers
Towards Verifiable Multimodal Deep Research: A Multi-Agent Harness for Interleaved Report Generation
Chenghao Zhang, Guanting Dong, Yufan Liu +3
Large Language Models (LLMs) have advanced autonomous agents from deep search, which retrieves concise factual answers, to deep research, which synthesizes scattered evidence into…
Beyond Dataset Distillation: Lossless Dataset Concentration via Diffusion-Assisted Distribution Alignment
Tongfei Liu, Yufan Liu, Bing Li +1
The high cost and accessibility problem associated with large datasets hinder the development of large-scale visual recognition systems. Dataset Distillation addresses these proble…
LongCat-Next: Lexicalizing Modalities as Discrete Tokens
Meituan LongCat Team, Bin Xiao, Chao Wang +86
The prevailing Next-Token Prediction (NTP) paradigm has driven the success of large language models through discrete autoregressive modeling. However, contemporary multimodal syste…
Arbitrary Ratio Feature Compression via Next Token Prediction
Yufan Liu, Daoyuan Ren, Zhipeng Zhang +4
Feature compression is increasingly important for improving the efficiency of downstream tasks, especially in applications involving large-scale or multi-modal data. While existing…
QVLA: Not All Channels Are Equal in Vision-Language-Action Model's Quantization
Yuhao Xu, Yantai Yang, Zhenyang Fan +4
The advent of Vision-Language-Action (VLA) models represents a significant leap for embodied intelligence, yet their immense computational demands critically hinder deployment on r…
The Illusion of Forgetting: Attack Unlearned Diffusion via Initial Latent Variable Optimization
Manyi Li, Yufan Liu, Lai Jiang +3
Text-to-image diffusion models (DMs) are frequently abused to produce harmful or copyrighted content, violating public interests. Concept erasure (unlearning) is a promising paradi…
AutoPrune: Each Complexity Deserves a Pruning Policy
Hanshi Wang, Yuhao Xu, Zekun Xu +5
The established redundancy in visual tokens within large vision-language models allows pruning to effectively reduce their substantial computational demands. Previous methods typic…
Pay Less Attention to Deceptive Artifacts: Robust Detection of Compressed Deepfakes on Online Social Networks
Manyi Li, Renshuai Tao, Yufan Liu +5
With the rapid advancement of deep learning, particularly through generative adversarial networks (GANs) and diffusion models (DMs), AI-generated images, or ``deepfakes", have beco…
Visual-Instructed Degradation Diffusion for All-in-One Image Restoration
Wenyang Luo, Haina Qin, Zewen Chen +6
Image restoration tasks like deblurring, denoising, and dehazing usually need distinct models for each degradation type, restricting their generalization in real-world scenarios wi…
Token Caching for Diffusion Transformer Acceleration
Jinming Lou, Wenyang Luo, Yufan Liu +5
Diffusion transformers have gained substantial interest in diffusion generative modeling due to their outstanding performance. However, their computational demands, particularly th…
I2V-Adapter: A General Image-to-Video Adapter for Diffusion Models
Xun Guo, Mingwu Zheng, Liang Hou +9
Text-guided image-to-video (I2V) generation aims to generate a coherent video that preserves the identity of the input image and semantically aligns with the input prompt. Existing…