14 citations · 22 across the 24 of their papers we have counts for
27 papers
Sparse Attention for Video Generation Acceleration via Growing Sparsity and Reduced Search
Yun Dai, Yuyun Chen, Jiarui Wen +3
Diffusion Transformers (DiTs) dominate video generation but suffer from high inference cost due to the quadratic complexity of self-attention. Sparse attention mitigates this by sk…
PARK: Accurate Block Retrieval for Sparse Attention in Video Diffusion Transformers
Yun Dai, Jiarui Wen, Huiping Zhuang +2
Diffusion Transformers (DiTs) have become a dominant architecture for video generation, but their efficiency is limited by the quadratic complexity of full attention. Sparse attent…
EgoSafe: A First-Person Mobile-Captured Benchmark for Visual Safety Understanding
Yuyun Chen, Tianao Li, TianQuan Feng +4
Reliable visual safety understanding in real-world scenarios demands more than just object recognition; it requires causal reasoning under epistemic uncertainty. While Large Vision…
FreoStream:Enhancing Stream Guardrails via Future-Aware Reasoning and Safety-Aligned Optimization
Jianwei Wang, Guoyang Shen, Yanhong Wu +5
Stream guardrails enable token-level safety detection before full responses are generated. However, they often make overly conservative judgements and block those sensitive but saf…
MixKVQ: Query-Aware Mixed-Precision KV Cache Quantization for Long-Context Reasoning
Tao Zhang, Ziqian Zeng, Hao Peng +2
Long Chain-of-Thought (CoT) reasoning has significantly advanced the capabilities of Large Language Models (LLMs), but this progress is accompanied by substantial memory and latenc…
ARGUS: Defending Against Multimodal Indirect Prompt Injection via Steering Instruction-Following Behavior
Weikai Lu, Ziqian Zeng, Kehua Zhang +5
Multimodal Large Language Models (MLLMs) are increasingly vulnerable to multimodal Indirect Prompt Injection (IPI) attacks, which embed malicious instructions in images, videos, or…
RCP-Merging: Merging Long Chain-of-Thought Models with Domain-Specific Models by Considering Reasoning Capability as Prior
Junyao Yang, Jianwei Wang, Huiping Zhuang +2
Large Language Models (LLMs) with long chain-of-thought (CoT) capability, termed Reasoning Models, demonstrate superior intricate problem-solving abilities through multi-step long…
Decompose, Plan in Parallel, and Merge: A Novel Paradigm for Large Language Models based Planning with Multiple Constraints
Zhengdong Lu, Weikai Lu, Yiling Tao +6
Despite significant advances in Large Language Models (LLMs), planning tasks still present challenges for LLM-based agents. Existing planning methods face two key limitations: heav…
L3A: Label-Augmented Analytic Adaptation for Multi-Label Class Incremental Learning
Xiang Zhang, Run He, Jiao Chen +5
Class-incremental learning (CIL) enables models to learn new classes continually without forgetting previously acquired knowledge. Multi-label CIL (MLCIL) extends CIL to a real-wor…
Analytic Subspace Routing: How Recursive Least Squares Works in Continual Learning of Large Language Model
Kai Tong, Kang Pan, Xiao Zhang +5
Large Language Models (LLMs) possess encompassing capabilities that can process diverse language-related tasks. However, finetuning on LLMs will diminish this general skills and co…
Semantic Shift Estimation via Dual-Projection and Classifier Reconstruction for Exemplar-Free Class-Incremental Learning
Run He, Di Fang, Yicheng Xu +5
Exemplar-Free Class-Incremental Learning (EFCIL) aims to sequentially learn from distinct categories without retaining exemplars but easily suffers from catastrophic forgetting of…
RewardDS: Privacy-Preserving Fine-Tuning for Large Language Models via Reward Driven Data Synthesis
Jianwei Wang, Chengming Shi, Junyao Yang +5
The success of large language models (LLMs) has attracted many individuals to fine-tune them for domain-specific tasks by uploading their data. However, in sensitive areas like hea…
SEA: Low-Resource Safety Alignment for Multimodal Large Language Models via Synthetic Embeddings
Weikai Lu, Hao Peng, Huiping Zhuang +2
Multimodal Large Language Models (MLLMs) have serious security vulnerabilities.While safety alignment using multimodal datasets consisting of text and data of additional modalities…
X-SGS: Safe and Generalizable Gaussian Splatting with X-dimensional Watermarks
Zihang Cheng, Wentao Bao, Huiping Zhuang +6
3D Gaussian Splatting (3DGS) has been widely used in 3D reconstruction and 3D generation. However, the rapid adoption of 3D Gaussian Splatting raises growing concerns about informa…
AIR: Analytic Imbalance Rectifier for Continual Learning
Di Fang, Yinan Zhu, Runze Fang +4
Continual learning (CL) agents incrementally learn from sequentially arriving data and adapt to the dynamic, ever-changing nature of real-world environments. However, many existing…
GenderAlign: An Alignment Dataset for Mitigating Gender Bias in Large Language Models
Tao Zhang, Ziqian Zeng, Yuxiang Xiao +4
Large Language Models (LLMs) are prone to generating content that exhibits gender biases, raising significant ethical concerns. Alignment, the process of fine-tuning LLMs to better…
PrivacyRestore: Privacy-Preserving Inference in Large Language Models via Privacy Removal and Restoration
Ziqian Zeng, Jianwei Wang, Junyao Yang +4
The widespread usage of online Large Language Models (LLMs) inference services has raised significant privacy concerns about the potential exposure of private information in user i…
Online Analytic Exemplar-Free Continual Learning with Large Models for Imbalanced Autonomous Driving Task
Huiping Zhuang, Di Fang, Kai Tong +4
In autonomous driving, even a meticulously trained model can encounter failures when facing unfamiliar scenarios. One of these scenarios can be formulated as an online continual le…
AFL: A Single-Round Analytic Approach for Federated Learning with Pre-trained Models
Run He, Kai Tong, Di Fang +5
In this paper, we introduce analytic federated learning (AFL), a new training paradigm that brings analytical (i.e., closed-form) solutions to the federated learning (FL) with pre-…
Eraser: Jailbreaking Defense in Large Language Models via Unlearning Harmful Knowledge
Weikai Lu, Ziqian Zeng, Jianwei Wang +5
Jailbreaking attacks can enable Large Language Models (LLMs) to bypass the safeguard and generate harmful content. Existing jailbreaking defense methods have failed to address the…
DS-AL: A Dual-Stream Analytic Learning for Exemplar-Free Class-Incremental Learning
Huiping Zhuang, Run He, Kai Tong +3
Class-incremental learning (CIL) under an exemplar-free constraint has presented a significant challenge. Existing methods adhering to this constraint are prone to catastrophic for…
F-OAL: Forward-only Online Analytic Learning with Fast Training and Low Memory Footprint in Class Incremental Learning
Huiping Zhuang, Yuchen Liu, Run He +5
Online Class Incremental Learning (OCIL) aims to train models incrementally, where data arrive in mini-batches, and previous data are not accessible. A major challenge in OCIL is C…
GACL: Exemplar-Free Generalized Analytic Continual Learning
Huiping Zhuang, Yizhu Chen, Di Fang +5
Class incremental learning (CIL) trains a network on sequential tasks with separated categories in each task but suffers from catastrophic forgetting, where models quickly lose pre…
REAL: Representation Enhanced Analytic Learning for Exemplar-free Class-incremental Learning
Run He, Di Fang, Yizhu Chen +5
Exemplar-free class-incremental learning (EFCIL) aims to mitigate catastrophic forgetting in class-incremental learning (CIL) without available historical training samples as exemp…