25 citations · 41 across the 19 of their papers we have counts for
19 papers
AgentSpec: Speculative Decoding for Batch Inference of LLM Agents
Xin Wang, Ziming Miao, Yi Zhu +4
Large language model (LLM)-based agent applications often incur high response time. Speculative decoding is a promising solution to improve the inference efficiency of LLM agents w…
Dynamic Linear Attention
Xin Wang, Hui Shen, Boyuan Zheng +7
The scalability of Large Language Models (LLMs) to long contexts is fundamentally constrained by the quadratic complexity of standard attention, motivating the adoption of linear a…
QuantVLA: Scale-Calibrated Post-Training Quantization for Vision-Language-Action Models
Jingxuan Zhang, Yunta Hsieh, Zhongwei Wan +5
Vision-language-action (VLA) models unify perception, language, and control for embodied agents but face significant challenges in practical deployment due to rapidly increasing co…
MMDeepResearch-Bench: A Benchmark for Multimodal Deep Research Agents
Peizhou Huang, Zixuan Zhong, Zhongwei Wan +12
Deep Research Agents (DRAs) generate citation-rich reports via multi-step search and synthesis, yet existing benchmarks mainly target text-only settings or short-form multimodal QA…
Enhancing Test-Time Scaling of Large Language Models with Hierarchical Retrieval-Augmented MCTS
Alex ZH Dou, Zhongwei Wan, Dongfei Cui +6
Test-time scaling has emerged as a promising paradigm in language modeling, leveraging additional computational resources at inference time to enhance model performance. In this wo…
PhyX: Does Your Model Have the "Wits" for Physical Reasoning?
Hui Shen, Taiqiang Wu, Qi Han +16
Existing benchmarks fail to capture a crucial aspect of intelligence: physical reasoning, the integrated ability to combine domain knowledge, symbolic reasoning, and understanding…
SVD-LLM V2: Optimizing Singular Value Truncation for Large Language Model Compression
Xin Wang, Samiul Alam, Zhongwei Wan +2
Despite significant advancements, the practical deployment of Large Language Models (LLMs) is often hampered by their immense sizes, highlighting the need for effective compression…
MEDA: Dynamic KV Cache Allocation for Efficient Multimodal Long-Context Inference
Zhongwei Wan, Hui Shen, Xin Wang +3
Long-context Multimodal Large Language Models (MLLMs) that incorporate long text-image and text-video modalities, demand substantial resources as their multimodal Key-Value (KV) ca…
Autoregressive Models in Vision: A Survey
Jing Xiong, Gongye Liu, Lun Huang +17
Autoregressive modeling has been a huge success in the field of natural language processing (NLP). Recently, autoregressive models have emerged as a significant area of focus in co…
Famba-V: Fast Vision Mamba with Cross-Layer Token Fusion
Hui Shen, Zhongwei Wan, Xin Wang +1
Mamba and Vision Mamba (Vim) models have shown their potential as an alternative to methods based on Transformer architecture. This work introduces Fast Mamba for Vision (Famba-V),…
Evolver: Chain-of-Evolution Prompting to Boost Large Multimodal Models for Hateful Meme Detection
Jinfa Huang, Jinsheng Pan, Zhongwei Wan +2
Recent advances show that two-stream approaches have achieved outstanding performance in hateful meme detection. However, hateful memes constantly evolve as new memes emerge by fus…
Scaling Laws with Vocabulary: Larger Models Deserve Larger Vocabularies
Chaofan Tao, Qian Liu, Longxu Dou +5
Research on scaling large language models (LLMs) has primarily focused on model parameters and training data size, overlooking the role of vocabulary size. We investigate how vocab…
LOOK-M: Look-Once Optimization in KV Cache for Efficient Multimodal Long-Context Inference
Zhongwei Wan, Ziang Wu, Che Liu +5
Long-context Multimodal Large Language Models (MLLMs) demand substantial computational resources for inference as the growth of their multimodal Key-Value (KV) cache, in response t…
SVD-LLM: Truncation-aware Singular Value Decomposition for Large Language Model Compression
Xin Wang, Yu Zheng, Zhongwei Wan +1
The advancements in Large Language Models (LLMs) have been hindered by their substantial sizes, which necessitates LLM compression methods for practical deployment. Singular Value…
Zero-Shot ECG Classification with Multimodal Learning and Test-time Clinical Knowledge Enhancement
Che Liu, Zhongwei Wan, Cheng Ouyang +3
Electrocardiograms (ECGs) are non-invasive diagnostic tools crucial for detecting cardiac arrhythmic diseases in clinical practice. While ECG Self-supervised Learning (eSSL) method…
MEIT: Multimodal Electrocardiogram Instruction Tuning on Large Language Models for Report Generation
Zhongwei Wan, Che Liu, Xin Wang +6
Electrocardiogram (ECG) is the primary non-invasive diagnostic tool for monitoring cardiac conditions and is crucial in assisting clinicians. Recent studies have concentrated on cl…
The Internet of Things in the Era of Generative AI: Vision and Challenges
Xin Wang, Zhongwei Wan, Arvin Hekmati +4
Advancements in Generative AI hold immense promise to push Internet of Things (IoT) to the next level. In this article, we share our vision on IoT in the era of Generative AI. We d…
Efficient Large Language Models: A Survey
Zhongwei Wan, Xin Wang, Che Liu +9
Large Language Models (LLMs) have demonstrated remarkable capabilities in important tasks such as natural language understanding and language generation, and thus have the potentia…
ETP: Learning Transferable ECG Representations via ECG-Text Pre-training
Che Liu, Zhongwei Wan, Sibo Cheng +2
In the domain of cardiovascular healthcare, the Electrocardiogram (ECG) serves as a critical, non-invasive diagnostic tool. Although recent strides in self-supervised learning (SSL…