11 citations · 11 across the 14 of their papers we have counts for
8 papers · 1 filter
SAFE-Mem: Secure Agentic Memory with Fine-Grained Authority and Governance-Aware Retrieval
Haoming Li, Dingyi Kang, Xu Hu +8
Large language model (LLM) agents increasingly rely on persistent memory to support long-term reasoning and personalized interactions. However, information collected from external…
EAServe: Encode-Aware Disaggregated Serving for Multimodal Large Language Models
Kunxiong Zhu, Zhihao Shu, Hangyu Zheng +4
Disaggregating the two stages, Prefill and Decode, onto separate GPU pools is now a standard optimization for (text-only) LLM serving. However, multimodal LLMs (MLLMs), which add a…
Allocation Before Ranking: Decoupled Token Compression for OmniLLMs
Zhenghui Guo, Yilin Yang, Yuanbin Man +5
Token compression in OmniLLMs is typically posed as a single saliency-ranking problem: score each multimodal token, keep the top-K. We argue this abstraction is mis-specified. The…
Splaxel: Efficient Distributed Training of 3D Gaussian Splatting for Large-scale Scene Reconstruction via Pixel-level Communication
Wenqi Jia, Zhewen Hu, Ying Huang +10
3D Gaussian Splatting (3DGS) enables high-fidelity and real-time 3D scene reconstruction, but scaling training to large-scale scenes requires optimizing hundreds of millions of Gau…
Gaussians on a Diet: High-Quality Memory-Bounded 3D Gaussian Splatting Training
Yangming Zhang, Jian Xu, Chaojian Li +7
3D Gaussian Splatting (3DGS) has revolutionized novel view synthesis with high-quality rendering through continuous aggregations of millions of 3D Gaussian primitives. However, it…
FlashMem: Supporting Modern DNN Workloads on Mobile with GPU Memory Hierarchy Optimizations
Zhihao Shu, Md Musfiqur Rahman Sanim, Hangyu Zheng +4
The increasing size and complexity of modern deep neural networks (DNNs) pose significant challenges for on-device inference on mobile GPUs, with limited memory and computational r…
CoSA: Compressed Sensing-Based Adaptation of Large Language Models
Songtao Wei, Yi Li, Bohan Zhang +6
Parameter-Efficient Fine-Tuning (PEFT) has emerged as a practical paradigm for adapting large language models (LLMs) without updating all parameters. Most existing approaches, such…
Event-VStream: Event-Driven Real-Time Understanding for Long Video Streams
Zhenghui Guo, Yuanbin Man, Junyuan Sheng +8
Real-time understanding of long video streams remains challenging for multimodal large language models (VLMs) due to redundant frame processing and rapid forgetting of past context…