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Transformer-based large models excel in natural language processing and computer vision, but face severe computational inefficiencies due to the self-attention's quadratic complexity with input tokens. Recently, researchers have proposed a…

Computation and Language · Computer Science 2026-05-26 Haojie Ouyang , Jianwei Lv , Lei Ren , Chen Wei , Xiaojie Wang , Fangxiang Feng

Large Language Models (LLMs) exhibit pronounced memory-bound characteristics during inference due to High Bandwidth Memory (HBM) bandwidth constraints. In this paper, we propose an L2 Cache-oriented asynchronous KV Cache prefetching method…

Machine Learning · Computer Science 2025-11-11 Yanhao Dong , Yubo Miao , Weinan Li , Xiao Zheng , Chao Wang , Jiesheng Wu , Feng Lyu

Large language models (LLMs) have shown exceptional performance and vast potential across diverse tasks. However, the deployment of LLMs with high performance in low-resource environments has garnered significant attention in the industry.…

Artificial Intelligence · Computer Science 2024-07-11 Pujiang He , Shan Zhou , Wenhuan Huang , Changqing Li , Duyi Wang , Bin Guo , Chen Meng , Sheng Gui , Weifei Yu , Yi Xie

Multimodal Large Language Models (MLLMs) have become a powerful tool for integrating visual and textual information. Despite their exceptional performance on visual understanding benchmarks, measuring their ability to reason abstractly…

Computer Vision and Pattern Recognition · Computer Science 2026-02-26 Nilay Yilmaz , Maitreya Patel , Yiran Lawrence Luo , Tejas Gokhale , Chitta Baral , Suren Jayasuriya , Yezhou Yang

We introduce two multilingual, multimodal foundation language models that power Apple Intelligence features across Apple devices and services: i a 3B-parameter on-device model optimized for Apple silicon through architectural innovations…

Machine Learning · Computer Science 2025-08-28 Ethan Li , Anders Boesen Lindbo Larsen , Chen Zhang , Xiyou Zhou , Jun Qin , Dian Ang Yap , Narendran Raghavan , Xuankai Chang , Margit Bowler , Eray Yildiz , John Peebles , Hannah Gillis Coleman , Matteo Ronchi , Peter Gray , Keen You , Anthony Spalvieri-Kruse , Ruoming Pang , Reed Li , Yuli Yang , Emad Soroush , Zhiyun Lu , Crystal Xiao , Rong Situ , Jordan Huffaker , David Griffiths , Zaid Ahmed , Peng Zhang , Daniel Parilla , Asaf Liberman , Jennifer Mallalieu , Parsa Mazaheri , Qibin Chen , Manjot Bilkhu , Aonan Zhang , Eric Wang , Dave Nelson , Michael FitzMaurice , Thomas Voice , Jeremy Liu , Josh Shaffer , Shiwen Zhao , Prasanth Yadla , Farzin Rasteh , Pengsheng Guo , Arsalan Farooq , Jeremy Snow , Stephen Murphy , Tao Lei , Minsik Cho , George Horrell , Sam Dodge , Lindsay Hislop , Sumeet Singh , Alex Dombrowski , Aiswarya Raghavan , Sasha Sirovica , Mandana Saebi , Faye Lao , Max Lam , TJ Lu , Zhaoyang Xu , Karanjeet Singh , Marc 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Zhang , Chang Gao , Chris Bartels , Sushma Rao , Nathalie Tran , Simon Lehnerer , Co Giang , Patrick Dong , Junting Pan , Biyao Wang , Dongxu Li , Mehrdad Farajtabar , Dongseong Hwang , Grace Duanmu , Eshan Verma , Sujeeth Reddy , Qi Shan , Hongbin Gao , Nan Du , Pragnya Sridhar , Forrest Huang , Yingbo Wang , Nikhil Bhendawade , Diane Zhu , Sai Aitharaju , Fred Hohman , Lauren Gardiner , Chung-Cheng Chiu , Yinfei Yang , Alper Kokmen , Frank Chu , Ke Ye , Kaan Elgin , Oron Levy , John Park , Donald Zhang , Eldon Schoop , Nina Wenzel , Michael Booker , Hyunjik Kim , Chinguun Erdenebileg , Nan Dun , Eric Liang Yang , Priyal Chhatrapati , Vishaal Mahtani , Haiming Gang , Kohen Chia , Deepa Seshadri , Donghan Yu , Yan Meng , Kelsey Peterson , Zhen Yang , Yongqiang Wang , Carina Peng , Doug Kang , Anuva Agarwal , Albert Antony , Juan Lao Tebar , Albin Madappally Jose , Regan Poston , Andy De Wang , Gerard Casamayor , Elmira Amirloo , Violet Yao , Wojciech Kryscinski , Kun Duan , Lezhi L

Time series analysis provides essential insights for real-world system dynamics and informs downstream decision-making, yet most existing methods often overlook the rich contextual signals present in auxiliary modalities. To bridge this…

Machine Learning · Computer Science 2026-03-24 Yushan Jiang , Wenchao Yu , Geon Lee , Dongjin Song , Kijung Shin , Wei Cheng , Yanchi Liu , Haifeng Chen

Vision-language models (VLMs) have demonstrated strong capabilities in multimodal perception and reasoning. However, deploying large VLMs on mobile devices remains challenging due to their substantial computational and memory demands. A…

Artificial Intelligence · Computer Science 2026-05-05 Yuanyuan Jia , Shunpu Tang , Qianqian Yang

Large Language Models (LLMs) are widely used across various domains, processing millions of daily requests. This surge in demand poses significant challenges in optimizing throughput and latency while keeping costs manageable. The Key-Value…

Distributed, Parallel, and Cluster Computing · Computer Science 2024-07-23 Jiale Xu , Rui Zhang , Cong Guo , Weiming Hu , Zihan Liu , Feiyang Wu , Yu Feng , Shixuan Sun , Changxu Shao , Yuhong Guo , Junping Zhao , Ke Zhang , Minyi Guo , Jingwen Leng

Mixture of Experts (MoE) LLMs, characterized by their sparse activation patterns, offer a promising approach to scaling language models while avoiding proportionally increasing the inference cost. However, their large parameter sizes…

Distributed, Parallel, and Cluster Computing · Computer Science 2025-04-15 Yichao Yuan , Lin Ma , Nishil Talati

Multimodal large language models (MLLMs) have demonstrated great performance on visual question answering (VQA). When it comes to knowledge-based Visual Question Answering (KB-VQA), MLLMs may lack the specialized domain knowledge needed to…

Computer Vision and Pattern Recognition · Computer Science 2025-02-04 Weixi Weng , Jieming Zhu , Xiaojun Meng , Hao Zhang , Rui Zhang , Chun Yuan

Inference of Large Language Models (LLMs) across computer clusters has become a focal point of research in recent times, with many acceleration techniques taking inspiration from CPU speculative execution. These techniques reduce…

Computation and Language · Computer Science 2024-11-19 Branden Butler , Sixing Yu , Arya Mazaheri , Ali Jannesari

Recent advancements in multimodal large language models (MLLM) have shown a strong ability in visual perception, reasoning abilities, and vision-language understanding. However, the visual matching ability of MLLMs is rarely studied,…

Computer Vision and Pattern Recognition · Computer Science 2025-07-10 Yikang Zhou , Tao Zhang , Shilin Xu , Shihao Chen , Qianyu Zhou , Yunhai Tong , Shunping Ji , Jiangning Zhang , Lu Qi , Xiangtai Li

Recent Multimodal Large Language Models (MLLMs) excel on benchmark vision-language tasks, yet little is known about how input visual quality shapes their responses. Does higher perceptual quality of images already translate to better MLLM…

Computer Vision and Pattern Recognition · Computer Science 2025-06-19 Shuo Xing , Lanqing Guo , Hongyuan Hua , Seoyoung Lee , Peiran Li , Yufei Wang , Zhangyang Wang , Zhengzhong Tu

In-context learning (ICL) allows large models to adapt to tasks using a few examples, yet its extension to vision-language models (VLMs) remains fragile. Our analysis reveals that the fundamental limitation lies in an inductive gap, models…

Computer Vision and Pattern Recognition · Computer Science 2026-05-05 Haoyu Wang , Haonan Wang , Yuyan Chen , Jun Chen , Gang Liu , Qian Wang , Jiahong Yan , Yanghua Xiao

State-of-the-art Vision-Language Models (VLMs) ground the vision and the language modality primarily via projecting the vision tokens from the encoder to language-like tokens, which are directly fed to the Large Language Model (LLM)…

Computer Vision and Pattern Recognition · Computer Science 2024-07-18 Sivan Doveh , Shaked Perek , M. Jehanzeb Mirza , Wei Lin , Amit Alfassy , Assaf Arbelle , Shimon Ullman , Leonid Karlinsky

Multimodal Large Language Models (MLLMs) have demonstrated strong performance across a wide range of vision-language tasks, yet their internal processing dynamics remain underexplored. In this work, we introduce a probing framework to…

Computer Vision and Pattern Recognition · Computer Science 2025-08-29 Zhuoran Yu , Yong Jae Lee

DL inference queries play an important role in diverse internet services and a large fraction of datacenter cycles are spent on processing DL inference queries. Specifically, the matrix-matrix multiplication (GEMM) operations of…

Hardware Architecture · Computer Science 2020-12-02 Benjamin Y. Cho , Jeageun Jung , Mattan Erez

Modern approaches to enhancing Large Language Models' factual accuracy and knowledge utilization face a fundamental trade-off: non-parametric retrieval-augmented generation (RAG) provides flexible access to external knowledge but suffers…

Computation and Language · Computer Science 2026-03-02 Rubin Wei , Jiaqi Cao , Jiarui Wang , Jushi Kai , Qipeng Guo , Bowen Zhou , Zhouhan Lin

The discovery of new ionizable lipids for efficient lipid nanoparticle (LNP)-mediated RNA delivery remains a critical bottleneck for RNA-based therapeutics development. Recent advances have highlighted the potential of machine learning (ML)…

While multimodal fusion has been extensively studied in Multimodal Sentiment Analysis (MSA), the role of fusion depth and multimodal capacity allocation remains underexplored. In this work, we position fusion depth, scalability, and…

Computation and Language · Computer Science 2025-04-16 Efthymios Georgiou , Vassilis Katsouros , Yannis Avrithis , Alexandros Potamianos
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