Edge-Cloud Collaborative Inference

Latest papers

26 papers2 papers in the last four weeks, down 33% on the four weeks before. 0.0% of all new papers.

Empowering Edge Agents: A Systematic Analysis of Edge-Cloud Collaboration in LLM Agent Execution
Oct 13, 2026Pengcheng Huang, Zhenghao Liu, Shixin Ren +5Edge-Cloud Collaborative Inference Edge-Cloud Computing
School of Computer Science and Engineering, Northeastern University, Shenyang, China · Department of Computer Science and Technology, Tsinghua University, Beijing, China · China Mobile Online Services Co., Ltd. Beijing, China
EdgeWise: Bringing Long-Horizon Agents to the Edge via Selective Cloud Delegation
Oct 13, 2026Zeru Shi, Jindong Jiang, Ryo Hachiuma +10Edge-Cloud Collaborative Inference LLM Agents
NVIDIA · Rutgers University
EV-JEPA: Elastic V-JEPA for Any Device and Cross-Device Inference
Oct 9, 2026Anxhelo Diko, Jieyu Lin, Stefano Ambrogio +5Elastic Neural Networks Edge-Cloud Collaborative Inference
Meta Reality Labs Research · FAIR at Meta
DySCo: Dynamic Sharding for Collaborative Edge-Cloud LLM Inference with Depth-Synchronized Batching
Oct 6, 2026Jingpo Xu, Paul Joe Maliakel, Ivona Brandic +1LLM Inference Edge-Cloud Collaborative Inference
University of Amsterdam Amsterdam, Netherlands · TU Wien Vienna, Austria
EMMI: Edge Multi-Modal Intelligence for Communication-Efficient MLLM Inference via Fused Representation Compression
Sep 10, 2026Motahare Mounesan, Irfan KhanEfficient Multimodal Inference Edge-Cloud Collaborative Inference
Texas A&M University
Streaming Hierarchical Inference with Tabular Foundation Models
Sep 7, 2026Vitor Crista, Afonso Lourenço, Diogo Martinho +1Tabular Foundation Models Efficient Inference
GECAD, Polytechnic of Porto, Portugal
Pro-Router: Token-Aware Progressive Model Routing with Adaptive Edge-Cloud Collaboration for Efficient Multimodal LLM Inference
Aug 28, 2026Xinyuan Gui, Shaowen Wang, Sheng Sun +3Edge-Cloud Collaborative Inference LLM Routing
Anyscale · Department of Computer Science and Engineering, Mississippi State University · Institute of Computing Technology, Chinese Academy of Sciences +1
SPADE: Speculative Decoding for Precise and Low Cost Distributed Edge Cloud Inference
Aug 13, 2026Divya Jyoti Bajpai, Kishan Kumar Upadhyay, Manjesh Kumar HanawalEdge-Cloud Collaborative Inference Edge Computing
MLiONS, Department of IEOR, IIT Bombay Mumbai, Maharashtra-400076, India
Cloud-Boosted Low-Compute Multi-Channel Speech Enhancement
Aug 7, 2026Xulin Fan, Juan Azcarreta, Ashutosh Pandey +5Edge-Cloud Collaborative Inference Edge Computing
University of Illinois Urbana-Champaign, USA · Meta Reality Labs Research, USA
Latency-Tolerant Cloud-Edge Collaborative Vision-Language-Action Models via Emergent Representational Specialization
Aug 1, 2026Daojie Peng, Fulong Ma, Bingtao Wang +2Robotic Control Edge-Cloud Collaborative Inference
HKUST(GZ) · Shandong University · HKUST
LAST: The Last Query Token Guides Visual Token Pruning for Edge-Cloud Collaborative MLLM Inference
Jul 30, 2026Feng Yang, Xinrui Ju, Keyang Zhang +6Edge-Cloud Collaborative Inference Efficient VLM Inference
1City University of Hong Kong · 2Peng Cheng Laboratory
Efficient and Privacy Aware Edge Cloud Collaborative Inference for Large Language Models
Jul 14, 2026Yi Li, Chen Li, Jiexiong LiuOn-Device Language Model Inference Privacy-Preserving Language Models
KunlunMeta
When Cloud Agents Meet Device Agents: Lessons from Hybrid Multi-Agent Systems
May 28, 2026Corrado Rainone, Davide Belli, Bence Major +1Multi-Agent LLM Systems On-Device Language Model Inference
Qualcomm Technologies, Inc. 1Qualcomm AI Research†
INAR-VL: Input-Aware Routing for Edge-Cloud Vision-Language Inference
May 13, 2026Ahmed Šabanović, Paul Joe Maliakel, Ivona BrandićEfficient VLM Inference Edge-Cloud Computing
TU Wien
Large Language Models over Networks: Collaborative Intelligence under Resource Constraints
May 9, 2026Liangqi Yuan, Wenzhi Fang, Shiqiang Wang +2LLM Inference Efficiency On-Device Language Model Inference
L. Yuan, W. Fang, and C. G. Brinton are with Purdue University, IN, USA. · S. Wang is with University of Exeter, UK. · H. V. Poor is with Princeton University, USA.
Progressive Semantic Communication for Efficient Edge-Cloud Vision-Language Models
Apr 29, 2026Cyril Shih-Huan Hsu, Wig Yuan-Cheng Cheng, Chrysa PapagianniEdge-Cloud Collaborative Inference Efficient VLM Inference
Informatics Institute, University of Amsterdam, The Netherlands. · Open-EP Community, Taoyuan City 330049, Taiwan (R.O.C.).
SparKV: Overhead-Aware KV Cache Loading for Efficient On-Device LLM Inference
Apr 23, 2026Hongyao Liu, Liuqun Zhai, Junyi Wang +3On-Device Language Model Inference KV-Cache Offloading
Department of Computer Science, City University of Hong Kong, Hong Kong SAR.
SAGE: Training-Free Semantic Evidence Composition for Edge-Cloud Inference under Hard Uplink Budgets
Apr 21, 2026Inhyeok Choi, Hyuncheol ParkEfficient Inference Edge-Cloud Computing
School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea
Rethinking AI Hardware: A Three-Layer Cognitive Architecture for Autonomous Agents
Apr 15, 2026Li ChenEdge-Cloud Collaborative Inference Cognitive Architectures for AI Agents
CIAR: Interval-based Collaborative Decoding for Image Generation Acceleration
Mar 26, 2026Keming Ye, Zhou Zhao, Fan Wu +1Edge-Cloud Collaborative Inference Image Generation
Zhejiang University · Shanghai Jiao Tong University
ConsRoute:Consistency-Aware Adaptive Query Routing for Cloud-Edge-Device Large Language Models
Mar 22, 2026Haoyu Qiao, Hao Zhang, Shanwen Mao +2Edge-Cloud Collaborative Inference LLM Routing
the Department of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China · the National Key Laboratory of Smart Farm Technologies and Systems, Harbin 150001, China · the Department of Computer Science and Technology, Harbin Institute of Technology (ShenZhen), ShenZhen 518055, China
RoboECC: Multi-Factor-Aware Edge-Cloud Collaborative Deployment for VLA Models
Mar 21, 2026Zihao Zheng, Hangyu Cao, Jiayu Chen +6Edge-Cloud Collaborative Inference Efficient VLA Model Inference
School of Computer Science, Peking University, Beijing, China · School of Computer Science, South China University of Technology, Guangzhou, China · School of Artificial Intelligence, Beijing Normal University, Beijing, China +1
Noise-Aware Misclassification Attack Detection in Collaborative DNN Inference
Mar 18, 2026Shima Yousefi, Saptarshi DebroyAdversarial Example Detection Anomaly Detection
City University of New York
Ruyi2.5 Technical Report
Mar 18, 2026Huan Song, Shuyu Tian, Qingfei Zhao +5Edge-Cloud Collaborative Inference Multimodal Foundation Models
Institute of Artificial Intelligence (TeleAI), China Telecom
Edge-Cloud Collaborative Speech Emotion Captioning via Token-Level Speculative Decoding in Audio-Language Models
Mar 12, 2026Xiangyuan Xue, Jiajun Lu, Yan Gao +3Edge-Cloud Collaborative Inference Speculative Decoding
The University of Auckland, New Zealand · The University of Melbourne, Australia · The University of Cambridge, United Kingdom +1
RAPID: Redundancy-Aware and Compatibility-Optimal Edge-Cloud Partitioned Inference for Diverse VLA Models
Mar 9, 2026Zihao Zheng, Sicheng Tian, Hangyu Cao +7Temporal Modeling for VLA Models Edge-Cloud Collaborative Inference
School of Computer Science, Peking University, Beijing, China. · School of Artificial Intelligence, Beijing Normal University, Beijing, China. · School of Software Engineering, South China University of Technology, Guangzhou, China. +1

Oct 13, 2026J/K move · Enter open · S save

Empowering Edge Agents: A Systematic Analysis of Edge-Cloud Collaboration in LLM Agent Execution

Pengcheng Huang, Zhenghao Liu, Shixin Ren, Shi Yu+4

School of Computer Science and Engineering, Northeastern University, Shenyang, China · Department of Computer Science and Technology, Tsinghua University, Beijing, China · China Mobile Online Services Co., Ltd. Beijing, China

Edge-Cloud Collaborative Inference Edge-Cloud Computing AI Agent Benchmarks LLM Agent Evaluation Long-Horizon Agent Evaluation

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Edge-cloud collaboration combines the lower inference overhead and greater data locality of edge-side execution with the stronger capabilities of cloud-hosted models. However, as LLM agents increasingly operate in long-horizon scenarios, cloud participation becomes interleaved throughout execution rather than a one-shot decision for each query. Such interleaved participation introduces trajectory-level effects that remain insufficiently understood. To address this gap, we introduce EC2-Bench, a benchmark for evaluating edge-cloud collaboration in long-horizon agent execution. It provides executable digital-workspace agent tasks and a unified evaluation framework covering task performance, resource usage, and privacy exposure. Using this benchmark, we empirically study diverse forms of edge-cloud collaboration. Our study reveals three findings. First, collaboration achieves a favorable balance among task performance, resource usage, and privacy exposure compared with edge-only and cloud-only execution. Second, as edge models become stronger, collaborative execution approaches cloud-only performance, and performance differences across strategies shrink, while privacy exposure remains strongly strategy-dependent. Third, stronger edge capability shifts preferred collaboration from externally controlled cloud participation and direct cloud execution toward more edge-controlled collaboration. Further analyses identify cloud-invocation decision-making and instruction following as important edge-side abilities for effective collaboration. These findings provide a systematic understanding of edge-cloud collaboration in long-horizon agent execution and insights for designing future edge agents that effectively collaborate with cloud models. Code and resources are available at https://github.com/OpenBMB/EC2-Bench.