Saved in:
| Main Authors: | Tang, Xin, Li, Xiaohuan, Chen, Qian, Liao, Binhan, Zhang, Yaqi, Chen, Jianxin, Zhao, Changyuan, Fan, Junchuan, Tian, Junxi |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.07456 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Knowledge-driven Reasoning for Mobile Agentic AI: Concepts, Approaches, and Directions
by: Liu, Guangyuan, et al.
Published: (2026)
by: Liu, Guangyuan, et al.
Published: (2026)
PipeBoost: Resilient Pipelined Architecture for Fast Serverless LLM Scaling
by: Liu, Chongpeng, et al.
Published: (2025)
by: Liu, Chongpeng, et al.
Published: (2025)
GoodServe: Towards High-Goodput Serving of Agentic LLM Inferences over Heterogeneous Resources
by: Du, Boxiao, et al.
Published: (2026)
by: Du, Boxiao, et al.
Published: (2026)
CONCUR: High-Throughput Agentic Batch Inference of LLM via Congestion-Based Concurrency Control
by: Chen, Qiaoling, et al.
Published: (2026)
by: Chen, Qiaoling, et al.
Published: (2026)
Agentic AI Workload Characteristics
by: Yuan, Yichao, et al.
Published: (2026)
by: Yuan, Yichao, et al.
Published: (2026)
DualPath: Breaking the Storage Bandwidth Bottleneck in Agentic LLM Inference
by: Wu, Yongtong, et al.
Published: (2026)
by: Wu, Yongtong, et al.
Published: (2026)
HexAGenT: Efficient Agentic LLM Serving via Workflow- and Heterogeneity-Aware Scheduling
by: Peng, You, et al.
Published: (2026)
by: Peng, You, et al.
Published: (2026)
LLM-assisted Agentic Edge Intelligence Framework
by: Dehury, Chinmaya Kumar, et al.
Published: (2026)
by: Dehury, Chinmaya Kumar, et al.
Published: (2026)
LLMSched: Uncertainty-Aware Workload Scheduling for Compound LLM Applications
by: Zhu, Botao, et al.
Published: (2025)
by: Zhu, Botao, et al.
Published: (2025)
ReviveMoE: Fast Recovery for Hardware Failures in Large-Scale MoE LLM Inference Deployments
by: Li, Haley, et al.
Published: (2026)
by: Li, Haley, et al.
Published: (2026)
S-HPLB: Efficient LLM Attention Serving via Sparsity-Aware Head Parallelism Load Balance
by: Liu, Di, et al.
Published: (2026)
by: Liu, Di, et al.
Published: (2026)
Hierarchical Observe-Orient-Decide-Act Enabled UAV Swarms in Uncertain Environments: Frameworks, Potentials, and Challenges
by: Jia, Ziye, et al.
Published: (2026)
by: Jia, Ziye, et al.
Published: (2026)
PICO: Pipeline Inference Framework for Versatile CNNs on Diverse Mobile Devices
by: Yang, Xiang, et al.
Published: (2022)
by: Yang, Xiang, et al.
Published: (2022)
Deploying Foundation Model Powered Agent Services: A Survey
by: Xu, Wenchao, et al.
Published: (2024)
by: Xu, Wenchao, et al.
Published: (2024)
Duet instrumentation: An Agentic Approach to Improving Sensitivity in Cloud Service Benchmarking
by: Koch, Sebastian, et al.
Published: (2026)
by: Koch, Sebastian, et al.
Published: (2026)
PICE: A Semantic-Driven Progressive Inference System for LLM Serving in Cloud-Edge Networks
by: Zhan, Huiyou, et al.
Published: (2025)
by: Zhan, Huiyou, et al.
Published: (2025)
Amoeba: Runtime Tensor Parallel Transformation for LLM Inference Services
by: Chen, Haoyu, et al.
Published: (2025)
by: Chen, Haoyu, et al.
Published: (2025)
Aragog: Just-in-Time Model Routing for Scalable Serving of Agentic Workflows
by: Dai, Yinwei, et al.
Published: (2025)
by: Dai, Yinwei, et al.
Published: (2025)
HeRo: Adaptive Orchestration of Agentic RAG on Heterogeneous Mobile SoC
by: Li, Maoliang, et al.
Published: (2026)
by: Li, Maoliang, et al.
Published: (2026)
Accelerating Compound LLM Training Workloads with Maestro
by: Yuan, Xiulong, et al.
Published: (2026)
by: Yuan, Xiulong, et al.
Published: (2026)
Agent.xpu: Efficient Scheduling of Agentic LLM Workloads on Heterogeneous SoC
by: Wei, Xinming, et al.
Published: (2025)
by: Wei, Xinming, et al.
Published: (2025)
EACO-RAG: Towards Distributed Tiered LLM Deployment using Edge-Assisted and Collaborative RAG with Adaptive Knowledge Update
by: Li, Jiaxing, et al.
Published: (2024)
by: Li, Jiaxing, et al.
Published: (2024)
DynaServe: Unified and Elastic Execution for Dynamic Disaggregated LLM Serving
by: Ruan, Chaoyi, et al.
Published: (2025)
by: Ruan, Chaoyi, et al.
Published: (2025)
Deployment of Containerized Simulations in an API-Driven Distributed Infrastructure
by: Kraus, Tim, et al.
Published: (2025)
by: Kraus, Tim, et al.
Published: (2025)
Flash-KMeans: Fast and Memory-Efficient Exact K-Means
by: Yang, Shuo, et al.
Published: (2026)
by: Yang, Shuo, et al.
Published: (2026)
LLM-Emu: Native Runtime Emulation of LLM Inference via Profile-Driven Sampling
by: Da, Wei, et al.
Published: (2026)
by: Da, Wei, et al.
Published: (2026)
Fantasy: Efficient Large-scale Vector Search on GPU Clusters with GPUDirect Async
by: Liu, Yi, et al.
Published: (2025)
by: Liu, Yi, et al.
Published: (2025)
WWW.Serve: Interconnecting Global LLM Services through Decentralization
by: Wang, Huanyu, et al.
Published: (2026)
by: Wang, Huanyu, et al.
Published: (2026)
SageSched: Efficient LLM Scheduling Confronting Demand Uncertainty and Hybridity
by: Gan, Zhenghao, et al.
Published: (2026)
by: Gan, Zhenghao, et al.
Published: (2026)
AgentServe: Algorithm-System Co-Design for Efficient Agentic AI Serving on a Consumer-Grade GPU
by: Zhang, Yuning, et al.
Published: (2026)
by: Zhang, Yuning, et al.
Published: (2026)
Dynamic Resource Manager for Automating Deployments in the Computing Continuum
by: Samani, Zahra Najafabadi, et al.
Published: (2024)
by: Samani, Zahra Najafabadi, et al.
Published: (2024)
CIR: Lightweight Container Image for Cross-Platform Deployment
by: Li, Fengzhi, et al.
Published: (2026)
by: Li, Fengzhi, et al.
Published: (2026)
Cloud Native System for LLM Inference Serving
by: Xu, Minxian, et al.
Published: (2025)
by: Xu, Minxian, et al.
Published: (2025)
Unlock the Potential of Fine-grained LLM Serving via Dynamic Module Scaling
by: Wu, Jingfeng, et al.
Published: (2025)
by: Wu, Jingfeng, et al.
Published: (2025)
BanaServe: Unified KV Cache and Dynamic Module Migration for Balancing Disaggregated LLM Serving in AI Infrastructure
by: He, Yiyuan, et al.
Published: (2025)
by: He, Yiyuan, et al.
Published: (2025)
SLICE: SLO-Driven Scheduling for LLM Inference on Edge Computing Devices
by: Chow, Will
Published: (2025)
by: Chow, Will
Published: (2025)
SynergAI: Edge-to-Cloud Synergy for Architecture-Driven High-Performance Orchestration for AI Inference
by: Stathopoulou, Foteini, et al.
Published: (2025)
by: Stathopoulou, Foteini, et al.
Published: (2025)
Multi-stage Flow Scheduling for LLM Serving
by: Sun, Yijun, et al.
Published: (2026)
by: Sun, Yijun, et al.
Published: (2026)
Profiling-Driven Adaptive Distributed Transformer Inference on Embedded Edge Deployment
by: Qazi, Muhammad Azlan, et al.
Published: (2026)
by: Qazi, Muhammad Azlan, et al.
Published: (2026)
Green by Design: Constraint-Based Adaptive Deployment in the Cloud Continuum
by: D'Iapico, Andrea, et al.
Published: (2026)
by: D'Iapico, Andrea, et al.
Published: (2026)
Similar Items
-
Knowledge-driven Reasoning for Mobile Agentic AI: Concepts, Approaches, and Directions
by: Liu, Guangyuan, et al.
Published: (2026) -
PipeBoost: Resilient Pipelined Architecture for Fast Serverless LLM Scaling
by: Liu, Chongpeng, et al.
Published: (2025) -
GoodServe: Towards High-Goodput Serving of Agentic LLM Inferences over Heterogeneous Resources
by: Du, Boxiao, et al.
Published: (2026) -
CONCUR: High-Throughput Agentic Batch Inference of LLM via Congestion-Based Concurrency Control
by: Chen, Qiaoling, et al.
Published: (2026) -
Agentic AI Workload Characteristics
by: Yuan, Yichao, et al.
Published: (2026)