arXiv AI

AGENTSERVESIM: A Hardware-aware Simulator for Multi-Turn LLM Agent Serving

arXiv:2606. 09613v1 Announce Type: cross Abstract: Multi-turn LLM agents interleave model calls with external tool invocations, shifting serving from stateless request processing to stateful program execution.

Hugging Face Trending Papers

Jun 8

AGENTSERVESIM: A Hardware-aware Simulator for Multi-Turn LLM Agent Serving

Multi-turn LLM agents interleave model calls with external tool invocations, shifting serving from stateless request processing to stateful program execution. Serving these workloads requires scheduling, KV-cache management, and routing policies that use program-level context, including turn dependencies, tool-induced gaps, and reusable KV state.

llms agents

arXiv AI

1d ago

From LLM Inference to Agentic Workloads: Characterization and Implications for Serving Systems

arXiv:2608. 15127v1 Announce Type: cross Abstract: Agentic applications are shifting AI serving from isolated model inference to long-running workloads in which LLMs coordinate tools, environments, and persistent state.

By Chaokun Chang, Yukun Zhou, Kaihua Fu, Dakai An, Tianyu Feng, Hanfeng Lu, Sheng Yao, Pu Guo, Yinghao Yu, Yizhou Shan, Bo Li, Binhang Yuan, Wei Wang

llms agents benchmarks

arXiv AI

1d ago

Learning Agent Execution for KV-Cache Management in Agentic Serving

arXiv:2608. 14624v1 Announce Type: new Abstract: Multi-agent LLM systems have emerged as an important deployment paradigm for AI services, where each user request is decomposed into a sequence of specialized agents.

By Rui Zhang, Chaeeun Kim, Shaoting Feng, Kuntai Du, Yuhan Liu, Yi Zhong, Cheng-Wei Ching, Junchen Jiang, Liting Hu

llms agents efficiency

arXiv Machine Learning

Aug 10

Cascade: Exploiting SLO-Aware latency budget for fair and high goodput LLM inference serving

arXiv:2608. 06557v1 Announce Type: cross Abstract: The reasoning and agentic capabilities of large language models have expanded the range of applications they support, from short interactive exchanges to long, compute-heavy requests.

By Muhammad Adnan, Rohan Mahapatra, Prashant J. Nair, Daniel Berger, Pantea Zardoshti, Rodrigo Fonseca, Esha Choukse

llms agents safety

arXiv AI

Jul 23

AgentCgroup: Understanding and Controlling OS Resources of AI Agents

arXiv:2602. 09345v3 Announce Type: replace-cross Abstract: AI agents are increasingly deployed in multi-tenant cloud environments, where they execute diverse tool calls within sandboxed containers, each call with distinct resource demands and rapid fluctuations.

By Yusheng Zheng, Jiakun Fan, Quanzhi Fu, Yiwei Yang, Wei Zhang, Andi Quinn

llms agents benchmarks

arXiv AI

Jul 28

SpecBox: Speculative Sandbox Scheduling for Efficient LLM Agent Serving

arXiv:2607. 23933v1 Announce Type: cross Abstract: As LLM agents increasingly rely on the Model Context Protocol (MCP) to invoke isolated external sandboxes, disaggregated sandbox deployment introduces a fundamental tension between resource utilization and interactive tail latency.

By Yihui Zhang (Beihang University), Tianyu Wo (Beihang University), Jinghao Wang (Beihang University), Xiaoyang Sun (University of Leeds), Menghao Zhang (Beihang University), Cangzhou Yuan (Beihang University), Li Li (Beihang University), Chunming Hu (Beihang University), Albert Y. Zomaya (The University of Sydney), Renyu Yang (Beihang University)

llms rag agents

arXiv AI

Jun 16

Frontier: Towards Comprehensive and Accurate LLM Inference Simulation

arXiv:2605. 21312v2 Announce Type: replace-cross Abstract: Modern LLM serving is no longer homogeneous or monolithic.

By Yicheng Feng, Xin Tan, Yangtao Deng, Yimin Jiang, Yibo Zhu, Hong Xu

llms agents reinforcement-learning efficiency benchmarks

arXiv Machine Learning

Jun 16

MARS: Efficient, Adaptive Co-Scheduling for Heterogeneous Agentic Systems

arXiv:2604. 26963v2 Announce Type: replace-cross Abstract: Large language models (LLMs) are increasingly deployed as the execution core of autonomous agents rather than as standalone text generators.

By Yifei Wang, Hancheng Ye, Yechen Xu, Cong Guo, Chiyue Wei, Qinsi Wang, Dongting Li, Tingjun Chen, Hai "Helen" Li, Danyang Zhuo, Yiran Chen

llms agents efficiency

arXiv AI

Jun 6

RedKnot: Efficient Long-Context LLM Serving with Head-Aware KV Reuse and SegPagedAttention

arXiv:2606. 06256v1 Announce Type: new Abstract: As the input length of large language model (LLM) serving continues to grow, the KV cache has become a dominant bottleneck in AI infrastructure.

By Yang Liu, ZhaoKai Luo, HuaYi Jin, ZhiYong Wang, RuoZhou He, BoYu Wang, Guanjie Chen, Junhao Hu

llms fine-tuning efficiency

arXiv AI

Jul 24

Autonomous Topology Mutation: Safe Runtime Restructuring for Multi-Agent LLM Systems with Capability, State, and Shadow Invariants

arXiv:2607. 20488v1 Announce Type: new Abstract: Multi-agent LLM frameworks typically fix their team topology at boot time.

By Bronislav Sidik, Chaya Levi, Nizzan Kimhi

llms agents efficiency benchmarks safety

arXiv AI

Aug 6

Architectural Implications of Agentic AI Workflows

arXiv:2608. 04458v1 Announce Type: new Abstract: Agentic AI is emerging in datacenters, but its architectural implications remain unexplored.

By Jirong Yang, Peizhe Liu, Chaojie Zhang, Jovan Stojkovic

llms agents efficiency

arXiv AI

6d ago

Ready Cohorts: Bounding GPU Opportunity and Avoiding Host Round Trips in LLM-Agent Control

arXiv:2608. 12123v1 Announce Type: cross Abstract: LLM-agent services repeatedly execute small deterministic transitions between model and tool calls: route an outcome, update state, and emit the next effect.

By Josef Liyanjun Chen

llms agents

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