arXiv AI

Parallelizing Tool Execution and LLM Generation for Low-Latency Agent Serving

arXiv:2603. 18897v2 Announce Type: replace-cross Abstract: LLM-powered agents execute tasks through a sequential loop of model generation and tool execution.

arXiv Machine Learning

Aug 4

Agentic Coding in the Wild: Characterizing GitHub Copilot Traces at Production Scale

arXiv:2608. 00101v1 Announce Type: cross Abstract: AI coding agents like GitHub Copilot, Claude Code, and Codex interleave multi-step LLM inference with tool execution, creating a workload different from chatbots.

By Banruo Liu, Haoran Qiu, \'I\~nigo Goiri, Rodrigo Fonseca, Ricardo Bianchini, Esha Choukse

llms agents efficiency

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

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

Jun 30

TraceLab: Characterizing Coding Agent Workloads for LLM Serving

arXiv:2606. 30560v1 Announce Type: cross Abstract: Coding agents are rapidly becoming a major application of agentic LLMs, but serving them efficiently remains challenging.

By Kan Zhu, Mathew Jacob, Chenxi Ma, Yi Pan, Stephanie Wang, Arvind Krishnamurthy, Baris Kasikci

llms agents benchmarks

arXiv Machine Learning

Jul 10

Tool-Making and Self-Evolving LLM Agents in Low-Latency Systems

arXiv:2607. 08010v1 Announce Type: cross Abstract: Production LLM agents often waste latency and reliability by regenerating code for the same procedural steps on every request.

By Kalle Kujanp\"a\"a, Ning Liu, Shahnawaz Alam, Yeshwanth Reddy Sura, Tianyu Yang, Kristina Klinkner, Shervin Malmasi

llms agents benchmarks

arXiv Machine Learning

Jul 14

ToolAtlas: Learning Once, Reusing Everywhere with Tool-Side Memory

arXiv:2607. 11126v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly rely on external tools served by shared providers and accessed by heterogeneous downstream agents.

By Yue Fang, Zhibang Yang, Fangkai Yang, Xiaoting Qin, Liqun Li, Qingwei Lin, Saravan Rajmohan, Dongmei Zhang

llms agents benchmarks

arXiv AI

Jul 17

MCPEvol-Bench: Benchmarking LLM Agent Performance Across Dynamic Evolutions of MCP Servers

arXiv:2607. 14642v1 Announce Type: new Abstract: As Model Context Protocol (MCP) servers emerge as the core infrastructure for connecting LLMs with external tools, existing benchmarks leverage real-world MCP servers to evaluate LLM agents' tool-using capabilities.

By Huanxi Liu, Kun Hu, Jiaqi Liao, Qiang Wang, Pengfei Qian, YuanZhao Zhai, Dawei Feng, Bo Ding, Huaimin Wang

llms agents benchmarks

arXiv AI

Jul 28

E-Bench: Benchmarking Multi-Step Tool-Use Agents in Real-World Product Scenarios

arXiv:2607. 23722v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly deployed as agents that interact with stateful environments over multiple steps: gathering hidden information, composing tool calls, and committing state changes.

By Weihuang Zheng, Tianyuan Zou, Eileen Ye, Alphet Liu, Youyong Kong, Ya-Qin Zhang, Duran Zheng, Maxm Pan

llms agents benchmarks

arXiv AI

Jun 9

WeaveBench: A Long-Horizon, Real-World Benchmark for Computer-Use Agents with Hybrid Interfaces

arXiv:2606. 09426v1 Announce Type: new Abstract: Computer-use agents (CUAs) increasingly operate in runtimes that combine visual desktop control, command-line execution, code editing, browsers, and external tools.

By Wanli Li, Bowen Zhou, Yunyao Yu, Zhou Xu, Yifan Yang, Dongsheng Li, Caihua Shan

agents benchmarks

arXiv Machine Learning

Aug 12

FlowScout: From Execution Feedback to Reliable Tool-Using Agent Workflows

arXiv:2608. 10039v1 Announce Type: new Abstract: Agentic workflows have become an important abstraction for building reliable LLM-based automation systems by organizing large language models (LLMs), tools, and control logic into explicit execution structures.

By Shuo Hao, You Lu, Bihuan Chen, Xin Peng

llms agents

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

Hugging Face Trending Papers

Jul 9

Tool-Making and Self-Evolving LLM Agents in Low-Latency Systems

Production LLM agents often waste latency and reliability by regenerating code for the same procedural steps on every request. We replace this inference-time coding loop with an agentic tool-making pipeline that compiles repeated SOP steps into validated, versioned tools before deployment.

llms agents benchmarks

Related stories