arXiv AI August 14, 2026 By Xi Shi, Mengxin Zheng, Qian Lou

Learning Latency-Aware Orchestration for Multi-Agent Systems

arXiv:2601. 10560v2 Announce Type: replace-cross Abstract: Multi-agent systems (MAS) coordinate multiple LLM-powered agents through structured workflows, gaining reasoning power but incurring high inference latency from multi-step execution and repeated model invocations.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI

Jun 11

INFRAMIND: Infrastructure-Aware Multi-Agent Orchestration

arXiv:2606. 11440v1 Announce Type: new Abstract: Existing multi-agent LLM orchestration methods, ranging from brute-force ensembles to learned routers, select models and topologies based on task and model features.

By Ahasan Kabir, Jiaqi Xue, Mengxin Zheng, Qian Lou

llms agents reinforcement-learning benchmarks

arXiv AI

Sep 10

Inference-Time Graph Engineering for Multi-Agent LLM Workflows

arXiv:2609.05774v1 Announce Type: new Abstract: Recent multi-agent LLM systems increasingly rely on graph-structured communication to coordinate specialized agents. We revisit multi-agent orchestrati...

By Katherine Tieu, Dongqi Fu, Yinglong Xia, Hong Li, Hong Yan, Jingrui He

llms agents reinforcement-learning

arXiv AI

Jul 24

InferenceBench: A Benchmark for Open-Ended LLM Inference Optimization by AI Agents

arXiv:2607. 20468v1 Announce Type: new Abstract: AI agents are increasingly used to automate research and development tasks, yet existing benchmarks typically evaluate them on prescribed workflows or narrow action spaces.

By Jehyeok Yeon, Ben Rank, Maksym Andriushchenko

llms agents efficiency benchmarks

arXiv AI

Aug 6

EASy: Towards Efficient LLM-Based Agentic System

arXiv:2608. 04588v1 Announce Type: cross Abstract: Agentic systems have emerged as a promising paradigm for solving complex tasks by coordinating specialized LLM-based agents.

By Junnan Liu, Linhao Luo, Thuy-Trang Vu, Gholamreza Haffari

llms agents reinforcement-learning robotics benchmarks

arXiv AI

Jul 28

Focus Is All You Need: Adaptive Goal-aware Attention Orchestration for Multi-Agent Graph Systems

arXiv:2607. 23678v1 Announce Type: new Abstract: Large language models (LLMs) enable autonomous agents for reasoning, planning, and tool use.

By Mingzhou Fan, Siyuan Xu, Mingxuan Yuan

llms agents

arXiv Machine Learning

Jun 2

Multi-Agent Computer Use

arXiv:2606. 01533v1 Announce Type: cross Abstract: Computer use agents (CUAs) today are primarily deployed as single serial agents.

By Jing Yu Koh, Ruslan Salakhutdinov, Daniel Fried

agents benchmarks

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INFRAMIND: Infrastructure-Aware Multi-Agent Orchestration

Inference-Time Graph Engineering for Multi-Agent LLM Workflows

InferenceBench: A Benchmark for Open-Ended LLM Inference Optimization by AI Agents

EASy: Towards Efficient LLM-Based Agentic System

Focus Is All You Need: Adaptive Goal-aware Attention Orchestration for Multi-Agent Graph Systems

Multi-Agent Computer Use