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.
Spine-Branch Coordination for Multi-agent Computer Use
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arXiv AI
Jul 28
Beyond Sequential Interaction: Benchmarking Parallel Execution and Coordination for GUI Agents
arXiv:2607. 22689v1 Announce Type: new Abstract: Graphical user interface (GUI) agents are systems powered by large multimodal models (LMMs).
arXiv AI
Sep 10
Diamond Agent: Agentic Control of Federated HPC Resources as a Service
arXiv:2609.06181v1 Announce Type: cross Abstract: Efficiently aggregating and orchestrating computing power across heterogeneous clusters for HPC workflows faces four practical challenges: preserving...
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.
arXiv AI
4d ago
Systematic Multi-Agent Vision-and-Language Navigation: Formulation, Benchmark, and Method
arXiv:2609.35965v1 Announce Type: cross Abstract: Vision-and-Language Navigation (VLN) has largely focused on a single agent following a single instruction, yet many real-world applications require t...
arXiv AI
3d ago
cua-speedrun: Standardized Benchmarking of the Speed of Computer-Use Agents
arXiv:2609.40284v1 Announce Type: cross Abstract: Computer use agents (CUAs), which use graphical user interfaces (GUIs) to complete tasks on a computer, have recently surpassed human performance on...
arXiv AI
Aug 18
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.
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.
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.
arXiv Computation and Language
Sep 23
Agensh: Scaling Organizational Intelligence to 1,024 Agents
Agensh is a new multi‑agent harness that eliminates a central orchestrator by letting workers self‑organize through a continuous cooperation loop. The system uses a shared workspace, message interface, and shared context to coordinate tasks, verify results, and merge progress asynchronously. Experiments on ProgramBench and pandoc show that scaling from 1 to 1,024 agents improves test‑pass rates by up to 49% relative, demonstrating that agent count is a viable scaling dimension for complex tasks.
arXiv AI
3d ago
PANDA: A Decentralized Architecture with Flexible Orchestration for Scalable, Fault-Tolerant Multi-Agent Systems
PANDA is a decentralized architecture for large-scale, fault-tolerant multi-agent systems that enables heterogeneous agents to discover each other's capabilities and self-organize into specialized teams for each task. It decouples collective communication from team communication, allowing agents to participate in multiple teams simultaneously and load-balance tasks across the collective. PANDA supports three planning and execution patterns—star, chain, and mesh—detects and recovers from infrastructure and orchestration failures, and uses a web-of-trust model for governance without a central bottleneck.
arXiv AI
Jun 9
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.