arXiv AI
Jun 12
EurekAgent: Agent Environment Engineering is All You Need For Autonomous Scientific Discovery
arXiv:2606. 13662v1 Announce Type: new Abstract: LLM-based agents have shown increasing potential in automating scientific discovery.
Hierarchical Server Architecture for Agentic Science
arXiv:2608. 05332v1 Announce Type: cross Abstract: Agentic science is transforming the landscape of computational work, extending to scientific pipelines and workload managers.
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arXiv AI
Jun 8
Twelve quick tips for designing AI-driven HPC workflows
arXiv:2606. 07491v1 Announce Type: cross Abstract: High-performance computing (HPC) clusters remain the backbone of large-scale scientific computation, traditionally executing deterministic, linear pipelines optimised for predictable performance.
arXiv AI
Jul 14
A Formal Hierarchical Architecture for Agentic Orchestration with Stack-Based Execution and Lazy Discovery
arXiv:2607. 11138v1 Announce Type: new Abstract: The rapid expansion of capabilities in Large Language Model (LLM) agents has exposed a critical architectural bottleneck: when agents are given access to a flat, monolithic registry of tools, the model must evaluate hundreds or thousands of options simultaneously.
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
Jun 30
Clarus: Coordinating Autonomous Research Agents toward Web-Scale Scientific Collaboration
arXiv:2606. 30246v1 Announce Type: new Abstract: Existing autonomous research agents can support parts of the research process, but most systems still treat research as either an isolated assistant task or a closed workflow.
arXiv AI
Aug 12
DSAgentBench: Can Agents Automate End-to-End Data-Science Workflows in Real Computer Environments?
arXiv:2608. 10366v1 Announce Type: new Abstract: Real-world data science involves long-horizon workflows that span data wrangling, exploration, modeling, visualization, and validation, and require coordinated use of tools such as notebooks, IDEs, terminals, browsers, and databases within real operating environments.
arXiv AI
Jul 20
Scalable LLM Agent Tool Access in the Cloud
arXiv:2607. 15593v1 Announce Type: cross Abstract: LLM agents increasingly rely on tool calling to act on external systems, and the Model Context Protocol (MCP) has quickly become its de facto interface.
arXiv AI
Jun 30
SwarmX: Agentic Scheduling for Low-Latency Agentic Systems
arXiv:2606. 21401v2 Announce Type: replace-cross Abstract: Agentic AI applications compose multiple model calls and tool executions, creating new scheduling challenges for GPU-CPU clusters.
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.
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.
arXiv AI
Jun 4
Archi: Agentic Operations at the CMS Experiment
arXiv:2606. 04755v1 Announce Type: cross Abstract: We present Archi, an open-source, end-to-end framework for scientific collaborations that combines the systematic ingestion and organization of heterogeneous data sources with the deployment of configurable, private, and extensible agents that retrieve and reason over them.
arXiv AI
Jun 30
AI4EOSC: a Federated Cloud Platform for Artificial Intelligence in Scientific Research
arXiv:2512. 16455v4 Announce Type: replace-cross Abstract: The rapid growth of Artificial Intelligence and Machine Learning in scientific research has highlighted a gap between industry-standard MLOps tools and platforms, and the unique requirements of modern and Open Science, particularly regarding the FAIR (Findable, Accessible, Interoperable, and Reusable) principles.