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From the 1 of 11 linked papers with an AI index.

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11 papers

cs.DC2026

Toward Trustworthy Autonomous Science: A Two-Year Community Roadmap

Rafael Ferreira da Silva, Milad Abolhasani, Peter Beaucage +25

The paper updates a community roadmap for autonomous scientific laboratories, emphasizing trust, verification, reproducibility, safety, security, and governance as central challeng…

cs.AI2026

An Agentic AI Framework to Accelerate Scientific Discovery in Plant Phenotyping

Renan Souza, Daniel Rosendo, Kelsey Carter +6

High-throughput plant phenotyping now generates image derived datasets far faster than scientists can analyze them. At Oak Ridge National Laboratory's Advanced Plant Phenotyping La…

cs.DC2025

JANUS: Resilient and Adaptive Data Transmission for Enabling Timely and Efficient Cross-Facility Scientific Workflows

Vladislav Esaulov, Jieyang Chen, Norbert Podhorszki +4

In modern science, the growing complexity of large-scale scientific projects has led to an increasing reliance on cross-facility scientific workflows, where resources and expertise…

cs.DC2025

Data Management System Analysis for Distributed Computing Workloads

Kuan-Chieh Hsu, Sairam Sri Vatsavai, Ozgur O. Kilic +20

Large-scale international collaborations such as ATLAS rely on globally distributed workflows and data management to process, move, and store vast volumes of data. ATLAS's Producti…

cs.DC2025

CGSim: A Simulation Framework for Large Scale Distributed Computing Environment

Sairam Sri Vatsavai, Raees Khan, Kuan-Chieh Hsu +20

Large-scale distributed computing infrastructures such as the Worldwide LHC Computing Grid (WLCG) require comprehensive simulation tools for evaluating performance, testing new alg…

cs.AI2025

The (R)evolution of Scientific Workflows in the Agentic AI Era: Towards Autonomous Science

Woong Shin, Renan Souza, Daniel Rosendo +4

Modern scientific discovery increasingly requires coordinating distributed facilities and heterogeneous resources, forcing researchers to act as manual workflow coordinators rather…