As AI agents increasingly participate in scientific workflows, scientists are shifting from direct authorship toward oversight, inspection, and steering. LLM-driven visualization systems are a promising interface for this hand-off, yet they remain largely stateless, forcing users to reconstruct context across refinemen...
Ella Hugie, Alexandra Irger, Grace Guo et al.· 0 citations
The PanDA workload management system, developed for large-scale distributed computing in high-energy physics, is being enhanced through the integration of AI-assisted operational tools built on the Model Context Protocol (MCP). This paper describes two complementary efforts. The first is PanDA MCP, a FastAPI-based inte...
P. Nilsson, Joseph Boudreau, Tasnuva Chowdhury et al.· Journal of Instrumentation· 0 citations
The growing scale of scientific data makes lossy compression essential for reducing data volume under controllable error. Transformation-based compressors using multilevel decomposition, such as MGARD, achieve strong compression ratios but map poorly to GPU architectures. We propose BlockMGARD, an adaptive, Region-of-I...
Yan-Liang Li, Qian Gong, Qing Liu et al.· 0 citations
Exascale simulations generate data far faster than it can be stored or analyzed, making efficient data reduction essential. Error-controlled lossy compression offers high compression ratios under user-specified error bounds, but the target tolerance must be fixed at compression time. Progressive compression relaxes thi...
Wenbo Li, Xuan Wu, Qian Gong et al.· 0 citations
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