8 citations · 10 across the 4 of their papers we have counts for
5 papers
Experiences with Model Context Protocol Servers for Science and High Performance Computing
Haochen Pan, Ryan Chard, Reid Mello +12
Large language model (LLM)-powered agents are increasingly used to plan and execute scientific workflows, yet most research cyberinfrastructure (CI) exposes heterogeneous APIs and…
HiPerRAG: High-Performance Retrieval Augmented Generation for Scientific Insights
Ozan Gokdemir, Carlo Siebenschuh, Alexander Brace +21
The volume of scientific literature is growing exponentially, leading to underutilized discoveries, duplicated efforts, and limited cross-disciplinary collaboration. Retrieval Augm…
AdaParse: An Adaptive Parallel PDF Parsing and Resource Scaling Engine
Carlo Siebenschuh, Kyle Hippe, Ozan Gokdemir +10
Language models for scientific tasks are trained on text from scientific publications, most distributed as PDFs that require parsing. PDF parsing approaches range from inexpensive…
Connecting Large Language Model Agent to High Performance Computing Resource
Heng Ma, Alexander Brace, Carlo Siebenschuh +3
The Large Language Model agent workflow enables the LLM to invoke tool functions to increase the performance on specific scientific domain questions. To tackle large scale of scien…
LSHBloom: Memory-efficient, Extreme-scale Document Deduplication
Arham Khan, Robert Underwood, Carlo Siebenschuh +7
Contemporary large language model (LLM) training pipelines require the assembly of internet-scale databases full of text data from a variety of sources (e.g., web, academic, and pu…