3 papers
cs.AI2026
Screening LLM Judges Without Ground Truth: A Preliminary Evaluation of an AI Dental Hygiene Coach
William DeShong, Barath Kannan, Auberon Palma +3
Evaluating a fine-tuned coaching model with expert graders is slow and expensive, and an LLM judge cannot be trusted without ground truth. We present a staged pipeline that screens…
cs.DC2026
Validating LLM-Modernized Scientific Software Through Differential Fault Injection
Evan Coleman, Yuzhong Shen, Masha Sosonkina +1
Large language model (LLM) agents are increasingly used to modernize the legacy Fortran underlying production scientific software, but validation of these transformations emphasize…
cs.AI2026
An Agentic Workflow for Legacy HPC Modernization: Converting the Two-Electron-Integral Core of GAMESS
Yuzhong Shen, Masha Sosonkina, Peng Xu +1
Modernizing legacy Fortran is a problem of volume: the transformations are individually routine, but the codebases can be enormous, and across much of computational science the wor…