LLMs Assessed for Stabilizing Numerical Expressions in Scientific Software
arXiv·medium signal
Nguyen, Gulzar et al. evaluate whether LLMs can detect and fix floating-point precision errors in scientific software — a domain where finite representations can propagate catastrophic errors in safety-critical applications. Despite growing use of LLMs in scientific applications, their ability to reason about numerical stability has been insufficiently examined. Results reveal both capabilities and critical blind spots in current models' understanding of floating-point semantics.