Not All Errors Are Equal: A Systematic Study of Error Propagation in Large Language Model Inference 事件

Not All Errors Are Equal: A Systematic Study of Error Propagation in Large Language Model Inference arXiv:2606.02430v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly integrated into high-performance computing (HPC) workflows, accelerating scientific discovery through diverse perspectives such as code generation and domain-specific decision-making. Yet, how soft errors propagate and affect LLM inference remains largely unexplored. To bridge this gap, we present a co