Benchmarking and Improving Monitors for Out-Of-Distribution Alignment Failure in LLMs 事件

Benchmarking and Improving Monitors for Out-Of-Distribution Alignment Failure in LLMs arXiv:2605.21602v2 Announce Type: replace Abstract: Many safety and alignment failures of large language models (LLMs) occur due to out-of-distribution (OOD) situations: unusual prompt or response patterns that are unforeseen by model developers. We systematically study whether LLM monitoring pipelines can detect these OOD alignment failures by introducing a benchmark called Misalignment Out Of Distribution (M