The look-ahead bias benchmark introduced in Re(Visiting) Large Language Models in Finance is a set of structured event–year probes that test whether models correctly link financial events to the time period in which they actually occurred using only information available at that point in time. By evaluating how models perform on these probes, the benchmark detects whether predictions are influenced by future information, providing a clear and controlled way to identify information leakage and assess temporal alignment in financial machine learning models.