RULER generates configurable tasks at chosen sequence lengths so teams can see where accuracy drops relative to a model's claimed context window. The COLM 2024 paper reports that many models ace vanilla NIAH yet fail harder RULER tasks as length grows. It is a sanity check, not a replacement for realistic document or agent evals.