Context Is Not Comprehension

The dominant evaluation of Large Language Models has centered on their ability to surface explicit facts from increasingly vast contexts. While today's best models demonstrate near-perfect recall on these tasks, this apparent success masks a fundamental failure in multi-step computation when information is embedded in a narrative. We introduce Verbose ListOps (VLO), a novel benchmark designed to isolate this failure. VLO programmatically weaves deterministic, nested computations into coherent stories, forcing models to track and update internal state rather than simply locate explicit values. Our experiments show that leading LLMs, capable of solving the raw ListOps equations with near-perfect accuracy, collapse in performance on VLO at just 10k tokens. The VLO framework is extensible to any verifiable reasoning task, providing a critical tool to move beyond simply expanding context windows and begin building models with the robust, stateful comprehension required for complex knowledge work.