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.

Verbose ListOps (VLO): Beyond Long Context -- Unmasking LLM's Reasoning Blind Spots

Large Language Models (LLMs), whilst great at extracting facts from text, struggle with nested narrative reasoning. Existing long context and multi-hop QA benchmarks inadequately test this, lacking realistic distractors or failing to decouple context length from reasoning complexity, masking a fundamental LLM limitation. We introduce Verbose ListOps, a novel benchmark that programmatically transposes ListOps computations into lengthy, coherent stories. This uniquely forces internal computation and state management of nested reasoning problems by withholding intermediate results, and offers fine-grained controls for both narrative size \emph{and} reasoning difficulty. Whilst benchmarks like LongReason (2025) advance approaches for synthetically expanding the context size of multi-hop QA problems, Verbose ListOps pinpoints a specific LLM vulnerability: difficulty in state management for nested sub-reasoning amongst semantically-relevant, distracting narrative. Our experiments show that leading LLMs (e.g., OpenAI o4, Gemini 2.5 Pro) collapse in performance on Verbose ListOps at modest (~10k token) narrative lengths, despite effortlessly solving raw ListOps equations. Addressing this failure is paramount for real-world text interpretation which requires identifying key reasoning points, tracking conceptual intermediate results, and filtering irrelevant information. Verbose ListOps, and its extensible generation framework thus enables targeted reasoning enhancements beyond mere context-window expansion; a critical step to automating the world's knowledge work.