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Beyond Context Length: Characterising the Difficulty of Long-Context Reasoning

arXiv · AI, language, vision and robotics · article · Oct 15, 2026 · UTC

Long-context language models support ever longer context windows, and evaluations typically treat context length as the main indicator of difficulty. However, context length alone cannot explain why models fail on long-context tasks that require reasoning over complex information. Contexts of equal length can pose very different challenges: a task may be solvable from a single local passage, or it may require integrating evidence distributed across the full context. We quantitatively characterise this difficulty through three properties: evidence scope, how much necessary information must be f

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