Holding the task fixed and only lengthening the input degrades reasoning long before the context limit is reached.

Capability: Losing information in long inputs

Sources

supportsSame Task, More Tokens: the Impact of Input Length on the Reasoning Performance of Large Language Models
Holding the task fixed and only lengthening the input degrades reasoning long before the context limit is reached.

Reviewed by AI and Russ MillerLast checked: 2026-09-03Evidence activity

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Related claims

Managing context explicitly, paging information in and out of a bounded window, sustains performance on tasks that exceed the window.Keeping its own context clean
On rubric-graded long-context tasks, most of the gain from a context- compilation harness comes from extracting the context's rules, exact terms and output spec into an explicit checklist placed in the prompt, not from the executable verifiers built on top of it — and the gain appears only on rule-dense tasks and on models with enough capacity, disappearing or reversing on open-ended tasks and on a small-activation model.Losing information in long inputs · reviewed by AI
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