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.

Status: activeLast checked: 2026-09-03Evidence activity

Disagreeing is the most useful thing you can do here. Both sides of every contested claim in this catalog were assembled by the same person, which is its weakest point.

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 · unreviewed
A tiered memory system managed by the model itself sustains recall over conversations far longer than the context window.Remembering across sessions
A correction made in conversation fixes one exchange and is gone when the session ends; the same correction encoded as a guide rule, a sensor, or a permission fixes every future run — so an agent system improves over time only to the extent that failures are converted into harness structure rather than re-applied as prompts, and the rate of new guide rules per week falling is the sign the conversion is working.Following an unfamiliar procedure · unreviewed