Context rot

An informal label for task-performance loss caused by longer, noisier, conflicting, or poorly positioned context even though the request fits the model's stated limit.

Long-context evaluations have found failures from position, distractors, conflicts, and retrieval demands below the advertised token ceiling. Lost-in-the-middle is one observed pattern, not a rule that every model or task follows. Model updates can change the curve, so context rot should be measured on the actual workload rather than inferred from length alone.

Builder example

A large limit permits a large request but does not establish accurate use of every relevant detail. Compare full-context, retrieval, hierarchy, and compression designs on representative long cases, including facts at varied positions and conflicting versions.

You drop fifty contracts into a conversation and ask about a renewal clause. The model misses it because the relevant section was buried in the middle of a massive input.

Retrieve only the relevant contract and clause, put it near the start of the prompt, and ask the model to cite the specific passage.

Common confusion: Capacity, attention, retrieval, and answer correctness are different properties. A token can be inside the accepted request while the model still fails to use it for the task.