Through our experiments, we demonstrate that LLMs do not maintain consistent performance across input lengths. Even on tasks as simple as non-lexical retrieval or text replication, we see increasing non-uniformity in performance as input length grows.

Our results highlight the need for more rigorous long-context evaluation beyond current benchmarks, as well as the importance of context engineering. Whether relevant information is present in a model’s context is not all that matters; what matters more is how that information is presented. We demonstrate that even the most capable models are sensitive to this, making effective context engineering essential for reliable performance.