Long-Context Reasoning: Where Models Still Struggle
Despite huge context windows, models show measurable degradation retrieving and reasoning over information buried deep in long inputs.
The 'lost in the middle' problem
Research consistently finds that models are more accurate at using information near the start or end of a long context than information buried in the middle, even when technically within the context window.
Why length alone isn't the answer
Simply extending context windows doesn't guarantee proportionally better use of that context — attention and retrieval mechanisms inside the model don't automatically scale their effectiveness with window size.
Practical implications
For agent and RAG systems, this argues for careful placement of the most important information in a prompt, rather than assuming a bigger context window solves retrieval quality on its own.