Long-context understanding

Extending the effective context window of language models beyond millions of tokens while maintaining faithful retrieval and reasoning over the full context is an active research area. Current models exhibit degraded performance in the middle of long contexts ('lost in the middle' effect) and struggle with tasks requiring synthesis across distant passages. Efficient attention mechanisms, improved position encodings, and context compression techniques all show promise but have not fully solved the problem.