Long-Context Language Modeling

The field asks how models can retrieve relevant evidence and maintain useful context as sequences grow, for tasks such as document understanding, code analysis, and long-range recall. Approaches include sparse and hierarchical attention, positional-encoding extensions, recurrent and state-space memory, hybrid architectures, and compression of context or key-value caches. Training methods range from long-sequence continued pretraining and curricula to distillation and test-time adaptation, with evaluation often using retrieval and reasoning benchmarks such as RULER, LongBench, and Needle-in-a-Haystack. The central tension is reducing compute and memory without losing reliable access to distant information; performance also depends on information density and may not extrapolate from short training contexts.