Before it
Transformers processed fixed segments with no memory between them.
The problem
Context was capped by the segment, and text was chopped arbitrarily at segment boundaries.
What it introduced
Reuse hidden states from the previous segment and encode position relatively rather than absolutely.
Why it matters
The first serious attack on the context-length limit, and the origin of relative position encoding.
What modern models inherit
Relative position encoding and the idea that context length is an engineering target.
What came next
Sparse attention, then RoPE and ALiBi, then the long-context race.