Retrofitting Unbounded Context onto Stock Language Models

Mark Oskin

A language model that never had to forget would keep every document it has read and every turn it has spoken, and answer from any of it. This paper shows that two stock models can do that on unmodified weights by manipulating their geometry. Rotary attention turns each pair of key dimensions, a plane, at its own rate. The slow planes turn once in thousands of tokens and carry the address a query matches when it reads a far key; the fast planes turn once in a few tokens, and the order of the tokens inside a span is already written into its keys and values by the time the span leaves the near window. At distance the position blurs, and what attention reads is a bag rather than a sequence. A far span is read by its words: under competition it is read only if it is visible at the query of the question's own handle word, and a read through a description rather than a word fails at distance in every model measured, a 70B included. These facts allow a trick. What sits in the far part of attention does not have to be the history as it was; it can be installed ahead of the prediction. The design keeps a near window of 8K tokens at their true positions and changes what lies beyond it with…