Long-Context Memory Retention Behavior in Extended Transformer Windows

Abstract

Long-context reasoning in Transformer architectures depends on the ability to retain and propagate
contextual information across sequences that exceed the native attention window. Segment-level
recurrence addresses this limitation by passing compressed memory states from one window to the
next, preserving semantic continuity without incurring the quadratic cost of full-sequence attention.
However, this study shows that the retention provided by recurrent memory is selective: while high
level thematic and structural context remains stable across many segment transitions, fine-grained
lexical and referential detail decays progressively as memory representations are repeatedly
transformed. The model effectively retains what the context is about, but not always the exact details
needed for precise reasoning. Furthermore, retention strength depends on thematic alignment between
segments continuity reinforces memory, while topic shifts accelerate abstraction and decay. These
findings emphasize that segment-level recurrence is well suited to tasks requiring semantic coherence,
narrative flow, or conceptual reasoning, but tasks requiring precise long-range recall may require
supplemental retrieval mechanisms.