As context windows scale to tens or hundreds of thousands of tokens, KV cache compression has become essential for efficient LLM inference.
Existing methods fall into three families: score-based eviction, summary compensation, and offload-and-recall.
Yet all three decide what to keep or recall by content relevance to the current query.
We show this shared design is structurally incomplete.
A cache supports two access modes: associative lookup by content and sequential traversal by position; current compressors implement only the first.
The gap matters in practice: retrieval-augmented generation, code completion, and structured-data extraction all require the model to reproduce identifiers, field values, or code tokens verbatim from the context.
Under compression, content-based eviction retains the head of such a sequence but discards its continuation, causing verbatim copying to break irreversibly midway, a failure we call sequential forgetting.
This failure resists better scoring, larger budgets, summary compensation, and dynamic re-scoring; it is the dominant source of remaining quality loss under compression.
We propose KVFetch, a training-free, drop-in framework that opens a temporal recall channel for any score-based compressor.
It demotes evicted candidates to a quantized cold tier, detects active copying through a monotone read pointer, and prefetches positional successors into fixed-size hot-tier slots without increasing attention cost.
On RULER-16K under an iso-budget control, KVFetch recovers verbatim copying from 0.8 to 78.4 and raises the 13-task average by +8.4, with gains concentrating on tasks that require sequential access.
On LongBench, where no task requires sequential access, the channel remains dormant and imposes no cost.