KV Cache Compression as State Substitution: When Does KV Compression Yield a Reliable Replacement for the Full Cache?

Long-context autoregressive inference is increasingly constrained by KV caches, whose memory footprint grows with sequence length, motivating compression for memory-efficient inference. However, when compression replaces the full cache with a smaller one that continued decoding must use and update, matching the current attention output is insufficient in general: two cache states can agree now yet diverge after the same append because softmax hides normalization mass. We characterize when current-output error can nevertheless control cache-extension error. For unchanged-row selection, this control depends on omitted attention mass and the separation between retained and omitted value means. Cancellation is the failure boundary: substantial omitted mass can remain nearly invisible in the current output. Away from it, current error controls the hidden mass exposed by an append, yielding a sharp, attainable one-append guarantee. The same control extends perturbatively to more general compressed caches that remain close, in response and normalization mass, to an unchanged-row selection on the same support. Fresh Qwen and Mistral evaluations support this mechanism: transition-aware scoring improves pairwise post-extension concordance over current-output scoring by .320 and .211, respectively, with 95% confidence intervals excluding zero in both model families. CaM and KeepKV test perturbative transfer at different scales. Finally, we characterize exactly when these headwise guarantees survive fixed output projection. Together, the results show how the geometry of compressed caches can make current-output error informative for reliable KV-cache replacement despite its global insufficiency.