How Does KV-Cache Eviction Shape Output Divergence?

KV-cache eviction reduces inference memory while altering subsequent generation. Across the interventions studied, lower cumulative token mismatch with full-cache continuations is associated mainly with later and less frequent first divergence; exposure-weighted post-divergence mismatch rates are also lower but remain high. Under a specified stepwise maximal coupling, we decompose the expected mismatch fraction into a first-mismatch term plus post-divergence exposure multiplied by its mismatch rate. Across an exploratory cohort and an independent 90%-retention cohort of two language models, exposure accounts for approximately 85–92% of six pooled mismatch-gap point estimates, including matched-byte selector comparisons at 50% and 90% retention. In the 90% cohort, prespecified comparisons within fixed early-divergence cohorts show higher total variation in late than early post-divergence windows for both models and selectors. We characterize the sharp range of cumulative mismatch compatible with an exact profile of finite divergence-aligned observations over unrestricted autoregressive kernel pairs and construct unbiased estimators via conditional Monte Carlo.