Contiguity, Not Importance: Budgeted Repair of Stale KV Caches After Document Edits

2026-09-15

AI Summary

研究解决了KV缓存因文档编辑而过时的问题，通过局部连续修复方法有效恢复一致性，比全重填成本更低。

Abstract

KV-cache reuse can reduce inference cost in retrieval-augmented generation and agentic systems, but cached contexts may become stale when retrieved knowledge, working memory, or user state is edited. Under causal self-attention, even a local edit can affect downstream KV states. A full re-prefill reliably restores consistency but is costly, whereas refreshing only the edited span can leave downstream dependencies stale. We formulate in-place repair as budgeted recomputation and compare training-free position-selection policies on a factual RAG benchmark with matched direct and derived edits. Across three model families, all policies repair direct cases, but derived cases clearly separate them. At the primary budget, a contiguous edit-local window recovers at least 0.94 of the post-edit answer margin and substantially outperforms attention-based, KV-deviation, and structural selectors. Mechanistic analysis shows that position sets effective under clean-state transplantation can fail under actual recomputation because scattered positions inherit surrounding staleness. The edit-local advantage also depends on adjacency and largely disappears when the answer-bearing text moves downstream. Because answer-relevant edits almost always corrupt model behavior, failure severity is difficult to predict, and repair is 13-21 times faster than full re-prefill, our results support unconditional edit-local repair when the dependent text remains adjacent to the edit.

Problem

• KV-cache
• document edits
• staleness
• budgeted repair
• causal self-attention

Innovation

• budgeted recomputation
• contiguous edit-local window
• KV-cache repair
• stale contexts
• causal self-attention