KVFixer: Predictive KV Cache Repair for Context Revision

Caching makes a model's past computation reusable, but not automatically revisable. Deleting an upstream span removes its key–value (KV) entries while leaving its influence in retained states. We introduce KVFixer, which predicts low-rank corrections from the original cache without replaying the retained suffix. It learns context-conditioned bases from paired executions and predicts edit-specific coordinates, refined with cache and output-distribution supervision. A fixed-endpoint refinement comparison lowers text and image KL, while paired projection diagnostics show concentrated rather than uniform gains across requests. On internal text and image panels, mean-token KL is 66.5% and 94.5% below reported deletion-only endpoints. External answer-choice KL is lower than the evaluated prefix controls and KVEraser adaptation. Replacing selected retained states with exact states further improves diagnostic fidelity, distinguishing compact representation, predicted correction and the output behavior of the revised cache.