UniCache: Task- and Type-Aware KV Cache Compression for Unified Multimodal Models

Unified multimodal models combine understanding, generation, and editing within a single network, offering a promising foundation for versatile multimodal applications. However, growing multimodal contexts make KV cache storage and access increasingly costly. Existing KV cache compression methods can reduce these costs, but applying a single policy uniformly can degrade quality across tasks, as shared attention brings together heterogeneous cache types with different attention distributions and temporal dynamics. Based on these findings, we propose UniCache, a training-free framework for task- and type-aware KV cache compression. UniCache identifies the cache segments activated by each task and assigns suitable compression policies to different segment types. It coordinates their parallel execution under a shared storage budget through attention-guided allocation and task-aware temporal scheduling. Experiments show that UniCache maintains balanced quality at high compression ratios: compresses KV cache 5$\times$ for understanding and editing and 2.5$\times$ for generation with negligible loss, and increases throughput up to 1.78$\times$, significantly improving the practicality of scaling unfied multimodal model to longer context length.