TaskPress: Query-Agnostic KV Cache Compression via Task-Guided Pruning
Long-context inference with large language models is constrained by the linear growth of the key-value cache to sequence length. While pruning offers mitigation, prevailing methods determine query-specific token importance that cannot be reused across unseen queries. In contrast, we introduce TaskPress, a framework for task-guided, query-agnostic KV cache eviction. Instead of optimizing the cache for a single query, TaskPress constructs a reusable memory representation conditioned on a high-level task guide. The guide functions as a meta-query during prefill to filter irrelevant tokens before downstream queries are issued. In addition, TaskPress leverages quantization scale factors as a z...
| Authors | Wonpyo Park, Seung-won Hwang |
|---|---|
| Primary category | cs.AI |
| Published | 4 August 2026 |
| Last updated at source | 30 August 2026 |
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