arXiv:2602.08686v2 Announce Type: replace Abstract: Prefill-only KV compression freezes a token subset at the end of prefill and decodes from it without further eviction. The retention decision is therefore irreversible, yet existing methods estimate the corrective signals it relies on, per-head reliability and prompt-level compression sensitivity, online from a single noisy prompt. We argue this is the wrong statistical unit: these signals exhibit far higher cross-prompt regularity than within-prompt signal-to-noise. We introduce \textsc{CompilerKV}, a KV-retention policy whose corrective tab
The rapid development and scaling of large language models necessitate more efficient memory management techniques to reduce computational cost and latency.
Improved KV compression directly translates to more powerful, cost-effective, and accessible AI models, impacting a wide range of applications from chatbots to autonomous systems.
This research introduces a more robust and reliable method for KV cache management, moving beyond noisy real-time estimations to more stable, pre-compiled retention policies.
- · AI model developers
- · Cloud computing providers
- · End-users of AI applications
- · Less efficient KV compression methods
Reduced inference costs and latency for large language models will become more common.
This efficiency gain will enable the deployment of larger and more complex AI models in resource-constrained environments.
The democratization of advanced AI capabilities could accelerate innovation across various sectors, creating new products and services.
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Read at arXiv cs.LG