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Updated: Aug 21, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
Published on: December 6, 2024
AdaK: adaptive KV cache budget estimation framework for analyzing long-context large language model inference
1College of Information Science and Technology, Jinan University, Guangzhou, China.
Introduction:
The deployment of LLMs on resource-constrained hardware is hindered by the memory-intensive KV Cache mechanism.
Methods:
We propose AdaK, an adaptive KV cache budget estimation framework with three strategies: entropy-based thresholding, task-aware lookup table, and a lightweight policy network.
Results:
AdaK reveals estimated KV cache reductions of up to 17.9% relative to fixed-k = 2048 baselines across 16 settings on Qwen3-4B, Qwen3-8B, and Mistral-7B.
Discussion:
AdaK's decoupled design enables safe budget estimation as a dynamic ceiling for downstream sparse attention kernels.
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