KV Admission: Learning What to Write for Efficient Long-Context LLM Inference

Abstract:Long-context LLM inference is bottlenecked by the quadratic attention complexity and linear Key-Value (KV) cache growth. Prior approaches mitigate this via post-hoc selection or eviction but overlook the root inefficiency: indiscriminate token admission. In this paper, we formalize KV management as a causal system of three primitives: KV Admission, Selection, and Eviction. We instantiate KV Admission via Write-Gated KV (WG-KV), a lightweight mechanism that learns to predict token utility before cache entry. By filtering out redundant states early to maintain a compact global cache alongside a sliding local cache, WG-KV significantly reduces memory usage and accelerates both prefill and decode phases. Our results demonstrate that learning what to write is a principled and practical recipe for efficient long-context inference. Code is available at this https URL.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2512.17452 [cs.LG]

(or arXiv:2512.17452v4 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2512.17452 Focus to learn more arXiv-issued DOI via DataCite

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