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

• URL: http://arxiv.org/abs/2512.17452v2
• Date: Mon, 22 Dec 2025 10:23:36 GMT
• Title: Learning What to Write: Write-Gated KV for Efficient Long-Context Inference
• Authors: Yen-Chieh Huang, Pi-Cheng Hsiu, Rui Fang, Ming-Syan Chen,
• Abstract summary: We formalize KV cache management as a causal system of three primitives: KV Admission, Selection, and Eviction.<n>We instantiate KV Admission via Write-Gated KV, a lightweight mechanism that learns to predict token utility before it enters the cache.
• Score: 10.915483460983411
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Long-context LLM inference is bottlenecked by the quadratic attention complexity and linear KV cache growth. Prior approaches mitigate this via post-hoc selection or eviction but overlook the root inefficiency: indiscriminate writing to persistent memory. In this paper, we formalize KV cache management as a causal system of three primitives: KV Admission, Selection, and Eviction. We instantiate KV Admission via Write-Gated KV, a lightweight mechanism that learns to predict token utility before it enters the cache. By filtering out low-utility states early to maintain a compact global cache alongside a sliding local cache, Write-Gated KV reduces memory usage by 46-57% and delivers 3.03-3.45$\times$ prefill and 1.89-2.56$\times$ decode speedups on Llama model with negligible accuracy loss, all while remaining compatible with FlashAttention and paged-KV systems. These results demonstrate that learning what to write, is a principled and practical recipe for efficient long-context inference. Code is available at https://github.com/EMCLab-Sinica/WG-KV .

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