You Need an Encoder for Native Position-Independent Caching

• URL: http://arxiv.org/abs/2602.01519v1
• Date: Mon, 02 Feb 2026 01:23:13 GMT
• Title: You Need an Encoder for Native Position-Independent Caching
• Authors: Shiju Zhao, Junhao Hu, Jiaqi Zheng, Guihai Chen,
• Abstract summary: Key-Value cache of Large Language Models (LLMs) is prefix-based.<n>Position-Independent Caching (PIC) has been proposed to enable KV reuse without positional constraints.<n>We propose native PIC by reintroducing the encoder to prevalent decoder-only LLMs and explicitly training it to support PIC.<n>We further develop COMB, a PIC-aware caching system that integrates seamlessly with existing inference frameworks.
• Score: 28.778240400537175
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Key-Value (KV) cache of Large Language Models (LLMs) is prefix-based, making it highly inefficient for processing contexts retrieved in arbitrary order. Position-Independent Caching (PIC) has been proposed to enable KV reuse without positional constraints; however, existing approaches often incur substantial accuracy degradation, limiting their practical adoption. To address this issue, we propose native PIC by reintroducing the encoder to prevalent decoder-only LLMs and explicitly training it to support PIC. We further develop COMB, a PIC-aware caching system that integrates seamlessly with existing inference frameworks. Experimental results show that COMB reduces Time-to-First-Token (TTFT) by 51-94% and increases throughput by 3$\times$ with comparable accuracy. Furthermore, the quality improvement when using DeepSeek-V2-Lite-Chat demonstrates the applicability of COMB to other types of decoder-only LLMs. Our code is available at https://github.com/shijuzhao/Comb.

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