# Cross-model KV cache transfer in LLM families: a closed-form linear mapping for prefill reuse

> Source: <https://arxiv.org/abs/2608.03893>
> Published: 2026-09-09 09:00:00+00:00

# Computer Science > Machine Learning

  [Submitted on 4 Aug 2026]

# Title:Cross-Model KV Cache Transfer in LLM Families: A Closed-Form Linear Mapping for Prefill Reuse

[View PDF](/pdf/2608.03893)

[HTML (experimental)](https://arxiv.org/html/2608.03893v1)

Abstract:Production deployments often swap between different-sized models in a family for cost-quality cascading, mid-conversation switching, and routing, and each swap forces the receiver to repay the prefill from scratch. We propose cross-model KV cache transfer, where the receiver reuses the source's KV cache, skipping prefill. We find that cross-model KV has substantial linear structure across matched-KV pairs, where source and target share KV head count and per-head dimension. On Qwen3 14B->32B, one source layer explains 56% of variance in the target's keys and 32% in values, rising to 79% and 65% with multiple source layers. Building on this, we design a closed-form ridge mapper that operates per head and proceeds in three steps. First, for each target layer we select the top-k most predictive source layers and concatenate their KV as input. Second, we strip RoPE from the keys before mapping, so the fit is position-free and reusable across context lengths. Third, we fit ridge regression on a small calibration set of 500 FineWeb-Edu sequences of 1,024 tokens each. Surprisingly, across six pairs in three families, this linear mapper retains 73-98% of the receiver's standalone-prefill accuracy on four pairs, while two degrade sharply. A nonlinear MLP recovers up to +37 pp HellaSwag retention on the failures. The mapper runs 2.7-25x faster than re-prefill and remains stable across multi-turn handoff, making cross-model KV cache transfer practical.
    

### References & Citations

Loading...

# Bibliographic and Citation Tools

Bibliographic Explorer 

*(*[What is the Explorer?](https://info.arxiv.org/labs/showcase.html#arxiv-bibliographic-explorer))
Connected Papers 

*(*[What is Connected Papers?](https://www.connectedpapers.com/about))
Litmaps 

*(*[What is Litmaps?](https://www.litmaps.co/))
scite Smart Citations 

*(*[What are Smart Citations?](https://www.scite.ai/))
# Code, Data and Media Associated with this Article

alphaXiv 

*(*[What is alphaXiv?](https://alphaxiv.org/))
CatalyzeX Code Finder for Papers 

*(*[What is CatalyzeX?](https://www.catalyzex.com))
DagsHub 

*(*[What is DagsHub?](https://dagshub.com/))
Gotit.pub 

*(*[What is GotitPub?](http://gotit.pub/faq))
Hugging Face 

*(*[What is Huggingface?](https://huggingface.co/huggingface))
ScienceCast 

*(*[What is ScienceCast?](https://sciencecast.org/welcome))
# Demos

# Recommenders and Search Tools

Influence Flower 

*(*[What are Influence Flowers?](https://influencemap.cmlab.dev/))
CORE Recommender 

*(*[What is CORE?](https://core.ac.uk/services/recommender))
IArxiv Recommender

*(*[What is IArxiv?](https://iarxiv.org/about))
# arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? [**Learn more about arXivLabs**](https://info.arxiv.org/labs/index.html).