Translating KV Caches Between Hybrid-Attention Language Models: MLA Latents, Linear-Attention States and Sparse-Attention Indexers

Povilas Kvedaras, Tomas Rasymas

A model that takes over another model's conversation must normally re-read the whole history; a text hand-off shortens the re-read only by dropping content. KV-cache translation instead maps the source's cache into the target's own format, so the target continues from the full context without re-reading it. It works for grouped-query attention (GQA), but open-weights frontier models now cache compressed latents (MLA), sparse-attention indexer keys and fixed-size linear-attention states. We replicate KV-Lingo on a GQA pair and then give, for each of these objects, where to capture and inject it and a mechanism that reaches native quality. We study two hybrid pairs at 16-48B scale in both directions, Kimi-VL-A3B - Kimi-Linear-48B (MLA - MLA + Kimi Delta Attention) and Qwen3.5-27B - Qwen3.8-27B (Gated DeltaNet hybrids), plus a sparse-attention target (Qwen3.8-27B - Qwen3.8-Flash-Next). MLA latents translate with token-wise linear maps; recurrent states are copied with a closed-form correction or synthesised by replaying the target's own recurrence. Exact long-range recall needs token identity: decoding token ids from the source's layer-0 MLA latent raises needle recall from 0.09 to…