KV Cache Translation across Heterogeneous Large Language Models

arXiv:2607.28979v2 Announce Type: replace Abstract: Heterogeneous Large Language Model (LLM) systems increasingly share contexts, retrieved evidence, and multi-agent dialogue histories, yet their internal key-value (KV) caches remain model-specific and cannot be reused across architectures. Consequently, each model must repeatedly prefill or store caches for the same context, limiting the scalability of multi-model reasoning and long-context generation. We propose Mixture-of-Translators (MoT), a cache translation framework that maps context KV caches from a source LLM into the cache space of a target LLM. Unlike prior approaches that depend on a single projection path or global shared latent space, MoT uses multiple translator modules with token-level routing to capture diverse cache translations. We further introduce a Context Correction Loss that aligns the replayed target trajectory with the native target trajectory, reducing residual translation error. Our analysis reveals two competing failure modes: propagation error from early injection and correction-deficit error from late injection. We evaluate heterogeneous translation among Llama, Gemma, and Qwen, spanning substantially different architectures and KV-cache spaces. MoT achieves an average accuracy of 57.6% and F1 of 0.42. These scores recover 95.0% and 120.6% of native target performance and reach 1.5x and 3.5x the strongest-baseline scores, respectively. In practical case studies, MoT enables accurate heterogeneous-agent discussion with scale-invariant KV-memory behavior as the number of agents grows, while retaining near-native generation quality in long-context cache-augmented generation. These results demonstrate scalable KV-cache reuse across heterogeneous LLMs.