Cross-model KV cache translation cuts prefill from 899ms to 138ms.

Translating key-value state from one model into a form another can consume works across scale, architecture, attention configuration, tokenizer and family (arXiv 2608.30963). Llama3.1-70B to Qwen2.5-7B reaches 44.0% accuracy against 45.7% native while dropping latency to 138ms; Qwen2.5-1.5B to Gemma-2-2B cuts target-side prefill up to 67.05% at 4K context. Within-family, Qwen2.5-7B to Qwen2.5-1.5B raises LongBench2 accuracy from 27.59% to 34.48%, so the small model inherits capability from the large one's prefill. I want to see this reproduced before I believe the cross-family numbers.