"Frozen weights can still improve": Byte-Exact KV-Cache Grafting pushes Gemma-4-12B from 80% to 93.3% on AIME. Sietse Schelpe's paper, posted to arXiv (2607.14431) on July 15, presents a seemingly "unscientific" but extremely violent trick: change zero weights, but lift a frozen LLM directly to a larger one's level. The core is byte-exact KV-cache grafting — treating inference-time KV state as a "verified knowledge artifact" and grafting it byte-for-byte back into the next session. The paper's hardest promise is bit-exact reproducibility: under a fixed configuration, the grafted logits and a re-run result have identical SHA-256 hashes, KL divergence = 0, and 50-sample argmax agreement reaches 100%. The premise of this precise surgery is that under floating-point rotary encoding, own-position graft is the only numerically exact operation point. All three experimental results are explosive: ① Gemma-4-12B on AIME 2025 goes from 80.0% → 93.3%, exceeding its own 77.5% baseline and beating the 31B sibling (89.2%); ② 8 problems the base model couldn't solve in 401,026 tokens take only 61 tokens after graft — a 6574x token reduction, with energy consumption of about 1/8700; ③ the same byte-exact repository lifts usable context from 32,768 to 2,854,766 tokens, with zero extra memory, and byte-identical transfer across same-architecture machines. The value is a new capability lever for the industry: instead of relying only on the training side, verified inference trajectories can be treated as permanent caches and invoked at zero cost in new sessions. KV-cache shifts from "VRAM burden" to "replayable capability asset", forming a third independent engineering paradigm alongside prompt cache and KV cache compression. The limitations are also explicit — closed-source engine, all numbers self-certified via committed hash, graft cannot directly cross architectures.