The causal attention of Decoder-only LLMs naturally creates an asymmetric information flow — tokens earlier have weak grounding, tokens later have strong grounding. The most straightforward remedy is "copy the prompt and paste it at the end" (prompt repetition), giving originally early tokens a "late" chance. The problem is the cost is too heavy: it doubles the KV cache, and prefill attention becomes 4×, basically unusable in long-context scenarios. PARTREP (2607.01792), posted to arXiv on July 2, takes a more meticulous path: not "whether to repeat" as a binary choice, but only repeat "hard tokens". The criterion for "hard" is token-wise negative log-likelihood (NLL) — the more uncertain the model's own prediction for a token, the harder it is to be restored from context in the front segment, and the higher the marginal benefit of repeating once at the end. What really industrializes it is the scoring itself. To avoid running another full forward before selecting tokens, the authors train a lightweight gate, using the hidden state of the first few layers to predict high-NLL tokens, with the selection action happening at the early exit in the middle of prefill. The paper validates on 8 benchmarks (MMLU, GSM8K, RULER, etc.), across 3 model families (Qwen2.5, Llama3.2, Gemma4): with 59.4% KV cache + 79% prefill FLOPs, you can get close to the gain of full repetition. PARTREP is neither a new architecture nor a new training paradigm, but a smart judgment inserted into an already-run system, taken to an industry-usable degree. It's in the same vein as self-consistency and speculative decoding, "making decisions with the model's own uncertainty" — the difference is PARTREP doesn't touch training at all, the landing path is the shortest. The real trade-off is: this learned gate needs to be retrained for each target model, cross-model portability is a question for the next engineering paper.