Pretrained transformers use little of their depth to follow references in context.
Thirteen base models reliably follow only 1.4-3.6 lines, and extra pretrained loops add little.
A task-trained rank-8 LoRA at one early layer extends this computation with all model weights frozen.
Qwen3-8B improves from 15.5% to 99% exact accuracy on 24-line chains; a longer-trained LoRA reaches 50 lines.
Ouro-1.4B reaches 60 lines after four loops and at least 160 after eight.
The LoRA starts a relay: program lines pass on their chain identity through a short range of middle layers.
Frozen heads read progressively further up the chain, and removing parent-line attention stops the relay.
A frozen-model measurement locates the last useful intervention layer within tolerance in three of four held-out models.
Task-specific LoRAs also improve MuSiQue.
Default answers therefore understate the computation accessible through a tiny edit.
Code and an interactive demo are available at https://lunamos.github.io/stop-thinking-too-early/.