TL;DR: I ran a small replication of the paper "Prompt Repetition Improves Non-Reasoning LLMs": 100 MMLU questions, one non-reasoning model, prompt sent once versus twice. Baseline 59%, repetition 61%. Probably not statistically significant, and that turned out to be the interesting part. The gap between the paper's headline numbers and my +2% taught me more about transformer attention than the trick itself.
Why I tried this
The claim sounds like a joke: paste your prompt twice and a non-reasoning LLM gets better. No chain-of-thought, no fine-tuning, no extra instructions. The paper, Prompt Repetition Improves Non-Reasoning LLMs from Google Research, tested Gemini, GPT, Claude, and Deepseek models across seven benchmarks and found repetition won 47 of 70 model-benchmark combinations with zero losses, including improvements as large as 21% to 97% on long-context tasks like NameIndex, where the model has to pull one item out of a long list.
A trick that cheap deserves a replication, and I wanted to know two things. Does it show up on ordinary short questions? And if it does, why does duplicating text change anything at all inside a transformer?
The setup
