A new arXiv paper introduces DoubtLess, a training-free decoding method in which a smaller reasoning model steers a larger one toward brevity during chain-of-thought (CoT) generation. The small model reads the large model's CoT so far twice — once prompted to be brief and once to be verbose — and the scaled difference between its two logit vectors is added to the large model's logits at every step. On seven benchmarks spanning competition mathematics, graduate-level science and competitive programming, the authors report a 31% shorter CoT for Qwen3-32B with unchanged accuracy, outperforming five inference-time baselines; they also report 20% fewer thinking tokens per turn in a self-debugging coding agent with unchanged task performance.