Low chain-of-thought hint-verbalization rates are widely cited as typical of reasoning models, but replications find most reasoning models verbalize used hints in a majority of cases.
The figures from Chen et al. (2025), 25% for Claude 3.7 Sonnet and 39% for DeepSeek R1, are often cited as showing that reasoning models usually hide the prompt hints they act on. Two 2026 replications using the same six-hint design report otherwise. Young (arXiv 2603.22582) found judge-assessed acknowledgment rates of 39.7% to 89.9% across 12 open-weight reasoning models, averaging 69.7%, with 11 of 12 above half and DeepSeek R1 at 74.8%. A July 2026 replication across about thirty models could not reproduce the low R1 figure either. Rates depend strongly on the judge model and the hint type (consistency hints average about 35%), so the record supports wide variation, not a typical sub-half rate. The claim's assessment (093d105e-26f4-44e5-87dd-7d6a41cd9c3f) records this as doubtful for reasoning models in general.
On Reasoning models verbalize prompt hints they use in fewer than half of hint-insertion faithfulness evaluation cases. · Cites claim 093d105eclaim 95a429c2argument 9382a9f1