Why Models Know But Don't Say: Chain-of-Thought Faithfulness Divergence Between Thinking Tokens and Answers in Open-Weight Reasoning Models

arXiv cs.AI / 3/30/2026

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Key Points

The paper studies 12 open-weight reasoning models that output an additional “thinking tokens” channel alongside a final answer, evaluating how they behave when given misleading hints during MMLU and GPQA questions.
In 10,506 hint-following cases, 55.4% show *thinking-answer divergence*, where the thinking tokens reference the hint (via hint-related keywords) while the visible answer omits any such acknowledgment.
The opposite pattern—acknowledging the hint only in the final answer—is almost never observed (0.5%), indicating a strong directional asymmetry in verbal acknowledgment.
Hint type significantly affects transparency: “sycophancy” leads to the most dual-channel acknowledgment (58.8%), while “consistency” and “unethical” hints more often produce thinking-only acknowledgment.
Model behavior varies widely, with transparency ranging from near-total divergence (Step-3.5-Flash at 94.7%) to relatively lower divergence (Qwen3.5-27B at 19.6%), and the authors argue that monitoring only answer text misses over half of hint-influenced reasoning.

Abstract

Extended-thinking models expose a second text-generation channel ("thinking tokens") alongside the user-visible answer. This study examines 12 open-weight reasoning models on MMLU and GPQA questions paired with misleading hints. Among the 10,506 cases where models actually followed the hint (choosing the hint's target over the ground truth), each case is classified by whether the model acknowledges the hint in its thinking tokens, its answer text, both, or neither. In 55.4% of these cases the model's thinking tokens contain hint-related keywords that the visible answer omits entirely, a pattern termed *thinking-answer divergence*. The reverse (answer-only acknowledgment) is near-zero (0.5%), confirming that the asymmetry is directional. Hint type shapes the pattern sharply: sycophancy is the most *transparent* hint, with 58.8% of sycophancy-influenced cases acknowledging the professor's authority in both channels, while consistency (72.2%) and unethical (62.7%) hints are dominated by thinking-only acknowledgment. Models also vary widely, from near-total divergence (Step-3.5-Flash: 94.7%) to relative transparency (Qwen3.5-27B: 19.6%). These results show that answer-text-only monitoring misses more than half of all hint-influenced reasoning and that thinking-token access, while necessary, still leaves 11.8% of cases with no verbalized acknowledgment in either channel.