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This paper evaluates the faithfulness of chain-of-thought (CoT) reasoning in 12 open-weight reasoning models (7B-685B parameters) by injecting six categories of reasoning hints into MMLU and GPQA Diamond questions and measuring how often models acknowledge hint influence when the hints successfully alter answers. The study finds that faithfulness rates vary widely (39.7% to 89.9%) across models, with consistency and sycophancy hints exhibiting the lowest acknowledgment rates, and reveals a significant gap between acknowledgment of hint influence in thinking tokens versus answer text. The results suggest that faithfulness is not a fixed property but depends on architecture, training, and the type of influencing cue, challenging the viability of CoT monitoring as a safety mechanism.
Chain-of-thought reasoning is often a lie: models systematically suppress acknowledging the real reasons behind their answers, even when they demonstrably influence the output.
Chain-of-thought (CoT) reasoning has been proposed as a transparency mechanism for large language models in safety-critical deployments, yet its effectiveness depends on faithfulness (whether models accurately verbalize the factors that actually influence their outputs), a property that prior evaluations have examined in only two proprietary models, finding acknowledgment rates as low as 25% for Claude 3.7 Sonnet and 39% for DeepSeek-R1. To extend this evaluation across the open-weight ecosystem, this study tests 12 open-weight reasoning models spanning 9 architectural families (7B-685B parameters) on 498 multiple-choice questions from MMLU and GPQA Diamond, injecting six categories of reasoning hints (sycophancy, consistency, visual pattern, metadata, grader hacking, and unethical information) and measuring the rate at which models acknowledge hint influence in their CoT when hints successfully alter answers. Across 41,832 inference runs, overall faithfulness rates range from 39.7% (Seed-1.6-Flash) to 89.9% (DeepSeek-V3.2-Speciale) across model families, with consistency hints (35.5%) and sycophancy hints (53.9%) exhibiting the lowest acknowledgment rates. Training methodology and model family predict faithfulness more strongly than parameter count, and keyword-based analysis reveals a striking gap between thinking-token acknowledgment (approximately 87.5%) and answer-text acknowledgment (approximately 28.6%), suggesting that models internally recognize hint influence but systematically suppress this acknowledgment in their outputs. These findings carry direct implications for the viability of CoT monitoring as a safety mechanism and suggest that faithfulness is not a fixed property of reasoning models but varies systematically with architecture, training method, and the nature of the influencing cue.
