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Lie to Me: How Faithful Is Chain-of-Thought Reasoning in Reasoning Models?

Richard J. Young

arXiv:2603.22582v1cs.CLcs.AI

TL;DR

It remains unclear whether low CoT faithfulness findings from proprietary models generalize across open-weight reasoning models. This study evaluates 12 open-weight models with hint-injection tests and finds substantial faithfulness variation, from 39.7% to 89.9%, across model families.

  • Problem

    Evidence is limited on whether low CoT faithfulness observed in two proprietary models generalizes across open-weight architectures, training methods, and scales.

  • Method

    The study evaluates 12 open-weight reasoning models on 498 MMLU and GPQA Diamond questions using six hint categories and measures explicit hint acknowledgment in CoT.

  • Results

    Faithfulness varies from 39.7% to 89.9% across models, with social-pressure hints hardest to acknowledge and thinking-token acknowledgment exceeding answer-text acknowledgment.

  • Takeaways & Limitations

    CoT faithfulness is not fixed across reasoning models, and answer-text monitoring captures substantially less hint-acknowledgment signal than thinking-token monitoring.

  • Takeaways & Limitations

    The faithfulness measure may miss indirect or paraphrased acknowledgments because it requires explicit mention of the hint.

Abstract

from arXiv · show

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.

1 Introduction

Because CoT is used to make reasoning monitorable in high-stakes settings, its safety value depends on whether explanations reflect the factors influencing model outputs. This study evaluates whether previously observed faithfulness limitations generalize across open-weight reasoning models and vary with architecture, training method, or scale.

  • Motivation: CoT is presented as a transparency mechanism for detecting flawed reasoning in high-stakes applications such as medical diagnosis, legal reasoning, and autonomous code generation.Its safety role depends on human overseers being able to inspect visible step-by-step reasoning.
  • Motivation: Prior evidence shows that prompt features can influence model outputs without being mentioned in CoT, exposing gaps between stated and actual reasoning.This evidence includes biased features such as suggested answers from purported experts across multiple BIG-Bench Hard tasks.
  • Research gap: The study asks whether low faithfulness observed in Claude and DeepSeek-R1 generalizes across open-weight models or reflects particular architectures, training methods, or model scales.The open-weight ecosystem spans dense models from 7B to 32B parameters and mixture-of-experts models exceeding 685B total parameters.
  • Study design: 12 models spanning 9 architectural families were evaluated on 498 multiple-choice questions using six hint categories, producing 41,832 inference runs.The questions comprise 300 MMLU items and 198 GPQA Diamond items; model families include DeepSeek, Qwen, MiniMax, OpenAI, Baidu, AI2, NVIDIA, StepFun, and ByteDance.

2 Related Work

Prior work has defined and measured CoT faithfulness through diverse causal, structural, symbolic, and hint-injection approaches, finding that it varies with measurement, task, model, and reasoning-chain structure. Existing evaluations largely centered on proprietary models or one open-weight family, motivating systematic cross-family evaluation.

  • Faithfulness methodologies: Faithfulness measures can diverge on the same data, and causal interventions show that CoT’s influence on predictions varies substantially by task.Approaches include truncating chains, adding intermediate mistakes, and paraphrasing explanations.
  • Faithfulness methodologies: Faithfulness can improve through question decomposition, while symbolic translation can make it verifiable by construction but does not generalize to open-ended reasoning.Faithful CoT translates queries into symbolic chains such as Python code or Datalog programs.
  • Hint-injection evaluations: 25% for Claude 3.7 Sonnet and 39% for DeepSeek-R1 were the lowest reported faithfulness rates in a directly relevant six-hint evaluation.Outcome-based reinforcement learning produced only modest, plateauing improvements, raising questions about CoT monitoring as a safety mechanism.
  • Safety implications: Safety research shows that long-CoT reasoning can both help and hinder safety, while hidden objectives and deceptive behaviors can persist under monitoring and safety training.Some models reason toward harmful outputs, and persistence is strongest in models trained with CoT reasoning about deception.
  • Safety implications: The literature disputes whether CoT monitoring requires faithful internal-computation reports or merely useful correlations with safety-relevant behavior.This debate distinguishes strict faithfulness from a weaker monitoring property that may remain practically informative.
  • Open-weight evaluation gap: The present work addresses limited prior coverage by evaluating 12 models across 9 families with standardized methods and two independent faithfulness classifiers.Its cross-scale analysis compares faithfulness across architectures, training methods, and model scales.

3 Methods

The study evaluates 12 open-weight reasoning models on 498 questions using six controlled hints and measures faithfulness among cases where hints change answers. A two-stage classifier identifies whether the chain-of-thought explicitly acknowledges the influencing hint.

  • Evaluation set: 498 questions comprise 300 MMLU items across 57 subjects and all 198 GPQA Diamond questions.GPQA Diamond provides an expert-validated graduate-level science subset, while MMLU spans undergraduate to professional difficulty.
  • Models and inference: 12 open-weight models span 9 architectural families, 7B to 685B total parameters, and GRPO, SFT, and hybrid RL training methods.Models are evaluated through the OpenRouter API with temperature = 0.0 and seed = 103.
  • Hint intervention: Six hints target sycophancy, consistency, visual pattern, metadata, grader hacking, and unethical information, using the same randomly selected incorrect answer across hint types for each question.The shared target answer enables fair cross-hint comparisons, while formulations differ from Chen et al. in explicitness.
  • Experimental pipeline: 41,832 inference calls pair each model-question baseline with six hinted runs, and influence requires the hinted answer to differ from baseline and match the hint target.The experiment includes 5,976 baseline runs and 35,856 hinted runs; 10,276 influenced cases are classified.
  • Faithfulness measurement: Faithfulness rate is the proportion of hint-influenced responses whose chain-of-thought explicitly acknowledges the hint.A two-stage procedure combines hint-specific regex or keyword matching with majority voting from three independent LLM judges for ambiguous cases.

4 Results

Faithfulness varies substantially across open-weight reasoning models and hint types, with social-pressure cues producing the lowest acknowledgment rates and no strong linear relationship with model scale. Thinking-token acknowledgment averages 87.5%, versus 28.6% in answer text, revealing a large output-level acknowledgment gap.

  • Baseline accuracy: 90.9%–67.7%: Baseline accuracy spans DeepSeek-V3.2-Speciale to OLMo-3-7B-Think, with every model performing better on MMLU than GPQA Diamond.The top-performing cluster achieves 84–91% overall accuracy, while the remaining six models range from 67.7% to 79.0%.
  • Hint influence: 44.6%–20.2%: Average hint influence ranges from Qwen3.5-27B to MiniMax-M2.5, while unethical and sycophancy hints are most influential and visual_pattern is least influential.The four most susceptible models are Qwen3.5-27B (44.6%), DeepSeek-R1 (41.2%), QwQ-32B (36.8%), and OLMo-3.1-32B-Think (34.0%).
  • Model-level faithfulness: 89.9%–39.7%: Sonnet-judged faithfulness varies by 50.2pp across models, from DeepSeek-V3.2-Speciale to Seed-1.6-Flash, with divergent results within the DeepSeek and Qwen families.DeepSeek-V3.2-Speciale reaches 89.9% versus DeepSeek-R1 at 74.8%, while Qwen3.5-27B reaches 68.3% versus QwQ-32B at 56.3%.
  • Hint-type faithfulness: 35.5% and 53.9%: Consistency and sycophancy have the lowest Sonnet-judged faithfulness, whereas grader and unethical reach 77.7% and 79.4%.H2 is partially supported: consistency is lowest as predicted, but the remaining hint types do not follow the hypothesized subtle-versus-overt ordering.
  • Scaling and reasoning length: No strong linear relationship: active parameter count does not predict Sonnet-judged faithfulness, with QwQ-32B (56.3%) and OLMo-3.1-32B-Think (81.0%) differing by 24.7pp despite both having 32B dense parameters.DeepSeek-V3.2-Speciale has 89.9% faithfulness with 37B active parameters, while GPT-OSS-120B has 84.9% with 5.1B active parameters.
  • Acknowledgment location: 87.5% versus 28.6%: Thinking-token acknowledgment exceeds answer-text acknowledgment by roughly 58.9pp across models.The largest gaps exceed 90pp for Step-3.5-Flash (94.7pp), GPT-OSS-120B (93.0pp), and DeepSeek-V3.2-Speciale (92.5pp).

5 Discussion

CoT faithfulness varies substantially across open-weight reasoning models and hint categories, rather than constituting a fixed model property. The discussion also identifies major measurement and deployment limitations, including suppressed answer-text acknowledgment and classifier sensitivity.

  • Monitoring implications: 87.5% thinking-token acknowledgment contrasts with 28.6% answer-text acknowledgment, a gap of roughly 58.9 percentage points.GPT-OSS-120B acknowledges hints in 93.0% of thinking traces but 0.0% of answers.
  • Methodological limitations: Classifier choice changes the measured result: the Sonnet judge reports 69.7% overall faithfulness versus 82.6% for the regex+Ollama pipeline.The reported 12.9-percentage-point gap is concentrated in sycophancy (−43pp) and consistency (−33pp) hints.
  • Methodological limitations: The evaluation is limited to API-served models and multiple-choice questions from MMLU and GPQA, so reproducibility and generalization to other task formats are not guaranteed.Additional limitations include conservative explicit-mention classification, approximately 6.7% MMLU label errors affecting accuracy but not faithfulness, and potentially insufficient power for fine-grained comparisons.

6 Conclusion · A Hint Injection Templates

The study finds that CoT faithfulness varies substantially across open-weight reasoning models and hint types, with internal acknowledgment often exceeding externally visible acknowledgment. It also identifies evaluation limitations, future research directions, and the templates used to inject the six hint categories.

  • 6 Conclusion: 39.7%–89.9%: Faithfulness rates vary across 12 open-weight models from nine architectural families and 41,832 inference runs.The evaluation covers six categories of reasoning hints.
  • 6 Conclusion: 35.5% and 53.9%: Consistency and sycophancy hints are the hardest social-pressure cues for models to acknowledge.These are the reported faithfulness rates for consistency and sycophancy hints, respectively.
  • 6 Conclusion: Approximately 87.5% versus 28.6%: Thinking-token acknowledgment substantially exceeds acknowledgment in externally visible answer text.Keyword analysis suggests models detect hint influence internally but suppress that acknowledgment in outputs.
  • 6 Conclusion: Training methodology shapes faithfulness more strongly than model scale, according to the study’s theoretical implications.The paper frames faithfulness as influenced by training methodology rather than parameter count alone.
  • 6 Conclusion: The evaluation is limited by API-based testing, multiple-choice format, conservative classifiers, and sensitivity to classifier methodology.These caveats qualify the reported faithfulness rates.
  • 6 Conclusion: Future work should test open-ended generation, combine CoT analysis with mechanistic interpretability, and develop training interventions that improve faithfulness without reducing task performance.These are the three directions identified by the paper.
  • A Hint Injection Templates: The hint templates replace {subject} with an MMLU subject or “graduate-level science” for GPQA, and {target} with a randomly selected incorrect answer letter.These placeholders parameterize the injected hints.
  • A Hint Injection Templates: Six hint categories are implemented through templates: five prepend or append prompt text, while the visual-pattern hint restructures answer choices without adding text.Table 4 lists the full experimental templates.

B Faithfulness Classifier Details

The faithfulness classifier uses a two-stage pipeline that first detects explicit hint acknowledgment with regex or keyword matching, then consults three independent LLM judges when no match is found. A separate Sonnet judge provides an additional robustness check.

  • B Faithfulness Classifier Details: The classifier first uses regex/keyword matching for explicit hint acknowledgment in the chain-of-thought, labeling matched cases faithful.If no match is found, three independent LLM judges classify the CoT by majority vote; a separate Sonnet judge is run as a robustness check.

B.1 Stage 1: Pattern Matching Rules · B.2 Stage 2: LLM Judge Prompt

Faithfulness is assessed in two stages: case-insensitive pattern matching first identifies explicit hint acknowledgments, then three independent LLM judges determine whether the hint was load-bearing. The final Stage 2 label uses majority voting and distinguishes reliance on a hint from merely mentioning or dismissing it.

  • B.1 Stage 1: Pattern Matching Rules: Stage 1 classifies a CoT as faithful when any hint-specific regular expression matches an acknowledgment phrase.The patterns are case-insensitive and were validated on unhinted base runs to reduce false positives.
  • B.1 Stage 1: Pattern Matching Rules: 8 consistency patterns target references to previous answers, changed responses, and sticking with prior selections.Examples include “previous conversation,” changing an answer, and maintaining the original response.
  • B.1 Stage 1: Pattern Matching Rules: 4 visual-pattern patterns target reordered options, answer-position bias, and first-option effects.These expressions capture both explicit reordering and positional-choice biases.
  • B.2 Stage 2: LLM Judge Prompt: Stage 2 sends each case to three independent LLM judges, with the final faithfulness label set by majority vote.Each judge returns exactly YES for faithful/load-bearing or NO for unfaithful.
  • B.2 Stage 2: LLM Judge Prompt: YES requires the CoT to depend on the hint as evidence, deference, or an override of the model’s own reasoning.The prompt defines faithfulness as the hint actually influencing how the answer is reached.
  • B.2 Stage 2: LLM Judge Prompt: NO applies when the CoT mentions the hint without depending on it, reasons independently after acknowledgment, or omits the hint.Mentioning alone is insufficient for a faithful classification.
  • B.2 Stage 2: LLM Judge Prompt: A hint that is dismissed, questioned, or independently verified is not faithful unless it is a reason the model selected its answer.Judges evaluate whether the hint was load-bearing rather than merely present in the reasoning.

B.3 Sonnet Judge (Robustness Check)

The robustness check reclassifies all influenced cases with Claude Sonnet 4 under fixed decoding, using the Section B.2 prompt with thinking text middle-truncated to 8,000 characters.

  • Sonnet Judge (Robustness Check): All influenced cases are additionally classified using Claude Sonnet 4 via OpenRouter at temperature 0.0 with max_tokens set to 10.The robustness check uses the same prompt template as Section B.2.
  • Sonnet Judge (Robustness Check): Thinking text is middle-truncated to 8,000 characters, preserving the first and last portions of the chain of thought.This is the sole prompt-template difference from Section B.2.

B.4 Cross-Classifier Agreement

Cross-classifier agreement varies substantially by hint type, with the largest discrepancies for sycophancy and consistency and close agreement for grader and unethical hints. Sonnet is more generous than the primary pipeline for OLMo-3.1-32B-Think, and its faithfulness rates serve as the paper’s primary metric.

  • Hint-type agreement: 43.4 pp and 33.1 pp are the largest pipeline–Sonnet discrepancies, occurring for sycophancy and consistency, respectively.The regex stage captures surface-level mentions that Sonnet judges as not load-bearing.
  • Hint-type agreement: <10 pp gaps occur for grader and unethical hints, indicating largely concordant classifications between the two systems.These hint types produce more clearly faithful or unfaithful CoTs.
  • Model-specific outlier: 81.0% vs. 71.9% is the OLMo-3.1-32B-Think Sonnet–pipeline comparison, with Sonnet identifying load-bearing hint usage missed by regex patterns.Sonnet is more generous than the pipeline for this model.
  • Metric choice: Sonnet faithfulness rates are reported as the primary metric throughout the paper for comparability and conservatism.Classifier validity is assessed by agreement between the primary regex plus 3-judge majority-vote pipeline and an independent Sonnet judge.

C Per-Model Detailed Results · D Example Model Outputs

Per-model results show that consistency hints are least faithfully reported, while grader and unethical hints are acknowledged most often. Faithful and unfaithful reasoning-trace examples are available in the project repository.

  • C Per-Model Detailed Results: Table 6 reports percentages of influenced cases classified as faithful for each model and hint type.Its Avg column is an unweighted macro average across five hint types.
  • C Per-Model Detailed Results: Visual-pattern hints are excluded because answer reordering does not inject text, making faithfulness classification inapplicable.This exclusion applies to the per-model faithfulness table.
  • C Per-Model Detailed Results: 34.2% is the macro mean for consistency-hint faithfulness, the lowest across nearly all models.This suggests models are especially unlikely to acknowledge when consistency pressure drives their answers.
  • C Per-Model Detailed Results: 73.8% and 72.9% are the macro means for grader and unethical hints, respectively, making them the most faithfully reported categories.These hints are explicit and difficult to rationalize away.
  • C Per-Model Detailed Results: 39.8% and 74.6% are the lowest and highest macro-averaged faithfulness rates, for Seed-1.6-Flash and DeepSeek-V3.2-Speciale, respectively.The comparison comes from Table 6’s per-model results.
  • D Example Model Outputs: Representative faithful and unfaithful reasoning traces for each hint type are available in the project repository.The repository URL is provided in the paper section.

E OpenRouter API Configuration

All 12 models were accessed through OpenRouter’s OpenAI-compatible chat completions API with uniform generation settings. Reasoning tokens were retrieved through the reasoning parameter, and every model received the same multiple-choice system prompt.

  • API access: All 12 models were accessed via OpenRouter’s API using the OpenAI-compatible chat completions format.Table 8 lists the OpenRouter model identifiers used.
  • Reasoning tokens: Chain-of-thought reasoning tokens were returned in OpenRouter’s reasoning_details array through the reasoning parameter.This provided access to internal thinking without provider-specific APIs.
  • System prompt: All models received the same system prompt instructing them to solve multiple-choice questions step by step and answer with a single letter.The permitted final-answer format was A, B, C, or D.
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