Mapping Faithful Reasoning in Language Models
On the map: LLM reasoning → Prompting and chain-of-thought reasoning
Abstract (arXiv)
Chain-of-thought (CoT) traces promise transparency for reasoning language models, but prior work shows they are not always faithful reflections of internal computation. This raises challenges for oversight: practitioners may misinterpret decorative reasoning as genuine. We introduce Concept Walk, a general framework for tracing how a model's internal stance evolves with respect to a concept direction during reasoning. Unlike surface text, Concept Walk operates in activation space, projecting each reasoning step onto the concept direction learned from contrastive data. This allows us to observe whether reasoning traces shape outcomes or are discarded. As a case study, we apply Concept Walk to the domain of Safety using Qwen 3-4B. We find that in 'easy' cases, perturbed CoTs are quickly ignored, indicating decorative reasoning, whereas in 'hard' cases, perturbations induce sustained shifts in internal activations, consistent with faithful reasoning. The contribution is methodological: Concept Walk provides a lens to re-examine faithfulness through concept-specific internal dynamics, helping identify when reasoning traces can be trusted and when they risk misleading practitioners.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Placed in Prompting and chain-of-thought reasoning, one of 17 directions in LLM reasoning. Builds on 11 of its 28 references that are in the map. First posted on arXiv 25 Oct 2025.
Builds on
11 of 28 references.
Closest ancestors · 6
Same direction, newest first
- 2025Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safety15 Jul
- 2025When Chain of Thought is Necessary, Language Models Struggle to Evade Monitors7 Jul
- 2025Thought Anchors: Which LLM Reasoning Steps Matter?23 Jun
- 2025Reasoning Models Don't Always Say What They Think8 May
- 2023On Measuring Faithfulness or Self-consistency of Natural Language Explanations13 Nov
- 2023Measuring Faithfulness in Chain-of-Thought Reasoning17 Jul
Other references · 5
- 2025Qwen3 Technical Reportcited by 8,820
- 2023Mistral 7Bcited by 3,164
- 2023Steering Language Models With Activation Engineeringcited by 922
- 2023Gemini: A Family of Highly Capable Multimodal Modelscited by 872
- 2024SafetyAnalyst: Interpretable, Transparent, and Steerable Safety Moderation for AI Behaviorcited by 10
Built on by
Citing papers per year: 2026: 2.
2 papers.
Similar, not cited
Close in wording, no citation either way
- 2026Mechanistic Evidence for Faithfulness Decay in Chain-of-Thought Reasoning
- 2026From Concept Alignment to Causal Grounding: An Intervention Test of Chain-of-Thought Faithfulness
- 2026Reasoning Traces Shape Outputs but Models Won't Say So
- 2025Are language models aware of the road not taken? Token-level uncertainty and hidden state dynamics
- 2025Beyond Semantics: The Unreasonable Effectiveness of Reasonless Intermediate Tokens
- 2026Legibility is Not Interpretability: Comparing Judged and Actual Importance in Chain-Of-Thought Reasoning
- 2025Style over Substance: Distilled Language Models Reason Via Stylistic Replication
- 2026Reasoning as State Transition: A Representational Analysis of Reasoning Evolution in Large Language Models