Detecting Unfaithful Chain-of-Thought via Circuit-Guided Internal-External Discrepancy

• URL: http://arxiv.org/abs/2605.25603v1
• Date: Mon, 25 May 2026 08:54:55 GMT
• Title: Detecting Unfaithful Chain-of-Thought via Circuit-Guided Internal-External Discrepancy
• Abstract summary: Chain-of-thought (CoT) reasoning improves problem-solving ability of large language models.<n> generated reasoning traces may not faithfully reflect the model's actual decision process.<n>We propose Circuit-guided Internal-External Discrepancy Scorer (CIE-Scorer) for instance-level CoT unfaithfulness detection.
• Score: 43.942072229910515
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Chain-of-thought (CoT) reasoning improves the problem-solving ability of large language models (LLMs), but generated reasoning traces may not faithfully reflect the model's actual decision process. Existing CoT unfaithfulness detectors mainly rely on external signals from generated rationales, such as textual plausibility or answer consistency, while overlooking evidence from the model's internal computation. Although recent circuit tracing methods provide a way to obtain model-internal evidence by tracing how information flows through model components during reasoning, constructing full reasoning circuits for long CoTs is costly and difficult to scale. To address these challenges, we propose Circuit-guided Internal-External Discrepancy Scorer (CIE-Scorer), a framework for instance-level CoT unfaithfulness detection. The key idea is that faithful reasoning traces should align with the model's computational process, whereas unfaithful traces may diverge from it. CIE-Scorer efficiently traces compact sentence-level circuits from informative reasoning tokens, constructs internal and external reasoning graphs, and measures their discrepancy using Fused Gromov--Wasserstein distance. Experiments on four datasets from FaithCoT-Bench show that CIE-Scorer achieves state-of-the-art performance while reducing the cost of circuit construction, demonstrating the effectiveness of combining mechanistic interpretability signals with external reasoning traces for CoT unfaithfulness detection.

Related papers

• The Tell-Tale Trace: Detecting Reasoning Failures in LLMs Using Chain-of-Thought Dynamics [0.9668407688201359]
  Chain-of-thought (CoT) reasoning improves large language model (LLM) performance.<n>Existing approaches that leverage verbalized CoTs to monitor reasoning correctness largely evaluate the semantic correctness or consistency of individual intermediate steps.<n>We show that capability failures can manifest as distributed, task-dependent changes in the structure of visible reasoning.
  arXiv  Detail & Related papers  (2026-08-04T08:04:36Z)
• Faithfulness as Information Flow: Evaluating and Training Faithful Chain-of-Thought Reasoning [10.87972575497941]
  Chain-of-thought (CoT) reasoning is useful for monitoring language models.<n>Models can rely on prompt-to-answer shortcuts that bypass the CoT.<n>We study CoT faithfulness through a structural information-flow perspective.
  arXiv  Detail & Related papers  (2026-05-22T23:37:29Z)
• Structural Anchors and Reasoning Fragility:Understanding CoT Robustness in LLM4Code [13.598118096561775]
  Chain-of-Thought (CoT) prompting is widely used to elicit explicit reasoning from large language models for code (LLM4Code)<n>We study how CoT reshapes internal uncertainty dynamics and why it sometimes harms rather than helps code generation.
  arXiv  Detail & Related papers  (2026-04-14T02:48:29Z)
• FACT-E: Causality-Inspired Evaluation for Trustworthy Chain-of-Thought Reasoning [49.65751420291115]
  Chain-of-Thought (CoT) prompting has improved LLM reasoning, but models often generate explanations that appear coherent while containing unfaithful intermediate steps.<n>We propose FACT-E, a causality-inspired framework for evaluating CoT quality.<n>We show that FACT-E improves reasoning-trajectory selection and yields stronger in-context learnings.
  arXiv  Detail & Related papers  (2026-04-12T15:35:08Z)
• Balancing Faithfulness and Performance in Reasoning via Multi-Listener Soft Execution [79.98699884805636]
  Reasoning Execution by Multiple Listeners (REMUL) is a multi-party reinforcement learning approach.<n>REMUL builds on the hypothesis that reasoning traces which other parties can follow will be more faithful.<n>Speakers are rewarded for producing reasoning that is clear to listeners.
  arXiv  Detail & Related papers  (2026-02-18T02:55:55Z)
• Are Reasoning LLMs Robust to Interventions on Their Chain-of-Thought? [79.86483056611105]
  Reasoning LLMs generate step-by-step chains of thought before giving an answer.<n>How robust are these reasoning traces to disruptions that occur within them?<n>We introduce a controlled evaluation framework that perturbs a model's own CoT at fixed timesteps.
  arXiv  Detail & Related papers  (2026-02-07T10:02:58Z)
• Bypassing the Rationale: Causal Auditing of Implicit Reasoning in Language Models [0.0]
  Chain-of-thought (CoT) prompting is widely used as a reasoning aid and is often treated as a transparency mechanism.<n>We introduce a causal, layerwise audit of CoT faithfulness based on activation patching.<n>We find that CoT-specific influence is typically depth-localized into narrow "reasoning windows"
  arXiv  Detail & Related papers  (2026-02-03T20:27:49Z)
• CoT-Seg: Rethinking Segmentation with Chain-of-Thought Reasoning and Self-Correction [50.67483317563736]
  This paper aims to explore a system that can think step-by-step, look up information if needed, generate results, self-evaluate its own results, and refine the results.<n>We introduce CoT-Seg, a training-free framework that rethinks reasoning segmentation by combining chain-of-thought reasoning with self-correction.
  arXiv  Detail & Related papers  (2026-01-24T11:41:54Z)
• CoG: Controllable Graph Reasoning via Relational Blueprints and Failure-Aware Refinement over Knowledge Graphs [53.199517625701475]
  CoG is a training-free framework inspired by Dual-Process Theory that mimics the interplay between intuition and deliberation.<n>CoG significantly outperforms state-of-the-art approaches in both accuracy and efficiency.
  arXiv  Detail & Related papers  (2026-01-16T07:27:40Z)
• Project Ariadne: A Structural Causal Framework for Auditing Faithfulness in LLM Agents [0.0]
  We introduce textbfProject Ariadne, a novel XAI framework to audit the causal integrity of agentic reasoning.<n>Unlike existing interpretability methods that rely on surface-level textual similarity, Project Ariadne performs textbfhard interventions ($do$-calculus) on intermediate reasoning nodes.<n>Our empirical evaluation of state-of-the-art models reveals a persistent textitFaithfulness Gap.
  arXiv  Detail & Related papers  (2026-01-05T18:05:29Z)
• Deep Hidden Cognition Facilitates Reliable Chain-of-Thought Reasoning [33.30315111732609]
  Chain of Thought (CoT) reasoning has demonstrated remarkable deep reasoning capabilities.<n>However, its reliability is often undermined by the accumulation of errors in intermediate steps.<n>This paper introduces an approach to calibrate the CoT reasoning accuracy by leveraging the model's intrinsic veracity encoding.
  arXiv  Detail & Related papers  (2025-07-14T07:41:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.