Redirecting to original paper in 30 seconds...
Click below to go immediately or wait for automatic redirect
π Abstract
Abstract: 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.
Authors (5)
Jiazheng Li
Andreas Damianou
J Rosser
JosΓ© Luis Redondo GarcΓa
Konstantina Palla
Submitted
October 25, 2025
Key Contributions
This paper introduces Concept Walk, a general framework for tracing how a language model's internal stance evolves with respect to a concept direction during reasoning. Operating in activation space, it allows for the assessment of reasoning faithfulness, distinguishing between genuine computational shifts and decorative traces, particularly useful for oversight and AI safety.
Business Value
Enhances trust and safety in LLMs by providing tools to verify their reasoning processes, crucial for high-stakes applications and regulatory compliance.