Abstract

Chain-of-Thought (CoT) prompting has emerged as a foundational technique for eliciting reasoning from Large Language Models (LLMs), yet the robustness of this approach to corruptions in intermediate reasoning steps remains poorly understood. This paper presents a comprehensive empirical evaluation of LLM robustness to a structured taxonomy of 5 CoT perturbation types:\textit {MathError, UnitConversion, Sycophancy, SkippedSteps,} and\textit {ExtraSteps}. We evaluate 13 models spanning three orders of magnitude in parameter count (3B to 1.5 T\footnote {Assumed parameter count of closed models}), testing their ability to complete mathematical reasoning tasks despite perturbations injected at different points in the reasoning chain. Our key findings reveal heterogeneous vulnerability patterns: MathError perturbations produce the most severe degradation in small models (50-60\% accuracy loss) but show …

Date

2026

Authors

Ashwath Vaithinathan Aravindan, Mayank Kejriwal

Journal

arXiv e-prints

Pages

arXiv: 2603.03332