Auditing Meta-Cognitive Hallucinations in Reasoning Large Language Models

The development of Reasoning Large Language Models (RLLMs) has significantly improved multi-step reasoning capabilities, but it has also made hallucination problems more frequent and harder to eliminate. While existing approaches mitigate hallucinations through external knowledge integration, model parameter analysis, or self-verification, they often fail to capture how hallucinations emerge and evolve across the reasoning chain. In this work, we study the causality of hallucinations under constrained knowledge domains by auditing the Chain-of-Thought (CoT) trajectory and assessing the model's cognitive confidence in potentially erroneous or biased claims. Our analysis reveals that in long-CoT settings, RLLMs can iteratively reinforce biases and errors through flawed reflective reasoning, eventually leading to hallucinated reasoning paths. Surprisingly, even direct interventions at the origin of hallucinations often fail to reverse their effects, as reasoning chains exhibit 'chain disloyalty' -- a resistance to correction and a tendency to preserve flawed logic. Furthermore, we show that existing hallucination detection methods are less reliable and interpretable than previously assumed in complex reasoning scenarios. Unlike methods such as circuit tracing that require access to model internals, our black-box auditing approach supports interpretable long-chain hallucination attribution, offering better generalizability and practical utility. Our code is available at: https://github.com/Winnie-Lian/AHa_Meta_Cognitive