Advancing Oversight Reasoning across Languages for Audit Sycophantic Behaviour via X-Agent

Large language models (LLMs) have demonstrated capabilities that are highly satisfactory to a wide range of users by adapting to their culture and wisdom. Yet, this can translate into a propensity to produce responses that align with users' viewpoints, even when the latter are wrong. This behaviour is known as sycophancy, the tendency of LLMs to generate misleading responses as long as they align with the user's, inducing bias and reducing reliability. To make interactions consistent, reliable and safe, we introduce X-Agent, an Oversight Reasoning framework that audits human-model dialogues, reasons about them, captures sycophancy and corrects the final outputs. Concretely, X-Agent extends debate-based frameworks by (i) auditing user-model conversations, (ii) applying a defence layer that steers model behaviour and goes beyond user beliefs, and (iii) extracting reasoning traces from evaluations that serve as training signals for mitigating sycophancy, all in a completely unsupervised way. We evaluate X-Agent across diverse scenarios and languages, showing that it consistently detects sycophancy, reduces unwarranted agreement, and improves crossturn consistency, advancing a reasoning-asoverview paradigm for safer user-model and model-model interaction. Our approach introduces a novel paradigm in which reasoning is not merely a means to solve problems, but as a mechanism for overseeing the problem-solving processes of other models.