[2603.26846] Stable Reasoning, Unstable Responses: Mitigating LLM Deception via Stability Asymmetry

Computer Science > Machine Learning arXiv:2603.26846 (cs) [Submitted on 27 Mar 2026] Title:Stable Reasoning, Unstable Responses: Mitigating LLM Deception via Stability Asymmetry Authors:Guoxi Zhang, Jiawei Chen, Tianzhuo Yang, Lang Qin, Juntao Dai, Yaodong Yang, Jingwei Yi View a PDF of the paper titled Stable Reasoning, Unstable Responses: Mitigating LLM Deception via Stability Asymmetry, by Guoxi Zhang and 6 other authors View PDF HTML (experimental) Abstract:As Large Language Models (LLMs) expand in capability and application scope, their trustworthiness becomes critical. A vital risk is intrinsic deception, wherein models strategically mislead users to achieve their own objectives. Existing alignment approaches based on chain-of-thought (CoT) monitoring supervise explicit reasoning traces. However, under optimization pressure, models are incentivized to conceal deceptive reasoning, rendering semantic supervision fundamentally unreliable. Grounded in cognitive psychology, we hypothesize that a deceptive LLM maintains a stable internal belief in its CoT while its external response remains fragile under perturbation. We term this phenomenon stability asymmetry and quantify it by measuring the contrast between internal CoT stability and external response stability under perturbation. Building on this structural signature, we propose the Stability Asymmetry Regularization (SAR), a novel alignment objective that penalizes this distributional asymmetry during reinforcement le...