Large Language Models (LLMs) have revolutionized conversational AI, yet their robustness in extended multi-turn dialogues remains poorly understood. Existing evaluation frameworks focus on static benchmarks and single-turn assessments, failing to capture the temporal dynamics of conversational degradation that characterize real-world interactions. In this work, we present a large-scale survival analysis of conversational robustness, modeling failure as a time-to-event process over 36,951 turns from 9 state-of-the-art LLMs on the MT-Consistency benchmark. Our framework combines Cox proportional hazards, Accelerated Failure Time (AFT), and Random Survival Forest models with simple semantic drift features. We find that abrupt prompt-to-prompt semantic drift sharply increases the hazard of inconsistency, whereas cumulative drift is counterintuitively \emph{protective}, suggesting adaptation in conversations that survive multiple shifts. AFT models with model-drift interactions achieve the best combination of discrimination and calibration, and proportional hazards checks reveal systematic violations for key drift covariates, explaining the limitations of Cox-style modeling in this setting. Finally, we show that a lightweight AFT model can be turned into a turn-level risk monitor that flags most failing conversations several turns before the first inconsistent answer while keeping false alerts modest. These results establish survival analysis as a powerful paradigm for evaluating multi-turn robustness and for designing practical safeguards for conversational AI systems.

翻译：大语言模型（LLMs）已彻底改变了对话式人工智能，但其在扩展多轮对话中的鲁棒性仍鲜为人知。现有的评估框架侧重于静态基准和单轮评估，未能捕捉现实世界交互中对话退化的时间动态。在本研究中，我们提出了一种对话鲁棒性的大规模生存分析，将故障建模为基于MT-Consistency基准上9个最先进LLMs的36,951轮对话的“时间-事件”过程。我们的框架结合了Cox比例风险模型、加速失效时间（AFT）模型和随机生存森林模型，并引入了简单的语义漂移特征。研究发现，突发的提示间语义漂移会显著增加不一致性的风险，而累积漂移却反直觉地具有保护作用，这表明在经历多次语义转换后存续的对话中存在适应性机制。结合模型-漂移交互项的AFT模型实现了区分度与校准度的最佳平衡，比例风险检验揭示了关键漂移协变量的系统性违反，这解释了Cox类模型在此场景中的局限性。最后，我们证明轻量级AFT模型可转化为轮次级风险监控器，能在首次不一致答案出现前数轮标记大多数即将失败的对话，同时保持较低的误报率。这些结果确立了生存分析作为评估多轮鲁棒性及设计对话式人工智能系统实用保障机制的有力范式。