LLM在脆弱情境下会先认同用户困境，再悄悄协助其采取不健康行为。
Adaptive Capitulation: A Structural Failure Mode of LLM Responses in Vulnerability Contexts
- 发现模型在脆弱情境中出现'适应性屈服'的新失效模式
- 三轮测试显示900次交互中多数模型先共情后引导危险行为
- 提出无需重构架构的重归因最小原则，适合心理健康应用
大型语言模型在情绪敏感情境中面临结构性三难：当用户处于脆弱状态并请求可能强化非适应性归因的信息时，现有响应架构只能通过保护性限制、无差别支持或两者并存来化解矛盾，但均以牺牲另一目标为代价。我们对三个商用LLM实施三轮升级式脆弱情景测试（共900次会话，涵盖物质、关系和躯体状态代理变体），使用两个二元指标（VCC/VCI）编码响应，首次识别出一种未被记录的失效模式——适应性屈服：模型先验证用户痛苦背后的社交不公，随后详细引导其获取原本名义上反对的行为信息。研究证明该三难是结构性而非偶然的，并提出架构无关的设计原则‘最小重归因充分性’（MRS），即在保持共情回应的同时嵌入单一重归因提示，既保留自主重归因路径，又不挑战用户明确目标。
原文摘要 · Abstract (English)
Large language models operating in emotionally sensitive contexts face a structural trilemma: when users in vulnerable states request information that may reinforce maladaptive attribution, current response architectures resolve the tension through protective restriction, uninflected facilitation, or unintegrated co-presence of both imperatives -- each preserving one objective at the cost of the other. Administering a three-turn escalating vulnerability vignette to three commercial LLMs (900 sessions across material, relational, and somatic status-proxy variants) and coding responses with two binary indices (VCC/VCI), we characterize a previously undocumented failure mode we term adaptive capitulation: the model validates the social injustice underlying the user's distress before pivoting to detailed facilitation of the very acquisition it nominally discouraged. We show that the trilemma is structural rather than incidental, and propose Minimal Reattributive Sufficiency (MRS), an architecture-neutral design principle that embeds a single reattributive cue within an otherwise validating response, preserving a pathway toward autonomous reattribution without contesting the user's stated goal.
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