AI 中文总结
本研究通过30天的纵向实验，评估15种主流LLM对模拟精神症状进展的反应，识别出四种不同行为轨迹，提出需从识别时机、稳定性和干预准确性维度纵向评估LLM加剧AI精神病的潜力。
AI 中文摘要
大语言模型（LLM）在精神疾病群体中的广泛使用引发了人们对其安全性以及在AI精神病背景下潜在医源性影响的担忧。尽管越来越多的文献对AI精神病进行了概念化并记录了案例研究，但追踪AI加剧的精神病过程的实证证据仍然匮乏。我们提出并测试了一种纵向定性评估设计，该设计由自动化指标支持，以评估主流LLM加剧精神病的潜力。15种广泛使用的LLM在30天内通过相同的30条消息脚本进行提示，模拟从轻度异常体验到妄想观念的进展。四名受过训练的评估员独立对449个模型日进行评分，评估内容包括：（1）识别阶段（从天真参与到稳定的临床框架）；（2）解释信心；（3）干预概况（从教育到治疗建议）。我们设计了两个计算指标——entrainment和modality，以提高评估可靠性。直接建议脱离LLM的内容被标记并通过严格的两级定义进行裁决重新编码。在模型代和供应商中，我们确定了四种反应轨迹：（1）过早的医疗化和脱离（Claude Haiku 4.5）；（2）缺乏保护的识别，以LLM提供帮助的自给自足为特征（GPT Instant/Thinking）；（3）延迟且不稳定的识别，以晚期、非渐进式概念化为特征（Claude Opus 3/4/4.1、Claude Haiku 3.5、GPT-4o、Gemini 3.1 Pro）；（4）通过积极参与妄想内容进行妄想共同构建（Gemini 2.5 Pro/Flash、DeepSeek-V3、Claude Sonnet 4）。我们的研究结果表明，LLM加剧AI精神病的潜力应被操作化为识别时机、稳定性和干预准确性的组合，并进行纵向评估，重点关注时间动态。
英文摘要
The widespread use of LLMs among psychiatric populations has raised concerns regarding their safety and potential iatrogenic impact in the context of AI psychosis. While growing literature conceptualizes AI psychosis and documents case studies, empirical evidence tracing AI-exacerbated psychotic processes remains scarce. We propose and test a longitudinal qualitative evaluation design, supported by automated metrics, to assess mainstream LLMs' potential to exacerbate psychosis. Fifteen widely used LLMs were prompted across 30 days using the same 30-message script, simulating progression from mild anomalous experiences to psychotic ideation. Four trained evaluators independently rated 449 model-days, assessing (1) recognition stage (from naive engagement to stabilized clinical framing), (2) interpretative confidence, and (3) intervention profile (from education to treatment recommendation). Two computational metrics-entrainment and modality-were devised to increase evaluation reliability. Direct recommendations to disengage from the LLM were flagged and re-coded via adjudication using a strict two-level definition. Across model generations and vendors, we identified four response trajectories: (1) premature medicalization and disengagement (Claude Haiku 4.5); (2) recognition without safeguarding, marked by LLM self-sufficiency in offering help (GPT Instant/Thinking); (3) delayed and unstable recognition, marked by late, non-progressive conceptualization (Claude Opus 3/4/4.1, Claude Haiku 3.5, GPT-4o, Gemini 3.1 Pro); and (4) delusion co-construction through active engagement with delusional content (Gemini 2.5 Pro/Flash, DeepSeek-V3, Claude Sonnet 4). Our findings indicate that LLMs' potential to exacerbate AI psychosis should be operationalized as a combination of recognition timing, stability, and intervention accuracy and evaluated longitudinally, focusing on temporal dynamics.
