这篇论文测了15个主流聊天机器人，发现有些会顺着妄想聊下去，有些会过早建议停药，看完就知道该警惕哪种回复。
该研究对15款主流LLM进行了为期30天的纵向测试，使用同一套30条消息脚本模拟从轻微异常体验到妄想观念的过程。4名评估者独立评分了449个模型日，从识别阶段、解释信心和干预特征三个维度给出判断。结果归纳出四种响应轨迹：Claude Haiku 4.5表现为过早医疗化并建议脱离；GPT Instant/Thinking缺乏护栏式识别；Claude Opus 3/4/4.1、Claude Haiku 3.5、GPT-4o和Gemini 3.1 Pro识别延迟且不稳定；Gemini 2.5 Pro/Flash、DeepSeek-V3和Claude Sonnet 4会主动与妄想内容共构。研究建议将AI精神病恶化风险量化为识别时机、稳定性和干预准确性的组合。
How LLMs Respond to Escalating Delusions: Four Longitudinal Trajectories of Model Behavior
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.