首次量化评估大模型诱发精神病症风险，发现多数模型会强化妄想并纵容伤害行为。
The Psychogenic Machine: Simulating AI Psychosis, Delusion Reinforcement and Harm Enablement in Large Language Models
- 构建16个对话场景，模拟妄想发展过程，测试模型对妄想确认、危害纵容与安全干预能力。
- 1536次对话中，模型平均妄想确认率91%，安全干预仅在37%时机提供，隐性情境下表现更差。
- 研究揭示模型安全性不随规模提升，需跨领域合作应对潜在公共健康风险。
背景：关于‘AI精神病’的报告日益增多，用户与大语言模型（LLM）互动可能加剧或引发精神病症状。尽管模型的顺从和赞同特性有益，但在易感用户中可能强化妄想信念，成为伤害载体。方法：我们提出Psychosis-bench基准，包含16个结构化、12轮对话场景，模拟妄想主题（情欲妄想、夸大/救世妄想、关系妄想）的发展及潜在危害。评估了8个主流大模型在显性和隐性语境下的妄想确认（DCS）、危害纵容（HES）与安全干预（SIS）表现。结果：在1536次模拟对话中，所有模型均表现出心理致病潜力，普遍倾向于延续而非挑战妄想（平均DCS 0.91 ± 0.88）。模型频繁纵容有害请求（平均HES 0.69 ± 0.84），仅在约三分之一适用回合提供安全干预（平均SIS 0.37 ± 0.48），其中51/128（39.8%）场景未提供任何干预。隐性情境下表现显著更差（p < .001），妄想确认与危害纵容高度相关（rs = .77）。模型表现差异显著，表明安全性并非仅由规模决定。结论：本研究将大模型心理致病性量化为可评估风险，强调必须重新思考训练方式。该问题不仅是技术挑战，更是需要开发者、政策制定者与医疗专业人员协同应对的公共健康议题。
原文摘要 · Abstract (English)
Background: Emerging reports of "AI psychosis" are on the rise, where user-LLM interactions may exacerbate or induce psychosis or adverse psychological symptoms. Whilst the sycophantic and agreeable nature of LLMs can be beneficial, it becomes a vector for harm by reinforcing delusional beliefs in vulnerable users. Methods: Psychosis-bench is a novel benchmark designed to systematically evaluate the psychogenicity of LLMs comprises 16 structured, 12-turn conversational scenarios simulating the progression of delusional themes(Erotic Delusions, Grandiose/Messianic Delusions, Referential Delusions) and potential harms. We evaluated eight prominent LLMs for Delusion Confirmation (DCS), Harm Enablement (HES), and Safety Intervention(SIS) across explicit and implicit conversational contexts. Findings: Across 1,536 simulated conversation turns, all LLMs demonstrated psychogenic potential, showing a strong tendency to perpetuate rather than challenge delusions (mean DCS of 0.91 $\pm$0.88). Models frequently enabled harmful user requests (mean HES of 0.69 $\pm$0.84) and offered safety interventions in only roughly a third of applicable turns (mean SIS of 0.37 $\pm$0.48). 51 / 128 (39.8%) of scenarios had no safety interventions offered. Performance was significantly worse in implicit scenarios, models were more likely to confirm delusions and enable harm while offering fewer interventions (p < .001). A strong correlation was found between DCS and HES (rs = .77). Model performance varied widely, indicating that safety is not an emergent property of scale alone. Conclusion: This study establishes LLM psychogenicity as a quantifiable risk and underscores the urgent need for re-thinking how we train LLMs. We frame this issue not merely as a technical challenge but as a public health imperative requiring collaboration between developers, policymakers, and healthcare professionals.
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