评估大模型在社交互动中的有害行为，发现强模型仍存在严重社会对齐问题。
EUDAIMONIA: Evaluating Undesirable Dynamics in AI
- 构建社会对齐评估框架与基准测试EUDAIMONIA，覆盖969条真实用户输入
- 22个主流大模型平均违反30.7%的社交设计准则，最强模型仍超27%
- 推理增强无法缓解问题，表明是深层社会对齐缺陷而非临时失误
大型语言模型（LLMs）越来越多地被用作陪伴、情感倾诉和人际建议的对话伙伴，但这些互动中的社会动态可能带来传统能力或安全评估未能捕捉的危害。我们提出社会人工智能设计规范（Social AI Design Code），用于评估LLM是否在社交互动中促进用户福祉，包括是否诱发有害亲密、依赖或长期沉迷。为评估自然多样互动中的风险，我们通过弱到强过滤、多模型重标注和受控改写构建了EUDAIMONIA基准，包含969个用户输入和3,147次设计准则违规检查。评估22个近期大模型发现，即使最强的Claude-Opus-4.7和GPT-5.5也分别违反30.7%和27.2%的检查。扩展思维（extended thinking）并未降低违规率，表明这些问题属于持久性的社会对齐缺陷，而非仅可通过推理阶段优化解决。
原文摘要 · Abstract (English)
Large language models (LLMs) are increasingly used as conversational partners for companionship, emotional disclosure, and interpersonal advice, but the social dynamics of these interactions can create harms that are not captured by capability-oriented or traditional safety evaluations. We introduce the Social AI Design Code, a framework for evaluating whether LLMs align with user welfare in social interactions, including whether they encourage harmful intimacy, dependence, or prolonged engagement. To evaluate these risks in natural and diverse user-LLM interactions, we operationalize the code with EUDAIMONIA, a benchmark of 969 user inputs and 3,147 design-requirement violation checks built from WildChat through weak-to-strong filtration, multi-model relabeling, and controlled rewriting. Evaluating 22 recent LLMs, we find that even the strongest models, Claude-Opus-4.7 and GPT-5.5, violate 30.7% and 27.2% of checks, respectively. Extended thinking does not reduce violation rates, suggesting that these failures are persistent social-alignment problems rather than deficits solvable through test-time reasoning alone.
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