试试看这个新基准，FriendBench测AI能不能从20秒对话看出两人是不是熟人。最强模型和人类打了个平手，但AI有偏见：拿不准就说是陌生人。
FriendBench 是一个新基准，要求从20秒破冰对话片段判断两人是否熟识。研究对比了7家公司的26个模型与人类受试者在96组对话上的表现。最佳模型与人类在准确率上无显著差异，但模型更倾向于回答“陌生人”。音频和视频信息对模型和人类的好处不同，只有人类能从可见行为中获益。论文公开了全部刺激材料、人类评分和模型预测。
FriendBench: Benchmarking Dyadic Familiarity Inference in Humans and Multimodal Large Language Models
Reading a social situation often depends on behavior, not words alone. We introduce FriendBench, a benchmark for inferring whether two people are already familiar or are meeting as strangers, from a 20-second clip of a dyadic ice-breaker conversation. Every pair answers the same type of prompt, so only the manner of interaction can reveal the answer. Across text, audio, and video, we compare 26 models from seven companies against matched human panels over 96 balanced dyads. The best model and the human crowd are statistically indistinguishable on accuracy in every modality, but reach it differently: humans stay balanced across the two answers, while the strongest models lean toward "stranger"---a difference in effective prior, not discrimination. Richer channels help both unequally, and only humans gain from visible behavior on top of speech. We release the stimuli, human ratings, and model predictions.