FriendBench: Benchmarking Dyadic Familiarity Inference in Humans and Multimodal Large Language Models

Understanding social relationships from brief interactions is a subtle human skill; this paper asks whether AI models can match it. FriendBench tests whether models can tell if two people in a 20-second video clip are already friends or just meeting, using text, audio, and video across 26 models from seven companies. While top models match human accuracy, they show a different bias --- leaning toward guessing \"strangers.\" Applications include social robotics, video-conferencing analytics, and multimodal AI assistants that need to correctly infer relationship context from short behavioral cues rather than explicit words. Authors: Jeffrey M. Girard, Jason Z. Zheng, Jacqueline R. Vertino, Paper: https://arxiv.org/abs/2607.29602v1