Paper Detail
Jeffrey M. Girard, Jason Z. Zheng, Jacqueline R. Vertino, Antony D'Avirro, Benjamin Peloquin
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.
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@article{girard2026friendbench,
title = {FriendBench: Benchmarking Dyadic Familiarity Inference in Humans and Multimodal Large Language Models},
author = {Jeffrey M. Girard and Jason Z. Zheng and Jacqueline R. Vertino and Antony D'Avirro and Benjamin Peloquin},
year = {2026},
abstract = {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 th},
url = {https://arxiv.org/abs/2607.29602},
keywords = {cs.CL, cs.AI, cs.CV, cs.HC},
eprint = {2607.29602},
archiveprefix = {arXiv},
}
{}