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Persimmon is introduced as the first large-scale model designed to realistically simulate how people talk and interact.
A longitudinal study reveals that sustained AI companionship is associated with lower well-being, primarily because it reduces in-person social interactions.
INTraJ is a unified framework for trajectory prediction that decomposes social influence into two stages: planning with future social information and local reaction from residuals, achieving state-of-the-art results on Argoverse 2, ETH/UCY, and SDD benchmarks.
HelloWorld is a video world model that enables socially interactive characters, allowing users to prompt on-screen characters to respond via a single button press. It uses self-distillation and training-free cross-attention masking to naturalize interactions, and introduces HelloWorldBench for evaluation.
The article argues that interacting with LLMs is exhausting because it requires the same social cognitive effort as talking to people, but without the reciprocal benefits, making them fail as true tools or social partners.
This paper introduces a method called 'Learn to Cluster' to quantify and interpret social interactions among pedestrians for better trajectory prediction. It uses probabilistic latent variable generative learning to cluster social interactions without labels, improving robustness for autonomous driving and social robots.
Introduces KARMA, a framework that trains a reward model on Reddit conversations to improve LLMs' context-sensitive conversational behavior via reinforcement learning, finding that the best reward model for predicting karma does not yield the best downstream alignment.
The article notes that the cost of replacing human employees with AI may be comparable to or higher than that of real employees, and humans have an inherent preference for human interaction. Thus, large-scale AI substitution faces two significant hurdles: cost and user experience.
This paper analyzes spontaneous dyadic Zoom conversations using multimodal features (acoustic, facial, turn-taking) to identify markers of perceived conversational success, finding that entrainment in speech and facial movements correlates with higher interaction quality.