Temporal coupling as a design principle for social AI

Résumé fourni par la source

Despite their ability to emulate social reasoning, current AI systems consistently fail at the dynamic, real-time coordination that defines human social interaction. We argue this failure reflects a missing aspect in their design, not a limitation of scale or training data. Current AI systems model their partner from the outside and train on the data from interactions. Social intelligence, however, is not a property of an individual. It emerges when two coupled individuals interact and is developed through that coupling. We here argue that social AI should be built, and evaluated, as a coupled system with a human which targets an optimal, rather than maximal, alignment. This argument is based on two principles from research in social neuroscience. First, the training objective must change from predicting their interacting partner to a directed coupling that optimizes synchrony. Second, optimizing synchrony requires an embodied and organized temporal architecture which separates faster interactive generation with slower contextual comprehension. We show how coupling can be measured against benchmarks of human interaction and argue that this framing is a safety requirement. Together, these principles offer a neurobiologically grounded pathway toward AI systems capable of real-time social coordination with human partners.

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