Title: Investigating the Link Between Representational Similarity and Model Interactions
Primary area: alignment, fairness, safety, privacy, and societal considerations
Keywords: Representational similarity, multi-agent system, cooperation, creativity

Abstract: Researchers have shown that neural similarity among humans predicts social closeness and cooperative success, whereas innovation often emerges from interactions among dissimilar individuals. We investigate whether these principles extend to artificial intelligence by examining interactions between large language models (LLMs). In our experiments, 276 model pairs interact across eight collaborative tasks spanning both cooperation and novelty. We find that pairs with more similar representation spaces achieve significantly higher cooperation but exhibit reduced novelty and creativity. These findings suggest that representational similarity can be an important consideration in multi-agent system design.

TL;DR: We examine whether representational similarity can predict the interactive behaviors of models.