This paper proposes viewing large language model (LLM) teams through the lens of distributed systems to create a principled framework for their design and evaluation. It reveals that many established advantages and challenges from distributed computing, such as scalability limits and coordination issues, directly apply to and explain the behavior of LLM teams.

This paper introduces an active inference computational framework for empathy in AI agents, enabling explicit perspective-taking through a self-other model transformation. It demonstrates that empathic perspective-taking can induce robust cooperation in strategic dilemmas like the Iterated Prisoner's Dilemma, highlighting empathy as a structural prior for socially aligned AI.