The human ability to think about thinking (“theory of mind”) is a fundamental object of study in many disciplines. In recent decades, researchers across these disciplines have converged on a rich computational paradigm for modeling theory of mind, grounded in recursive probabilistic reasoning. However, practitioners often find programming in this paradigm challenging: first, because thinking-about-thinking is confusing for programmers, and second, because models are slow to run. This paper presents memo , a new domain-specific probabilistic programming language that overcomes these challenges: first, by providing specialized syntax and semantics for theory of mind, and second, by taking a unique approach to inference that scales well on modern hardware via array programming. memo enables practitioners to write dramatically faster models with much less code, and has already been adopted by several research groups.

A single panel of a comic book can say a lot: it can depict not only where the characters currently are, but also their motions, their motivations, their emotions, and what they might do next. More generally, humans routinely infer complex sequences of past and future events from a *static snapshot* of a *dynamic scene*, even in situations they have never seen before. In this paper, we model how humans make such rapid and flexible inferences. Building on a long line of work in cognitive science, we offer a Monte Carlo algorithm whose inferences correlate well with human intuitions in a wide variety of domains, while only using a small, cognitively-plausible number of samples. Our key technical insight is a surprising connection between our inference problem and Monte Carlo path tracing, which allows us to apply decades of ideas from the computer graphics community to this seemingly-unrelated theory of mind task.