一句话：提出领域特定概率编程语言 memo，以专用语法语义和数组编程推断，支持心智理论建模。
心智理论（theory of mind）建模通常基于递归概率推理，但实践者编写此类模型十分困难：思考“思考”本身令人困惑，且模型运行缓慢。论文提出 memo，一种面向心智理论的领域特定概率编程语言：一方面为 theory of mind 提供专用语法与语义，降低建模难度；另一方面采用独特的推断方法，通过数组编程（array programming）在现代硬件上良好扩展。memo 让研究者用更少代码写出显著更快的模型，并已被多个研究小组采用。
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.
@article{paperbot2895,
title = {A Domain-Specific Probabilistic Programming Language for Reasoning about Reasoning (Or: A Memo on memo)},
author = {Kartik Chandra and Tony Chen and Joshua B. Tenenbaum and Jonathan Ragan-Kelley},
journal = {Proceedings of the ACM on Programming Languages},
volume = {9},
number = {OOPSLA2},
year = {2025},
doi = {10.1145/3763078}
}