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Explore a 16-minute conference presentation from OOPSLA 2025 that introduces memo, a revolutionary domain-specific probabilistic programming language designed specifically for modeling theory of mind and recursive probabilistic reasoning. Learn how researchers from MIT have addressed two critical challenges in computational modeling of human thinking: the programming complexity of "thinking about thinking" and the computational inefficiency of existing approaches. Discover memo's specialized syntax and semantics that make theory of mind programming more intuitive, and understand its unique inference approach that leverages array programming to achieve dramatic performance improvements on modern hardware. Examine how this language enables practitioners to write significantly faster models with substantially less code, and see why multiple research groups have already adopted memo for their work. Gain insights into the intersection of cognitive science, probabilistic programming, and computational efficiency through this presentation by Kartik Chandra, Tony Chen, Joshua B. Tenenbaum, and Jonathan Ragan-Kelley, complete with reproducible artifacts and supplementary materials for hands-on exploration.
