Symbolic World Models - Top Piriyakulkij

Wasu "Top" Piriyakulkij, PhD student at Cornell University advised by Kevin Ellis, discusses his paper "PoE-World: Compositional World Modeling with Products of Programmatic Experts." The episode explores how symbolic, programmatic world models can achieve strong generalization and sample efficiency by composing many small causal programs instead of learning a single monolithic model.

The conversation traces how PoE-World emerged from earlier work on active concept learning and hypothesis testing, and how object-centric Atari environments became a natural testbed for scaling symbolic world models beyond grid worlds. Piriyakulkij reflects on design failures, surprising successes, and the moment the learned world model became interactive enough to serve as a real-time simulator.

In This Episode -

• Symbolic vs. neural world models

• Products of programmatic experts

• Modular causal rules as world models

• Object-centric Atari environments

• Montezuma’s Revenge as exploration benchmark

• Sample-efficient learning from demonstrations

• Weights as expert confidence signals

• World models as executable simulators

• Exploration as program testing

References -

• WorldCoder - https://arxiv.org/abs/2402.12275

• Object-Centric Atari - https://arxiv.org/abs/2306.08649v2

• ARC-AGI-3 - https://arcprize.org

• VisualPredicator - https://arxiv.org/abs/2410.23156

• People: Marvin Minsky, François Chollet, Armando Solar-Lezama

About the Paper -

"PoE-World: Compositional World Modeling with Products of Programmatic Experts"

Authors: Wasu Top Piriyakulkij, Yishou Wang, Hao Tang, Martha Lewis, Kevin Ellis

The paper introduces a symbolic world modeling framework in which many small, interpretable programs - each encoding a simple causal rule - are combined multiplicatively into a probabilistic world model. By learning weights over these programmatic experts from limited demonstrations, the system produces accurate, stochastic simulators that generalize to new environments with minimal data.

https://arxiv.org/abs/2505.10819

About the Guest -

Wasu Top Piriyakulkij is a PhD student at Cornell University advised by Kevin Ellis. His research focuses on symbolic world models, program synthesis, and human-like learning and exploration in artificial agents. He is particularly interested in how compositional structure enables generalization in complex environments.

• https://www.cs.cornell.edu/~wp237/

• https://scholar.google.com/citations?user=nlO1TkkAAAAJ&hl=en

Credits -

• Host & Music: Bryan Landers, Technical Staff, Ndea

• Editor: Alejandro Ramirez

• https://x.com/ndea

• https://x.com/bryanlanders

• https://ndea.com