Assessing World Models In Machines | ARC Prize @ MIT

2025-10-31 · Source: ARC Prize · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Mathematics & Computational Sciences · Depth: Expert, medium

Summary

A position paper, co-authored by Katie Collins, Lancing, and Jacob Francois, advocates for using games as a benchmark for artificial intelligence, emphasizing that human intelligence extends beyond game-playing. The paper explores hierarchical structures and abstraction within world models, particularly how systems learn, transfer knowledge, and adapt across tasks and domains in video game environments. It draws parallels to 20-year-old concepts of hierarchical hypothesis bases and Bayesian models of cognition, stressing the importance of learning and inference at multiple abstraction levels. Furthermore, the paper discusses novelty generation and problem-making in games, suggesting that AI benchmarking should consider how systems create new challenges, not just solve existing ones. This concept is extended to advanced human intellectual activities, such as fundamental mathematics, where researchers like Katie Collins apply similar principles of play and problem formulation.

Key takeaway

For AI scientists developing advanced cognitive systems, consider integrating game-based benchmarks that probe hierarchical abstraction and problem-making, not just problem-solving. Your evaluation metrics should extend beyond task completion to assess an AI's ability to generate novel challenges and transfer knowledge across varied domains, mirroring human intellectual activity in fields like mathematics.

Key insights

Games offer a robust benchmark for AI, revealing hierarchical abstraction and problem-making capabilities beyond mere problem-solving.

Principles

• Human intelligence transcends game-playing.
• World models require multi-level abstraction.
• Problem-making is as vital as problem-solving.

Method

The paper proposes using games to benchmark AI by evaluating hierarchical learning, knowledge transfer, and the generation of novel challenges and problem spaces.

In practice

• Design AI benchmarks using diverse game structures.
• Integrate novelty generation into AI evaluation.
• Explore abstraction hierarchies in AI world models.

Topics

• World Models
• Game Benchmarks
• Hierarchical Abstraction
• Problem Making
• Novelty Generation

Best for: AI Scientist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by ARC Prize.