# Assessing Adaptive World Models in Machines with Novel Games

arXiv: https://arxiv.org/abs/2507.12821 · HTML: https://mlsanity.com/paper/2507.12821/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Placed in World models for language agents, one of 11 directions in World models.
- Builds on 29 of its 71 references that are in the map.
- First posted on arXiv 17 Jul 2025.

## Placed in

- World models / [World models for language agents](https://mlsanity.com/wm/world-models-for-language-agents/)

## Builds on (most cited first, 29 of 29)

- [Deep Reinforcement Learning with Double Q-learning](https://mlsanity.com/paper/1509.06461/) (2015)
- [World Models](https://mlsanity.com/paper/1803.10122/) (2018)
- [Meta-World: A Benchmark and Evaluation for Multi-Task and Meta Reinforcement Learning](https://mlsanity.com/paper/1910.10897/) (2019)
- [Reasoning with Language Model is Planning with World Model](https://mlsanity.com/paper/2305.14992/) (2023)
- [Model-Based Reinforcement Learning for Atari](https://mlsanity.com/paper/1903.00374/) (2019)
- [Leveraging Procedural Generation to Benchmark Reinforcement Learning](https://mlsanity.com/paper/1912.01588/) (2019)
- [Emergent World Representations: Exploring a Sequence Model Trained on a Synthetic Task](https://mlsanity.com/paper/2210.13382/) (2022)
- [Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research](https://mlsanity.com/paper/1802.09464/) (2018)
- [ARC-AGI-2: A New Challenge for Frontier AI Reasoning Systems](https://mlsanity.com/paper/2505.11831/) (2025)
- [Understanding World or Predicting Future? A Comprehensive Survey of World Models](https://mlsanity.com/paper/2411.14499/) (2024)
- [Human-Timescale Adaptation in an Open-Ended Task Space](https://mlsanity.com/paper/2301.07608/) (2023)
- [Quality and Diversity Optimization: A Unifying Modular Framework](https://mlsanity.com/paper/1708.09251/) (2017)
- [Evaluating the World Model Implicit in a Generative Model](https://mlsanity.com/paper/2406.03689/) (2024)
- [Is Sora a World Simulator? A Comprehensive Survey on General World Models and Beyond](https://mlsanity.com/paper/2405.03520/) (2024)
- [Superintelligent Agents Pose Catastrophic Risks: Can Scientist AI Offer a Safer Path?](https://mlsanity.com/paper/2502.15657/) (2025)
- [Building Machines that Learn and Think with People](https://mlsanity.com/paper/2408.03943/) (2024)
- [Robin: A multi-agent system for automating scientific discovery](https://mlsanity.com/paper/2505.13400/) (2025)
- [Rapid trial-and-error learning with simulation supports flexible tool use and physical reasoning](https://mlsanity.com/paper/1907.09620/) (2019)
- [General Video Game AI: a Multi-Track Framework for Evaluating Agents, Games and Content Generation Algorithms](https://mlsanity.com/paper/1802.10363/) (2018)
- [Human-Level Reinforcement Learning through Theory-Based Modeling, Exploration, and Planning](https://mlsanity.com/paper/2107.12544/) (2021)
- [Investigating Human Priors for Playing Video Games](https://mlsanity.com/paper/1802.10217/) (2018)
- [GAVEL: Generating Games Via Evolution and Language Models](https://mlsanity.com/paper/2407.09388/) (2024)
- [Measuring General Intelligence with Generated Games](https://mlsanity.com/paper/2505.07215/) (2025)
- [General agents contain world models](https://mlsanity.com/paper/2506.01622/) (2025)
- [Goals as Reward-Producing Programs](https://mlsanity.com/paper/2405.13242/) (2024)
- [Are Large Language Models Reliable AI Scientists? Assessing Reverse-Engineering of Black-Box Systems](https://mlsanity.com/paper/2505.17968/) (2025)
- [Invention as a Combinatorial Process: Evidence from U.S. Patents](https://mlsanity.com/paper/1406.2938/) (2014)
- [ACES: Generating Diverse Programming Puzzles with with Autotelic Generative Models](https://mlsanity.com/paper/2310.10692/) (2023)
- [People use fast, goal-directed simulation to reason about novel games](https://mlsanity.com/paper/2407.14095/) (2024)

## Built on by (most cited first, 20 of 20)

- [3D and 4D World Modeling: A Survey](https://mlsanity.com/paper/2509.07996/) (2025)
- [Code World Models for General Game Playing](https://mlsanity.com/paper/2510.04542/) (2025)
- [AutoEnv: Automated Environments for Measuring Cross-Environment Agent Learning](https://mlsanity.com/paper/2511.19304/) (2025)
- [Act2Goal: From World Model To General Goal-conditioned Policy](https://mlsanity.com/paper/2512.23541/) (2025)
- [stable-worldmodel: A Platform for Reproducible World Modeling Research and Evaluation](https://mlsanity.com/paper/2605.21800/) (2026)
- [AI Gamestore: Scalable, Open-Ended Evaluation of Machine General Intelligence with Human Games](https://mlsanity.com/paper/2602.17594/) (2026)
- [One Life to Learn: Inferring Symbolic World Models for Stochastic Environments from Unguided Exploration](https://mlsanity.com/paper/2510.12088/) (2025)
- [Benchmarking World-Model Learning with Environment-Level Queries](https://mlsanity.com/paper/2510.19788/) (2025)
- [Tycho: Active Abstraction with Programmatic World Models for ARC-AGI-3](https://mlsanity.com/paper/2607.28287/) (2026)
- [BuilderBench: The Building Blocks of Intelligent Agents](https://mlsanity.com/paper/2510.06288/) (2025)
- [Graph-Based Exploration for ARC-AGI-3 Interactive Reasoning Tasks](https://mlsanity.com/paper/2512.24156/) (2025)
- [The ARC of Progress towards AGI: A Living Survey of Abstraction and Reasoning](https://mlsanity.com/paper/2603.13372/) (2026)
- [Evaluating Language Models' Evaluations of Games](https://mlsanity.com/paper/2510.10930/) (2025)
- [From Generative Engines to Actionable Simulators: The Imperative of Physical Grounding in World Models](https://mlsanity.com/paper/2601.15533/) (2026)
- [Scalable Environments Drive Generalizable Agents](https://mlsanity.com/paper/2605.18181/) (2026)
- [Hierarchical Experimentalist Agents](https://mlsanity.com/paper/2606.29315/) (2026)
- [Mini Amusement Parks (MAPs): A Testbed for Modelling Business Decisions](https://mlsanity.com/paper/2511.15830/) (2025)
- [Foundation World Models for Agents that Learn, Verify, and Adapt Reliably Beyond Static Environments](https://mlsanity.com/paper/2602.23997/) (2026)
- [Training Language Agents to Learn from Experience](https://mlsanity.com/paper/2605.20477/) (2026)
- [Hypothesis Generation and Inductive Inference in Children and Language Models](https://mlsanity.com/paper/2605.24528/) (2026)

## Abstract (arXiv)

Human intelligence exhibits a remarkable capacity for rapid adaptation and effective problem-solving in novel and unfamiliar contexts. We argue that this profound adaptability is fundamentally linked to the efficient construction and refinement of internal representations of the environment, commonly referred to as world models, and we refer to this adaptation mechanism as world model induction. However, current understanding and evaluation of world models in artificial intelligence (AI) remains narrow, often focusing on static representations learned from training on massive corpora of data, instead of the efficiency and efficacy in learning these representations through interaction and exploration within a novel environment. In this Perspective, we provide a view of world model induction drawing on decades of research in cognitive science on how humans learn and adapt so efficiently; we then call for a new evaluation framework for assessing adaptive world models in AI. Concretely, we propose a new benchmarking paradigm based on suites of carefully designed games with genuine, deep and continually refreshing novelty in the underlying game structures -- we refer to this class of games as novel games. We detail key desiderata for constructing these games and propose appropriate metrics to explicitly challenge and evaluate the agent's ability for rapid world model induction. We hope that this new evaluation framework will inspire future evaluation efforts on world models in AI and provide a crucial step towards developing AI systems capable of human-like rapid adaptation and robust generalization -- a critical component of artificial general intelligence.