Benchmarking World-Model Learning with Environment-Level Queries
Proposes WorldTest protocol to evaluate AI models' generality via environment-level queries; humans outperform AI in experiments.
Key Findings
Methodology
WorldTest protocol evaluates AI models' ability to handle environment-level queries through interaction and test phases. Interaction phase is reward-free; test phase uses modified challenge environments. AutumnBench, as a concrete implementation, includes 43 grid-world environments and 129 tasks.
Key Results
- Humans significantly outperform five frontier AI models on AutumnBench, especially in exploration and belief updating.
- Experiments show humans excel over AI in 129 tasks, particularly in prediction and planning tasks.
- AI models perform poorly in detecting changes in environment dynamics, indicating a lack of structural understanding.
Significance
This study provides a new framework for evaluating AI models' generality, emphasizing performance on diverse environment-level queries rather than task-specific rewards. Comparison with humans reveals current AI models' deficiencies in exploration and belief updating.
Technical Contribution
Introduces a reward-free, representation-agnostic evaluation framework allowing broader assessment of AI models. AutumnBench offers an extensible benchmark supporting tests of various environment dynamics and learning mechanisms.
Novelty
First to evaluate AI models' learning abilities via environment-level queries, differing from traditional task-specific evaluation methods by emphasizing model generality and structural understanding.
Limitations
- The current framework primarily tests in grid-world environments, which may not apply to more complex real-world scenarios.
- AI models' performance in the exploration phase is limited by computational resources and strategy design.
Future Work
Future work can extend to more complex environments and tasks, exploring different AI models' performance in diverse environments to further close the gap with humans.
AI Executive Summary
World models are central to building AI agents capable of flexible reasoning and planning. However, current evaluations are limited to properties measurable from observed interactions, such as next-frame prediction or task return, and do not test whether a learned model supports diverse queries about the environment. Humans, in contrast, build general-purpose models that can answer many different questions about an environment, including those requiring understanding of global structure and counterfactual consequences.
We propose WorldTest: a protocol for evaluating whether agents learn models that support multiple environment-level queries—questions whose answers depend on properties of the full environment, not just observed trajectories. We instantiate WorldTest as AutumnBench, a benchmark of 43 interactive grid-world environments and 129 tasks across three query families for both humans and learning agents. Experiments with 517 human participants and five frontier models show that humans substantially outperform these models, a gap we attribute to differences in exploration and belief updating.
AutumnBench provides a framework for evaluating world-model learning in grid-world environments with environment-level queries, and WorldTest provides a template for extending such evaluations to richer domains. We believe that learning models that support diverse environment-level queries is pivotal for the next step in AI progress.
Deep Analysis
Background
World models are crucial in AI agents' reasoning and planning. Traditional reinforcement learning benchmarks mainly assess agents through task-specific rewards, failing to directly test if agents learn the environment structure. Cognitive science shows humans can build flexible, predictive world models, offering important insights for AI development.
Core Problem
Current AI evaluation methods are limited to observed interaction properties, failing to test if models support diverse environment queries. The lack of evaluation for model generality and structural understanding limits AI's application in complex environments.
Innovation
WorldTest protocol evaluates AI models' learning abilities via environment-level queries, emphasizing model generality and structural understanding. AutumnBench, as a concrete implementation, provides an extensible benchmark supporting tests of various environment dynamics and learning mechanisms.
Methodology
- �� Interaction Phase: Agents autonomously explore the environment without external rewards, building internal models.
- �� Test Phase: Evaluation through modified challenge environments, testing agents' environment-level query abilities.
- �� AutumnBench: Includes 43 grid-world environments and 129 tasks, covering prediction, planning, and change detection.
Experiments
Experiments conducted on the AutumnBench benchmark, including 43 grid-world environments and 129 tasks. Participants include 517 humans and five frontier AI models. Evaluation is conducted through interaction and test phases, assessing agents' performance in various tasks.
Results
Humans significantly outperform five frontier AI models on AutumnBench, especially in exploration and belief updating. AI models perform poorly in detecting changes in environment dynamics, indicating a lack of structural understanding.
Applications
This study provides a new framework for evaluating AI models' generality, applicable to scenarios requiring diverse environment-level queries, such as robotic navigation and complex system simulations.
Limitations & Outlook
The current framework primarily tests in grid-world environments, which may not apply to more complex real-world scenarios. AI models' performance in the exploration phase is limited by computational resources and strategy design.
Plain Language Accessible to non-experts
Imagine cooking in a kitchen. You know when the food in the pot is done because you can infer from the steam and time. This is a kind of world model. AI needs similar models to predict and plan. WorldTest is like a kitchen test to see if AI can understand kitchen dynamics like you. By testing in different kitchen environments, researchers found humans excel in understanding and adapting to changes compared to AI.
ELI14 Explained like you're 14
Imagine playing a game where you need to understand the game's rules to win. AI also needs to learn these rules. WorldTest is like a super game test to see if AI can smartly understand the game world like you. Experiments found humans do better in these tests because we're better at spotting changes and adapting to new situations.
Glossary
WorldTest Protocol
A protocol for evaluating if AI models learn models that support multiple environment-level queries.
Used to test AI models' generality and structural understanding.
AutumnBench
A benchmark of 43 grid-world environments and 129 tasks for evaluating AI models' environment-level query abilities.
Concrete implementation of WorldTest.
Environment-Level Query
Queries requiring understanding of entire environment properties, not just observed trajectories.
Used to evaluate model generality.
Interaction Phase
Phase where agents autonomously explore the environment without external rewards, building internal models.
First phase in the WorldTest protocol.
Test Phase
Phase where agents are evaluated through modified challenge environments, testing their environment-level query abilities.
Second phase in the WorldTest protocol.
Open Questions Unanswered questions from this research
- 1 How to apply the WorldTest framework to more complex real-world scenarios? Current grid-world environments may not be complex enough.
- 2 How to improve AI models' performance in the exploration phase? Limited by computational resources and strategy design.
Applications
Immediate Applications
Robotic Navigation
Evaluate robots' navigation abilities in complex environments via environment-level queries, enhancing adaptability.
Long-term Vision
Complex System Simulation
Apply WorldTest framework in more complex simulation environments to evaluate AI models' generality and adaptability.
Abstract
World models are central to building AI agents capable of flexible reasoning and planning. Yet current evaluations (i) test only properties measurable from observed interactions, such as next-frame prediction or task return, and (ii) do not test whether a learned model supports diverse queries about the environment. In contrast, humans build $\textit{general-purpose}$ models that can answer many different questions about an environment$\unicode{x2014}$including questions that require understanding global structure and counterfactual consequences. We propose $\textit{WorldTest}$: a protocol for evaluating whether agents learn models that support multiple $\textit{environment-level queries}\unicode{x2014}$questions whose answers depend on properties of the full environment, not just observed trajectories. Individually, these queries can target properties (e.g., reachability or the effects of interventions) that no single rollout distribution determines. Collectively, they assess model generality across query types. We instantiate WorldTest as $\textit{AutumnBench}$, a benchmark of 43 interactive grid-world environments and 129 tasks across three query families for both humans and learning agents. Experiments with 517 human participants and five frontier models show that humans substantially outperform these models, a gap we attribute to differences in exploration and belief updating. AutumnBench provides a framework for evaluating world-model learning in grid-world environments with environment-level queries, and WorldTest provides a template for extending such evaluations to richer domains.