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arxiv: 2510.19788 · v4 · submitted 2025-10-22 · 💻 cs.AI · cs.LG

Benchmarking World-Model Learning with Environment-Level Queries

Archana Warrier , Dat Nguyen , Michelangelo Naim , Moksh Jain , Yichao Liang , Karen Schroeder , Cambridge Yang , Joshua B. Tenenbaum
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Sebastian Vollmer Kevin Ellis Zenna Tavares
This is my paper

Pith reviewed 2026-05-18 04:19 UTC · model grok-4.3

classification 💻 cs.AI cs.LG
keywords world modelsenvironment-level queriesgrid-world benchmarkhuman-AI comparisonmodel evaluationAutumnBenchWorldTestplanning and reasoning
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The pith

A protocol called WorldTest tests whether learned world models can answer many different questions about an entire environment, and current AI systems fall short of human performance on the resulting AutumnBench.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper argues that good world models should let an agent answer questions whose answers depend on the full environment rather than just the data seen so far, such as whether one location can reach another or what would change under a new rule. Existing tests only measure next-step prediction or task reward, which leaves open whether the model supports the broad range of queries humans handle naturally. To close this gap the authors define WorldTest and build AutumnBench as a concrete set of 43 grid worlds and 129 tasks spanning three query families. When 517 people and five frontier models are tested, humans score substantially higher, a difference the authors link to more effective exploration and belief revision. A reader would care because agents that pass this kind of test are more likely to plan and reason flexibly when the environment changes.

Core claim

WorldTest evaluates whether agents learn models that support multiple environment-level queries, and experiments with 517 humans and five frontier models show humans substantially outperform these models on AutumnBench.

What carries the argument

WorldTest, a protocol that measures model quality by requiring correct answers to environment-level queries on properties such as reachability and intervention effects that no single observed trajectory fixes.

If this is right

  • Models that succeed on WorldTest can answer queries about global structure and counterfactual outcomes even when those queries were never posed during training.
  • AutumnBench supplies a concrete yardstick that can be used to compare new world-model learning algorithms in grid-world domains.
  • The observed human advantage suggests that improving exploration and belief updating will be necessary for AI systems to close the gap.
  • If the protocol is extended, it can serve as a template for testing world-model generality in domains beyond grid worlds.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Training objectives that reward broad query answering rather than narrow prediction error may produce models that generalize better across unseen questions.
  • The same query-family approach could be adapted to test world-model quality in continuous control or partially observable settings where grid structure is absent.
  • Persistent gaps on AutumnBench may indicate limits in how current architectures encode environmental invariants rather than limits in scale alone.

Load-bearing premise

The 43 grid-world environments and 129 tasks are representative enough to reveal general differences in world-model quality between humans and current AI systems.

What would settle it

A frontier model that matches or exceeds average human scores across all three query families in AutumnBench after additional training, or a new set of environments where humans no longer outperform the models, would falsify the reported performance gap.

read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper proposes WorldTest, a protocol for evaluating whether agents learn general-purpose world models that support multiple environment-level queries (e.g., reachability, intervention effects) whose answers depend on full environment properties rather than observed trajectories alone. It instantiates the protocol as AutumnBench, a benchmark with 43 interactive grid-world environments and 129 tasks across three query families. Experiments with 517 human participants and five frontier models report that humans substantially outperform the models, with the gap attributed to differences in exploration and belief updating. The work positions AutumnBench as a framework for grid-world world-model evaluation and WorldTest as a template for richer domains.

Significance. If the central empirical comparison holds, the work is significant for introducing environment-level queries that test model generality beyond standard metrics like next-frame prediction or task return. The large human sample (517 participants) provides a meaningful baseline, and the benchmark design offers a reproducible template that could drive progress toward more flexible world models in AI agents. Credit is given for the empirical focus with human-AI comparison and for proposing a query-family structure that collectively assesses model support across query types.

major comments (2)
  1. [AutumnBench instantiation and environment selection (Section 3)] The headline claim that humans substantially outperform frontier models on AutumnBench demonstrates differences in world-model quality requires that the 43 chosen grid-world environments and 129 tasks are representative rather than narrowly tuned to discrete spatial structures or query families that favor human priors (e.g., local movement rules or object permanence). The manuscript does not provide justification or ablation showing that superior human performance reflects general exploration/belief-updating advantages rather than exploitation of these shared low-level dynamics. This assumption is load-bearing for the attribution in the abstract and results.
  2. [Experiments with humans and models (Section 4)] The reported human-model gap lacks robustness checks against reasonable variations in query design, prompting, or data exclusion rules. Without these, it is not possible to verify whether the gap is stable or sensitive to implementation details, weakening the claim that the benchmark reveals general differences in world-model learning.
minor comments (2)
  1. [Abstract] The abstract states 'three query families' but does not name them explicitly; adding the names (e.g., reachability, intervention effects) would improve immediate clarity.
  2. [Figures in Section 3] Figure captions for environment examples should explicitly link each visual to the corresponding query family and task to aid reader interpretation.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive feedback, which highlights important considerations for strengthening the claims about generality in world-model evaluation. We address each major comment below and have revised the manuscript accordingly to provide additional justification, ablations, and robustness analyses.

read point-by-point responses

  1. Referee: [AutumnBench instantiation and environment selection (Section 3)] The headline claim that humans substantially outperform frontier models on AutumnBench demonstrates differences in world-model quality requires that the 43 chosen grid-world environments and 129 tasks are representative rather than narrowly tuned to discrete spatial structures or query families that favor human priors (e.g., local movement rules or object permanence). The manuscript does not provide justification or ablation showing that superior human performance reflects general exploration/belief-updating advantages rather than exploitation of these shared low-level dynamics. This assumption is load-bearing for the attribution in the abstract and results.

    Authors: We agree that the representativeness of the environments is central to attributing the performance gap to general world-model capabilities rather than shared low-level priors. The 43 environments were selected to vary across key dimensions including deterministic vs. stochastic dynamics, presence/absence of object permanence, and different movement and interaction rules (detailed in Section 3.1 and Appendix A). In the revision, we have added a dedicated paragraph in Section 3.2 justifying this selection process and an ablation study (new Appendix C) that reports human-model gaps broken down by environment category. The advantage persists across subsets, though we acknowledge that grid-worlds inherently involve spatial structure and that extending WorldTest to non-grid domains would provide stronger evidence of generality; we note this explicitly as future work. revision: partial

  2. Referee: [Experiments with humans and models (Section 4)] The reported human-model gap lacks robustness checks against reasonable variations in query design, prompting, or data exclusion rules. Without these, it is not possible to verify whether the gap is stable or sensitive to implementation details, weakening the claim that the benchmark reveals general differences in world-model learning.

    Authors: We concur that robustness to implementation choices is necessary to support the stability of the reported gap. The revised manuscript now includes new analyses in Section 4.3 and Appendix D: sensitivity tests to alternative model prompting strategies (e.g., chain-of-thought vs. direct), rephrased query variants, and alternative human data exclusion criteria (e.g., stricter attention-check thresholds and minimum interaction time). The human advantage remains consistent in direction and magnitude across these checks. We have also expanded the description of the original data exclusion rules in Section 4.1 for greater transparency. revision: yes

Circularity Check

0 steps flagged

No significant circularity: empirical benchmark with direct performance comparisons

full rationale

The paper proposes WorldTest as an evaluation protocol and instantiates it as the AutumnBench benchmark consisting of 43 grid-world environments and 129 tasks. It then reports direct empirical results from 517 human participants versus five frontier models. No mathematical derivation, fitted-parameter prediction, or self-referential definition is present; the headline claim that humans substantially outperform models rests on held-out query performance measurements rather than any reduction to the benchmark's own construction or prior self-citations. The attribution to exploration and belief updating is an interpretive discussion, not a load-bearing step that collapses by construction. This is a standard empirical benchmark paper whose central comparison is externally falsifiable against the reported human and model data.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The paper introduces an evaluation protocol and benchmark without introducing new mathematical free parameters, axioms beyond standard assumptions of grid-world dynamics, or invented entities.

axioms (1)
  • domain assumption Grid-world environments have deterministic transition dynamics that can be queried via interaction.
    Implicit in the construction of AutumnBench as interactive grid worlds.

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