ArXiv

Hallucination in World Models is Predictable and Preventable

2026-06-25 · 17:38 UTC ·Nicklas Hansen, Xiaolong Wang ·1 min read
Authors
Nicklas Hansen, Xiaolong Wang
Categories
cs.LG, cs.CV, cs.RO
arXiv
https://arxiv.org/abs/2606.27326v1
PDF
https://arxiv.org/pdf/2606.27326v1

Brief

Hansen and Wang introduce MMBench2 (427 hours, 210 tasks) and train a 350M-parameter world model to study hallucination in generative, action-controllable rollouts. They categorize failures into perceptual, action-marginalized, and scene-diverging modes, develop three predictive signals, and use coverage-aware sampling plus curiosity-driven data collection to finetune on new environments with as few as 50 trajectories, arguing hallucination arises from data-coverage gaps.

Why it matters

MMBench2: a 427-hour, 210-task visual world-modeling dataset with ground-truth actions, rewards, and live simulators; authors train a 350M-parameter generative world model on it.

Key details

  • Three distinct hallucination modes are identified—perceptual, action-marginalized, and scene-diverging—and the paper develops three lightweight data-centric signals that accurately predict where the model will fail.
  • Mitigation via coverage-aware sampling and using the hallucination predictors as curiosity rewards enables data-efficient finetuning: the pretrained world model adapts to entirely unseen environments with as few as 50 real environment trajectories, supporting the claim that hallucination is primarily a data-coverage issue.
Source evidence

Abstract

Modern generative world models render increasingly realistic action-controllable futures, yet they frequently hallucinate: rollouts remain visually fluent while drifting from the ground-truth dynamics. We hypothesize that hallucination concentrates in low-coverage regions of the state-action space, where lightweight data-centric signals can both detect it and guide mitigation. To test this, we introduce MMBench2, a 427-hour, 210-task dataset for visual world modeling with ground-truth actions, rewards, and live simulators, and train a 350M-parameter world model on it. We identify three distinct hallucination modes: perceptual, action-marginalized, and scene-diverging -- each anchored to a different stage of the pipeline, and develop three signals that accurately predict where the model will fail. To close coverage gaps at training time, we develop a coverage-aware sampling technique; to close them online, our hallucination predictors serve as curiosity rewards for targeted data collection, yielding a data-efficient finetuning recipe that adapts the pretrained world model to entirely unseen environments with as few as 50 real environment trajectories. Overall, our findings reveal that hallucination in world models is inherently a data coverage issue, and that the same signals used to detect it can also be used for mitigation. An interactive web version of our paper is available at https://www.nicklashansen.com/mmbench2

Comment: Interactive paper, live demo, code, dataset, and models: https://www.nicklashansen.com/mmbench2