MMBench2

MMBench2 paper: hallucination in world models is predictable and preventable via coverage signals

Researchers introduce MMBench2, a 427-hour, 210-task dataset for visual world modeling, and train a 350M-parameter world model to study hallucination in generative world models. The paper identifies three distinct hallucination modes (perceptual, action-marginalized, scene-diverging) and develops lightweight signals that predict where models will fail. A coverage-aware sampling technique and curiosity-reward-based data collection enable efficient finetuning to unseen environments with as few as 50 real trajectories. The central finding is that world model hallucination is fundamentally a data coverage problem, with the same signals serving both detection and mitigation.