EXPLORE-Bench: Egocentric Scene Prediction with Long-Horizon Reasoning

Multimodal large language models (MLLMs) are increas-ingly considered as a foundation for embodied agents, yet it remainsunclear whether they can reliably reason about the long-term phys-ical consequences of actions from an egocentric viewpoint. We studythis gap through a new task, Egocentric Scene Prediction with LOng-horizon REasoning: given an initial-scene image and a sequence of atomicaction descriptions, a model is asked to predict the final scene afterall actions are executed. To enable systematic evaluation, we introduceEXPLORE-Bench, a benchmark curated from real first-person videosspanning diverse scenarios. Each instance pairs long action sequenceswith structured final-scene annotations, including object categories, vi-sual attributes, and inter-object relations, which supports fine-grained,quantitative assessment. Experiments on a range of proprietary andopen-source MLLMs reveal a significant performance gap to humans,indicating that long-horizon egocentric reasoning remains a major chal-lenge. We further analyze test-time scaling via stepwise reasoning andshow that decomposing long action sequences can improve performanceto some extent, while incurring non-trivial computational overhead. Over-all, EXPLORE-Bench provides a principled testbed for measuring andadvancing long-horizon reasoning for egocentric embodied perception.