arXiv:2603.09731v3 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) are increasingly considered as a foundation for embodied agents, yet it remains unclear whether they can reliably reason about the long-term physical consequences of actions from an egocentric viewpoint. We study this gap through a new task, Egocentric Scene Prediction with LOng-horizon REasoning: given an initial-scene image and a sequence of atomic action descriptions, a model is asked to predict the final scene after all actions are executed. To enable systematic evaluation, we introduce EXPLO
The proliferation of multimodal large language models and the increasing focus on embodied AI necessitates robust evaluation benchmarks for long-term reasoning in egocentric scenarios.
This development addresses a critical gap in assessing MLLMs' ability to predict complex, multi-step actions from an agent's perspective, which is crucial for reliable autonomous systems.
The introduction of EXPLO-Bench provides a standardized framework for evaluating and advancing egocentric long-horizon reasoning in embodied AI, driving future research and development.
- · AI researchers
- · Robotics companies
- · Embodied AI developers
- · MLLM developers
- · Developers of less robust AI evaluation methods
- · Companies relying on short-horizon AI
Improved benchmarks accelerate the development of more capable and reliable embodied AI agents.
Advanced egocentric reasoning enables new applications for robots in complex, unstructured environments.
The widespread deployment of highly autonomous agents could transform logistics, healthcare, and personal assistance sectors.
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Read at arXiv cs.AI