GameWorld: Towards Standardized and Verifiable Evaluation of Multimodal Game Agents

Understand

Towards an embodied generalist for real-world interaction, Multimodal Large Language Model (MLLM) agents still suffer from challenging latency, sparse feedback, and irreversible mistakes.

• Video games offer an ideal testbed with rich visual observations and closed-loop interaction, demanding fine-grained perception, long-horizon planning, and precise control.
• However, systematically evaluating these capabilities is currently hindered by heterogeneous action interfaces and heuristic verification.
• To this end, we introduce GameWorld, a benchmark designed for standardized and verifiable evaluation of MLLMs as generalist game agents in browser environments.