arXiv:2605.30931v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have shown strong capabilities in perception, reasoning, and action generation. However, their ability to sustain exploration in dynamic open worlds remains unclear. Existing embodied and game-based benchmarks often compress interaction into short-horizon tasks or entangle success with domain-specific game mechanics. In this paper, we introduce MineExplorer benchmark for evaluating open-world exploration capabilities of MLLM agents in Minecraft. We first filter atomic tasks whose solutions rely heavily on
The proliferation of advanced multimodal large language models and their increasing deployment in complex, dynamic environments necessitates rigorous evaluation beyond short-horizon tasks.
Evaluating MLLM agents in open-world exploration is critical for understanding their true capabilities and limitations, influencing future AI development and application in less constrained settings.
The introduction of a specialized benchmark like MineExplorer enables researchers to systematically assess and compare MLLM agent sustained exploration, accelerating progress in autonomous AI.
- · AI researchers
- · MLLM developers
- · Open-world simulation platforms
- · Gaming AI
- · Developers of less robust MLLMs
- · Benchmarks focusing only on short-horizon tasks
Improved MLLM agents capable of more autonomous exploration in complex environments.
Accelerated development of AI agents for real-world applications requiring sustained autonomy and dynamic adaptation.
Potential for MLLMs to manage and adapt within highly dynamic and unpredictable real-world systems, from logistics to scientific discovery.
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Read at arXiv cs.CL