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GameplayQA: A Benchmarking Framework for Decision-Dense POV-Synced Multi-Video Understanding of 3D Virtual Agents
arXiv – CS AI|Yunzhe Wang, Runhui Xu, Kexin Zheng, Tianyi Zhang, Jayavibhav Niranjan Kogundi, Soham Hans, Volkan Ustun|
🤖AI Summary
Researchers introduce GameplayQA, a new benchmarking framework for evaluating multimodal large language models on 3D virtual agent perception and reasoning tasks. The framework uses densely annotated multiplayer gameplay videos with 2.4K diagnostic QA pairs, revealing substantial performance gaps between current frontier models and human-level understanding.
Key Takeaways
- →GameplayQA provides dense video annotations at 1.22 labels/second for evaluating AI agents in 3D environments.
- →The framework organizes perception around Self, Other Agents, and World - a natural decomposition for multi-agent scenarios.
- →Current frontier multimodal LLMs show significant gaps from human performance in temporal grounding and agent attribution.
- →The benchmark addresses critical needs for autonomous agents in robotics and virtual worlds applications.
- →Common model failures include temporal reasoning, cross-video understanding, and handling high decision density environments.
#multimodal-llm#benchmarking#3d-agents#video-understanding#embodied-ai#gameplayqa#autonomous-agents#perception#reasoning
Read Original →via arXiv – CS AI
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