OmniGameArena：面向VLM游戏智能体的统一UE5基准与改善动态

AI 导读

OmniGameArena是一个基于十二个Unreal Engine 5新构建游戏的实时基准，涵盖单人（7个）、PvP（3个）和合作（2个）模式，提供统一动作接口。除冷启动排行榜分数外，还引入Improvement Dynamics Curve (IDC)，一种智能体反射评估机制：通过工具调用反射大语言模型自动优化技能提示词，追踪多轮反射中的分数变化以及习得技能在任务变体上的泛化表现。论文报告了12个VLM智能体在冷启动排行榜上的表现，以及4个顶级智能体在IDC下的指标。

推荐理由

在 UE5 里直接测 agent 的自我改进，这个思路让游戏 benchmark 从一次性的刷榜变成动态成长观测，对做多模态 agent 的团队是个新标尺。

正文

Authors:Mingxian Lin, Shengju Qian, Yuqi Liu, Yi-Hua Huang, Yiyu Wang, Wei Huang, Yitang Li, Fan Zhang, Zeyu Hu, Lingting Zhu, Xin Wang, Xiaojuan Qi

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Abstract:Vision-language model (VLM) agents are increasingly deployed in interactive game environments. Yet game benchmarks for VLM agents typically report a single first-attempt score per (agent, game) pair, focus on single-agent Solo play, and lack unified protocols for evaluating heterogeneous agent classes (commercial VLMs, open-weight VLMs, and specialized game policies) on the same footing. We address these gaps with OmniGameArena, a real-time benchmark of twelve newly built Unreal Engine 5 games spanning Solo (7), PvP (3), and Coop (2) with unified action interfaces, and the Improvement Dynamics Curve (IDC), an agentic-reflection harness in which a tool-using reflector LLM autonomously refines a bounded skill prompt across multiple rounds. Beyond cold-start leaderboard scores, IDC exposes two additional observables for each (agent, game) pair: how the score evolves across reflection rounds, and how the learned skill behaves on held-out task variants. We report these observables for twelve VLM agents on the cold-start leaderboard and four top agents under IDC.

Subjects:	Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)
Cite as:	arXiv:2606.09826 [cs.CV]
	(or arXiv:2606.09826v1 [cs.CV] for this version)
	https://doi.org/10.48550/arXiv.2606.09826 arXiv-issued DOI via DataCite

Submission history

From: Mingxian Lin [view email]
[v1] Mon, 8 Jun 2026 17:59:43 UTC (36,521 KB)