arXiv cs.AI· Zhaojian Yu·· 1 天前AI 评分42
LOLPlay：在《英雄联盟》中评测实时视觉智能体的基准
LOLPlay: Benchmarking Real-Time Visual Agents in League of Legends
AI 导读
研究者推出 LOLPlay，一个在《英雄联盟》原生环境中评测实时视觉智能体的基准，包含 36 个英雄专属任务，覆盖资源管理、战斗、协作与目标控制，智能体仅通过截图和鼠标键盘操作交互。
正文 · 原文
Abstract:Visual agents need to coordinate reasoning and action while their environment evolves during inference. We introduce LOLPlay, a benchmark in native League of Legends with 36 champion-specific tasks spanning resource management, combat, cooperation, and objectives. Agents interact through screenshots and mouse-keyboard actions; automated initialization and 82 executable subgoal checks distinguish partial progress from completion. Across 324 task evaluations of three models at three reasoning-effort settings, GPT-6 Astra achieves the highest task success rate of 25.0% at low and medium effort, and the highest subgoal success rate of 40.3% at medium effort. However, high effort improves neither metric over low effort for any model. For Astra, longer requests leave fewer interaction rounds, suggesting a cost to additional reasoning in real-time control. Trajectories reveal incomplete subgoal composition and actions executing after the observed scene changes, motivating agents that combine visual reasoning with timely action.
| Subjects: | Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2611.03685 [cs.AI] |
| (or arXiv:2611.03685v2 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2611.03685 arXiv-issued DOI via DataCite |
Submission history
From: Zhaojian Yu [view email]
[v1]
Sun, 25 Oct 2026 11:39:59 UTC (25,589 KB)
[v2]
Wed, 11 Nov 2026 15:28:15 UTC (25,598 KB)
来源：arXiv cs.AI · arxiv.org