| title | LOLPlay: Benchmarking Real-Time Visual Agents in League of Legends |
|---|---|
| authors | Zhaojian Yu |
| date | 2026-10-25 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2611.03685 |
| upvotes | 6 (Hugging Face Daily Papers, 2026-11-05) |
| links | abs pdf hf code |
| cite | |
| 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. |
bibtex
@misc{yu2026lolplay,
title={LOLPlay: Benchmarking Real-Time Visual Agents in League of Legends},
author={Zhaojian Yu},
year={2026},
eprint={2611.03685},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2611.03685}
}