LOLPlay: Benchmarking Real-Time Visual Agents in League of Legends

Zhaojian Yu

LOLPlay: Benchmarking Real-Time Visual Agents in League of Legends: 6 upvotes on Hugging Face Daily Papers, #25 of 46 papers on 2026-11-04. Day-by-day upvote history.

Upvotes over time

It had 6 upvotes by its first day, 6 upvotes by the next day, 6 upvotes by day 3, 6 upvotes by day 7, and has 6 now. It ranked #79 of 130 papers in its week and #79 of 130 in its month. Its code repository has 4 GitHub stars.

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.

Paper page on Hugging Face · arXiv

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