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LOLPlay: Benchmarking Real-Time Visual Agents in League of Legends — arXiv:2611.03685

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.03685
License (as archived): CC0-1.0
Archived: 2026-11-04T12:26:55.716Z
SHA-256 of the archived text: 5472847b10a11057766944c47c623f0088f2f36e4c1345d3dec81764d341fa25
Stable URL of this page: https://metraipolis.com/documento/dc_c8e575aaaae1

How to cite

LOLPlay: Benchmarking Real-Time Visual Agents in League of Legends — arXiv:2611.03685 — arXiv (en), https://arxiv.org/abs/2611.03685 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_c8e575aaaae1 (Metraipolis archive dc_c8e575aaaae1, sha256 5472847b10a11057766944c47c623f0088f2f36e4c1345d3dec81764d341fa25)

BibTeX

@misc{dcc8e575aaaae1,
  title = {LOLPlay: Benchmarking Real-Time Visual Agents in League of Legends — arXiv:2611.03685},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.03685},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_c8e575aaaae1; sha256 5472847b10a11057766944c47c623f0088f2f36e4c1345d3dec81764d341fa25},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.03685. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Visual agents need to coordinate reasoning and action while their environment evolves during inference. We introduce LOLPlay, a benchmark in native \emph{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.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.AI, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI) ; Computer Vision and Pattern Recognition (cs.CV).
Pagina del lavoro: https://arxiv.org/abs/2611.03685 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-11-04T12:26:47.091Z dalla pagina https://arxiv.org/list/cs.AI/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).