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Recursive Game Creator: An Agentic Product-Level Experience-Oriented Game Harness — arXiv:2610.08621

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.08621
License (as archived): CC0-1.0
Archived: 2026-10-07T11:17:26.307Z
SHA-256 of the archived text: 492caeabb191afaca87b58d8a4dfeba54c7d924ec7c580335f6df64f1cb03c4d
Stable URL of this page: https://metraipolis.com/documento/dc_54697dae90fa

How to cite

Recursive Game Creator: An Agentic Product-Level Experience-Oriented Game Harness — arXiv:2610.08621 — arXiv (en), https://arxiv.org/abs/2610.08621 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_54697dae90fa (Metraipolis archive dc_54697dae90fa, sha256 492caeabb191afaca87b58d8a4dfeba54c7d924ec7c580335f6df64f1cb03c4d)

BibTeX

@misc{dc54697dae90fa,
  title = {Recursive Game Creator: An Agentic Product-Level Experience-Oriented Game Harness — arXiv:2610.08621},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.08621},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_54697dae90fa; sha256 492caeabb191afaca87b58d8a4dfeba54c7d924ec7c580335f6df64f1cb03c4d},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.08621. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Recent game design agents have made substantial progress in generating playable games. However, program correctness does not ensure an enjoyable experience for players. We present Recursive Game Creator, an experience-oriented harness to advance agentic game development from rough game prototypes into entertaining games. Recursive Game Creator organizes recursive development around four components: Designer, Builder, Player, and Reviewer. The Designer translates user instructions and Reviewer's feedback into detailed plans. The Builder turns these plans into candidate games. The coding-native Player creates and executes reusable policies through programmatic interfaces to efficiently collect diverse gameplay trajectories, mitigating evaluation bias caused by slow GUI-based collection. The Reviewer uses carefully designed trajectory-based metrics to induce player preferences, integrating with visual evidence and explicit textual preferences to evaluate games against game-specific criteria. Finally, the Reviewer accepts the better version and provides improvement reviews for the next round, closing the recursive loop. Our method achieves state-of-the-art overall performance of 77.89 on GameCraft-Bench. On GameASG-Bench, it achieves a strict task success rate of 53.2%, a 34.1% improvement over the same-model baseline, and the highest mean runtime-check pass rate at 93.4% among compared methods. A user study shows longer playtime and higher ratings. Code is coming soon.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.MA, nuovi lavori del giorno).
Categorie arXiv: Artificial Intelligence (cs.AI) ; Multiagent Systems (cs.MA); Software Engineering (cs.SE).
Pagina del lavoro: https://arxiv.org/abs/2610.08621 — 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 2026-10-07T11:15:09.488Z dalla pagina https://arxiv.org/list/cs.MA/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).