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VibeGame: Prompt-to-Game Development with AI-Native Engine and Self-Evolving Adversarial Agent Team — arXiv:2610.33106

Source: arXiv (en)
Original: https://arxiv.org/abs/2610.33106
License (as archived): CC0-1.0
Archived: 2026-10-27T15:22:45.814Z
SHA-256 of the archived text: 6f34d551e92285eae7cd2501f4027c8604f12765e373207066da21f43009607e
Stable URL of this page: https://metraipolis.com/documento/dc_e87f2672be70

How to cite

VibeGame: Prompt-to-Game Development with AI-Native Engine and Self-Evolving Adversarial Agent Team — arXiv:2610.33106 — arXiv (en), https://arxiv.org/abs/2610.33106 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_e87f2672be70 (Metraipolis archive dc_e87f2672be70, sha256 6f34d551e92285eae7cd2501f4027c8604f12765e373207066da21f43009607e)

BibTeX

@misc{dce87f2672be70,
  title = {VibeGame: Prompt-to-Game Development with AI-Native Engine and Self-Evolving Adversarial Agent Team — arXiv:2610.33106},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2610.33106},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_e87f2672be70; sha256 6f34d551e92285eae7cd2501f4027c8604f12765e373207066da21f43009607e},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2610.33106. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Large language model agents have enabled software development through natural-language interaction, but extending this paradigm to end-to-end game development remains challenging. Building a playable game requires coordinating code, scenes, assets, animations, physics, and configurations, while allowing developers to validate their integrated behavior through interactive testing. We introduce VibeGame, a framework for creating complete 2D game projects from natural-language prompts and optional reference images. Its AI-native game engine represents project state as schema-validated structured text and provides frame-synchronous runtime control, enabling agents to inspect game states, execute actions, and verify behavior at their own pace. Built upon this interface, we design an Adversarial Agent Team of eight specialized agents to separate implementation from review, so that problems are found and fixed through multi-round loops. For each conducted and accepted project, we further introduce a training-free self-evolution mechanism to distill it into reusable skeletons, modules, and collaboration contracts for future development. We evaluate prompt-assets-to-game (PA2G), where agents are given an asset library, and prompt-to-game (P2G), where agents generate their own art. On a GameCraft-Bench subset, VibeGame in P2G consistently outperforms a range of agent harnesses in either P2G or PA2G with the same underlying model, including GPT-6.1-Sol.

Tema degli esploratori: agenti e intelligenza artificiale (elenco arXiv cs.MA, nuovi lavori del giorno).
Categorie arXiv: Multiagent Systems (cs.MA).
Pagina del lavoro: https://arxiv.org/abs/2610.33106 — 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 2026-10-27T15:22:44.274Z dalla pagina https://arxiv.org/list/cs.MA/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).