arXiv:2605.28258v1 Announce Type: cross Abstract: Generating a game is not the same as making one that can be played. Despite advances in code generation, existing approaches treat game generation as one-shot translation from prompt to artifact, leaving interaction-level failures undetected. We argue that evaluating and improving game generation requires a player, and study two roles for graphical user interface (GUI) agents in this process: (1) as an objective evaluator, for which we introduce PlaytestArena, a new evaluation environment that pairs 200 browser-based game generation tasks acros
Advances in AI code generation are now mature enough to expose the limitations of current game creation approaches, necessitating more advanced evaluation methods.
This research highlights the critical gap between AI-generated code and functional, playable game experiences, suggesting a new paradigm for evaluating AI creativity and utility.
The focus for AI-driven game generation shifts from mere artifact output to interactive, playtest-driven evaluation, emphasizing agentic involvement in the iterative development process.
- · AI game developers
- · Game testing platforms
- · Creative industries using AI
- · AI agent developers
- · One-shot AI code generation models
- · Manual game testing firms
AI models will be developed with built-in evaluative capabilities, capable of assessing their own outputs through simulated interaction.
This iterative, agent-driven creation and evaluation loop will accelerate the development of complex, functional AI-generated content across various creative domains.
The role of human creators could evolve to supervising AI agents that generate and self-critique artifacts, leading to unprecedented content production scales and speeds.
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Read at arXiv cs.AI