arXiv — NLP / Computation & Language · · 3 min read

GameCraft-Bench: Can Agents Build Playable Games End-to-End in a Real Game Engine?

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Computer Science > Computation and Language

arXiv:2606.17861 (cs)
[Submitted on 16 Jun 2026]

Title:GameCraft-Bench: Can Agents Build Playable Games End-to-End in a Real Game Engine?

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Abstract:Game generation is an emerging application of coding agents, requiring models to transform natural-language specifications into playable interactive systems. Unlike traditional coding tasks, game generation takes place within a game engine, where scripts, scenes, assets, rendering, and runtime interactions must jointly produce coherent gameplay. We formalize end-to-end game generation as the problem of producing a complete game artifact that realizes a specification through observable player-game interaction in a target environment. We argue that evaluating this setting requires three desiderata: Engine Grounding, Artifact Completeness, and Interactive Verification. We propose an interaction-grounded evaluation framework that assesses executable gameplay through replayed demonstrations and rubric-guided multimodal judging. We instantiate this framework as GameCraft-Bench, a benchmark comprising 140 Godot tasks across 15 game families. Evaluations of frontier coding agents show that end-to-end game generation remains highly challenging: the strongest agent achieves only 41.46%, and most agents score below 40%. Further analysis reveals that while agents often implement recognizable mechanics, they struggle to deliver complete games with sufficient content, functional visual feedback, and coherent presentation. See this https URL for demos, code, and data.
Subjects: Computation and Language (cs.CL)
Cite as: arXiv:2606.17861 [cs.CL]
  (or arXiv:2606.17861v1 [cs.CL] for this version)
  https://doi.org/10.48550/arXiv.2606.17861
arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Tongxu Luo [view email]
[v1] Tue, 16 Jun 2026 12:34:39 UTC (5,503 KB)
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