GameReplica: A Benchmark for Black-Box Visual Game Replication by Vision-Language Agents
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
The Flow has not summarised this story yet — read it at arXiv Computer Vision.
SWE-Game is a benchmark comprising 247 tasks based on 41 Godot games across 13 gameplay categories, testing coding agents on tasks such as brief-to-game, design-document implementation, skeleton completion, fault repair, and Godot-to-Unity porting. Evaluation uses engine-state checks, replay of certified reference inputs, and agent-authored demonstrations to judge mechanic correctness, playability, and post-repair behavior, supplemented by vision‑language rubrics for presentation. Across six models, Opus5 leads but overall scores stay below 60/100, highlighting common issues like omitted requirements and gameplay logic errors.
A2Z GameSpec-Bench introduces a benchmark of 100 long‑form Game Design Documents (GDDs) to evaluate how faithfully coding agents can generate complete games from detailed specifications. The benchmark measures faithfulness by checking that the game satisfies the GDD requirements and preserves the relationships among them, using a dependency‑aware contract and a combination of source‑code inspection and agent‑generated test policies. Evaluations show that current agents struggle to meet interdependent requirements, but requirement‑specific feedback improves GDD fidelity by 10.9% after two revision rounds.
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