V-GameGym: Visual Game Generation for Code Large Language Models
Code large language models have demonstrated remarkable capabilities in
programming tasks, yet current benchmarks primarily focus on single modality
rather than visual game development. Most existing code-related benchmarks
evaluate syntax correctness and execution accuracy, overlooking critical
game-specific metrics such as playability, visual aesthetics, and user
engagement that are essential for real-world deployment. To address the gap
between current LLM capabilities in algorithmic problem-solving and competitive
programming versus the comprehensive requirements of practical game
development, we present V-GameGym, a comprehensive benchmark comprising 2,219
high-quality samples across 100 thematic clusters derived from real-world
repositories, adopting a novel clustering-based curation methodology to ensure
both diversity and structural completeness. Further, we introduce a multimodal
evaluation framework with an automated LLM-driven pipeline for visual code
synthesis using complete UI sandbox environments. Our extensive analysis
reveals that V-GameGym effectively bridges the gap between code generation
accuracy and practical game development workflows, providing quantifiable
quality metrics for visual programming and interactive element generation.
68d53af0f9b028b804d3c2c6
Wei Zhang
68d53af0f9b028b804d3c2c7
Jack Yang
68d53af0f9b028b804d3c2c8
Renshuai Tao
68d53af0f9b028b804d3c2c9
Lingzheng Chai
68d53af0f9b028b804d3c2ca
Shawn Guo
68d53af0f9b028b804d3c2cb
Jiajun Wu
68d53af0f9b028b804d3c2cc
Xiaoming Chen
68d53af0f9b028b804d3c2cd
Ganqu Cui
admin_assigned
2025-09-26T16:14:06.579Z
650eba9555dc1e841746f132
/avatars/af6f5ee78f161d25ec0afc45d2def8eb.svg
Ganqu Cui
user
ganqu
68d53af0f9b028b804d3c2ce
Ning Ding
68d53af0f9b028b804d3c2cf
Xander Xu
68d53af0f9b028b804d3c2d0
Hu Wei
68d53af0f9b028b804d3c2d1
Bowen Zhou
V-GameGym: Visual Game Generation for Code Large Language Models
Code large language models have demonstrated remarkable capabilities in
programming tasks, yet current benchmarks primarily focus on single modality
rather than visual game development. Most existing code-related benchmarks
evaluate syntax correctness and execution accuracy, overlooking critical
game-specific metrics such as playability, visual aesthetics, and user
engagement that are essential for real-world deployment. To address the gap
between current LLM capabilities in algorithmic problem-solving and competitive
programming versus the comprehensive requirements of practical game
development, we present V-GameGym, a comprehensive benchmark comprising 2,219
high-quality samples across 100 thematic clusters derived from real-world
repositories, adopting a novel clustering-based curation methodology to ensure
both diversity and structural completeness. Further, we introduce a multimodal
evaluation framework with an automated LLM-driven pipeline for visual code
synthesis using complete UI sandbox environments. Our extensive analysis
reveals that V-GameGym effectively bridges the gap between code generation
accuracy and practical game development workflows, providing quantifiable
quality metrics for visual programming and interactive element generation.
V-GameGym is a comprehensive benchmark for evaluating code generation in game development, focusing on multimodal evaluation including playability, visual aesthetics, and user engagement.
code large language models V-GameGym multimodal evaluation visual code synthesis UI sandbox environments playability visual aesthetics user engagement