# V-GameGym: Visual Game Generation for Code Large Language Models

arXiv: https://arxiv.org/abs/2509.20136 · HTML: https://mlsanity.com/paper/2509.20136/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2025.

## Built on by (most cited first, 9 of 9)

- [From Code Foundation Models to Agents and Applications: A Comprehensive Survey and Practical Guide to Code Intelligence](https://mlsanity.com/paper/2511.18538/) (2025)
- [WorldCoder-Bench: Benchmarking Physically Grounded 3D World Synthesis](https://mlsanity.com/paper/2606.01869/) (2026)
- [Agentic Environment Engineering for Large Language Models: A Survey of Environment Modeling, Synthesis, Evaluation, and Application](https://mlsanity.com/paper/2606.12191/) (2026)
- [GameLogicBench: Evaluating Coding Agents on Runtime Game Logic with Tick-Level State Assertions](https://mlsanity.com/paper/2609.21562/) (2026)
- [IndustryCode: A Benchmark for Industry Code Generation](https://mlsanity.com/paper/2604.02729/) (2026)
- [GameASG-Bench: Benchmarking Autonomous Software Generation for Game Development](https://mlsanity.com/paper/2609.21293/) (2026)
- [World Agent: Can Language Models Keep a World Running?](https://mlsanity.com/paper/2609.32692/) (2026)
- [SWE-Game: Can Coding Agents Build the Games We Want?](https://mlsanity.com/paper/2609.33678/) (2026)
- [GAMEGO: Training Game-Dev Agents with Synthetic Trajectories Anchored in Real-World Assets](https://mlsanity.com/paper/2610.06910/) (2026)

## Abstract (arXiv)

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.