Challenge: Existing code-related benchmarks focus on single modality rather than visual game development.
Approach: They propose a multimodal benchmark for evaluating code large language models in visual game generation that integrates a clustering-based curation methodology and a pipeline for visual code synthesis.
Outcome: The proposed framework assesses code generation and visual game generation using a sandbox environment.

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Challenge: Existing benchmarks for evaluating the code understanding and generation capacities of Large Language Models are insufficient . existing benchmarks focus on a narrow range of popular programming languages and specific tasks .
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PlotGen-Bench: Evaluating VLMs on Generating Visualization Code from Diverse Plots across Multiple Libraries (2026.findings-acl)

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Challenge: PlotGen-Bench evaluates vision-language models' ability to generate executable visualization code from plots under realistic and complex visualization requirements.
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VGBench: A Comprehensive Benchmark of Vector Graphics Understanding and Generation for Large Language Models (2024.emnlp-main)

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Challenge: Current vision models use pixels to rasterize the visual world, but vector graphics are not the best or unique way to represent visual content.
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XCodeEval: An Execution-based Large Scale Multilingual Multitask Benchmark for Code Understanding, Generation, Translation and Retrieval (2024.acl-long)

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Challenge: Recent advances in large language models have shown impressive abilities in generating codes from natural language descriptions, repairing buggy codes, translating codes between languages, and retrieving relevant code segments.
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TURTLEAI: Benchmarking Multimodal Models for Visual Programming in Turtle Graphics (2026.findings-acl)

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Challenge: Vision-language models have been explored for visual programming, but performance is unclear . most prior work focuses on visual programming for productivity .
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mHumanEval - A Multilingual Benchmark to Evaluate Large Language Models for Code Generation (2025.naacl-long)

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Challenge: Current evaluations focus on English-to-Python conversion tasks with limited test cases . code generation from low-resource language prompts remains largely unexplored .
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MMCode: Benchmarking Multimodal Large Language Models for Code Generation with Visually Rich Programming Problems (2024.findings-emnlp)

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Challenge: Programming often involves translating detailed and complex specifications into code . current state-of-the-art models struggle to solve these problems, a new study shows .
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Turning the Tide: Repository-based Code Reflection (2025.findings-emnlp)

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Challenge: Code large language models (LLMs) enhance programming by understanding and generating code across languages.
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Cardiverse: Harnessing LLMs for Novel Card Game Prototyping (2025.emnlp-main)

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Challenge: Recent advances in Large Language Models (LLMs) have enhanced the possibilities for game prototyping, but they face significant challenges.
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DynaCode: A Dynamic Complexity-Aware Code Benchmark for Evaluating Large Language Models in Code Generation (2025.findings-acl)

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Challenge: Existing code benchmarks for large language models remain static, resulting in data contamination and unreliable evaluation results.
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