Summary

This repository serves as a comprehensive, curated collection of research papers focused on Multimodal Large Language Models (MLLMs) for code generation. It addresses the growing need for models that can understand and generate code based on visual inputs and complex multimodal scenarios, targeting researchers, engineers, and practitioners in AI and software development. The primary benefit is providing a structured overview of the state-of-the-art, benchmarks, and methodologies in this rapidly advancing field.

How It Works

The project functions as a living bibliography, systematically cataloging academic publications related to MLLMs for code generation. It organizes papers by application domain, including UI code generation, scientific visualization, program repair, and more. The curated list highlights key methods, datasets, and evaluation frameworks, offering a structured pathway to understanding the diverse capabilities and challenges in multimodal code synthesis.

Quick Start & Requirements

This repository is a curated list of research papers and does not include executable code or a direct setup process. Users can explore the linked papers for detailed information on methods, datasets, and benchmarks.

Highlighted Details

• Extensive coverage of UI code generation, featuring numerous papers on converting designs, screenshots, and prototypes into front-end code.
• Significant focus on scientific visualization and data representation, including papers on generating plots, charts, and diagrams from various inputs.
• Broad application scope encompassing program repair, CAD, poster generation, game development, and multimodal document generation.
• Regular updates with recent publications (Arxiv 2024-2026), indicating an active research landscape.

Maintenance & Community

The repository is actively maintained, with a clear contribution guide encouraging community additions of relevant papers and tools. This suggests an ongoing effort to keep the list current with the latest advancements in multimodal LLMs for code.

Licensing & Compatibility

No specific license is mentioned for the curated list itself. The content consists of links to research papers, each with its own licensing terms. Compatibility for commercial use would depend on the licenses of the individual research artifacts linked.

Limitations & Caveats

As a curated list, its comprehensiveness is dependent on community contributions and author updates, and it may not encompass all ongoing or unpublished work. The rapid pace of MLLM development means the field is constantly evolving.