# MetaGen: A DSL, Database, and Benchmark for VLM-Assisted Metamaterial Generation

arXiv: https://arxiv.org/abs/2508.17568 · HTML: https://mlsanity.com/paper/2508.17568/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 55 references that are in the map.
- First posted on arXiv 25 Aug 2025.

## Builds on (most cited first, 14 of 14)

- [LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models](https://mlsanity.com/paper/2407.07895/) (2024)
- [Holodeck: Language Guided Generation of 3D Embodied AI Environments](https://mlsanity.com/paper/2312.09067/) (2023)
- [3D-GPT: Procedural 3D Modeling with Large Language Models](https://mlsanity.com/paper/2310.12945/) (2023)
- [LLaMA-Mesh: Unifying 3D Mesh Generation with Language Models](https://mlsanity.com/paper/2411.09595/) (2024)
- [L3GO: Language Agents with Chain-of-3D-Thoughts for Generating Unconventional Objects](https://mlsanity.com/paper/2402.09052/) (2024)
- [BlenderAlchemy: Editing 3D Graphics with Vision-Language Models](https://mlsanity.com/paper/2404.17672/) (2024)
- [Design2GarmentCode: Turning Design Concepts to Tangible Garments Through Program Synthesis](https://mlsanity.com/paper/2412.08603/) (2024)
- [Data-Driven Design for Metamaterials and Multiscale Systems: A Review](https://mlsanity.com/paper/2307.05506/) (2023)
- [METASET: Exploring Shape and Property Spaces for Data-Driven Metamaterials Design](https://mlsanity.com/paper/2006.02142/) (2020)
- [CADTalk: An Algorithm and Benchmark for Semantic Commenting of CAD Programs](https://mlsanity.com/paper/2311.16703/) (2023)
- [VLMaterial: Procedural Material Generation with Large Vision-Language Models](https://mlsanity.com/paper/2501.18623/) (2025)
- [Guided Diffusion for Fast Inverse Design of Density-based Mechanical Metamaterials](https://mlsanity.com/paper/2401.13570/) (2024)
- [MIND: Microstructure INverse Design with Generative Hybrid Neural Representation](https://mlsanity.com/paper/2502.02607/) (2025)
- [GenPara: Enhancing the 3D Design Editing Process by Inferring Users' Regions of Interest with Text-Conditional Shape Parameters](https://mlsanity.com/paper/2503.14096/) (2025)

## Built on by (most cited first, 3 of 3)

- [SciTextures: Collecting and Connecting Visual Patterns, Models, and Code Across Science and Art](https://mlsanity.com/paper/2511.01817/) (2025)
- [AutoMS: Multi-Agent Evolutionary Search for Cross-Physics Inverse Microstructure Design](https://mlsanity.com/paper/2603.27195/) (2026)
- [A property-registry contract for retrieve-or-refuse thermal-mechanical lattice search](https://mlsanity.com/paper/2609.14741/) (2026)

## Abstract (arXiv)

Metamaterials are micro-architected structures whose geometry imparts highly tunable-often counter-intuitive-bulk properties. Yet their design is difficult because of geometric complexity and a non-trivial mapping from architecture to behaviour. We address these challenges with three complementary contributions. (i) MetaDSL: a compact, semantically rich domain-specific language that captures diverse metamaterial designs in a form that is both human-readable and machine-parsable. (ii) MetaDB: a curated repository of more than 150,000 parameterized MetaDSL programs together with their derivatives-three-dimensional geometry, multi-view renderings, and simulated elastic properties. (iii) MetaBench: benchmark suites that test three core capabilities of vision-language metamaterial assistants-structure reconstruction, property-driven inverse design, and performance prediction. We establish baselines by fine-tuning state-of-the-art vision-language models and deploy an omni-model within an interactive, CAD-like interface. Case studies show that our framework provides a strong first step toward integrated design and understanding of structure-representation-property relationships.