---
license: apache-2.0
task_categories:
- feature-extraction
- question-answering
- text-generation
language:
- en
tags:
- multimodal
- vision-language
- vlm
- video-generation
- diffusion-transformer
- world-models
- dit
- computer-vision
pretty_name: Multimodal Vision-Language & Video Foundation Models 2026
size_categories:
- n<1K
---

# 👁️ Multimodal Vision-Language & Video Foundation Models Dataset (2026 Edition)

A structured research dataset featuring **1,000 domain-verified research papers and code repositories** focused on Multimodal Vision-Language Models (VLM), Video Foundation Models, Diffusion Transformers (DiT), Visual Grounding, and World Simulators.

Built with **Universal Scientific Engine V15.1 Gold**, providing 47 schema attributes with verified repository attribution, modality capability matrix, vision backbones, and native 384-dimensional PyTorch embeddings.

---

## 📊 Dataset Schema Highlights (47 Columns)

| Field | Type | Description |
| :--- | :--- | :--- |
| `paper_id` | String | Unique ArXiv identifier |
| `title` | String | Research paper title |
| `supported_modalities` | List[String] | Categorized modalities (Video QA, OCR, Grounding, DiT, World Models) |
| `vision_backbone_architecture` | String | Granular vision encoder (SigLIP, Qwen-VL, InternViT, 3D Video VAE) |
| `tested_benchmarks` | List[String] | Benchmarks evaluated (MMMU, MathVista, DocVQA, Video-Bench) |
| `commercial_ip_safety_score` | Integer | 0–100 commercial compliance index (92% Enterprise Safe) |
| `title_vector_384d` | List[Float] | 384d PyTorch embedding (`all-MiniLM-L6-v2`) |
| `abstract_vector_384d` | List[Float] | 384d PyTorch embedding (`all-MiniLM-L6-v2`) |
| `reproduction_recipe` | String | 1-line bash setup command |

---

## 💻 1-Click Python Quickstart

```python
import pyarrow.parquet as pq

# Load Sample Parquet
table = pq.read_table("MULTIMODAL_VISION_LANGUAGE_VIDEO_FOUNDATION_MODELS_2026_30_SAMPLE.parquet")
df = table.to_pandas()

print(f"Loaded {len(df)} sample Multimodal AI papers.")
print(f"Top Paper: {df['title'].iloc[0]} (Backbone: {df['vision_backbone_architecture'].iloc[0]})")
```

---

## 🚀 Get the Full 1,000-Paper Enterprise Edition

The complete commercial production dataset (1,000 papers in Parquet, SQLite DB, Clean CSV, and JSON) is available here:

👉 **[BeatsProm Multimodal AI Dataset Full Edition](https://beatsprom.gumroad.com/l/multimodal-vision-language-video-models-2026)**