Update README.md

### Key Components:
1. **Cognitive Hierarchy:**
- Video encoder extracts spatiotemporal features at multiple scales
- Text encoder provides semantic context
- Fusion transformer establishes cross-modal relationships

2. **Diffusion-Based Prediction:**
- Conditional UNet generates future frames
- Training via masked future prediction

3. **Contextual Reasoning:**
- Joint embedding space enables multimodal understanding
- Temporal coherence through video-text alignment

### Requirements:
- PyTorch 2.0+
- Hugging Face Transformers
- Diffusers library
- CUDA 11.7+

This architecture provides a foundation for building world models that understand temporal dynamics and contextual relationships through multimodal fusion and generative prediction.

README.md

@@ -14,3 +14,26 @@ Joint Embedding Space
 
 Diffusion-Based Decoder
 
+
+### Key Components:
+1. **Cognitive Hierarchy:**
+   - Video encoder extracts spatiotemporal features at multiple scales
+   - Text encoder provides semantic context
+   - Fusion transformer establishes cross-modal relationships
+
+2. **Diffusion-Based Prediction:**
+   - Conditional UNet generates future frames
+   - Training via masked future prediction
+
+3. **Contextual Reasoning:**
+   - Joint embedding space enables multimodal understanding
+   - Temporal coherence through video-text alignment
+
+### Requirements:
+- PyTorch 2.0+
+- Hugging Face Transformers
+- Diffusers library
+- CUDA 11.7+
+
+This architecture provides a foundation for building world models that understand temporal dynamics and contextual relationships through multimodal fusion and generative prediction.
+