Model Architecture Deep Dive

Understanding the architectural foundations of modern multimodal AI systems, from transformers to vision-language models

Overview

This section provides comprehensive coverage of the architectural components that power modern Vision-Language Models (VLMs). From fundamental transformer mechanisms to state-of-the-art multimodal architectures, these resources form the technical foundation for understanding robustness challenges in medical AI systems. Understanding these architectures is crucial for addressing phrasing brittleness in medical VLMs, as architectural choices directly impact how models handle paraphrased questions, attention consistency, and safe deployment.

Core Architecture Components

🔧 Fundamental Building Blocks

• Transformer Architecture — Self-attention, multi-head attention, positional encodings, and layer normalization
• Tokenization & Encoding — BPE, WordPiece, and multimodal token representations
• Large Language Models — Scaling laws, emergent capabilities, and training dynamics

🎯 Vision-Language Integration

• VLM Fundamentals — Cross-modal alignment, fusion strategies, and contrastive learning
• Modern Architectures — Latest design patterns in Gemma, LLaMA, and other foundation models

Key Architectural Patterns

Attention Mechanisms

1. Self-Attention: Intra-modal relationships within text or vision
2. Cross-Attention: Inter-modal connections between vision and language
3. Sparse Attention: Efficiency improvements for long sequences
4. Flash Attention: Hardware-optimized implementations

Fusion Strategies

1. Early Fusion: Combined embeddings before transformer layers
2. Late Fusion: Separate processing with final integration
3. Cross-Modal Fusion: Iterative exchange between modalities
4. Hierarchical Fusion: Multi-scale feature integration

Medical Domain Adaptations

• Specialized tokenizers for medical terminology
• Domain-specific pre-training objectives
• Clinical knowledge injection methods
• Radiological feature extractors

Performance Considerations

Computational Efficiency

• Model quantization techniques (INT8, INT4)
• Knowledge distillation for deployment
• Efficient attention variants (Linear, Performer)
• Mixed precision training strategies

Robustness Features

• Architectural defenses against adversarial attacks
• Regularization techniques for medical domains
• Ensemble architectures for uncertainty estimation
• Attention-based interpretability mechanisms

Relevance to Robustness Gauntlet

Architectural Impact on Robustness Testing

• Attention Mechanisms: Different attention types affect how models handle paraphrases
• Fusion Strategies: Impact visual perturbation sensitivity
• Model Size: Larger models may be more robust but harder to interpret
• Training Objectives: Pre-training affects distribution shift performance

Key Considerations for Medical VLMs

• Interpretability: Architecture must support attention extraction for grounding analysis
• Efficiency: Clinical deployment requires balance between robustness and speed
• Safety: Architectural choices affect triage system integration
• Adaptability: Fine-tuning capabilities for domain-specific robustness

Related Resources

• MedGemma — Google’s medical-specific architecture adaptations
• Attack Surfaces — How architecture choices impact vulnerability
• Architectural Evaluation — Metrics for model comparison