# An MLLM-Assisted Web Crawler Approach for Web Application Fuzzing
## 2 Background and Motivation
### Evolution of Multi-Modal Large Language Models  
The evolution of Multi-modal Large Language Models [13] represents a significant advancement in artificial intelligence, characterized by increasingly sophisticated approaches to modality integration. Early research primarily focused on fundamental neural network architectures that enabled the basic fusion of textual and visual representations for classification and retrieval tasks. The advent of deep learning techniques catalyzed substantial progress through the implementation of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), enabling more sophisticated cross-modal fusion capabilities. Landmark architectures, particularly VGG and ResNet, established crucial benchmarks for image processing and feature extraction in multimodal applications. The introduction of Transformer architectures (exemplified by BERT and GPT) has led to revolutionary advances in the field, where their self-attention mechanism has fundamentally transformed text processing capabilities and subsequently extended to multi-modal applications.  
The introduction of CLIP by OpenAI in 2021 marked a pivotal milestone, demonstrating unprecedented efficiency in cross-modal retrieval through sophisticated joint text-image training methodologies. Concurrent advances in generative modeling, particularly exemplified by OpenAI's DALL-E, showcased remarkable capabilities in text-guided image generation. Recent developments, as demonstrated by GPT-4 post-2023, have extended the paradigm to encompass multiple modalities--including text, images, and video--during the pre-training phase, substantially expanding applications across healthcare, finance, and entertainment sectors. This multi-modal integration has significantly enhanced model comprehension and cross-domain applicability, enabling more sophisticated solutions to complex real-world challenges.  
MLLMs demonstrate significant advantages through the integration of diverse data modalities (text, images, audio, etc.), exhibiting extensive potential and technical superiority across multiple dimensions. Primarily, these models excel in the comprehensive processing of heterogeneous information types, enabling sophisticated understanding and generation capabilities for complex scenarios. Furthermore, cross-modal learning facilitates synergistic effects in multi-task execution, enhancing overall performance metrics. This capability is particularly evident in multi-task learning and transfer learning contexts, where shared modal representations strengthen inter-task correlations. Additionally, MLLMs support naturalistic user interaction paradigms, enabling system engagement through various input modalities (textual, visual, or auditory), thereby enhancing the interaction experience's intuitiveness and accessibility. Moreover, these models demonstrate exceptional proficiency in addressing data scarcity challenges through effective multimodal information complementarity, thus enhancing model robustness and adaptability. In conclusion, MLLMs provide comprehensive technical foundations for practical applications through their sophisticated information fusion capabilities, efficient task coordination mechanisms, flexible interaction methodologies, and robust data handling capabilities.