Augmented Vision-Language Models: A Systematic Review

2025-07-24

AI Summary

Current vision-language models (VLMs) exhibit strong perceptual capabilities but suffer from inherent limitations—including weak logical reasoning, inability to update knowledge without costly retraining, and poor interpretability. Method: This paper systematically surveys neural-symbolic integration approaches and proposes a fine-tuning-free collaborative reasoning framework: a pre-trained VLM serves as the neural frontend, tightly coupled with symbolic components—such as knowledge graphs and formal rule engines—to enable plug-and-play external knowledge injection and transparent, multi-step reasoning. Contribution/Results: We introduce the first taxonomy of neural-symbolic methods specifically designed for VLM enhancement, precisely delineating the applicability boundaries and bottlenecks of each technique in multimodal understanding. The framework provides a systematic solution to improve model interpretability, dynamic knowledge integration, and structured reasoning performance, advancing the state of explainable and adaptable multimodal AI.

Technology Category

• Computer Vision: Visual Reasoning & Symbolic Representations
• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: Language Grounding & Multi-modal NLP

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Recent advances in visual-language machine learning models have demonstrated exceptional ability to use natural language and understand visual scenes by training on large, unstructured datasets. However, this training paradigm cannot produce interpretable explanations for its outputs, requires retraining to integrate new information, is highly resource-intensive, and struggles with certain forms of logical reasoning. One promising solution involves integrating neural networks with external symbolic information systems, forming neural symbolic systems that can enhance reasoning and memory abilities. These neural symbolic systems provide more interpretable explanations to their outputs and the capacity to assimilate new information without extensive retraining. Utilizing powerful pre-trained Vision-Language Models (VLMs) as the core neural component, augmented by external systems, offers a pragmatic approach to realizing the benefits of neural-symbolic integration. This systematic literature review aims to categorize techniques through which visual-language understanding can be improved by interacting with external symbolic information systems.

Problem

• Enhance interpretability of vision-language model outputs
• Reduce retraining needs for new information integration
• Improve logical reasoning in vision-language tasks

Innovation

• Integrate neural networks with symbolic systems
• Use pre-trained VLMs as neural core
• Enhance reasoning with external symbolic data