Beyond Generation: Multi-Hop Reasoning for Factual Accuracy in Vision-Language Models

• URL: http://arxiv.org/abs/2511.20531v1
• Date: Tue, 25 Nov 2025 17:34:32 GMT
• Title: Beyond Generation: Multi-Hop Reasoning for Factual Accuracy in Vision-Language Models
• Authors: Shamima Hossain,
• Abstract summary: Visual Language Models (VLMs) are powerful generative tools but often produce factually in- accurate outputs.<n>This work introduces a framework for knowledge-guided reasoning inVLMs, leverag- ing structured knowledge graphs for multi-hop verification.<n>We evaluate the framework using hi- erarchical, triple-based and bullet-point based knowledge representations, analyzing their ef-fectiveness in factual accuracy and logical infer- ence.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Visual Language Models (VLMs) are powerful generative tools but often produce factually in- accurate outputs due to a lack of robust reason- ing capabilities. While extensive research has been conducted on integrating external knowl- edge for reasoning in large language models (LLMs), such efforts remain underexplored in VLMs, where the challenge is compounded by the need to bridge multiple modalities seam- lessly. This work introduces a framework for knowledge-guided reasoning in VLMs, leverag- ing structured knowledge graphs for multi-hop verification using image-captioning task to il- lustrate our framework. Our approach enables systematic reasoning across multiple steps, in- cluding visual entity recognition, knowledge graph traversal, and fact-based caption refine- ment. We evaluate the framework using hi- erarchical, triple-based and bullet-point based knowledge representations, analyzing their ef- fectiveness in factual accuracy and logical infer- ence. Empirical results show that our approach improves factual accuracy by approximately 31% on preliminary experiments on a curated dataset of mixtures from Google Landmarks v2, Conceptual captions and Coco captions re- vealing key insights into reasoning patterns and failure modes. This work demonstrates the po- tential of integrating external knowledge for advancing reasoning in VLMs, paving the way for more reliable and knowledgable multimodal systems.

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