Visual Graph Scaffolds for Structural Reasoning in Large Language Models
The paper discusses the use of visual graph scaffolds to enhance structural reasoning in large language models (LLMs). It highlights that graphs can serve not only as external knowledge sources but also as internal reasoning aids. The study shows that visual graph guidance improves reasoning efficiency and answer quality compared to flattened text structures.
- ▪Graphs can enhance large language models by organizing reasoning.
- ▪Visual graph guidance remains effective even without direct answer clues.
- ▪The study reveals a significant difference in performance between visual graphs and flattened text structures.
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Computer Science > Artificial Intelligence arXiv:2606.02673 (cs) [Submitted on 1 Jun 2026] Title:Visual Graph Scaffolds for Structural Reasoning in Large Language Models Authors:Runlin Lei, Xiaokui Xiao, Zhewei Wei View a PDF of the paper titled Visual Graph Scaffolds for Structural Reasoning in Large Language Models, by Runlin Lei and 2 other authors View PDF HTML (experimental) Abstract:Graphs have been used to enhance large language models (LLMs) for structured reasoning, mostly as external knowledge sources are provided to models at test time. In this paper, we take a different view: the value of graphs for LLMs lie not only in supplying information, but also in organizing reasoning.
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