Visual Graph Scaffolds for Structural Reasoning in Large Language Models

This research explores using visual graph scaffolds to organize reasoning in Large Language Models (LLMs), inspired by human mind maps. Experiments on multi-hop question answering reveal that visual graph guidance significantly improves reasoning efficiency and answer quality compared to flattened text representations.