Colorful Talks with Graphs: Human-Interpretable Graph Encodings for Large Language Models

ArXi:2602.10386v2 Announce Type: replace Graph problems are fundamentally challenging for large language models (LLMs). While LLMs excel at processing unstructured text, graph tasks require reasoning over explicit structure, permutation invariance, and computationally complex relationships, creating a mismatch with the representations of text-based models. Our work investigates how LLMs can be effectively applied to graph problems despite these barriers. We