When Structure Doesn't Help: LLMs Do Not Read Text-Attributed Graphs as Effectively as We Expected

ArXi:2511.16767v2 Announce Type: replace Graphs provide a unified representation of semantic content and relational structure, making them a natural fit for domains such as molecular modeling, citation networks, and social graphs. Meanwhile, large language models (LLMs) have excelled at understanding natural language and integrating cross-modal signals, sparking interest in their potential for graph reasoning. Recent work has explored this by either designing template-based graph templates or using graph neural networks (GNNs) to encode structural information.