G-reasoner: A Unified Framework for Graph and Language Foundation Models

G-reasoner presents a cohesive framework that merges graph and language foundation models, facilitating scalable reasoning across various graph-structured knowledge. While large language models (LLMs) are proficient in intricate reasoning, they face limitations due to static and incomplete parametric knowledge. Although retrieval-augmented generation (RAG) incorporates external information, current RAG implementations struggle with knowledge-intensive tasks because of fragmented data and inadequate modeling of knowledge structures. Graphs effectively represent relationships in knowledge, yet LLMs lack the organization needed to reason with graph-structured data. Recent advancements like graph-enhanced RAG (GraphRAG) aim to close this gap by creating customized graphs for LLM reasoning. Nonetheless, these approaches often rely on makeshift graph designs and costly agent pipelines, which impede scalability and generalization. G-reasoner overcomes these obstacles by offering a scalable solution for reasoning over diverse graph-structured knowledge.