UniGFM-RAG paper trains the graph retriever and LLM end-to-end for GraphRAG
JeremyCMorgan · x · 2026-11-07
The arXiv paper UniGFM-RAG proposes a unified end-to-end GraphRAG framework coupling a Graph Foundation Model retriever with an LLM generator. Existing GraphRAG uses decoupled retrieve-then-generate pipelines with misaligned objectives, and training GFM retrievers needs costly annotations. Here the GFM retriever scores and retrieves document nodes, whose embeddings pass through a fully differentiable graph bridge into soft-weighted graph prefixes combined with raw texts as graph-augmented prompts. Answer loss back-propagates to the retriever, enabling joint end-to-end training; training cost remains an open question.
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