Retrieving Minimal and Sufficient Reasoning Subgraphs with Graph Foundation Models for Path-aware GraphRAG
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866912951465672704 |
|---|---|
| author | Yuan, Haonan Sun, Qingyun Shi, Junhua Liu, Mingjun Yuan, Jiaqi Zhang, Ziwei Fu, Xingcheng Li, Jianxin |
| author_facet | Yuan, Haonan Sun, Qingyun Shi, Junhua Liu, Mingjun Yuan, Jiaqi Zhang, Ziwei Fu, Xingcheng Li, Jianxin |
| contents | Graph-based retrieval-augmented generation (GraphRAG) exploits structured knowledge to support knowledge-intensive reasoning. However, most existing methods treat graphs as intermediate artifacts, and the few subgraph-based retrieval methods depend on heuristic rules coupled with domain-specific distributions. They fail in typical cold-start scenarios where data in target domains is scarce, thus yielding reasoning contexts that are either informationally incomplete or structurally redundant. In this work, we revisit retrieval from a structural perspective, and propose GFM-Retriever that directly responds to user queries with a subgraph, where a pre-trained Graph Foundation Model acts as a cross-domain Retriever for multi-hop path-aware reasoning. Building on this perspective, we repurpose a pre-trained GFM from an entity ranking function into a generalized retriever to support cross-domain retrieval. On top of the retrieved graph, we further derive a label-free subgraph selector optimized by a principled Information Bottleneck objective to identify the query-conditioned subgraph, which contains informationally sufficient and structurally minimal golden evidence in a self-contained "core set". To connect structure with generation, we explicitly extract and reorganize relational paths as in-context prompts, enabling interpretable reasoning. Extensive experiments on multi-hop question answering benchmarks demonstrate that GFM-Retriever achieves state-of-the-art performance in both retrieval quality and answer generation, while maintaining efficiency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_07179 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Retrieving Minimal and Sufficient Reasoning Subgraphs with Graph Foundation Models for Path-aware GraphRAG Yuan, Haonan Sun, Qingyun Shi, Junhua Liu, Mingjun Yuan, Jiaqi Zhang, Ziwei Fu, Xingcheng Li, Jianxin Information Retrieval Graph-based retrieval-augmented generation (GraphRAG) exploits structured knowledge to support knowledge-intensive reasoning. However, most existing methods treat graphs as intermediate artifacts, and the few subgraph-based retrieval methods depend on heuristic rules coupled with domain-specific distributions. They fail in typical cold-start scenarios where data in target domains is scarce, thus yielding reasoning contexts that are either informationally incomplete or structurally redundant. In this work, we revisit retrieval from a structural perspective, and propose GFM-Retriever that directly responds to user queries with a subgraph, where a pre-trained Graph Foundation Model acts as a cross-domain Retriever for multi-hop path-aware reasoning. Building on this perspective, we repurpose a pre-trained GFM from an entity ranking function into a generalized retriever to support cross-domain retrieval. On top of the retrieved graph, we further derive a label-free subgraph selector optimized by a principled Information Bottleneck objective to identify the query-conditioned subgraph, which contains informationally sufficient and structurally minimal golden evidence in a self-contained "core set". To connect structure with generation, we explicitly extract and reorganize relational paths as in-context prompts, enabling interpretable reasoning. Extensive experiments on multi-hop question answering benchmarks demonstrate that GFM-Retriever achieves state-of-the-art performance in both retrieval quality and answer generation, while maintaining efficiency. |
| title | Retrieving Minimal and Sufficient Reasoning Subgraphs with Graph Foundation Models for Path-aware GraphRAG |
| topic | Information Retrieval |
| url | https://arxiv.org/abs/2603.07179 |