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[2509.24276] G-reasoner: Foundation Models for Unified Reasoning over Graph-structured Knowledge
Summary
The G-reasoner paper introduces a unified framework that enhances reasoning over graph-structured knowledge using a new graph foundation model (GFM) integrated with large language models (LLMs).
Why It Matters
This research addresses the limitations of existing retrieval-augmented generation methods in handling knowledge-intensive tasks by providing a scalable solution that effectively integrates graph structures with language models. The implications of this work could significantly improve performance in AI applications that rely on complex reasoning and knowledge representation.
Key Takeaways
- G-reasoner integrates graph and language models for improved reasoning.
- QuadGraph standardizes knowledge representation across diverse sources.
- A 34M-parameter graph foundation model enhances LLM capabilities.
- Mixed-precision training and distributed message-passing ensure scalability.
- G-reasoner outperforms existing methods on multiple benchmarks.
Computer Science > Artificial Intelligence arXiv:2509.24276 (cs) [Submitted on 29 Sep 2025 (v1), last revised 26 Feb 2026 (this version, v2)] Title:G-reasoner: Foundation Models for Unified Reasoning over Graph-structured Knowledge Authors:Linhao Luo, Zicheng Zhao, Junnan Liu, Zhangchi Qiu, Junnan Dong, Serge Panev, Chen Gong, Thuy-Trang Vu, Gholamreza Haffari, Dinh Phung, Alan Wee-Chung Liew, Shirui Pan View a PDF of the paper titled G-reasoner: Foundation Models for Unified Reasoning over Graph-structured Knowledge, by Linhao Luo and 11 other authors View PDF HTML (experimental) Abstract:Large language models (LLMs) excel at complex reasoning but remain limited by static and incomplete parametric knowledge. Retrieval-augmented generation (RAG) mitigates this by incorporating external knowledge, yet existing RAGs struggle with knowledge-intensive tasks due to fragmented information and weak modeling of knowledge structure. Graphs offer a natural way to model relationships within knowledge, but LLMs are inherently unstructured and cannot effectively reason over graph-structured data. Recent graph-enhanced RAG (GraphRAG) attempts to bridge this gap by constructing tailored graphs and enabling LLMs to reason on them. However, these methods often depend on ad-hoc graph designs, heuristic search, or costly agent pipelines, which hinder scalability and generalization. To address these challenges, we present G-reasoner, a unified framework that integrates graph and language foun...
