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
Venue: ICLR 2026 Poster (2026)
Keywords: GraphRAG
RAG
LLM
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: We present G-reasoner, a unified framework that integrates graph and language foundation models for reasoning over diverse graph-structured knowledge.

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 foundation models for scalable reasoning over diverse graph-structured knowledge. Central to our approach is QuadGraph, a standardized four-layer abstraction that unifies heterogeneous knowledge sources into a common graph representation. Building on this, we introduce a 34M-parameter graph foundation model (GFM) that jointly captures graph topology and textual semantics, and is integrated with LLMs to enhance reasoning in downstream applications. To ensure scalability and efficiency, mixed-precision training and distributed message-passing are implemented to scale GFM with more GPUs. Extensive experiments on six benchmarks show that G-reasoner consistently outperforms state-of-the-art baselines, significantly enhances LLM reasoning, and achieves strong efficiency and cross-graph generalization.

Published as a conference paper at ICLR 2026
G G-REASONER: FOUNDATION MODELS FOR UNIFIED REASONING OVER GRAPH-STRUCTURE KNOWLEDGE
Linhao Luo', Zicheng Zhao², Junnan Liu', Zhangehi Junnan Dong", Serge Panev Chen Gong', Thuy-Trang Vul, Alan Wee-Chung Liew", Gholamreza Haffari', Dinh Phung', Shirui Pan
Junnan
Monash University, *Nanjing University of Science and Technology, *Griffith University, *Shanghai Jiao Tong University, Teeneent Youtu Lab, NVIDIA Li nhao. Iuo@monash. edd, s. s.pa@grriffthh.edi
Q Project page: http::::mmmaan...gtttuubbbooob. rag/
ABSTRACT
Large language models (LLMs) excel at complex reasoning but remain lim- ited by static and incomplete parametric knowledge. aetmmetelaugmmented gen- eration (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 natural way to model relationships within knowledge, but LLMs are inherently unstructured and can- not effectively reason over graph-structured data. Recent graph-enhanced RAG (GraphRAG) attempts to bridge this gap by constructing tailored graphs and en- abling LLMs to reason them. However, these methods often depend ad-hoc graph designs, heuristic search, or costly agent pipelines, which hinder scalability and generalization. To address these challenges, present G-reasoner, unified framework that integrates graph and language foundation models for scalable rea- soning over diverse graph-structur Graph, standardized four-layer abstraction that unifies heterogeneous knowledge sources into common graph representation. Building on this, we introduce a 34M-parameter graph foundation model (GFM) that jointly captures graph topol- ogy and textual semantics, and integrated with LLMs to enhance reasoning downstream applications. To ensure scalability and efficiency, mixed precision training and distributed passsag-passsing are implemented to seale GFM with more GPUs. Extensive experiments six benchmarks show that G-reasoner consistently outperforms state-of-the-art baselines, significantly enhances LLM reasoning, and achieves strong efficiency and cross-graph generalization.
knowledge. Central our approach is Quad-
INTRODUCTION
Large language models (LLMs) have demonstrated remarkable reasoning capabilities and serve as the foundation model to solve complex tasks across diverse domains (Achiam et al. 2023 Yang al. 2025; Liu et al. 2024). However, their effectiveness is often constrained by limitations in accessing up-to-date and domain-specifer knowledge (Mousavi et al., 2024] [Song et al.) 20256) Recently, retrieval-augee -uggeneed generation (RAG) (Gao et 2023) addresses this challenge by en- abling LLMs to reason over external knowledge sources, thereby enhancing their applicability in real-world applications, such as legal judgments (Kang et al., 2024) and medical diagnoses (Jin et al. 2019). While RAG improves access external knowledge, current RAG approaches strug- gle with showledge-intessin reasoning due to the scattered nature of related information (Li et al.) 20256). This requires not only retrieving relevant information but also effectively capturing the associations and structure among knowledge to facilitate reasoning (Jiang et al.) 2025).
Graphs provide natural and flexible representation for modeling the structure and relationships within knowledge (Hogan et al. 2021 Safavi & Koutra 2021), making them particularly well-suited
"oorresooniigg author.

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Multi--ommann Knowledge
Graph-structuned Knowledge
Unified QuadGraph
Graph Foundation
Language Foundation Model Reasoning
Model Reasoning
W
Encyclopedia
Knowledge groph (HippORAG, To.. GFM-RAG..)
Community
Loyer
User's s Query
Community
Company
Quastion
Apple
Answering
Layer
Records
Graph Foundation
Large Language Model
Document
Hierorchical Graph (GrophRAG, KAS, Youtu-GrophRAG...
Model
nelease
Medical Diagnasis
Knowledge Groph Layer
Apple
Iphone
Legal Cases
Triple
1000
color, price...
Attribute
Financial Report
Document Graph (KGP, RAPTOR..)
Layer
Attribute
Assistant
Virtual
Figure 1: The overall framework G-reasoner. First, G-reasoner provides a unified graph inter- face, QuadGraph. that integrates diverse graph-structured knowledge from different domains into standard format. Then, adopts a GNN-powered foundation model to jointly reason over the graph- structured knowledge and make versatile predictions. Last, we enhance the LLMs with the graph reasoning results to improve the performance on downstream applications.
for capturing complex knowledge associations to enhance reasoning. However, due to the unstruc-
tured nature of L.LMs, they struggle handle graph data (Guo al., 2023 Jin et al. 2024). This motivates the need for approaches that enhance LLMSs to effectively reason over graph-structured
knowledge with graph-enhanced retrieval augmented generation (GraphRAG) (Peng al., 2024
Han et al. 2024).
Existing works in GraphRAG have primarily focused two components. (1) Graph construction focuses on designing graph structure to effectively organize and capture relationships within the knowledge, such as document graphs (Wang al., (2024), knowledge graphs (Jimenez Gutierrez et al., 2024), and hierarchical graphs (Edge et al. 2024 Dong et al. 2025). The well-designed graph structure could enhance the retrieval process by providing more context and relationships among knowledge. (2) Graph-enhunced reasoning explores to enhance L.MMs* ability reason over these graph structures. For example, HippoRAG (Jimenez Gutierrez et al. 2024) adopts the PageRank algorithm to search over knowledge graphs, ToG (Sun et al.) (0224) employs agent- based approach with tool calling to interact with the graph for reasoning, GNN-R MIN-RAG (Mavromatis) & Karypis 2025b) leverages graph neural networks (GNNs) to facilitate complex reasoning graphs.
Despite the effectiveness, existing methods face several limitations. First, they often rely spe- cific graph structures, which may not generalize well to diverse domains or tasks (Edge et al. [024] Jimenez Gutierrez et al. 2024). This limits their adaptability and generalizability in real-world ap- plications. Second, intuitive graph search-based methods (Jimenez Gutierrez et al. 2024) may not fully leverage the power of foundation models for reasoning, while agent-based methods (unn al.) 2024 can be computationally expensive and suffer from high latency. Although GFM-RAG (Luo) al. 2025) proposes GNN- nowerede graph foundation model (GFM) with 8M parameters to efñ- ciently reason over graphs, it is still limited to specific knowledge graphs and cannot generalize to other graph structures. Therefore, crucial to develop a unified method that can adapt to various graph structures and effectively reason over graph- structrt knowledge.
this paper, we propose G-reasoner, which integrates graph and language foundation models to enable scalable training and generalized reasoning over diverse graph-structured knowledge, as shown in Figure I To reason over diverse graph structures, we first define a novel 4-layer graph structure, QuadGraph, which unifies heterogeneous graph-structured knowledge into standard- ized format. This allows G-reasoner to flexibly adapt to various graph structures. With the unified QuadGraph, further unleash the power of graph foundation models (GFM) powered by GNNs
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to jointly reason over the topology and text semantics of the graph. To support large- scale training and reasoning, we implement a mixed seexeeiireiiion training and propose a distributed pessage-passiing mechanism, allowing G-reasoner to scale effectively across multiple GPUs and datasets.
Finally, we derive a 34M-parameter GFM that efficiently captures complex relationships and depen- dencies within the knowledge to make versatile predictions graphs. The graph reasoning results can be flexibly integrated with LLMs to enhance their reasoning in downstream applications. Exper- iments six benchmark datasets demonstrate that G-reasoner achieves superior performance over state-of-the-art baselines and significantly boosts the performance of LLMs on complex reasoning tasks. Moreover, G-reasoner exhibits strong efficiency and generalization capabilities across various graph structures, making it versatile solution for real-world applications.
The main contributions of this work are summarized as follows:
We propose G-reasoner, a novel framework that integrates graph and language foundation models to enable unified reasoning over diverse graph graph-structured knowledge.
We develop a 34M -aM-aaeteterr graph foundation model that jointly reasons the graph topology and text semantics, featuring a distributed message-passing mechanism to support large-scale training and reasoning.
* We conduct extensive experiments on six benchmark datasets, demonstrating that G- reasoner achieves superior performance over sfate-of-the-arr baselines and exhibits strong efficiency and generalization capabilities across various graph structures and domains.
2 RELATED WORK
Graph Construction. Graph construction is key for graph-based reasoning. Early methods like KGP (Wang et al.] 2024) use hyperlinks and KNN similarity, but miss semantic associations.
RAPTOR (Sarthi al. 2024) builds hierarchical trees via recursive summarization. GraphRAG (MS) (Edge et al.) 2024) use LLMs to extract entities and relations, forming hierarchical graphs with community detection and summarization. LightRAG (Guo al. (024), ArchRAG (Wang al., 2025) and Youtu-GraphRAC (Dong et al.] 2025) further enrich graph structures with attributes and documents. HippoRAG 1 & 2 (Jimenez Gutierrez et al.) 2024, Gutiérrez et al., 2025) apply Ope- nIE to induce knowledge graphs capturing factual relationships. Despite their achievements, these methods are typically tailored for specific graph structures, and thus exhibit limited generalizability across different types of graphs. For example, the hierarchical graphs constructed by GraphR AG (MS) (Edge al.) | (024) and LightRAG (Guo et al. a (024) are primarily designed for summarization tasks, and may not be suitable for multi-hop reasoning tasks compared to the knowledge graphs used
HippoRAG (Jimenez Gutierrez et al. 2024). Youtu Noutu-GraphRAG (Dong et al.) 2025) introduces a vertically unified framework that exploits the graph schema to guide the graph construction.
Graph-enhanced Reasoning. Graph-enhanced reasoning seeks to enable LLMs to reason on the graph-structtred knowledge and improve their performance knowledge-intensive applications. HippoRAG (Jimenez Gutierrez et al.) 2024) adopts personalized PageRank to support efficient re- trieval on knowledge graphs. LightR AA (Guo et al. 2024) employs dual-level retrieval strategy with both the mubbedding-basee based retrieval and graph-based neighborhood expansion. However, these graph search-based methods still fall short of fully exploiting the power of foundation models for reasoning. Agent-based methods, such as ToG (Sun et al. 2024), KAG (Liang et al. 2025). and Youtu-GraphR... (Dong et al., 2025) employ LLM agents iteratively interact with graphs to conduct reasoning. Despite the effectiveness. these methods often incur substantial computational costs and suffer from high latency due to the multiple invocations of LLMSs. More recent efforts leverage graph neural network (GNNs) to reason over graphs and enhance LLMS Mavromatis & Karypis 2025b);; He et al. (0024); Li et al. (2025a). For example, SubgraphRAAG (Li et al.| (025a)) employs GNNs to encode the graph structure into the node representations. which are then used to retrieve relevant information for LLMs. More recently, GFM-RAG (Luo et al.) 2025) proposes a graph foundation model powered by GNNs designed to enable reasoning over different knowledge graphs. However, these approaches remain tailored for specific graphs and cannot generalize well across diverse types of graph structure. More detailed related work can be found in Section A
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3 PRELIMINARY
In this section, we formally define the problem of reasoning over graph-structured knowledge with LLMs, which can be unified into two-stage framework: (1) graph structure construction and (2) graph-enhanced retrieval and LLM reasoning. Specifically, given a set of documents 'D, (V, E), such as knowledge
first extract the knowledge and construct a structured graph graph (Jimenez Gutierrez et al.) (024) and document graph (Wang et al. 2024). The V denotes the set of nodes (e.g., entity and document) and denotes the edges that model the connection between knowledge, facilitating efficient retrieval and reasoning. Based on the constructed graph g and user query q. we aim to retrieve the relevant knowledge from G and reason the final answer a with LLMss. The general pipeline can be formulated as:
g = GraphhCons.ructoor(()
a LIM(Retriever(y.90
APPROACH
The proposed G-reasoner aims to design foundation model that unifies the reasoning diverse graph structures, enabling more effective and efficient reasoning over graph-structured knowledge with LLMs. The overall framework of G-reasoner is illustrated in Figure I which consists of three main components: (1) a unified graph interface. QuadGraph, that standardizes diverse graph- structured knowledge from different domains into a unified format; (2) a GNN- -owered foundation model that jointly reasons over the graph- graph-structured knowledge and makes versatile predictions; and (3) an LLM-enhanced reasoning that incorporates the graph reasoning results to improve perfor- mance on downstream applications. In the following, we will introduce each component in detail.
4.1 UNIFIED GRAPH INTERFACE: QUADGRAPH
The real-world knowledge is often complex and multi-relational, which can be naturally repre- sented graph structures (Hogan et al. 2021 Safavi & Koutra 2021). To effectively leverage graph-structured knowledge for reasoning, existing methods typically construct different types of graphs based on the specific characteristics of knowledge and requirements of downstream tasks. For example, knowledge graphs (Jimenez Gutierrez et al.) (024) are often used to represent factual information between entities, while document graphs (Wang et al.) 2024) are used capture the relationships between documents based on their content similarity or citation links. However, these methods usually focus on a specific type of graph structure, which limits their applicability to other types of graph- struhustructured knowledge and hinders the generalization of reasoning models.
To address this limitation, G-reasoner proposes unified graph interface called QuadGraph that stan- dardizes diverse graph-structured knowledge from different domains into unified format. Specifi- cally, we design a 4-layer graph structure that con- sists of the following layers: (1) attribute layer that captures the common attributes of the nodes; (2) knowledge graph layer that represents the entities and their relationships as triples, which stores the structured factual knowledge; (3) document layer that contains the unstructured textual information, such as documents and passages; and (4) commu- nity layer that groups related nodes into communi- ties based on their semantic similarity or structural connectivity to provide global level information. As shown in Figure 2 the QuadGraph can effectively unify various types of graph-structured knowledge, such as knowledge graphs (Jimenez Gutierrez et al.)
Unified QuadGraph
Knowledge Graph Layer
Document Layer
HippoRAG 1
HippoRAG 2
RAPTOR
GFM-RAG
LightRAG
KGP
Graph RAG (MS)
KAG
ArchRAG
Youtu-Graphrag
Community Layer
Attribute Layer
Figure 2: Illustration of QuadGraph for uni- fying existing graph-structured knowledge.
2024). document graphs (Wang et al. (202)). and hierarchical graphs (Edge al. 2024 Liang al.
2025
Dong et al. 2025), into a standard format, facilitating the design of generalizable reasoning models.
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Definition. The QuadGraph is defined as G fattribute, entity document, community) denotes the set of node types, R denotes the set of edge types that model the relations between nodes, (e.g., born in, city of) and special relations across layers, (e.g., has attribute, i inc uded in, graph formulated & = (o",,,"" (er T r E R),, where ty denotes the type of node e. The S denotes the set of node semantic features, such as the name of an entity or the text content of a document.
(,,,R,T,S,) where T
to). The edges in the
4.2 GRAPH FOUNDATION MODEL REASONING
To effectively reason over the unified graph-structured knowledge, G-reasoner proposes GNN- powered foundation model that jointly reasons over the QuadGraph and makes versatile predictions. Graph neural networks (GNNs) (Mavromatis & Karypis, 2025a He et al. (024) have shown great success in reasoning over graph-structured data due to their ability of capturing complex relation-
ships and dependencies between nodes. Recently, GFM-RAG (Luo et al. 2025) proposes graph foundation model (GFM) for reasoning over knowledge graphs, which demonstrates the effective- ness of GNNs in enhancing LLMs with structured knowledge.
However, GFM-RA
specifically designed for knowledge graphs and cannot be directly applied to other types of graph-structured knowledge with versatile node types and rich text semantics, such as document graphs or hicrarchical graphs. To address this limitation, G-reasoner further unleashes generalizable GFM that (1) synergizes graph topology and
the power of GNNs by designing text semantics for reasoning and (2) enables versatile predictions on arbitrary node types.
Synergized Reasoning over Structure and Semantics. G-reasoner adopts the query-dependee GNN Galkin et al., [2224] Luo et al., (025) as the backhone of the GFM, which can capture the complex relationships and dependencies between query and knowledge the graph. Unlike GFM-
RAG (Luo et al. 2025) that only considers the semantics of relations, G-reasoner further incorpo- rates the rich text semantics of nodes S into the reasoning process.
Given a graph G, we first encode the text features of each node s, E S into node embeddings h. E Rd using a pre-trained text embedding model (e.g., BGE (Chen al., 2024), Qwen3 Embedding model (Zhang et al. 2025b)). The relation embeddings h, E IR" are also initialized using the same text embedding model encode the text description of each relation embeddings, we can effectively capture the semantic information in the graph and unify them into the same embedding space, facilitating the following reasoning.
R. With the help of text
During the reasoning, the graph G together with the user's query q are input into the GFM. The model first encodes the query into a query embedding h, E IRd using the same text embedding model to understand the user's intent and align it with the graph knowledge. Then, a L-layer query-dependent GNN applied to jointly reason over the graph topology and text semantics via message-passing and make versatile predictions of each node type, which can be formulated as:
h Init thi LoEV, * 1,y),E)
(3)
h Update (h--1, ,gg (MMsg(h!-) n4, 1 (e,,,, EE))), [1,
(4)
P(v) Predictor,, (h!, hy hg),
(5)
where h² denotes the embedding of node at the 1-th GNN layer, the Init function initializes the node embedding by combining the original node embedding h, and the query embedding h. if the node v is in the query-related nodes V,, with a single MLP layer.
At each GNN layer, the Msg function uses DistMult (Yang et al., 2015) to generate the message from the neighbors based on their nodes embeddings h, h and relation embedding hi. which are then aggregated by the Agg function (e.g., sum). The Update function updates the target node embedding h by combining its previous embedding and the aggregated messages using another MLP, and relation embeddings are also updated with a layer-specific MLP, i.c., h-= g'.))..
=
Finally, a type-specific predictor P Predictors is applied to make versatile predictions for each node based on its final embedding h. original text embedding h. and query embedding hg The predictor can be designed as binary classifier for arbitrary node types E T. such entity nodes in the knowledge graph layer or document nodes in the document layer, to predict whether the node relevant to the query.
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Optimization. The GFM conducts unified reasoning by integrating the graph topology E)) and text semantics S in G to predict the relevance of nodes to the query. The GFM is optimized by maximizing the likelihood of the ground-truth -tunth relevant nodes V.J which can be formulated as:
(
Σ VEVT
(oglo,
where the Vir denotes the set of labeled relevant nodes for the query q that can be of arbitrary types t TT However, the scarcity of labeled nodes | < |V] makes difficult to capture the complex relationships between the query and knowledge on the graph.
To mitigate this challenges, we propose to train the GFM on large-scale datasets with weak super- vision by leveraging the abundant unlabeled nodes on the graph. The pre-trained text embedding models (Devlin et al., 2019) have shown strong semantic understanding and can effectively capture the relevance between the query and nodes based on their text features S. Therefore, we propose to leverage the pre-trained text embedding model as a teacher to provide pseudo-labels for nodes on the graph, which can be formulated
Po S) Si gmo i d (H hn),
where hy denotes the query embedding and h, E Hy denotes the text embeddings of all nodes encoded by the pre-trained text encoder 6, which is frozen during training.
Following the knowledge distillation (Hinton et al. 2015), we train the GFM as student to mini- mize the KL divergence between the pseudo-label distribution P& (V'g, S) and the prediction distri- bution pe (V9g. G) over all nodes. As they both follow the Bernoulli distribution, the KL divergence can be efficiently calculated as:
Σ = (o) log (o(v)
P 1 (o(
(o) + (1 1--p-(() pa()))
(8)
VEV
where (v) = (ulg, h..) and po(u) = po (ul4,
The final unified objective of the GFM training can be formulated as:
0 Σ
log logpo(ula, 9) S
(9)
VEVT
where hyper-parameter that balances the two terms. The unified objective not only distill the semantic understanding from the pre-trained text encoder into the GFM but also alleviate the issue of scarce labeled data by leveraging the pscudo-label distribution over the graph. Empirical experiments in Section 5.4 demonstrate the effectiveness of the proposed objectives.
Large-scale Training and Reasoning. To enable the generalizable reasoning ability over diverse graph-structured knowledge, G-reasoner is trained on large-scale datasets with weak supervision. Specifically, we collect a large number of query- -rayg pairs {(g: V* G.) from various do- mains (Luo et al., (2025), where graphs G constructed with diverse graph constructors (e.g., knowledge graphs (Jimenez Gutierrez et al. 2004)), document graphs (Gutiérrez et al.) 2025). hi- erarchical graphs (Dong et al.) 2025)) and unified into the QuadGraph interface introduced in Sec- tion 4.1 The weak supervision Vi is obtained by labeling the relevant nodes for each query q, such as answer entities supporting documents. The GFM is then trained by optimizing the unified ob- jective in eq. (9) over the collected dataset, which can effectively capture the complex relationships between the query and knowledge on the graph and generalize various types of staph-structured knowledge.
To support large-scale training and reasoning, we first enable mixed precision training, yielding an 2. times increase in training throughput and a 17.5% reduction in GPU memory. To further scale up the model and graph size, we implement a distributed message-passingg mechanism that enables distributed training and reasoning across multiple GPUs. Specifically, partition the full graph into balanced subgraphs using the METIS algorithm (Karypis & Kumar 1997), with each device storing only subset of the graph in memory. During the message- passing, each device first aggregates information locally and then exchanges messages with other devices to finalize the node embedding updates. Thus, the memory complexity of G-reasoner per device is O((V)/N) d),

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where N denotes the number of devices and d denotes the latent dimension. This design allows G-reasoner to scale effectively to larger graphs and model size by leveraging more GPUs. Detailed implementation and efficiency analysis provided in Sections and C.3 and Section 3.5
4.3 LANGUAGE FOUNDATION MODEL REASONING
With the unified QuadGraph and GNNN-powers foundation model, G-reasoner can efficiently reason over the graph-structured knowledge and provide versatile predictions for arbitrary node types, such as attributes, entities, documents, and communities. This enables G-reasoner to flexibly select the most relevant information from different layers of the graph at varying granularities, enhancing LLM reasoning and boosting performance in downstream applications.
Specifically, given a user's query g. the GFM first reasons over the QuadGraph & and predicts the relevance score p(e) for each node (V E T are selected based on the predicted scores to provide the most relevant information and enhance LLM reasoning, which can be formulated as:
V. Then, the top-k relevant nodes of each type V
D* Top (oppk<((p(v))ee e V,t. t)}
(10)
a = (L (ail((rromot((( (q. v²)), D =
T}.
(11)
where P (rompt(-) denotes the prompt template that formats the query and information from the selected nodes V* 9 into prompt. which then input into the LLM (e.g.. GPT-4 (Achiam et al., 2023), DeepSeek (Liu et al. 2024)) to generate the final answer a. Detailed prompt templates are provided in Figure
EXPERIMENT
In experiments, we aim to answer the following research questions: RQ1:
Can G-reasoner achieve
state-of-the-art performance on reasoning over graph-structured knowledge? RQ2: Can G-reasoner effectively generalize across different graph structures? RQ3: How do the key components of G- reasoner contribute to its overall performance? RQ4: How efficient is G-reasoner in terms of training and inference?
5.1 EXPERIMENTAL SETUP
Datasets. We first evaluate the effectiveness of G-reasoner on three widely-used multi-hop
Table 1: Statistics of the evaluation datasets.
QA datasets, including HotpotQA Yang et al.
2018), MuSiQue (Trivedi et al. 2022). and
Dataset
# Query
# Document
2WikiMultHHoQAA (2Wiki) (Ho et al.] 2020))
HotpotQA (Yang et al. 2018
1,000
9,221
following the settings used in Jimenez Gutier-
MuSiQue (Trivedi el 2013
1,000
6,119
rez et al. 2024); Gutiérrez et al. (2025): Luo
2Wiki (Ho al. 2020
1,000
11,656
et al. (2025) for fair comparison. To further assess the generalization ability of G-reasoner across domains, we employ three GraphRAG benchmarks: G-bench (Novel) (Xiang et al. 2025). G-bench (Medical) (Xiang et al. (2025). and G-bench (CS) (iao al.) 2025) evaluate G-reasoner on complex reasoning across medical, novel, and computer science (CS) knowledge.
G-bench (NoveJ Xuaig ct al.
2025
2,010
461
G-bench (Medical) (Xlng el 2025
2,062
2,406
(i-bench (CS) (iaao
1,018
24,534
The statistics of the datasets are summarized Table More details about datasets can be found in
Section
B
Baselines. We compare with two groups of baselines: (1) Non-structure methods: BM25 (Robertson
& Walker 1994), ColBERTv2 (Santhanam et al. (022). Qwen3-Emb-8B (Zhang et al. (025b): (2) Graph-enhanced methods: RAPTOR (Sarthi et al. (024), GraphRAG (MS) (Figee al. 2024).
LightRAG (Guo et al., 2024h. KAG (Liang et al. 2025), HippoRAG 1 & (Jimenez Gutierrez al.
2024 Gutiérrez et al. 2025).
SubgraphRAG (Li et al. 2025a), G-retriever (He et al. 2024). and
GFM-RAC (Luo al. (025).
We fixed bug of GFM-RAG in R@k calculation and re-evaluated in our experiments.

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Table 2: QA reasoning performance comparison. GPT-4o-mini is used the LLM for reasoning.
G-bench (Novel)
G-bench (Medical)
G-bench (CS)
HotporQA
MuSiQue
2Wiki
Method
EM
F1
EM
F1
EM
F1
ACC
ACC
ACC
Non-structure Methods
None (GPT--o-mini) (OpenAl 2024
28.6
41.0
11.2
36.3
30.2
36.3
51.4
67.1
70.7
BM25 [Rohertson & Walker 1994
52.0
63.4
20.3
28.8
47.9
51.2
56.5
68.7
71.7
ColBERTvZ v2 SSanihammm
43.4
57.7
15.5
26.4
33.4
43.3
56.2
71,9
Owen3-Emil [8B) Zhang 2025b
53.4
67.6
31.9
44.1
57.2
63.2
56.2
70.4
73.5
Graph-enhanced
Methods
RAPTOR (Sarthi et al. 2024
50.6
64.7
27.7
39.2
39.7
48.4
43.2
57.1
73.6
GraphR (CT [Eage ct 2024
51.4
67.6
27.0
42.0
34.7
61.0
50.9
45.2
72.5
LightRAG (Guo el al 2024
9.9
20.2
2.0
9.3
2.5
12.1
45.1
63.9
71.2
KAG (Liang al 22/5 20/5
72.2
33.8
46.0
67.3
75.1
HippokAG Jimenez Gutierrez
2024
46.3
60.0
24.0
35.9
59.4
67.3
44.8
59.1
72.6
HippoRAG 2 [Gütterrez et 2025
56.3
71.1
35.0
49.3
60.5
69.7
56,5
64.9
SubgraphRAGi (Li al. 20255
44.5
57.0
25.1
35.7
62.7
69.0
G-retriever (He 2024
41.4
53.4
23.6
34.3
33.5
39.6
69.8
GFM-RAG Luo et al.
69.5
30.2
49.2
77.7
58.6
72.1
G-reasoner
61.4
76.0
38.5
52.5
74.9
82.1
58.9
73.3
73,9
Metrics. For QA reasoning performance, we use the exact match (EM) and Fl score on multi-hop
QA following previous works (Jimenez Gutierrez et al. [2244] Luo et al. 2025) and accuracy (ACC)
on G-benchs following their settings (Xiang et al. 2025 Xiao et al., 2025). For retrieval perfor-
mance, we use document recall @2 (R @2) and recall @5 (R @ for multi-hop QA and evidence recall (Recall) for G-benchs (Xiang et al. 2025) as evaluation metrics.
Implementation Details. We gather the training data from Luo et al. (2025). which consists of 277,83 query samples and 2,972,931 documents, and we construct diverse graph structures using Jimenez Gutierrez et al. (2024); Gutiérrez et al. (2025); Guo et al. (2024); Dong et al. (2025) train
our GFM. We use GPT-4o-mini as the reasoning LLM. More traiiig and implementation details can be found in Section
5.2 MAIN RESULTS (RQ1)
QA Reasoning Results. Table 2 shows QA results on six datasets requiring complex reasoning. G-reasoner consistently outperforms all baselines across these datasets, proving its effectiveness in reasoning over graph-structured knowledge in various domains. Non-structure methods (e.g., BM25, Co Qwen3-Emb) perform poorly on multi-hop QA due to their inability to cap- ture knowledge structure. Graph-enhanced methods (e.g., HippoRAG) generally outperform non- structure methods by leveraging graph structures. However, some approaches relying on specifically designed graphs and heuristic searches (e.g., GraphRAG, LightRAG) struggle to generalize across different datasets and tasks (e.g., G-bench). While the GNN-based GFM-RAG performs well on multi-hop QA, it also underperforms
G-bench datasets, likely due to limited generalization of GNNs across diverse graph structures. In contrast, G-reasoner achieves the best performance across all datasets, demonstrating superior reasoning and generalization capabilities.
To further demonstrate the effectiveness of G-reasoner, we compare it against advanced multi-step (agentic) RAG methods (e.g., IRCoT (Trived et al. 2023), ReSearcher (Song al.] 2025a), and Search-R1 (Jin et al. 2025)). From the results in Table IO, we observe that hat G-reasoner con- sistently outperforms them across all datasets, highlighting its superior ability to leverage graph- structured knowledge for efficient and accurate multi-hop question answering. Unlike agentic RAG approaches, G-reasoner achieves end-to-end reasoning in a single forward pass, offering both im- proved performance and computational efficiency. The detailed results can be found in Section D.
Retrieval Results. Table shows retrieval results on multi-hop QA and G-bench datasets. G- reasoner consistently delivers the best performance across all datasets, demonstrating its effec- tiveness in retrieving relevant information from graph-structured knowledge. Although advanced umbedding-baasee methods (e.g.. Qwen3-Emns to capture semantic similarity. they still fall short of graph-enhanced approaches on some datasets. This underscores the importance of utilizing graph topology for effective retrieval in complex rea-
perform well by leveraging srgge-scale pre-training
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Published as a conference paper at ICLR 2026
Table 3: Retrieval performance comparison. @ (R @ ) used for multi-hop QA datasets,
and evidence recall (Recall) is used for G-bench (Xiang et al. 2025).
G-bench (Novel)
G-hench (Medical)
HotpotQA
MuSiQue
2Wiki
Method
R@2
R@5
R@2
R@5
R@2
R@5
Recall
Recall
Non-structure Methods
BM25 (Robertson & Walker ColBERTvZ (Santhanam
1994
55.4
72.2
32.3
41.2
51.8
61.9
82.1
87.9
2022
64.7
79.3
37.9
49.2
59.2
68.2
82.4
89.5
Qwen3-Eni (8B) Zhhang
2025b
74.1
88.8
46.8
62.1
66.2
74.1
82.6
92.7
Graph-enhanced Methods
RAPTOR Sarthi 2024
58.1
71.2
35.7
45.3
46.3
53.8
84.2
GraphRAG (MSS [Edge
2024
58.3
76.6
35.4
49.3
61.6
77.3
67.4
56.4
LightRAG (uoo et al.. 2024
38.8
54.7
24.8
34.7
45.1
59.1
79.6
82.6
KAG (Liang
2025
59.4
86.1
42.2
62.4
61.4
88.3
HippoRAG Jumenez Gulierrez et al.
2024
60.1
78.5
41.2
53.2
68.4
87.0
81.2
84.0
HippoRAG 2 (Gutterez a.. 2025
80.5
95.7
53.5
74.2
80.5
95.7
66.2
73.6
SubgraphRAG [Lr al. 2025g
58.1
71.7
40.6
48.1
70.2
85.3
G-retriever (He et al. 2024
51.8
63.6
35.6
43.5
60.9
66.5
GFM-RAG Luo et 2025
75.6
89.6
43.5
57.6
79.1
92.4
75.9
82.2
G-reasoner
85.9
97.7
54.8
74.9
81.2
98.2
87.7
93.8
Table 4: Generalization of G-reasoner across different graph structures.
QuadGraph Layer
HotpotQA
MuSiQue
2Wiki
Retriever
Graph Structure
KG
Doc.
Attr.
Com.
EM
F1
EM
F1
EM
F1
Personalized
PageRank
HippoRAG
46.3
60.0
24.0
35.9
67.3
Embedding+
Graph Search
LightRAG
9.9
20.2
2.0
9.3
2.5
12.1
HippoRAG
54.0
68.3
28.9
41.0
72.0
80.0
G-reasoner
LightRAG
49.7
62.0
25.3
35.9
59.4
64.4
Yratu-GraphRAG
52.3
65.9
30.3
42.5
69.7
77.7
soning tasks beyond text semantics. Notably, G-reasoner significantly outperforms existing meth- ods, highlighting the superior ability our GFM to integrate graph topology and text semantics for efficient retrieval.
5.3 GENERALIZATION ACROSS GRAPH STRUCTURES (RQ2)
To evaluate the generalization ability of G-reasoner across different graph structures, we conduct ex- periments using various graph constructors, including HippoRAG (Jieene Gutierrez et al. 2024), LightRAG (Guo et al., 2024), and Youtu-GraphRAG (Dong et al. 2025), whose statistics are pre- sented Table[8 The G-reasoner directly tested on graphs generated by each constructor without further fine-tuning. As shown in Table G-reasoner shows strong generalization ability across different graph structures, consistently outperforming the retrievers specifically designed for cach graph type. This demonstrates the robustness and adaptability of G-reasoner in handling diverse graph-structured knowledge for reasoning tasks.
5.4 ABLATION STUDY (RQ3)
Table 5: Ablation studies of G-reasoner.
HotpoiQA
MuSiQue
2Wiki
In this section, we conduct an ablation study to assess the contributions of key components in G-reasoner. We evaluate the impact (1) dis- tillation loss (Distill), (2) node text semantics (Text), and (3) graph foundation model (GFM) on the performance of G-reasoner. The results are presented Table 5 Removing the distillation loss leads to the performance drops on all datasets, indicating its importance in enhancing the GFM's ability under weak supervision. Ex-
Variant
R@2
R@5
R@2
R@5
R@2
G-reasoner
81.1
96.9
52.1
72.4
75.6
96.1
w/o Distill
77.4
96.1
50.7
71.9
75.9
96.0
w/o Text
79.4
96.3
50.0
71.9
74.6
95.2
w/o GFM
11.6
19.7
3.8
7.1
4.9

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Published as a conference paper at ICLR 2026
Compute Scaling
#GPU x2.56 10
Total GPU Memory Required (GB)
More
2560
(GB)
FP32
6F16
T GPC (80GB)
GPP (80GB)
80
-17.5%
640
2.72
20
60
+111%
Samplesis
GB
40
1.29
20
0
100kx1024 2001xx2048 400kx4096 800kx8192
Memory
Throughput
Compute Cost [1] d
Figure 3: Memory and throughput gain
Figure 4: Compute scaling of G-reasoner.
brought by mixed precision training.
cluding node text semantics also results in performance degradation, highlighting the crucial role of textual information in reasoning tasks. Notably, removing the GFM causes drastic drop in perfor- mance, underscoring its essential role in effectively integrating graph topology and text semantics for reasoning over graph-structured knowledge.
5.5 EFFICIENCY ANALYSIS (RQ4)
Table 6: Efficiency and performance comparison
on G-bench (CS) (Xiao al. 2025).
Inference Efficiency. We compare the infer- ence efficiency (time per sample) of G-reasoner on G-bench (CS) (Xiao al. 2025 with (1) agent-based, (2) graph search, and (3) GNN- based methods. As shown in Table 6 G- reasoner achieves the lowest latency and high- est performance among all methods. This demonstrates the efficiency of our method for
G-bench (CS)
Method
Time (s) ACC
Agent- based Methods
KGP Wang al 2024
89.4
71.9
ToG (Sun et ar. 2024
70.5
71.7
DALK Li
26.8
69.3
Gruph Search Methods
reasoning over graph-structured knowledge.
GraphRAG (MS) (Edge et al. 2024
44.9
72.5
LightRAG Guo el al. 2024
14.0
71.2
Training Efficiency. Mixed precision train- ing enables G-reasoner to significantly reduce memory usage and improve training through- put. As shown in Figure 3 mixed precision training reduces memory consumption from 80GB to 66GB (-17.5%) and increases throughput from 1.29 to 2.72 samples/s (+1 11%) on a single A100 GPU. This allows G-reasoner to be trained efficiently on large-scale graph-structured knowl- edge with limited computational resources.
HippoRAG Ummenez Gulierezz et 2024
2.4
72.6
GNN based Melhods
G-retriever He ct al. 2024
23.8
69.8
GFM-RAG L.00 ct 2025
2.0
72.1
G-resonner
0.2
73.9
Compute Scaling. The compute cost of G-reasoner defined [V] which linearly grows with both the graph node size [V] and the model's hidden dimension d. Thanks to the distributed message-passiee mechanism, as shown in Figure G-reasoner can efficiently scale large graphs and larger model sizes with more computational resources. Detailed analysis of compute scaling can be found in Section D.4
CONCLUSION
In this paper, we present G-reasoner, a novel framework that synergizes graph foundation model and language foundation model for reasoning over graph-structured knowledge. With the proposed QuadGraph, G-reasoner unifies diverse graph types into standardized four-layer graph structure. A (NW--poeeeed graph foundation model is further developed to jointly reason over graph topology and text semantics, enabling versatile prediction on graphs and enhancing LLM reasoning. Exten- sive experiments on six complex reasoning benchmarks demonstrate that G-reasoner consistently outperforms state-of-the-at baselines. substantially improves LLM reasoning, and exhibits strong efficiency and cross-graph generalization. We believe G-reasoner would pave the road for future search in integrating graph and language foundation models for wewlleeeeenneesiii applications.
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Published as a conference paper at ICLR 2026
ACKNOWLEDGMENTS
This work is partially supported by the DARPA Assured Neuro Symbolic Learning and Reasoning (ANSR) program under award number TA8750-23-2-1016. D Phung is supported by the Australian Research Council (ARC) Discovery Project DP250100262 and DP230101170 S Pan was partly funded by Australian Research Council (ARC) under grants FT210100097 and DP240101547 and the CSIRO National Science Foundation (US) AI Research Collaboration Program.
ETHICS STATEMENT
Our research addresses only scientific questions and involves no human subjects, animals, or en- vironmentally sensitive materials. Therefore, we anticipate no ethical risks conflicts of interest. We are committed to upholding the highest standards of scientific integrity and ethics to ensure the validity and reliability of findings.
REPRODUCIBILITY STATEMENT
Our model is clearly formalized in the main text for clarity and thorough understanding. Detailed implementation, including dataset information, baselines, experimental settings, and model config- urations, are provided in Sections B and 5.I Experimental settings and baselines have been rigorously verified to ensure fair comparison. Code and pre-trained model weights will released upon acceptance.
USAGE OF LLMS
LLMs are used to proofread and polish the writing of this paper. We have carefully reviewed and verified all content generated by LLMs to ensure accuracy and integrity, Any errors inaccuracies in the final manuscript are solely our responsibility.
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Zhaocheng Zhu, Zuobai Zhang, Louis-Pascal Xhonneux, and Jian Tang. Neural bellman-foo net- works: general graph neural network framework for link prediction. Advances in Neural Infor- mation Processing Systems, 34:29476-29490, 2021.
Appendix
Table of Contents
A Detailed Related Work
16
A.1 I Graph Construction
16
A.2 Graph-enhanced Reasoning
16
Datasets Details
17
C
implementation Details
18
Training Details
18
Mixed Precision Training
Distributed bassage-passing
20
D
Additional Experiment
20
D.I
Comparison with Multi-step RAG methods
................................
20
Comparison on Full Musique Dataset
................................
21
D.3
Reasoning Explanation
21
D.4
Model Scaling Case Study
23
D.5
G-reasoner Case Study
23
Prompts
25
imitations and Future Work
25
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A DETAILED RELATED WORK
A.1 GRAPH CONSTRUCTION
Recently, graph retrievll-augmented generation (GraphRAG) has emerged as a promising approach to leverage structured knowledge to enhance the reasoning capabilities of large language models (LLMs). Nevertheless, suitable graphs are often unavailable for supporting complex multi-hop rea- soning task that span across scattered documents. To address this limitation, prior work has explored diverse graph construction strategies tailored to different types of reasoning tasks.
Document Graph. KGP (Wang et al., 2024) constructs document graphs using existing hyperlinks and KNN-based similarity. yet the resulting graphs fail to capture the nuanced semantic associations. RAPTOR (Sarthi et al., 2024) builds hierarchical tree through recursive summarization based on similarities of documents, and SiReRAG (Zhang et al., 2025a) further integrates relatedness with similarity to build tree-like indexing structures for documents.
Hierarchical Graph. To better model hierarchical structure, Microsoft GraphRAG (GraphRAG (MS) (Edge et al. 2024) utilizes LLMs to extract entities and relations from raw texts, and fur- ther incorporates community detection with summarization to generate hierarchical graph structure. Building on this line of work, LightRAG (Guo al. 2024) employs dual-level graph indexing pro- cess to facilitate efficient retrieval, whereas Youtu-Graphin GraphRAG (Dong et al. (025) introduces a ver- tically unified framework that exploits the graph schema to guide the graph construction. Similarly, ArchRAG (Wang et al.) 2025) leverages attributed communities (ACs) and introduces an efficient hicrarchical retrieval strategy.
Knowledge Graph. Beyond document graphs and hierarchical graphs, HippoRAG (Jimenez Gutier- rez et al. 2024) and HippoRAG 2 (Gutiérrez et al. 2025) leverage OpenlE techniques to induce knowledge graphs (KGs) that capture the relationships among factual knowledge. To mitigate the noise induced by OpenIE, KAG (Liang et al., (025) introduces the conceptual semantic reasoning and human-annotated annotated schemas to curate domain expert knowledge.
Despite their achievements, these methods are typically tailored for specific graph structures, and thus exhibit limited generalizability across different types of graphs. For example, the hierarchical graphs constructed by GraphRAG (MS) (Edge et al., (202]] and LightRAG (Guo et al., (2024) are primarily designed for summarization tasks, and may not be suitable for multi-hop reasoning tasks compared to the knowledge graphs used in HippoRAG (Jimenez Gutierrez et al. 2024).
A.2 GRAPH-ENHANCED REASONING
Graph-enhanced reasoning seeks enable LLMs to reason on the graph-structured knowledge to im- prove their performance on knowledge-intensee applications.
Graph Search. Inspired by hippocampal memory indexing theory, HippoRAG (Jmeenzz Gutier- rez et al. 2024) combines open knowledge graphs with personalized PageRank to support efficient knowledge retrieval knowledge graphs. Extending this, HippoRAG2 (Gutiérrez et al.) 2025) further incorporates documents into the knowledge graphs, thereby enabling deeper contextual un- derstanding. LightRAG (Guo al., (024) employs dual-level retrieval strategy with both the embedding-bbsedd retrieval and graph- based neighborhood expansion to enhance the retrieval per- formance. However, these graph search-based methods still fall short of fully exploiting the power of foundation models for reasoning.
Agent-based Reasoning, Another line of research explores the agent-driven graph reasoning and retrieval. For example, ToG (Sun et al.) (2024) employs LLM agents to sequentially interact with graphs and expands relevant reasoning paths for given queries, while ToG2 (Ma al., 2025) en- hances this process by interactively retrieving from both knowledge graphs and documents, thereby achieving context-aware retrieval for reasoning. KAG (Liang et al., (025) integrates the logical query solver during the agent-based reasoning, which will be called with the query generated by LLMs to perform symbolic reasoning on knowledge graphs. Youtu-Graphie GraphRAG (Dong et al.) 2025) further proposes agentic framework that leverages graph schema to guide the LLMs to inter- act with the graph for reasoning. Despite the effectiveness, these methods often incur substantial computational costs and suffer from high latency due to the multiple invocations of LLMss.
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GNN-based Reasoning. More recent efforts leverage graph neural network (GNNs) Wu et al. 2020 to reasoning over graph and enhance LLMs. GNN-RAG (Mavromatis & Karypis 2025b) firslly applies a GNN-based retriever to identify candidate entities for given question, and then verbalizes entities-induced reasoning paths to support LLMs reasoning. G-retriever (He et al. 2024) combines GNNs with L.Mss enhance the structure understanding of LLMs for reasoning knowledge graphs. SubgraphRAG (Li et al. 2025a) employs GNNs to encode the graph structure into the node representations, which are then used to retrieve relevant information for LLMs. More recently, GFM-RAG (Luo et al. 2025) proposes graph foundation model designed to enable rea- soning over different knowledge graphs. However, these approaches remain tailored for specific graphs and cannot generalize well across diverse types of graph structure. Although some GFMs have been designed, they primarily focus on graph-related tasks (e.g., node classífication (Zhao et al. 2024) and link prediction (Galkin et al. 2024)), making them unsuitable for GraphR tasks.
B DATASETS DETAILS
We first evaluate the effectiveness of G-reasoner on three widely-used multi-hop QA datasets, in- cluding HotpotQA (Yang et al.) 2018), MuSiQue (Trivedi et al. 2022), and 2WikiMultHHopAA (2Wiki) (Ho et al., 2020) and three GraphRAG benchmarks: G-bench (Novel) (Xiang el al. 2025),
G-bench (Medical) (Xiang et al., 2025). 3, and G-bench (CS) (Xiao al. 2025). We provide brief description of each dataset below.
HotpotQA (Yang et al. 2018) is a multi-hop QA dataset that requires reasoning over mul- tiple documents to answer questions. The dataset consists of 97k question-answer pairs, where each question is associated with up to 2 supporting and several distracting docu- ments. The questions are designed be answerable using multiple pieces of information from the supporting documents.
MuSiQue (Trivedi et al. 2022 a challenging multi-hop QA dataset with 25k 2-4 hop questions. It requires coherent multi-step reasoning answer questions that span multiple documents.
2WikiMultiHopQA (2Wiki) (Ho et al.) 2020) is a multi-hop QA dataset that requires reasoning over multiple Wikipedia articles answer questions. The dataset consists of 192k questions, which are designed to be answerable using information from 2 or 4 articles.
G-bench (Novel) & G-bench (Medical) (Xiag et al., 2025) are two Nomain-specitn datasets that are specially designed to evaluate GraphRAG models on both hierarchical knowledge retrieval and deep contextual reasoning. They feature comprehensive datasets with tasks of increasing difficulty, covering fact retrieval, complex reasoning, contextual summarization, and creative generation. G-bench (Medical) collects both domain data from NCCN medical guidelines to provide dense conceptual relationships (c.g., treatment pro- tocols linking symptoms, drugs, and outcomes). G-bench (Novel) collects novels from Gutenberg library to simulate real-world documents with implicit, non-linear narratives.
* G-bench (CS) Xiao et al. 2025) is a dataset that focuses college-level, domain-specific questions that demand multi-hop reasoning. G-bench (CS) provides comprehensive assess- ment across the entire GraphRAG pipeline, knowledge retrieval, answer generation, and logical coherence of the reasoning process. It contains 1018 questions in 5 question types spanning 16 topics and a corpus of 7 million words from 20 computer science (CS) text- books.
In experiments, for multi-hop QA datasets, we adhere existing methods (Jimenez Gutierrez et al., 2024 Luo et al.) 2025) to use the same 1,000 samples from each validation set to avoid data leakage. We merge the supporting and distractor passages as the document corpus for graph construction and retrieval. This setup allows to evaluate the model's ability to retrieve relevant information from challenging yet controlled environment, reflecting practical scenarios where the model must discern relevant knowledge from a large pool of documents. For G-bench datasets, follow (Xiang al.) 2025| Xiao et al., (025) to use the provided test sets and document corpus for evaluation. The statistics of the datasets summarized in Table
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Table Statistics of the training datasets.
# Query
Document
# Node
Relation
Edge
277,839
2,972,931
18,785,120
3,920,541
77,336,005
C IMPLEMENTATION DETAILS
C.I TRAINING DETAILS
Training Data. We gather the training data from Luo et al. (2025). which is based on the training sets of HotpotQA. MuSiQue, and 2Wiki, and construct diverse graph structures to train GFM. Specifically, the training data consists of 277,839 query samples and 2,972,931 document corpus. Each query is labeled with 2-4 supporting documents. We construct three types of graphs from documents, including knowledge graphs (KG) using HippoRAG (Gutiérrez et al. 2025), , knowledge graph document graph using LightRAG (Guo al. 2024). and hierarchical graphs using Youtu- GraphRAG (Dong et al. 2025).
The proposed QuadGraph presents comprehensive schema that integrates four layers: Community, Document, Knowledge Graph, and Attribute, which enables the representation of various graph types within single framework for training. The construction steps for HippoRAG, LightRAG, and Youtu-GraphRee are as follows:
HippoRAG Graph Construction (Jimenez Gutierrez et al. 2024 Gutiérrez et al. 2025):
HippoRAG contains the knowledge graph layer. We follow the original HippoRAG method to first extract entities, relations, and triples from the document corpus using an LLM--ased information extraction approach. Then, build knowledge graph layer by connecting entities based on the extracted triples.
LightRAG Graph Construction (Guo et al. 2024): LightRAG employs a dual-Ievel graph indexing process with knowledge graph and document graph. It also first extracts entities and relations from the documents to build a knowledge graph layer. The document layer is constructed by linking documents to the entities they mention.
Youtu-GraphRAC Graph Construction (Dong et al. (025):: Youtu-GraphhRG proposes hierarchical graph structure with community, document, knowledge graph, and attribute layers, which cover all four layers of QuadGraph. We follow their method to build each layer and connect them accordingly. The knowledge graph first constructed with schema- bound extraction, and then documents are linked to the entities they mention. Communities are formed by clustering entities with the consideration of both their topographical connec- tivity and semantic similarity. Attributes are extracted from documents and linked to the corresponding entities.
To ensure efficiency, we split large graphs into smaller subgraphs with around 100k nodes and group the relevant queries for each subgraph during training. The statistics of the training data are summarized in Table 7
Model Settings. The GFM used in G-reasoner is implemented with a 6-layer guery-dependent GNN with a hidden dimension of 1024, DistMult message function, and sum aggregation. The relation update function g-()) is implemented as 2-layer MLP. We use the sentence embedding model with a dimension of 1024. The total training parameters the GFM 34M.
-...edddnn-O.BBB as the
Training Settings. The GFM is trained with 16 1 GPUs (80G) for epochs with a batch size of 2. We use AdamW optimizer with learning rate set to 5e-4. The À for KL divergence is set to 0.01. We also include the ranking loss used in GFM-RAG (Luo et al. 2025) to improve training stability. We apply B mixed precision training to reduce memory usage and improve training throughput. The training takes around 7 days to complete. The detailed hyperparameter settings are summarized in T
Evaluation Settings. During the evaluation, for multi-hop QA datasets, we merge the supporting and distractor passages for each query as the document corpus for graph construction and retrieval.
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Table 8: Statistics of evaluation graphs constructed by different graph constructor.
Graph Constructor
HippoRAG
LightRAG
Youtu-GraahRRAG
# Node
105,256
85,130
200,533
HotporQA
# Relation
24,117
54,725
7,317
# Edge
447,131
186,922
556,055
# Node
112,504
92,637
219,408
MusiQue
# Relation
27,973
65,404
8,471
# Edge
464,638
210,456
636,276
# Node
54,898
47,361
90,258
2Wiki
# Relation
10,375
101,987
2,259
# Edge
227,628
25,237
265,287
# Node
29,825
G-hench (Novel)
# Relation
11,244
# Edge
108,221
# Node
10,515
G-bench (Medical)
# Relation
3,373
# Edge
61,056
# Node
217,071
G-bench (CS)
# Relation
36,797
# Edge
1,750,491
Table 9: The detailed implementation and training settings of G-reasoner.
Layer Hidden dim
1024 DistMult Sum
Message Aggregation
GFM
g'(.)
2-layer MLP Qwen3--inbeddinng-..6
Sentence embedding model
X Optimizer Learning rate Batch size Precision
0.01
AdamW
5e-4
Training
16
Training epochs
10
We use the trained GFM to predict the relevance scores of nodes for each query and select the top-k nodes from each node type to construct the prompt for LLMs. We set k 5 for multi-hop QA datasets, and A 10 for G-bench datasets for fair comparison with existing results. To test the generalizability of G-reasoner across different graph structures, we evaluate G-reasoner on three graph constructors (HippoRAG, LightRAG, Grauu-GraphRAG) for each evaluation dataset. The statistics of the constructed graphs are summarized in Table [ The results reported in Table 2 and Table are obtained with the graph constructed by HippoRAG.
C.2 MIXED PRECISION TRAINING
We apply BFloat16 mixed-precision training to reduce memory usage and improve through- put. Mixed precision runs compute-heavy operations (e.g., message-passing) while keeping numerically sensitive operations (e.g., reductions) in float32, which typically boosts throughput and reduces memory footprint. This enables training larger models or using larger batch sizes without exhausting GPU memory. However, enabling mixed precision for graph foundation models non-trivial: we must carefully manage numerical stability during gradient computation in message passing. To address this and fully exploit hardware acceleration, implemented custom
lower precision
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Lorge -cale Graph
METIS Graph Partition
Local Message-pant passsigg
Reduce and Aggregation
GPU
GPU
GPU
GPU 2
GPU
GPU
GPU N
GPU N
Figure 5: The illustration of distributed message passing in G-reasoner.
Table 10: Performance and efficiency comparison with multi-step RAG methods.
HotporQA
MuSiQue
2Wiki
Method
EM
F1
Time / sample
EM
F1
Time / sample (s)
EM
F1
Time sample (8)
IRCoT
45.5
58.4
1.146
19.1
1.152
35.4
45.1
2.095
R1-searcher
61.2
73.8
0.532
34.7
48.4
58.3
71.1
0.713
Search-R1
60.8
74.3
0.496
37.4
54.6
68.7
0.652
G-reasoner
61.4
76.0
0.114
38.5
52.5
0.125
74.9
82.1
0.058
CUDA backward kernels for custom relational message-passing that accumulate gradients in float32, mitigating precision loss while preserving the speed benefits of nower-precision compute.
C.3 DISTRIBUTED MESSAGE-PASSINGG
With the customized message-passing CUDA kernels, the memory complexity of GFM is reduced to O(V d) (Zhu et al. 2021). According to the neural scaling law observed for GFM (Luo et al. 2025) the performance of GFM improves as we increase the model size (i.e., hidden dimension) and the training data size (i.e., number of nodes in graphs). However, the memory consumption of GFM still grows linearly with the number of nodes and hidden dimension, which limits the scalability of GFM
single GPU. To address this, algorithm that partitions the graph across multiple GPUs and performs message-passing in parallel. As shown in Figure 5 partition the nodes of the graph into N disjoint sets using the METIS algorithm [aryypi & Kumar 1997) and assign each set to different GPU. During the message- passing, each GPU computes the messages for its assigned nodes and exchanges the messages with other GPUs as needed. This allows us to scale GFM to larger graphs and model sizes by leveraging more GPU resources. Different from the existing distributed GNN training methods (e.g.. PyG (Fey et al. 2025). DGL (Wang et al. 2019)) that use graph sampling, our distributed message-passing algorithm enables full-graph training. This is crucial for preserving the graph structure and ensuring effective reasoning with GFM by passing messages across the entire graph.
implement distributed message-passinn
D ADDITIONAL EXPERIMENT
D.1 COMPARISON WITH MULTI-STEP RAG METHODS
To demonstrate the effectiveness of G-reasoner, we compare the performance with advanced multi- ReSearcher (Song et al.) 2025a), and Search- R1 (Jin et al., 2025).. From the results in Table 10 we observe that G-reasoner outperforms these advanced RAG systems across all three datasets, demonstrating its effectiveness in multi-hop ques- tion answering tasks. While these RAG systems, powered by powerful LLM agents, are designed for iterative retrieval and reasoning, they often lack the ability to effectively capture and leverage the rich relational structure present in graph-structured knowledge. In contrast, G-reasoner 's inte- gration of GFM-based graph reasoning allows it to better utilize this structure, leading to improved performance. Moreover, the iterative nature of these RAG systems be computationally expen-
step RAG methods (e.g., IRCoT (Trivedi et al. 2023
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Table 11: Dataset Statistics of MuSiQue-Full dataset.
Dataset
# Test
# Document
Node
# Relation
Edge
MuSiQue-Full
2,417
21,100
19,4817
45,437
3,024,388
Table 12: Evaluation of G-reasoner on MuSiQue-Full dataset.
MuSiQue-Full
EM
Tive3--Em--83
29.21
42.04
HippoRAG
24.62
36.16
GFM-RAG
23.40
33.87
G-reasoner
33.64
47.89
sive due to multiple rounds of retrieval and LLM reasoning, whereas G-reasoner achieves efficient end-to-end reasoning in single forward pass.
D.2 COMPARISON ON THE FULL MUSIQUE DATASET
To further validate the effectiveness of G-reasoner real-world scenarios with a larger and noisier document corpus, we conducted additional experiments on the full dev set of the MuSiQue dataset using an expanded corpus that includes all supporting and distractor passages. The dataset statistics summarized in Table I1] From the results in Table 12] can observe that with the larger corpus, the performance of previous graph-based baselines (HippoRAG, GFM-RAG) drops significantly due worse than conventional embedding-basse
methods (Qwen3-emb-- In contrast, G-reasoner maintains strong performance, demonstrating its robust- ness and effectiveness handling larger, more complex graphs. This validates our claim that G- reasoner is applicable to real-world scenarios where knowledge is vast and diverse. Moreover, in real-world applications, G-reasoner can be further integrated with some pre-filterigg retrieval meth- ods (e.g., dense retrieval) to first narrow down the candidate documents before graph construction, making it scalable to even larger corpora.
the increased retrieval difficulty and
D.3 REASONING EXPLANATION
In addition achieving high accuracy in final answers, G-reasoner also excels at generating reason- Following [iao et al. (2025), we evaluate each method's reasoning explanations using the reasoning score (Avg R) to measure semantic alignment and con- sistency with ground-truth explanations, along with the Avg AR metric to assess whether the model provides correct reasoning when it answers questions accurately.
ing explanations, as shown in Table 13
The results in Table demonstrate that G-reasoner outperforms existing methods in both Avg R and Avg AR, indicating its superior ability to generate coherent and accurate reasoning explanations, reducing the hallucination of LLMs and enhancing the interpretability of the reasoning process. The case studies of the generated reasoning explanations are presented Table 14
Table 13: Comparison of reasoning explanation on G-bench (CS)
Xiao et 2025
Method
Avg
Avg AR
GPT-44-mini OOpenAl
2024
55.5
39.8
BM-25 (Roberison & Walker 1994
59.2
44.2
DALK (Li el al. 2024
58.9
42.1
KGP WWng al. (0044
58.7
43.3
GraphRAG (Edge et al
2024
59,4
43.3
ToG Sun et al. 2024)
60.1
44.0
G-reasoner
60.2
44.7
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Table 14: Case studies for reasoning explanation of G-reasoner on G-bench (CS) (iiao et al. 2025
Query
An upper ontology is named for position at the top of hierarchical graphs, where includes general concepts, with more specific concepts placed below.
Answer
True
Ground-truth Rational
An upper ontology is a framework for general concepts placed the top of hierarchical graphs, with more specific concepts arranged beneath it.
Retrieved Document
The general framework of concepts is called an upper ontology because of the convention of drawing graphs with the general concepts at the top and the more specific concepts below them.
Generated Rational
The statement describes the structure of an upper ontology accurately. An upper ontology is indeed characterized by its hierarchical organiza- tion, where general concepts
positioned at the top, and more specific concepts placed below them. This aligns with the provided context. which explains that the upper ontology serves gorizing knowledge. The use of hierarchical graphs represent these relationships further supports the truth of the statement. ANSWER: True
framework for cate-
Which methods are effective for preventing SQL Injection?
A: Parameterized queries
Query
B: Input sanitization
C: Disahling HTTP methods
D: Web Application Firewalls (WAF)
Answer
ABD
Ground-truth Rational
Parameterized queries, input sanitization, and WAFs are effective strate- gies to prevent SQL. Injection by ensuring safe data handling and filtering malicious requests.
Retricved Document
1. Use of prepared statement would prevent this problem because the input string would have escape characters inserted.
To avoid this kind of SQI. injection, the application should ensure that the orderAt t r bute variable value one of the allowed values.
Programmers must pass user-input strings the database only through parameters of prepared statements; creating SQL queries by concatenat- ing strings with user-input values is an extremely serious security risk and should never he done in any program.
Generated Rational
To determine the effective methods for preventing SQL Injection, we can analyze each option:
A: Parameterized queries - This is well-known and effective method for preventing SQI. injection. By using parameterized queries (or pre- pared statements), user input is treated as data rather than executable code, which prevents attackers from injecting malicious SQL. B: Input sanitization - This method involves cleaning and validating user input to ensure it does not contain harmful characters patterns. While it can help reduce the risk of SQL injection. it is not foolproof on its own, as it can be difficult to cover all edge cases. However, it is still a useful practice when combined with other methods
C: Disabling HTTP methods This option is not directly related to preventing SQL injection. Disabling certain HTTP methods (like PUT or DELETE) can enhance security in general but does not specifically address SQL injection vulnerabilities.
D: Web Application Firewalls (WAF) A WAF can help detect and block SQL injection attempts by filtering out malicious requests before they reach the application. While it is not a substitute for secure coding practices, it is effective layer of defense against SQL injection attacks. Based this analysis, the correct answers are A, B, and D. C. is not relevant to SQL injection prevention. ANSWER: ABD
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D.4 MODEL SCALING CASE STUDY
With the implemented mixed precision training and distributed message-passing. G-reasoner can efficiently scale to larger graphs and model sizes with computational resources. The number of required GPUs can be empirically estimated as
#PPU (1) + dd 2.56 10
(12)
GPU Memory
where [V] is the number of nodes in the graph, d is the hidden dimension of GFM. It can be helpful to estimate the required GPUs for using G-reasoner on different graph sizes and model sizes.
We illustrate some example configurations in Figure From the results, with 32 A100 GPUs (80G), G-reasoner can scale to graphs with 800k nodes and a hidden dimension of 8192, which around 2B parameters. With more GPUs, G-reasoner can further scale to larger graphs and model sizes and achieve better performance as suggested by the neural scaling law (Luo et al. 2025
Graph Size ||]
100K
200K
400K
800K
8192 (2R)
Dimension d (Params)
(5166)
2048 (132M)
1024 (34M)
10
20
25
30
# GPU (80GB)
Figure Scaling of G-reasoner with different model sizes and graph sizes.
D.5 G-REASONER CASE STUDY
In this section, we first illustrate the versatile prediction results of G-reasoner. As shown Table 15 given query, G-reasoner can not only retrieve relevant documents to support the reasoning of LLMs, but also predict relevant entities that can be used to guide the reasoning process of LLMs. The G-reasoner exhibits great interpretability by quantifying the importance of reasoning paths. The paths' importance to the final prediction can be quantified by the partial derivative the prediction score with respect to the triples each layer (hop), defined as:
82, 81. = arg top- k Ope(q)
(13)
Os:
The top-k paths are selected based on the product of gradient scores over triples forming the path, which approximates the contribution of that path the final prediction via the chain rule. This allows us to identify influential multi-hop reasoning chains and interpret the model's behavior. We illustrate the top-2 path interpretations in the Table [6] the first example, the GFM identifies the path from the film entity to the director entity through the "created by" relation, and then links to the document mentioning the director. In the second example, it traces from Lady Dorothy Macmillan to her father through the "is the daughter of" relation, and then to the document mentioning him. These paths illustrate how the GFM leverages graph structure to connect entities and documents, providing interpretable reasoning chains that lead to the final answer.
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Table 15: Case studies for versatile prediction of G-reasoner. Relevant predictions are highlighted in bold.
Query
which county the lown which Raymond Robertsen was born ?
Answer
Finnmark county,
Raymond Robertsen Hammerfest
Supporting Documents (Tidl)
1. cumberland county
Entity Prediction (Top-3)
2. finnmark
3. pacific county
Raymond Robertsen Hammerfest
Document Prediction (Top-3)
Raymond, Maine
Query
Who the president of newly declared independent country that formed the Timor Leste Commission of Truth and Friend- ship with the country where Pantar is found?
Answer
Francisco Guterres
Blagar language
Supporting Documents (Title)
Indonesia Timor Leste Commission of Truth and Friendship
Fast Timor
1. indonesia timor leste commission of truth and friendship francisco guterres democratic republic of timor leste
Entity Prediction (Top-3)
Indonesia Timor Leste Commission Truth and Friendship East Timor
Document Prediction (Top-3)
Blagar language
Table 16: Path interpretations G-reasoner for multi-hop reasoning, where - denotes the inverse of the original relation, and bold highlights the supporting documents occurred in the paths.
Question
Where was the director of film Flagy And Waves bom??
Answer
Toronto
Supporting Docs.
("William Reeves (animator)", "Flags and Waves"]
2.1465: [flags and waves (entity), is w.mmeniionedinn Flags and Waves (document) 1.3665: [flag and waves (entity). created by. bill reeves (entity)) [bill reeves (entity). (entity)) [william Reeves (animator) (document)
Paths
equivalent, william
(entity), mentioned. in. William
Question
Where was the place of death of Lady Domothy Macmillon's 's father?
Answer
Derbyshire
Supporting Docs.
"Victor Cavendish. 9th Duke of Devonshire". "Lady Dorothy Macmillan")
1.4286: [lady dorothy evelyn macmillan (entity), the daughter of, victor cavendish (en-
tity).] -> [victor cavendish (entity). is me.ntioonddiin. Victor Cavendish, 9th Duke of De- vomshire (document) ]
0.7685: lady dorothy evelyn macnillan (entity), m.ntiinnedd in, Lady Dorothy Macmil- lan (document)
Paths
Lady Dorothy Macmillan (document), is fy_mentione_in -1, 9th duke of devonshire (entity) J | 9th duke of devonshire (entity). holds the title of Victor Cavendish, 9th Duke of Devonshire (cutity) [ 9th duke of devonshire (entity), is E_mennionnd__nn in, Victor Cavendish, 9th Duke of Devonshire (document) ]
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E РROMPTS
The prompts used our experiments are presented in Figure 7 G-reasoner (i.e., supporting documents and entities) the LLMs guide the reasoning process.
We feed the versatile predictions of
LLM Reasoning Prompt
As an advanced reading comprehension assistant, your task to analyze text passages and corresponding questicns meticulously. Your response start after "Thought: ", where you will methodically break down the reasoning process, illustrating how you arrive conclusions, Conolude with "Answer: to present a concise, definitive response, devoid additional elabcrations."
#+# Document:
<Document 1>
<Document 2>
<Dccument n>
HI Entity: <Entity
<Entity 2>
<Entity
++ Question:
<Question>
Thought:
Figure 7: The prompt template for LLM Reasoning.
F LIMITATIONS AND FUTURE WORK
The limitations of G-reasoner are as follows: (1) The current framework single-mddality focused on text-based graphs. However, real-world knowledge often contains multi-modal data (e.g., images, audio). Extending G-reasoner to handle multi-modal graphs is an important future direction. (2) The GFM and LLMs used are integrated as separate modules. Despite the flexibility, tighter end-to-end integration may yield further performance gains, where the GFM and LLM can be co-trai co-trained to better identify and utilize graph graph-structured knowledge to support reasoning.
(3) The G-reasoner currently focuses question answering tasks. Extending it to other reasoning tasks (e.g., agent planning) an interesting direction for future work.
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Reviews:
[{"review_id": "KPDmkoYPVt", "reviewer": "Reviewer_qUas", "summary": "The paper introduces G-Reasoner, a framework designed to unify graph reasoning and language reasoning by integrating Graph Foundation Models (GFMs) with Large Language Models (LLMs). The core idea is that while LLMs are powerful reasoners, they lack structural awareness; conversely, graph structures effectively represent relational knowledge but are underutilized in LLM reasoning. To address this, the authors propose three components: QuadGraph, Graph Foundation Model (GFM),  and a LLM-enhanced reasoning.\nExperiments on six benchmarks show consistent improvement over state-of-the-art methods such as GraphRAG, HippoRAG, and GFM-RAG.", "questions": "See weakness.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. The paper introduce clear motivation and organization. The introduction frames the LLM-graph reasoning gap well. I also like the figures and tables that effectively summarize the pipeline and results.\n2. The paper proposes a four-layer unified graph schema, which is elegant and general enough to cover multiple graph types (KGs, document graphs, hierarchical graphs).\n3. The paper also conduct comprehensive evaluation. Benchmarks include both general QA datasets and domain-specific GraphRAG benchmarks. The ablations also clearly demonstrate component contributions. Efficiency experiments are also included.", "weaknesses": "1. The paper has an incremental novelty. The core idea (combining GNN-based reasoning with LLM retrieval) closely follows existing works like GFM-RAG and GNN-RAG; the contribution feels like a well-engineered unification rather than a conceptual breakthrough.\n2. The overall methodological depth is limited. For example, the GFM architecture largely reuses standard DistMult-style message passing and MLP updates without introducing new graph learning techniques. The “QuadGraph” abstraction appears more of a schema design than a learnable innovation.\n3. The paper can benefits from some interpretability analysis or case-study. The paper shows gains but does not deeply analyze why G-Reasoner helps LLM reasoning or how graph structure contributes. A case-study with visualization of reasoning paths showing the predicted top-k scores from the graph will be great.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "LE8EJRdX7n", "reviewer": "Reviewer_gBkL", "summary": "The paper proposes G-reasoner, a novel framework integrating graph and language foundation models for reasoning over graph-structured knowledge. The authors introduce QuadGraph, a unified graph abstraction, and a 34M-parameter Graph Foundation Model (GFM) powered by Graph Neural Networks (GNNs). The paper is theoretically sound and demonstrates strong experimental performance across six benchmarks, including QA datasets as well as domain-specific benchmarks.", "questions": "**Model Complexity and Interpretability:**\n\nThe GFM model, while powerful, could benefit from further discussion on its interpretability. Complex models like GNN-based foundation models often suffer from interpretability issues, making it hard to explain the reasoning behind predictions. This is particularly important for high-stakes applications such as legal | medical | safety reasoning.\n\n**Lack of Comparison with State-of-the-Art GNN Methods:**\n\nWhile the model is compared with existing graph-enhanced RAG methods, there is no direct comparison with state-of-the-art GNN-only models or pure graph models that don’t involve retrieval-augmented generation. This comparison could provide clearer insight into the unique benefits of GFM.", "limitations": "", "rating": 6, "confidence": 3, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "**Novel Contribution:**\n\nThe QuadGraph abstraction is a promising innovation that unifies different types of graph structures, such as knowledge graphs and document graphs, into a single standardized representation. This is a key step toward addressing the challenge of generalizing reasoning models across diverse graph types.\n\nThe integration of GNN-based reasoning into a graph foundation model that leverages both graph topology and textual semantics is a novel approach that aligns well with current trends in deep learning and knowledge representation.\n\n**Strong Empirical Results:**\n\nG-reasoner consistently outperforms state-of-the-art methods, including graph-enhanced techniques like GraphRAG and HippoRAG, in multi-hop reasoning tasks across diverse domains.\n\nThe approach demonstrates strong cross-graph generalization, which is critical for real-world applications involving diverse knowledge domains.\n\n**Efficient Training and Reasoning:**\n\nThe use of mixed-precision training and a distributed message-passing mechanism allows the model to scale effectively across large datasets and graph structures, ensuring both efficiency and performance.", "weaknesses": "See Questions.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "2CoWKEbPQK", "reviewer": "Reviewer_jHd3", "summary": "This paper proposes G-Reasoner, a GNN foundation model-based system for unified reasoning over graph-structured knowledge.  The G-Reasoner system contains three main modules. It first constructs a hierarchical heterogeneous QuadGraph, which contains Community, Document, Knowledge Graph, and Attribute four layers. Then it devises a query-dependent GNN as the graph foundation model for relevant context retrieval. Given a question q, the model predicts whether each of the node (can be entity, document, passage, community and attribute) contribute positively to answer the question. To mitigate the label scarcity issue, the authors proposed to leverage pre-trained language models for knowledge distillation. In order to support training on large-scale graph, the authors propose mixed precision training.  Finally, the LLM reasoning step receives top-k relevant node, and use them to augment the LLM for final answer generation.", "questions": "1. The reviewer suggests the authors to add the following experiments:\n\na)\tSince the performance reported in Table 2 is not directly comparable to the multi-hop QA results in [1–8], re-evaluating some baselines on the current evaluation knowledge corpus (same subset of distractive passages) may help clarify the advantages and necessities of the proposed graph search over iterative dense/sparse document retrieval.\n\nb)\tScaling-up the scope for the document corpus used in evaluation. For example, the MuSiQue dataset only contains 25k questions. Using all supporting documents can be less challenging than the 2Wiki dataset. This may help in validating the effectiveness of the proposed method when being adapted to real-world scenarios.\n\n2. [Minor] Apart from providing references for graph construction solutions, the reviewer suggests the author to include a short description to introduce how we can construct the unified graph in this paper. \n\n3. For the HotpotQA, MuSiQue, and 2Wiki datasets, *performance can vary substantially under different evaluation settings* (such as open-domain, distractor, etc.)  To avoid confusion, apart from providing relevant references, the reviewer suggests the authors to explicitly state the retrieval scope for evaluation. It can be included in the appendices if the main sections do not have enough space.\n\nThe reviewer is willing to raise the score if the aforementioned issues are addressed.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 4, "contribution": 2, "strengths": "1.\tThe concept of a Unified QuadGraph is novel and, to the best of the reviewer’s knowledge, is introduced here for the first time.\n\n2.\tThe proposed method G-Reasoner, outperforms competitive GNN/Graph Retriever-based methods in multi-hop question answering. \n\n3.\tThe prerequisite knowledge from sentence transformers shows to be useful in guiding the GNN. \n\n4.\tThe proposed mix precision training technique achieves higher throughput with lower GPU memory usage.", "weaknesses": "1.\tG-reasoner does not propose new graph construction methods. Instead, it reuses graph construction techniques from three baseline methods, namely HippoRAG2, LightRAG, and Youtu-GraphRAG. \n\n2.\tG-reasoner is not compared with various RAG systems, including but not limited to Search-o1 [1], Search-R1 [2], RAG-Gym[3], R1-Searcher [4], Collab-RAG [5] and RAG-Star [6], ReSearch [7], IRCoT [8]. \n\n3.\tThis manuscript and its related baselines adopt an alternative evaluation setup than the commonly-adopted Multi-hop QA evaluation setting. The commonly-adopted setup constructs the retrieval corpus with  the full Wikipedia dump [2,3,4,6,7] or all supporting plus distractor passages from all training, validation, and test questions [8]. \n\nIn contrast, as stated in section 5.1, the manuscript follows the settings used in Gutierrez et al., 2024 (HippoRAG), which “*collect all candidate passages (including supporting and distractor passages) from our selected (the 1000) questions and form a retrieval corpus for each dataset*”.  This retrieval environment is overly idealized and does not reflect real-world conditions. \n\nThe reviewer understands that constructing a graph over the full text corpus is practically challenging. However, based on the existing experimental results, one cannot conclude that: (1) the proposed graph retrieval method outperforms iterative RAG approaches (or can achieve competitive performance), and (2) the proposed G-reasoner is robust to larger graphs and applicable to open-domain question answering.\n\n[1] Li et al., Search-o1: Agentic Search-Enhanced Large Reasoning Models\n\n[2] Jin et al., Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning\n\n[3] Xiong et al., RAG-Gym: Systematic Optimization of Language Agents for Retrieval-Augmented Generation\n\n[4] Song et al., R1-Searcher: Incentivizing the Search Capability in LLMs via Reinforcement Learning\n\n[5] Xu et al., Collab-RAG: Boosting Retrieval-Augmented Generation for Complex Question Answering via White-Box and Black-Box LLM Collaboration\n\n[6] Jiang et al., RAG-Star: Enhancing Deliberative Reasoning with Retrieval Augmented Verification and Refinement\n\n[7] Xie et al., Interleaved Reasoning for Large Language Models via Reinforcement Learning\n\n[8] Trivedi et al., Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "TavNnAeSUT", "reviewer": "Reviewer_voRT", "summary": "LLMs reason well but are constrained by static, incomplete parametric knowledge. RAG helps by fetching external info, yet current methods falter on knowledge-intensive tasks due to fragmented sources and weak structure modeling. They propose G-reasoner, which unifies graph and language foundation models for reasoning over diverse graph-structured knowledge. Its core is QuadGraph, a four-layer abstraction that standardizes heterogeneous sources into a single graph. They also introduce a 34M-parameter Graph Foundation Model (GFM) that captures both topology and text and integrates with LLMs. With mixed-precision training and distributed message passing for scalability, experiments on six benchmarks show state-of-the-art results, stronger LLM reasoning, better efficiency, and cross-graph generalization.", "questions": "There is no mention of this paper’s limitations. What are some of the limitations of this paper?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The experimental validation of the method was well conducted based on a variety of experiments.\n\n2. A general-purpose framework that can be extended to any data type was proposed.\n\n3. It achieved higher performance compared to other baselines.\n\n4. Despite its small size of 34M, it demonstrated efficient and strong performance.", "weaknesses": "Overall, this is a well-written paper with solid experiments. However, one weakness is that each component of QuadGraph (such as the document-level hierarchy and the knowledge graph hierarchy) integrates existing concepts rather than introducing entirely new ones, which limits its novelty. The same observation applies to the GFM component as well.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)