Can Knowledge-Graph-based Retrieval Augmented Generation Really Retrieve What You Need?
Authors: Junchi Yu
Yujie Liu
Jindong Gu
Philip Torr
Dongzhan Zhou
Venue: NeurIPS 2025 spotlight ()
Keywords: graph machine learning
knowledge graph
LLM
Primary area: applications

Abstract: Retrieval-Augmented Generation (RAG) based on knowledge graphs (KGs) enhances large language models (LLMs) by providing structured and interpretable external knowledge.
However, existing KG-based RAG methods struggle to retrieve accurate and diverse information from text-rich KGs for complex real-world queries.
Process Reward Models (PRMs) offer a way to align the retrieval process of KG-based RAG with query-specific knowledge requirements, 
but they heavily rely on process-level supervision signals that are expensive and hard to obtain on KGs.
To address this challenge, we propose GraphFlow, a framework that efficiently retrieves accurate and diverse knowledge required for real-world queries from text-rich KGs.
GraphFlow employs a transition-based flow matching objective to jointly optimize a retrieval policy and a flow estimator.
The flow estimator factorizes the reward of the retrieval outcome into the intermediate retrieval states.
Such reward factorization guides the retrieval policy to retrieve candidates from KGs in proportion to their reward.
This allows GraphFlow to explore high-quality regions of KGs that yield diverse and relevant results. 
We evaluate GraphFlow on the STaRK benchmark, which includes real-world queries from multiple domains over text-rich KGs. 
GraphFlow outperforms strong KG-RAG baselines, including GPT-4o, by 10\% on average in hit rate and recall. 
It also shows strong generalization to unseen KGs, demonstrating its effectiveness and robustness.

Can Knowledge-Graph-basee Retrieval Augmented Generation Really Retrieve What You Need?
Junchi Yu', Yujie Liu?, Jindong Gu', Philip Torr',
Dongzhan Zhou?*
Department of Engineering Science, University of Oxford, UK
Shanghaii Artificial Intelligence Laboratory, China
junchi. yudeng. ox. uk, liuyujie1@pjaat org. en, jindong guueng ox ac. philip. torrdeng.... ox. ac. uk, zhoudongzhan@pjlabb.oo org.
sunchiyyueng.
Abstract
Retrieval Augmentted Generation (RAG) based knowledge graphs (KGs) hances large language models (LLMs) with structural and textual external knowl- edge. Yet, existing KG-based RAG methods struggle retrieve accurate and diverse information when handling complex queries. By modeling KG-based retrieval multi-step decision process, Process Reward Models (PRMs) offer a promising solution to align the retrieval behavior with the query-specific knowledge requirements. However, PRMs heavily rely on process-level supervision signals that are expensive and hard to obtain on KGs. To address this challenge, we propose GraphFlow, framework that efficiently retrieves accurate and diverse knowledge required for complex queries from text-rich KGs. GraphFlow employs a detailed balance objective with local exploration to jointly optimize retrieval policy and a flow estimator. The flow estimator factorizes the outcome reward of the retrieval results into the intermediate retrieval steps. Such reward factorization guides the retrieval policy to retrieve candidates from KGs in proportion to their outcome reward. This allows GraphFlow to explore relevant regions KGs that yield diverse and accurate results. We evaluate GraphFlow on STaRK benchmark, which includes real-world queries from multiple domains over text-rich KGs. GraphFlow outperforms strong KG-based RAG baselines including GPT-4o by 10% perfor- mance gain on both retrieval accuracy and diversity metrics. GraphFlow also shows strong generalization by effectively retrieving information from unseen KGs support new-domain queries, highlighting its effectiveness and robustness
Introduction
Cetrieval-Augmenne Generation (RAG) 336 has emerged as promising framework to reduce the hallucination of Large Language Models (LLMs) by mitigating the gap between model knowledge and factual knowledge 28
Traditional RAG usually employs an unstructured vector-indexed database the external knowledge source, where the text corpus is indexed using pretrained encoders to support retrieval. Recent work explored knowledge graphs (KGs) 68 a structural alternative to the external knowledge source of RAG [59]. KGs enjoy several advantages over the vector-indexed database traditional RAG, such as representing relational information with graph structures (88), integrating knowledge from heterogeneous resources [59], and enhancing interpretability by neural- symbolic reasoning (87). Thus, KG-based RAG has demonstrated great potential enhancing LLMs in many domains, including medical diagnosis [67] biochemistry (77), and physics [72]
89 26
"Corresponding Author
"oode available https: httpp/ttth..cee/m/mmooo/ee///aap
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Recent KG-based RAG methods employ two approaches to retrieve information from KGs when receiving an imput query 47
19 48
Retrieval-based approaches [2] 37 80 leverage pretrained
language models [62 to encode KG text into embeddings, and use retriever models 31 to identify relational triplets or subgraphs in KGs that most support the query. And agent-based methods treat
LLMs as searching agents to navigate across KGs and retrieve a relational path with supporting
information for a given query 668 47 50].
While KG-based RAG methods show promise in retrieving structural information for simple relational queries, their effectiveness is limited in more complex ones. As shown in Figure I structural information in KGs alone is often sufficient for many relational queries. For example, retrieving the relation triplet (Alice, daughter, Bob) adequately answers the question "Who the father of Alice?".
However, complex queries typically require leveraging both structural and textual information during retrieval [50] Consider the paper searching query "Please list the papers published by Uni- versity A relevant to research topic B". Addressing such query requires understanding authorship and affiliation rela- tionships, as well as text de- scriptions of paper content, research topies, institutions, and authors. The fusion of relational and text knowledge presents a significant challenge for accurate retrieval with KG-based RAG methods. Another challenge is the diversity of retrieval targets in complex queries. Unlike relational queries
Who the father Alice?
@: Li the papers published by University related 5?
Author counte Author cilatoon 7124
ABice
Bcb
Popper
Toackey
Sam Prind
Ashersipy
infeemeeee ofher chiter solleee.
Figure 1: Comparison of the retrieval tasks between relational and complex queries.
corresponding to single deterministic retrieval target (e.g., Alice daughter, Bob), complex queries require retrieving a diverse set of candidates. For example, the paper searching query in Figure| [] corresponds to multiple retrieval targets. Therefore, KG-based RAG must emphasize both retrieval accuracy and diversity when supporting complex queries. Unfortunately, our empirical findings reveal that existing KG-based RAG methods face challenges in achieving this goal.
To overcome the above challenge,
essential to align the retrieval process of KG-based RAG with the diversity and accuracy demands for complex queries. Process Reward Models (PRMs) [54] 90) [2] 85) offer a promising framework for this goal. By providing step-wise guidance, PRMs have been widely used in LLM alignment [40, reasoning 7 and planning [6] when treating these tasks as the multi-step decision. In KG-based RAG, the retrieval process can be naturally viewed as multi-step decision process, where agent traverses KG and expands its retrieval trajectory at each step. PRMs can provide step-wise guidance for the agent to retrieve desired information for complex queries. However, training PRM needs high- preference datasets with fine- mne-grained and process-level reward signals [57 76 27 In KG-based RAG, assessing the process-level reward at each step in retrieval trajectories is expensive. Only the outcome reward is easily available (i.e., whether the retrieval trajectory can support a query or not).
Proposed Work. We present GraphFlow, a novel framework for supporting complex queries by retrieving accurate and diverse knowledge from knowledge graphs (KGs), without relying on process-level reward supervision. Inspired by GFlowNet B3) GraphFlow formulates the problem of retrieving from KGs as learning retrieval policy that generates retrieval trajectories with probabilities proportional to their outcome rewards. Thus, the retrieval trajectory that better supports the query is retrieved with higher probability, leading to diverse and accurate retrieval results. To achieve this, GraphFlow jointly trains the retrieval policy with a flow estimator, which assigns non non-negative flow values to partial trajectories. These flow values decompose the final outcome reward across intermediate retrieval steps, thereby providing rich supervision signals without requiring explicit process-level rewards. The retrieval policy is guided by these flow values and receives proeess-level supervision "for free". We adopt the detailed balance objective [64] to co-train the retrieval policy and the flow estimator. To further enhance training efficiency, we introduce local exploration strategy that reduces visits to low-reward regions of the KG. Thus, GraphFlow effectively explores high-reward regions of the retrieval space, leading to more accurate and diverse retrievals that better support complex query.
2

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We evaluate the effectiveness of GraphFlow on the STaRK benchmark (74), which involves retrieving from text-rich KGs for real-world queries across multiple domains. Extensive experiments demon- strate that GraphFlow consistently produces high-quality and diverse retrieval results, outperforming both Process Reward Models (PRMs) and existing KG-based RAG methods. Notably, GraphFlow surpasses strong KG-based RAG baselines instantiated with GPT-4o, achieving average improve- ment of 10% in both retrieval accuracy and diversity metrics. In addition, GraphFlow enjoys strong generalization capabilities and can retrieve from unseen KGs to support queries in new domains.
2
Preliminary and Notations
2.1 KG-based RAG
[V where V and E are the sets of nodes and edges. The node V2 is associated with a short text description of an entity (e.g. Vi = 'Alice"). And the edge eij denotes the relationship between node V; and Vj. For example, Eaj 'daughter' represents the relationship between V; = 'Alice' and V = 'Bob'.
We denote knowledge graph (KG)
Retrieval-based Approach. For an input query 2, the retrieval-based method 2 37 first encodes the texts in nodes and edges into embeddings using pretrained LM 62
Then, retriever Rct(-) is employed to retrieve subgraph Gaub from [4 that can support answering Q:
max P(AIQ, suub), G Ret(G). Gub
(1)
Here A is the answer to the input query. Some works employ non- con-paraeetric retrievers, such as dense retriever with vector similarity M and Prize-Collecting Steiner Tree (PCST) algorithm [2] to retrieve from KGs. Other works train parameterized retriever models based on Multi-layer Perceptron (MLP) and Graph Neural Network (GNN) (2) to retrieve from KGs.
Agent-based Approach. Recent work formulates retrieval on KGs as a multi-step decision process [68] 47 388 and employs LLM agents search on KGs due their superior capability in plan- ning. For an input query 2, the agent LLM(-) starts from an initial node Vo and searches T steps incrementally in a KG to produce a retrieval trajectory T Vo
VT + Vr support Q:
P(A TET
LLM(G).
(2)
The inital node Vo can be identified using Entity name recognition (ENR) [8 or vector similarity matching [63]. At step f, the searching agent expands the trajectory conditioned on the input query V-- V/:
Q, the partial trajectory at step Tse Vo
V4+1 (L,M(V+nlQ,
V44 ((V).
(3)
Here PLIM is the policy instantiated by an LLM, and ((V) is the neighborhood node set of Vz.
2.2 Process Reward Models
Process Reward Models (PRMs) have emerged as a promising framework for aligning large language
models (LLMs) with human preferences [85 73 40 For multi-step decision problem, denote E S as the state and E A as the action. Training a PRM requires a preference dataset D [(a,t, , a, 8:)
i = 1,... N). where at and a, are positive and negative actions at state 8,, respectively. Such datasets can be constructed through human supervision 40), rule-based heuristics (55), or LLM- generated annotations (76). The goal of PRM is to learn scoring function (a(a, () : A S - IR that assigns real-valued preference scores to action- state pairs by minimizing the following objective:
LPAM = - Bott (aatta7.e0 .D)= (rg(a(ro(a; 8,) ro((a,,
where o-)) denotes the sigmoid function. Once trained, the PRM can be used to provide step-wise preference signals for LLM alignment 40 73). Additionally, it can directly guide the multi-step
decision with soft policy P(s (s)) x (s 7 73 76 92
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An overview of the proposed GraphFlow framework. (a). GraphFlow employs a flow estimator F(.) to factorize the outcome reward R(T) of a retrieval trajectory T to flow value F(x)). The flow value guides to learn policy P($+4118t) that leads to accurate and diverse retrieval results for complex queries. (b) We introduce an LLM--based implementation of GraphFlow to enhance KG-based RAG on text-rich KGs.
Figure 2:
3
Method
3.1 KG-based RAG as Multi-step Decision
Problem Formulation. Given an input query 2, our goal is to retrieve a set of K target nodes 1, ,K) from text-rich knowledge graph (KG) 9 {V, E, D) such that the ".....KKK K} can support answering Q. Here, V, E V is
== (V |
associated documents I'* = {D; | 1..... node, Eij e E is edge between and V3 indicating their relation. Each D, denotes the textual document associated with node V; (e.g., the content of a paper).
Agent-based Retrieval as a Multi-step Decision Process. effectively leverage both relational and text information in the KG, we employ the agent-based approach initiated with an LLM due to superior text understanding and planning ability. We formulate the agent- based retrieval as multi-step decision problem, consisting of the following components:
The agent starts retrieval from the initial state 80 = (2, (Do)). where Do is the document associated with the source node Vo from which the retrieval process is initiated. At step t, the agent arrives at node Vú and the current state is defined as 8 (Q, {Dj} =))) where {Dz}i= is the set of documents collected along the partial retrieval trajectory
State.
Tgt Vo
Action. Given state st, the agent selects an action a, E A(s)), corresponding to moving from V; to adjacent node V++1 E N(V)) along edge E,,41. The agent then retrieves the documents D++1 associated with Vi+1-
Transition. The agent transits to state 8t+1 = (2, {D, (D, (2 This process continues until either the document D++1 is deemed sufficient to support the query Q, or a predefined maximum number of steps is reached.
Reward. Upon termination, the agent receives reward R(T) for the retrieval trajectory The reward is calculated whether the document Dr associated with the terminal node Vi of trajectory the query (i.e. Dr IT*))
Energy-based Modeling for Accurate and Diverse Retrieval. As shown in Figure ( (a), the goal of GraphFlow is to learn the policy P($+41 st) that can effectively retrieve accurate and diverse information from a knowledge graph (KG) support answering input query. To this end, formulate the retrieval process as an anergy-based distribution over trajectories:
P(r)
II P(st1 8t) R(T)- t=0
(5)
The equality in Eq. 5 due to the Markov property of the state transition.
In contrast to objectives prior KG-based RAG methods in Eq. and Eq. 2 that maximize the likelihood of the most relevant information in KG, the objective of GraphFlow Eq. reflects the intuition that high-reward retrieval trajectories (i.e., trajectories ending in high-quality supporting documents) should be sampled more frequently. Thus, GraphFlow naturally promotes diverse yet

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Table 1: Performance of retrieval accuracy of KG-based RAG methods on STaRK benchmark. Our GraphFlow outperforms baselines with higher hit rates and MRR scores. GraphFlow also surpasses strong baselines implemented with GPT-4o most metrics.
Dataset
STaRK-AMAZON
STaRK-MAG
STaRK PRIME
Method
Metric
Hit@1 1 Hit@5t MRR Hit@1
H1 MRR1
Hit@ 11
Hit@5t MRR
DenseRetriver
6.10
15.85
10.61
24.44
40.23
32.41
5.43
13.07
8,99
Retrieval -based
G-Retriever
6.10
11.59
8.54
24.44
31.95
28.08
5,43
8.94
6.95
AubgraphRAG
8.03
12.43
9.90
9.30
25.59
16.11
4.82
8.00
6.17
ToG+LLaMA3
4.21
6.16
5.25
12,0
14.09
12.67
21.92
34,0
26.61
Agent- based
ToG+G
20.67
41.38
30,90
23.33
56.67
36.38
16.67
39.77
27.02
SFT
8.16
15.30
13.54
26.53
28.57
29.10
27.5
40.07
33.06
(w/o Rerank)
PRM
20.09
26.25
28.16
26.05
28.0
28.52
21.01
46.72
31.25
GraphFlow
19.63
44.17
31,66
29.32
58.64
41.32
39.84
71.71
54.58
ToG+LLaMA3
4.21
6.16
5.25
12.0
14.09
12.67
21.92
34.0
26.61
Agent- based
ToG+G
27.58
51.72
39.08
26.67
56.67
39.65
53.33
63.73
57.78
SFT
12.24
30.61
21.54
27.55
44.89
36.37
23.75
52.5
35.98
(Rerank)
PRM
21.25
42.50
31.97
27.31
44.09
33.69
22.86
28.24
26.94
GraphFlow 47.85
65.03
55.49
39.09
57.51
47.82
51.39
72.11
61.37
accurate retrieval results since the retrieval trajectories resulting in highly relevant documents are more likely to be explored. Moreover, GraphFlow also enjoys good generalization ability by avoiding strict likelihood maximization and does not overfit to few dominant candidates.
3.2 Flow Estimation as Credit Assignment
major challenge in learning the policy P(s++1 | 8t) satisfy Eq. is the lack of process-level supervision. When collecting retrieval trajectories T E T for training, only the outcome reward R(T) is observable, indicating whether the final retrieved document supports answering the query. Annotating the process-level reward signals for every intermediate state and action is expensive. This gives rise the credit assignment problem [56] 91], which attributes the terminal reward of trajectory back to the intermediate decisions. To address this, adopt the GFlowNet framework (3), which implicitly performs credit assignment by estimating a non-negative flow value for each state.
Rather than directly maximizing the reward or value of a full trajectory. GFlowNets introduce flow function Fs)) S - R2 for each intermediate state s. The learning objective enforces local consistency constraint between transitions, which is known as the detailed balance condition:
F(t:) P(s++1 sx)) = F(++11) (B(S: | (841),
where P(s St) the forward policy we want to obtain, and Ps(se $(41) is the backward policy. When this condition holds for all transitions, the retrieval trajectory induced by the policy P(s++1 st) satisfics the objective in Eq. 5 leading to diverse and accurate retrieval results on KGs.
While alternative GFlowNet objectives, such trajectory balance 52 or subtrajectory balance [51] can also promote diversity, they require computation over entire trajectories sub-trajectories. In KG-based RAG, the retrieval trajectory involves multi-hop transitions, and each node is associated with long documents. These objectives are computation-ineee (OOM) issues. To ensure scalability and efficiency, we thus adopt the detailed balance objective that operates on state transitions.
and often lead to oft-of-memory
Detailed Balance with Local Exploration. Enforcing the detailed balance condition globally across all transitions in a knowledge graph (KG) is computationally inefficient, since the vast state space makes many nodes and transitions unreachable during training. To address this, introduce local exploration strategy that focuses the detailed balance objective on the neighborhoods of states observed in sampled trajectories.
For retrieval trajectory T = Vi +.... Vr with reward R(r), we apply local exploration to each non-terminal state s; (Q, (D) where t * T. Specifically, the agent takes exploratory action af E A(s,) that moves from node V; to neighboring node V/41 E (VV) different from the original next node V4+1. This results in a new exploratory state 5/+1 = (2, (D,)}=o U (D(41)) corresponding to the partial trajectory T5 Vo ..... V/41-
5

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Table 2: Performance of retrieval diversity of KG-based RAG methods. GraphFlow retrieves more correct documents to support queries with high diversity.
Dataset
STaRK- AAARON
STaRK MAG
BHARK-PRIME
Method
Metric
R@201
D-R@201
R@201
D-R@201
R@201
D-R D-R@2001
DenseRetriver
13.63
13.63
41.80
41.80
13.92
13.92
Retrieval -based
G-Retriever
5.35
5.35
25.37
25.37
6.75
6.75
SuhgraphRAG
6.53
6.53
27.83
26.95
6.49
6.49
ToG+ILLaM33
2.61
2.61
6.77
6.77
33.84
33.84
ToG+GPT4o
25.81
23.70
48.03
47.71
54.35
54.35
Agent
SFT
25.22
24.97
37.48
35,90
47.72
45.36
-based
PRM
35.72
18.94
36,73
36.73
45.97
45,97
GraphFlow
36.15
36.15
57.18
57.18
79.71
79.59
By performing k such explorations, we generate & exploratory actions (d .0,, and their resulting states {,1+1,1 1 With the ground-truth next state denoted as $/4 = 81+1, we obtain a + transitions from to candidate next states for optimizing the detailed balance objective.
The forward policy is then defined P(s++1 (,,1,));)) Ei_ge (olee Here ra(s, a) is
learned process reward function parameterized by a neural network with parameters Since the retrieval process is inherently irreversible (i.e., backtracking disallowed), we follow prior work [22] and set the backward policy PB (s; $141) = 1 in Eq. We yield the following objective for state by taking the log function to both sides of Eq. 5
CDBLE((1)=
Σ Dlo (st) log F(5(41.)) log P(s++1 [644]
==0
k
Σ 1=0
Dog F(s)) log (61(+1,)) ro(ss, a/,)) log
To (e
Boundary Condition. We impose boundary conditions on the initial and terminal states to ensure proper propagation of flow values along the retrieval trajectory: log F(so) log F(sr) = 1. Here so the initial state and ST is the terminal state. The reason is that we only collect retrieval trajectories that reach target documents during model training. With such boundary conditions, ensure that the total incoming and outgoing flow consistent across the trajectory and enable flow estimator to correctly distribute the outcome reward of the terminal state to the intermediate states.
Termination Condition. To allow the retrieval policy P(s++1 8t) to decide when to stop, we introduce special self-loop action that retrieves the current node again. At each step, this action included among the candidate actions in Eq. 7 Hence, Eq. can also be applied for the terminal state. If the policy chooses retrieve the current document (i.e., selects the self-loop), the trajectory is terminated, indicating that the current document is relevant to the query. Otherwise, the policy continues explore the KG. This mechanism enables the agent to adaptively determine when to stop retrieval based on its experience, rather than relying on a fixed number of steps.
Difference Between GraphFlow, SFT, and PRM. SFT and PRM learn the retrieval policy by treating the action leading to the ground-truth next state 81+1 as positive sample, and exploratory actions leads {s1+1,11-
> as negative ones, akin to behavior cloning (70]. GraphFlow
generalizes this by learning state-dependent flow values F(s) and factorizes the outcome reward via Eq. 7 When setting log Fs,, and log F(5(41,) 0, GraphFlow reduces to behavior cloning. However, such as hard objective limits generalization and cannot learn a policy leading to diverse and accurate retrieval results for complex queries.
3.3 Instantiating GraphFlow with LLMs
We implement GraphFlow with an LLM due to ability of text understanding and decision-making, as shown in Figure (b). The state and state-action pair are decorated with flow prompt and policy prompt template, which are encoded using a shared L.M.. The embeddings of the final tokens are used as representations of the state and the state-action pair, respectively. On top of the shared

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Table 3: Quantitative retrieval quality of different KG-based retrieval methods.
STaRK-Amane
SAARK-MAG
STaRK PRIME
Mothod
Siep-ASreper
Answer- Saper
Step-ASeper
Answer-A Seper
Step-A Seper
Answer-A
ToG-GPT-Ho
0.031 + 0.109
0,092 1: 0.128
0.011 = 0.172
0.065 = 0.150
0.065 + 0.125
0.148 + 0.165
ToG+LLaMA3
0.010 0.118
0.046 0.149
0.68 0.108
0,105 0.145
0.009 0.102
0.021 1: 0.106
0,079
0.141 0.160
0.101
0.84
0.132
0.158 +
PRM
0.029 + 0.089
0,071 0.112
0.037 0.117
0.060 = 0.115
0.057 + 0.106
0.131 = 0.174
G-Retriever
0.024 0.110
0.012 0.089
0.029 1: 0.117
SuhgraphRAG
0.399
GraphFlow
0.0M7 + 0.15M
0,219 1: 0.257
0 0.137
0.145
0.112
0 + 0.147
0,206 : 0.192
encoder, employ two separate multi-layer perceptrons (MLPs) as the policy head and the flow head, respectively. The policy head predicts the forward transition probability, while the flow head estimates logarithm of the flow value of the state. During model training, we apply LoRA 23 to inject learnable adapters into the frozen backbone of the LLM, and update the parameters of the flow head and the policy head. This design enables joint optimization of policy learning and flow estimation in parameter-efficine manner, while also capturing rich contextual information through the LLM encoder. We present detailed implementation in Supplementary Material due space limit.
Experiment
4.1 Dataset
We employ the STaRK [74] benchmark to validate the retrieval quality of the proposed GraphFlow to support complex queries. STaRK a recently proposed benchmark designed to evaluate the retrieval performance of KG-based RAG methods on text-rich KGs spanning three domains:
STaRK-AMAZON is an e-commerce KG where the nodes contain detailed product infor- mation and the edges denotes the properties of products and co-purchase between products. The retrieval task is to retrieve the diverse products to satisfy the recommendation query.
STaRK-MAG is an academic graph constructed based on OGB [24] and Microsoft Aca- demic Graph [66]. The nodes contain author information, institute, and publications. The retrieval task is to address academic queries such as paper searching.
STaRK-PRIME is biomedical KG where the nodes are associated with the detailed description of drugs, disease, genes, and pathways, and the edges are their relationship. The retrieval task is to address the biomedical query.
The StaRK benchmark challenges KG-based RAG methods by complex queries corresponding to diverse retrieval targets and fusion of text and structure information that complicates accurate retrieval.
4.2 Baseline and Evaluation Metries
Baseline. We choose representative retrieval-basse and agent-based baselines with explicit retrieval results (i.e., the retrieved node index) on the STaRK benchmark 774. All detailed implementations shown in Supplementary Material due space limit.
For retrieval-based baselines, we consider Dense-Retriewe graphRAG 37. RetrieRe is implemented with SentenceBERT
30
G-Retriever 21 and Sub-
62 to encode both questions and the documents of KG nodes into dense embeddings and retrieve the documents with top vector similarity. G-Retriever employs the Prize-Collecting Steiner Tree (PCST) (2) algorithm to extract subgraph from KGs relevant to the query. Since computing PCST on STaRK benchmark is infeasible, we follow the hybrid setting 33 34 that first identifies source node in KG via Dense-Retrieve and only computes PCST around the ego-graph up to hops around the identified node. We also adopt the same hybrid setting for other baselines ensure computational feasibility STaRK. SubgraphRAGG integrates a learnable subgraph retrieval module to retrieve from KG.
For agent methods, we consider ToG 668] SFT, and PRM 44 as baselines. ToG employs an L.MM agent to search from the KG to retrieve supporting documents. We instantiate ToG using both LLaMA3-88 and GPT-4o as backbone models, denoted as ToG+LLaMA3 and ToG+GPT40.
7

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Suave TTALOEP
[A]. Generaliaation Results without Rerank
Generalization Results Rarank
Figure 3: Generalization Performance of KG-based RAG methods. GraphFlow shows superior cross-domain generalization performance, especially under the rerank setting (best viewed in color).
respectively. SFT and PRM are two popular approaches that fine-tune the LLM agent to enhance RAG in recent works [76, 12 25), SFT, PRM, and GraphFlow are deployed with LLaMA3-BB-Inttne 17]. For agent- based methods, use the agent to rerank all the retrieved results.
Evaluation Metrics. All KG-based RAG methods retrieve every input query 20 times and generate 20 retrieval results for diversity and accuracy evaluation following the standard setting in STaRK (74). We employ the following metrics to evaluate the retrieval performance. Hit@ k denotes whether the ground truth is retrieved in the top-k results. We employ @it@1 1 and Hit@5 measure the retrieval precision of the different KG-based RAG methods. Mean Reciprocal Rank (MRR) measures the average of reciprocal ranks of the first ground-truth item in the retrieval results and encourages the ground-truie item to be retrieved in higher rank. Recall @ (R@k) is standard metric to measure the percentage of ground- -oound-truth items that appear the top-k retrieved results. We employ Recall @ 20 (R @2) for evaluation. De-duplicate Recall @k (D-R @k) measures the percentage of unique ground-truth items that appear the top-k retrieved results. This metric used to evaluate the diversity of the correctly retrieved results. We use De-duplicate Recall @20 20 (D-R @20).
4.3 Main Results
Accuracy. Table presents the retrieval accuracy of various KG-based RAG methods on STaRK. GraphFlow consistently outperforms other KG-based RAG approaches on most metrics. In particular, it achieves higher Hit rates and MRR scores than the strong baseline ToG+GPT-4o with an average 10% im- provement retrieval accuracy. Interestingly. ToG's performance is highly sensitive to the choice of back- end model. When instantiated with LLaMA3-8B, ToG shows significant drop in performance compared to using GPT-4o. Additionally, rerank has no effect in the ToG+LLaMA3-SB setup, all retrieved results receive equally high scores, leaving the ranking unchanged.
Diversity Difficltty
Graphtllon
0.5
PAN
T&C LLLM433
6.4
TeC
02-
5-00
Figure 4: GraphFlow shows improved re- trieval diversity on different difficulty levels of retrieval queries on STaRK-PRIME.
Two finetuned agent-based baselines, SFT and PRM, outperform ToG without finetuning. but still fall short of GraphFlow. While PRM training can leverage curated preference datasets with fine-grained process-level re- wards, such labeling is prohibitively expensive. Instead, GraphFlow achieves high-quality retrieval with only outcome rewards of retrieval trajectories. For retriever-based approaches, DenseRetriever, G- Retrieeeer, and SubgraphRAG show moderate performance but generally inferior to agent-based

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methods. Overall, retriever- eetriever-based methods remain more lightweight but trail behind agent-based approaches in retrieval accuracy.
Diversity. Table 2 reports the retrieval diversity of different KG-based RAG methods on the STaRK benchmark. We evaluate both Recall 20 (R @ (0) and its de-duplicated variant (D-R@20), which better captures retrieval diversity. GraphFlow achieves the highest retrieval diversity across all datasets, outperforming both retriever- based and agent- based baselines. Its results not only match more ground-truth contents but also avoid redundancy. Notably, GraphFlow exceeds the strongest baseline (ToG+GPT-4o) by a large margin on the STaRK-PRIMA dataset, highlighting its ability to retrieve results that both relevant and diverse. Compared with PRM and SFT, GraphFlow also demonstrates superior diversity. In contrast, retriever-based methods retrieve less diverse content and cover fewer retrieval targets.
4.4 Quantifying Retrieval Quality
We further employ the Seper score (Aseper) 9 to quantify the retrieval quality of different KG-based retrieval methods. The Seper score is a recently proposed metric for evaluating retrieval utility by measuring semantic perplexity reduction after retrieval: Seper = PM (a gg d) PM (a/g). Here, is the question, d the document associated with the retrieval item, and M is an LLM used for question answering. In our case, we use retrieval model. Since there no ground-trute answer for the questions in the STaRK benchmark, we use the title or summarized description of the ground-trute retrieval item as a. We design the following metrics for comprehensive evaluation and report their mean and standard deviation (std).
to instantiate M to keep consistent with the
Slep-Asepent the Seper score that quantity the retrieval quality of intermediate retrieval.
* -nswwr--Seepee Seper: the Seper score that quantity the retrieval quality of the final result.
As shown in Table 3 GraphFlow consistently achieves higher Step Seper and Answer- Seper than all the baselines, demonstrating stronger information utility during retrieval. These results further confirm that GraphFlow can significantly improve the information utility when retrieving from text- rich KGs. Notice that all the methods have high variance in Step-Asepa Seper and auswer-Aseper The reason is the high variance in natural language entailment when calculating Seper scores. Moreover, observe that some retrieval samples have negative Seper scores for all methods, indicating negative impact on question answering when retrieving bad contents.
4.5 Further Discussion
Cross-domain Generalization. Figure reports the cross-domain generalization ability different KG-based RAG methods. We use Hit@ to evaluate the retrieval accuracy Compared with Sub- graphRAG using small model for retrieval, SFT, PRM, and GraphFlow that finetune the L.LM show better cross-domain generalization ability due to the pneerparrmeterizaaionn [35] [5]. GraphFlow demonstrates superior cross-domain generalization, since it avoids from likelihood maximization objectives used by SFT and PRM. Instead, GraphFlow adaptively assigns the outcome reward of the retrieval trajectory to the flow values of intermediate states and guides the retrieval policy, leading to better generalization ability. More results are shown in Supplementary Material due space limit.
Performance on Hard Cases. We categorize the retrieval queries with different numbers of retrieval targets into 4 difficulty levels. Figure shows the D-R @ 20 scores of KG-based RAG methods retrieval queries on approaches by large margin by covering more diverse and accurate retrieval targets, especially on the hard cases containing more than 15 retrieval targets. The performance on hard cases shows the superior performance of GraphFlow in retrieving more relevant and diverse results. More results of hard cases are shown in Supplementary Materials due to space limit.
at different levels. GraphFlow outperforms the other agent-based
5 Related Work
KG-based RAG. Knowledge graphs (KGs) are widely used as knowledge sources in retrieval- augmented generation (RAG) 20 systems to enhance large language models (LLMM)) with both relational and textual information for answering complex queries [1. 13 g 75). A core challenge in

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KG-based RAG lies in retrieving relevant knowledge from KGs in response to given query. Recent methods addressing this problem can be broadly categorized into two approaches. Retrieval-based methods [0 53 61 93 leverage pretrained language models to encode the textual content in KGs into embeddings and use small models, such as MI.P and GNN, to retrieve relevant information. In contrast, agent-based methods [68 [8] 550
employ LLMs as agents that iteratively traverse the KG to locate supporting evidence. While both paradigms have shown promise in knowledge graph question answering (KGQA) (37], their effectiveness in retrieving diverse and high-quality candidates for complex queries remains limited. Furthermore, complex queries often require retrieval from text-rich KGs, necessitating the joint consideration of both relational structure and text content. Although recent studies [50] 34] have begun to explore this setting and tackle complex queries, enhancing the diversity and accuracy of KG-based RAG is still underexplored.
47 45
Process Reward Models. Process Reward Models (PRMs) 40 [2] have shown great promise in guiding LLMs with process supervision and have been adopted in many domains such as complex reasoning 77. alignment (58). and planning [6] The key to PRMs is to construct a preference dataset with process supervision 660 Previous works obtain the process supervision from human feedback and LLM evaluation. However, fine-grat aneeggriied process-level supervision is expensive for
RAG due the potentially vast search space of KGs and the difficulty of accessing the intermediate state during retrieval. Although early explorations are made to use PRM to guide the retrieval process of RAG on unstructured knowledge bases 39 65 25 76), they still need a preference dataset with process-level supervision. How to guide the retrieval process of KG-based RAG on structured KGs without process supervision data still challenging.
GFlowNet. GFlowNet [3 aims to sample diverse and high-qualit, candidates from unnormalized density and has received increasing attention in sampling from discrete and vast spaces [16 46 IO
[49] The goal of GFlowNet is to learn a policy that can lead to the terminal states with the likelihood in proportion to their rewards [86]. Some objects are proposed to optimize GFlowNet by regularizing
the state flows and their transitions 669 52 51). Recently, GFlowNet has also been introduced to improve the generative performance of L.MSs and diffusion models by promoting diversity in the decoding process [71 22
Differently, our work focuses aligning the retrieval results of KG-based RAG with the knowledge required for real-world queries by estimating the state flow in multi-step retrieval. Moreover, we introduce local exploration strategy to avoid visiting less-valued states, thus efficiently optimizing the detailed balance.
6 Conclusion
We introduce GraphFlow, a novel framework that enhances existing KG-based RAG methods by enabling accurate and diverse retrieval from text-rich KGs. By jointly optimizing a retrieval policy and flow estimator via detailed balance objective, GraphFlow effectively aligns the retrieval process with query-specific knowledge demands without explicit process-level reward. Extensive evaluation on the STaRK benchmark demonstrates that GraphFlow not only surpasses strong baselines deployed with GPT-4o, but also generalizes well to unseen KGs. These findings underscore the effectiveness and robustness of GraphFlow in supporting complex queries using textual and structured knowledge. Our future work will incorporate causality into KG- based RAG to improve the reasoning ability of
LLMs 44 5 83 reduce forgetting [41 42), and explore their scientific applications [78].
Acknowledgments and Disclosure of Funding
This work is supported by the UKRI grant: Turing AI Fellowship EP/W002981/1. This work is jointly supported by the Shanghai Municipal Science and Technology Major Project and Shanghai Artificial Intelligence Laboratory. The authors would like to thank PCs, ACs, and all the reviewers for their insightful comments and suggestions.
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A Introduction of GFlowNet
Generative Flow Networks (GFlowNets) 3 aim to learn a stochastic policy that generates objects X' through sequential decisions, such that the marginal probability of generating is proportional
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to reward function R(x)) >> O. Given a complete trajectory 7 = (su, a1, ..,...; or, ST = =x) that
terminates in object 2, the forward trajectory probability is:
T-1
Pr(T)
II Pr(a441(ss
and the backward probability (used to reverse the trajectory) is:
PB(T) II Pa(ay)s)
t=1
Trajectory Balance (TB) Loss [52].. The Trajectory Balance objective ensures the ratio of forward to backward probability matches the reward:
PF(T)
R(x)
log Pr(T) log Pr(()) = log R(x)) log
(T(
where Z is the global partition function. The loss function is then defined as:
LT (log PF(T) - log PB(T) log R(x) + log 7)²
In practice, log Z is treated as learnable scalar parameter.
Subtrajectory Balance (SubTB) Loss [5T].. To enable learning from partial trajectories, the Subtrajectory Balance loss generalizes TB to arbitrary subpaths. For any subtrajectory Tej (8), 0i+11.......................11.. ,5) from state s; to Sj, the balance condition becomes:
(T(T))) ((s;)
log PF(Teg) log PA(TTT) = log ((s,) - log ((x))
PA((Te)) (x:)
This leads the Subtrajectory Balance loss:
S (log PF(Teg) log (TT(TT)) log (s,) + log 2ss)))
Here, (s)) denotes the flow partition function at state 8, typically parameterized by neural network as (s) = log (s). SubTB enables more flexible and sample-efficient training. especially for long-horizon generation tasks. However, directly implementing GFlowNet on KG-based RAG faces several challenges. First, the objectives such as Trajectory balance and sub-trajectory balance computed the whole trajectories, leading to computational burden in KG-based RAG where entities are associated with long texts. Second, many states and transitions in KGs are less-valued and visited, making the traditional GFlowNet objective inefficient. Second, the discrete and symbolic nature of KGs poses difficulty in defining state transitions and flow dynamics, especially when integrating pretrained language models to interpret semantic relevance. These factors collectively make it challenging to directly apply GFlowNet to KG-based retrieval without significant adaptations in trajectory design, reward shaping, and exploration strategy.
B Implementation of GraphFlow with LLMs
Model Architecture. We use LLaMA3-8B-Insttuu Instructt as the backbone LLM to implement GraphFlow. Specifically, we first employ the following flow prompt template wrap the retrieval trajectory Tge at state into text sequence for flow estimation.
###Information trajectory you have visited: [history]
###Question: {question]
Please predict the reward of the Information trajectory to the question:
Here (history) is the concatenation of documents of previously visited entities. {question} is the input complex query. The backbone LLM encodes the above wrapped text sequence. The embedding of the last token is treated as the representation the wrapped sequence used for flow estimation. We employ 1- l-layer MLP as the flow head, which receives the representation of the wrapped sequence and outputs the log value of the estimated flow log st.
Then we employ the following policy prompt template to warp the retrieval trajectory Tge at state 8 into a text sequence for policy learning.
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ttaitinggtrrting locs
training total amizon
oval loss
amazon prire
prine
smazon prms
13
2.6
Steg
eval policy accuracy
training end
training transition amazon prins
aTazon
prine
101
amazon
print
66
05
Stpp
Figure 5: Training dynamics of GraphFlow.
###Information trajectory you have visited: [history)
### Question: {question] ###Candidate Information: [candidate]
Please predict the score of the candidate to help you find the answer to the question:
Here (history) is the concatenation of documents of previously visited entities. [question] is the input complex query. And (candidate) is one action a, that leads to the next state sc+1- The backbone LLM encodes the above wrapped text sequence. The embedding the last token is treated as the representation of the wrapped sequence used for learning P(s++1|8e). Specifically, employ 1-layer MLP with a ReLU function to parameterize o0(8;; az). The forward policy P(8(+1)8)) is calculated below:
ap(st, az)
P(se41|5:) E., op(s),
Training Configuration. we apply LoRA 26 to inject learnable adapters into the frozen backbone of the LLM,, and update the parameters of the flow head and the policy head. This design enables joint optimization of policy learning and flow estimation in parameter-efficien manner, while also capturing rich contextual information through the LLM encoder. The parameters of these modules are trained by optimizing the detailed balance with local exploration (DBLE) objective:
(DBLE(S:)
Ellog Fs.:) log F(5(41,)) log P(st+1 = 1,411,
1=0
(9)
b
Σ
[og F(st) log F(5(41,) re(s log ro(ooaah.))
Experimental Settings. To facilitate training the LoRA module and the flow head and the policy head on the STaRK benchmark, we first collect training dataset consisting of transitions between states. For a given question with the set of ground truth retrieval entities VT in the training set, we first identify the initial entity Vo using vector similarity between the embedding of Q and Vo. Then, we sample the trajectory TST = Vo the all the transitions between st 8t-1 in the example the trajectory T&T = Vo + ... Vr
- Vr staring from Vo and ending at Vr. We collect
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Table 4: Parameters of GraphFlow training on STaRK benchmark.
STaRK-AMAZON
STaRK-MAG
STaRK-PRIME
Accumulation steps
2
alpha
batch_size
1
num_gpi
8
depth_ cutoff
6
doc ccttof
400
eval ratio
0.8
eval_step
100
lora_ dro_dropout
0.05
Ir
1.00E-05
max_ max_length
1024
n_epochs
aum_exploration
4
r
32
window size
3
Table 5: We provide data statistics of STaRK. The statistics are from the STaRK benchmark 74
entity type
relation type
avg. degree
entities
relations
tokens
STARK-AMAZON
5
18.2
1,035,542
9,443,802 39,802,116
592,067,89 212,602.571
STARK-MAG
4
4
43.5
1,872,968
STARK-PRIME
10
18
125.2
129,375
8,100,498
31,844,769
to implement local exploration as introduced in Section 3.2 in the main paper. For every training step, We construct mini-batch of traditions between states to calculate the loss in Eq. 9 The training dynamic is shown in Figure 5 Here, training transition loss is calculated using the transition between non-terminal states. And training starting loss and training end loss are calculated using boundary condition Fso) = F(T)) 0. Training total loss and eval loss are calculated on all the transitions between states the training and evaluation dataset. Eval policy accuracy is the accuracy of policy P(s (st) on the evaluation dataset. We training GraphFlow on these dataset for one epoch, other important parameters are shown in Table
C Implementations of Baselines
To the best of our knowledge, few KG-based RAG methods are implemented the text-rich STaRK benchmark. Instead, many KG-based RAG methods employ simple KGQA datasets such as CWQ WEBQSP. Thus, choose representative retrieval-basee based and agent-based baselines with explicit retrieval results (i.e., the retrieved node index) on the STaRK benchmark [74]. We provide the implementation details of the used baseline methods as below.
implemented with SentenceBERT (62) to encode both questions and the documents of KG nodes into dense embeddings and retrieve the documents with top vector similarity. We choose SentenceBERT as the text document to consistent with prior works [21], where SentenceBERT is used to encode the text information in KGs. Although STaRK benchmark provide the pre-processed text embedding of entities and relationships in KGs using -al--embedding-ddd-------- the inconsistency between the entities IDs and the entities embeddings. Some entities in KGs are not converted into embeddings. Thus, we rerun the encoding model using SentenceBERT to obtain the full entities embeddings. After encoding the text information into embeddings, employ the vector similarity between the question embedding and text embeddings for retrieval. We evaluate the retrieval performance top 20 retrieval results.
model, we find
G-Retriever a two-stage method for KG-based RAG. It first employs the Prize-Collecting Steiner Tree (PCST) 2 algorithm to retrieve subgraph from KGs relevant the query. Then, the
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retrieved subgraph is encoded into the token space of LLM using a GNN for question answering (QA). To further improve the QA performance, G-Retriever also applies LoRA module to fine-tune LLM. Since we focus on the evaluating the retrieval performance of different KG-based RAG methods, we do not fine-tune the GNN and L.M for QA. To make PCST algorithm feasible on STaRK benchmark, we adopt hybrid approach that first identify the 20 seed nodes and implement the PCST algorithm to extract the subgraphs around 2-hop ego graph around the seed nodes. We drop the seed nodes with dense neighborhoods to avoid computation overhead [33 34).
SubgraphRAG [37] integrates learnable subgraph retrieval module to retrieve from KGs. Since training the subgraph retrieval module on the STaRK benchmark is infeasible, we employ the ego- graph setting similar G-Retriever. We identify the up-to hop neighbor hood graph around the seed node to construct the training and testing set for SubgraphRAG, We also drop the the seed node that has dense neighborhood to avoid computation overhead. This ego-graph setting is also employed to construct the test set for the other KG-based RAG models. We follow the default setting of SubgraphRAG to reproduce on STaRK benchmark.
ToG [68] employs an LLM agent to search from the KG to support KG-based question anwsering. ToG is implemented with frozen LLMs by prompt engineering instead of fine-tuning. Specifically, ToG employs tree-based search [79 to transverse the KG and search the relevant information for KG-based QA. Since we focus on evaluating the retrieval performance of KG-based RAG models, we modify ToG to retrieve the relevant document at each searching steps instead of incorporating the retrieved document update the question answering results. Since running ToG on the whole KGs in STaRK is infeasible, we identify the seed node for ToG searching using vector similarity and constrain the searching area around the 2-hop neighborhood of the seed node. We instantiate ToG using both LLaMA3-8B and GPT-4o as backbone models, denoted as ToG+LLaMA3 and ToG+GPT40, respectively.
We also implement SFT and PRM as two fine-tuning baselines build upon ToG and LLaMA3-8B- Instruct. We use the sample training dataset train ToG using SFT and PRM as GraphFlow for fair comparison. We employ the TRL (Transformer Reinforcement Learning) package to fir SFT and PRM fine-tuning. We apply LoRA funetuning to improve the efficiency.
Other potential baselines but hard to implement on STaRK. There are alternative KG-based RAG baseline methods for evaluation. However, we find it hard to implemented these baseline on STaRK, mostly due to the compatibility issues. We list some examples as below.
QAGNN [0] is designed for improving the QA performance on KG-based QA task. Although its retrieval performance is reported on STaRK benchmark, detailed implementation code on STaRK not publicly available. Although recent concurrent work [34 333 tried implement QAGNN on STaRK, the reported performances of QAGNN diverge from the reported results on STaRK benchmark.
RoG [47] adopts similar approach as it finetunes the LLM to search from KGs. It first employs an LLM to generate retrieval trajectories for the input queries and the generated retrieval trajectories to construct training dataset to fine-tune the retrieval agent by SFT. However, we find that LLM usually generate invalid retrieval trajectory, leading low quality training datasets for SFT fine- tuning. Thus, we finetune the retrieval agent using the valid retrieval trajectories by SFT in the main paper.
ToG-2.0 (50 is a recently proposed method to retrieve from the structured database and unstructured database. The key ToG-2.0 is to identify the topic entitiies for a given questions. However, the implementation of topic entity recognition absent, making difficult to reproduce ToG-2.0 on STaRK benchmark.
HybridRAG (33), Mixture of RAG 34 and KAR 75] are recent pre-prints on Arxiv focusing on retrieving from text-rich KGs. However, their codes are not available yet, making it difficult for us to reproduce these methods.
D More results of Cross-domain Generalization
We show more generalization performance in terms of Hit in Figure
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Suger
[a]. Generalization Results without Rerank
(b). Generalization Results with Rerank.
Figure 6: Generalization Performance (Hit@5) of KG-based RAG methods. GraphFlow shows superior cross- cross-domain generalization performance, especially under the rerank setting (best viewed in color).
Retrieval Diversity of Different Difficulty Level
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Figure 7: GraphFlow shows improved retrieval diversity on different difficulty levels of retrieval queries STaRK-PRIME.
E
More results of Hard Cases
categorize the retrieval queries with different numbers of retrieval targets into difficulty levels. We provide the performance of different KG-based RAG STaRK-PRIME, STaRK-MAG, and STaRK-AMAZON different difficulty levels. The results are shown Figure 7 Figure g and
Figure
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Figure 8: GraphFlow shows improved retrieval diversity different difficulty levels of retrieval queries on STaRK-AMAZON.
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Figure 9: GraphFlow shows improved retrieval diversity on different difficulty levels of retrieval queries on STaRK-MAG.
F Benchmark Information
We provide benchmark information in Table
G Computing Resources
We run all experiments on 8/16 NVIDIA- NVDDIAAAA000S GPUs and Intel(R) Xeon(R) Platinum 8336C CPUs.
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H Broader Impact and Limitations
GraphFlow introduces a novel framework for eetrievl--augmente generation over text-rich knowl- edge graphs, enabling Large Language Models (LLMs) to reason more effectively through process supervision using GFlowNets. By modeling retrieval as generative process that balances diverse and relevant paths, GraphFlow promotes both interpretability and coverage in knowledge based reasoning. This has broad implications for applications such as scientific discovery, open-domain question answering, and medical decision support, where combining structured knowledge with free-text reasoning is crucial. Moreover, GraphFlow can serve as foundation for future research in integrating generative decision decision-making with symbolic structures, thereby pushing forward the synergy between LLMs and knowledge graphs. One potential limitation that we only evaluate the generalization ability of GraphFlow on two new domains.
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Reviews:
[{"review_id": "mbsLU2sAKw", "reviewer": "Reviewer_PQ1s", "summary": "This paper tackles the problem of retrieval-augmented generation over text-enriched knowledge graphs for complex, multi-facet queries. The authors observe that prior KG-based RAG methods underperform when the query demands (i) fusing structural and textual evidence, and (ii) returning a diverse set of supporting items, and while procedural reward models could in principle give step-wise guidance, they need costly process-level supervision unavailable for large KGs Thus, the paper introduces GraphFlow, a GFlowNet-inspired framework that models KG retrieval as a stochastic decision process. GraphFlow jointly learns a retrieval policy that samples multi-hop sub-graphs, and a flow estimator that factorizes the terminal reward backward onto each intermediate step, promoting both accuracy and diversity without explicit step-wise labels. Experiments on the STaRK benchmark show that GraphFlow exceeds strong baselines, becoming an effective and robust solution for complex KG-based RAG scenarios.", "questions": "Why can this method outperform ToG by such a large margin? Is it because of the gap between the limited context window size and the large degree of nodes in STaRK, or other reasons?", "limitations": "yes", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:\n\n- This paper is very well written and joyful to read. It clearly and reasonably frames related works to retrieval-based and agent-based and uses proper examples to illustrate their idea, showing the authors' good understanding of this area.\n\n- The paper's core technical idea for providing step-wise guidance using a trained LLM is both intuitive and elegant. The experiments also demonstrate the effectiveness and generalizability of the proposed method.\n\nWeaknesses:\n\n- Missing baselines and analysis. The experimental section omits some widely‐used STaRK baselines such as AVATAR [1], MFAR [2], KAR [3], and MOR [4]. My own inspection shows that on the Amazon and MAG domains the proposed method still lags these baselines by a noticeable margin. The impressive result of this paper lies in the PRIME domain, where by far it's the best results I've seen and leads by a large margin. However, there is a lack of analysis of this domain-variant result in this paper.\n\n- Does the paper sufficiently answer the question in the title? More analysis is needed to illustrate whether a retrieval method (previous and proposed) retrieves useful documents. For example, you may use SePer [5] to quantitatively estimate the utility of information at the intermediate steps.\n\n- Efficiency comparison unclear. One of the key claimed advantages is the efficiency of using the flow estimator for graph retrieval. However, there is a lack of experiments on the cost in time and money compared to retrieval-based and other agent-based retrieval methods.\n\nWould consider improving the score if more sufficient experiments are shown.\n\n[1]  Avatar: Optimizing llm agents for tool usage via contrastive reasoning\n\n[2]  Multi-field adaptive retrieval\n\n[3] Knowledge-Aware Query Expansion with Large Language Models for Textual and Relational Retrieval\n\n[4] Mixture of Structural-and-Textual Retrieval over Text-rich Graph Knowledge Bases\n\n[5] SePer: Measure Retrieval Utility Through The Lens Of Semantic Perplexity Reduction", "was_revised": true, "final_justification": "The authors provided sufficient experiments to address my concerns, covering different aspects including baseline comparison, in-depth analysis, and algorithm efficiency. The results make a good supplement to the original paper, demonstrating its solidity and completeness. Overall, I recommend accepting this paper due to the following highlights:\n1. The method efficiently and effectively addresses the diversity problem in complex retrieval, which is an important aspect often overlooked by similar works.\n2. The algorithm is solid and interpretable. During the rebuttal, the authors add experiments to quantify the usefulness of retrieved documents in the middle steps. This strengthens their claim in the title, adding good supportive evidence apart from end-to-end blackbox QA performance.\n3. The related works sorted in this paper are very clear and helpful, which would make this paper a good reference to guide future researchers.\n4. The added experiments and explanations cover a complete aspects of a classical good paper, including ablations, interpretation, and efficiency analysis. The completeness and efficiency of the method convince me that it is easy and safe to use for this task.", "rebuttal": "We really appreciate your time and effort in reviewing and providing valuable comments for us to improve our manuscript. Especially, we like the recent $\\Delta_{seper}$ metric introduced by the reviewer. We will add the $\\Delta_{seper}$ results to Experiment Section and other analysis in their corresponding sections. Our responses are as follows:\n\n**Q1: More comparison with the baselines and analysis of the domain-variant result in the paper.**\n\n**A1**: We will add a section in the revised manuscript to compare GraphFlow with recent STaRK baselines, including AVATAR, MFAR, KAR, and MoR, and compare their empirical performance. We summarize the main difference below:\n\n- **Comparison with AVATAR [1]**: AVATAR is an agentic framework built on API-based LLMs (e.g., GPT‑4, Claude‑3‑Opus) and leverages external tools like WEB_SEARCH and WIKI_SEARCH. It also employ an API-model-based Comparator module to optimize retrieval instructions. Its implementation incurs substantial token and computation costs (e.g., very high API usage), which we confirm is a practical barrier to reproduction (in Appendix D, p. 20 of the AVATAR paper). In contrast, GraphFlow relies on fine-tuning an 8B-scale LLM to traverse text-rich KGs directly, avoiding external APIs or tool dependencies. **This leads to significantly lower resource consumption while maintaining comparable performance to the GPT‑4–based AVATAR baseline** (Table 4 in AVATAR paper).\n\n- **Comparison with MFAR/KAR/MoR [2,3,4]**: They integrate multiple retrievers while GraphFlow only employs one retriever.  MFAR ensembles multiple retrieval models (lexical and dense), with learned weighting functions. KAR and MoR integrate both text and graph retrievers in intermediate retrieval steps. Differently, GraphFlow relies on a pure graph retriever to retrieve from text-rich KGs. While integration of multiple retrievers leads to promising results, **GraphFlow still shows strong performance over other one-retriever baselines**. Given time and complexity constraints, we leave “GraphFlow + multiple retrievers” to future work.\n\n- **Domain-Variant Analysis**: PRIME SKB is significantly denser (avg. degree=125.2) with more relation types and textual annotations, compared to MAG (avg. degree=43.5) and Amazon (avg. degree=18.2). GraphFlow’s graph-based inductive bias aligns closely with PRIME’s dense structure, making it particularly effective in that domain. Performance gaps on MAG and Amazon likely stem from their sparser graph structure. This also explains why other agent-based baselines show similar trends. This finding also motivates us to explore “GraphFlow + multiple retrievers” in future work.\n\n**Q2: More analysis on the retrieval steps. For example, using SePer to evaluate the utility of information at the intermediate steps**\n\n**A2**: Thank you for this insightful suggestion! In the current manuscript, we evaluate retrieval performance using conventional metrics like Hit@1 and Hit@5. Our results highlight that both retriever-based and agent-based baselines struggle to retrieve diverse and accurate contents from text-rich KGs, leading to our motivation for proposing GraphFlow. We appreciate the reviewer introducing **SePer**, a recent ICLR 2025 metric [5] for evaluating retrieval utility by measuring semantic perplexity reduction after retrieval: $\\Delta_{seper}=P_{M}(a|q,D)-P_{M}(a|q)$. where 𝑀 is an LLM (here we use LLaMA3–8B–Instruct),  𝑞 is the question, 𝐷 is retrieved content, and 𝑎 is the ground truth answer.\n\nIn STaRK benchmark, since there is no ground-truth answer, we use the title or summarized description of the ground-truth retrieval item as 𝑎. We design the following metrics for comprehensive evaluation, and we report their mean and std. \n- $Step \\Delta_{seper}$: the $\\Delta_{seper}$ score per retrieval step, \n- $Answer \\Delta_{seper}$: the $\\Delta_{seper}$ score of the final retrieval results. \n\nAs shown in Table 1-3 below, GraphFlow consistently achieves higher $Step \\Delta_{seper}$ and $Answer \\Delta_{seper}$ than baselines, demonstrating stronger information utility during retrieval. This confirms our motivation:\n- Retrieving from text-rich KGs indeed poses huge challenges to current KG-based RAG methods.\n- GraphFlow can significantly improve the information utility when retrieving from text-rich KGs.\n\n**Table 1: $\\Delta_{seper}$ on Prime.**\n| Method               | $Step \\Delta_{seper}$      | $Answer \\Delta_{seper}$      |\n|---------------------------|----------------------|------------------------|\n| ToG + GPT‑4              | 0.065 ± 0.125        | 0.148 ± 0.165          |\n| ToG + LLaMA3‑8B‑Instruct  | 0.009 ± 0.102        | 0.021 ± 0.106          |\n| SFT                       | 0.062 ± 0.132        | 0.158 ± 0.183          |\n| PRM                       | 0.057 ± 0.106        | 0.131 ± 0.174          |\n| G‑Retriever               | —                    | 0.029 ± 0.117          |\n| SubgraphRAG               | —                   | 0.046 ± 0.083          |\n| GraphFlow                 | 0.091 ± 0.147        | 0.206 ± 0.192          |\n\n**Table 2: $\\Delta_{seper}$ on Amazon.**\n| Method                      | $Step \\Delta_{seper}$       | $Answer \\Delta_{seper}$       |\n|-----------------------------|------------------------|-------------------------|\n| ToG + GPT‑4                 | 0.031 ± 0.109          | 0.092 ± 0.128           |\n| ToG + LLaMA3‑8B‑Instruct     | 0.010 ± 0.118          | 0.046 ± 0.149           |\n| SFT                         | 0.079 ± 0.151          | 0.141 ± 0.160           |\n| PRM                         | 0.029 ± 0.089          | 0.071 ± 0.112           |\n| G‑Retriever                 | —                      | 0.024 ± 0.110           |\n| SubgraphRAG                 | —                      | 0.021 ± 0.093           |\n| GraphFlow           | 0.097 ± 0.158          | 0.219 ± 0.257           |\n\n**Table 3: $\\Delta_{seper}$ on MAG.**\n| Method                      | $Step \\Delta_{seper}$       | $Answer \\Delta_{seper}$      |\n|-----------------------------|------------------------|-------------------------|\n| ToG + GPT‑4                 | 0.041 ± 0.172          | 0.065 ± 0.150           |\n| ToG + LLaMA3‑8B‑Instruct     | 0.068 ± 0.108          | 0.105 ± 0.146           |\n| SFT                         | 0.035 ± 0.101          | 0.084 ± 0.095           |\n| PRM                         | 0.037 ± 0.117          | 0.060 ± 0.115           |\n| G‑Retriever                 | —                      | 0.012 ± 0.089           |\n| SubgraphRAG                 | —                      | 0.039 ± 0.076           |\n| GraphFlow                   | 0.081 ± 0.137          | 0.145 ± 0.112           |\n\nNotice that all the methods have high variance in $\\Delta_{seper}$ scores. This may be due to \n\n- High variance in natural language entailment when calculating  $\\Delta_{seper}$ scores; \n- For all methods, we observe that some retrieval samples have negative $\\Delta_{seper}$, indicating a negative impact on question answering when retrieving bad contents.\n\n**Q3: Efficiency comparison**.\n\n**A3**: When we say “to further enhance training efficiency” (Line 85), we refer to making the **training process** of GraphFlow both scalable and GPU-friendly. Applying the GFlowNet objective directly to large KGs introduces prohibitive computation and often triggers out-of-memory errors (Section 3.2, Line 178). To address the efficiency issue, we employ detailed balance with local exploration strategy to make the training process of GraphFlow efficient and scalable.  **We also report retrieval latency and inference cost at test time in Table 4**. SubgraphRAG enjoys fast inference speed since it employs a light-weight retriever. However, its retrieval performance on text-rich KGs is limited as reported in Table 1 in our manuscript.\n\nTable 4: Inference-time of different methods.\n| Method        | Inference Time |\n|---------------|----------------|\n| SubgraphRAG   | 0.094 s        |\n| G-Retriever   | 6.79 s        |\n| ToG + LLaMA   | 10.35 s        |\n| ToG + GPT‑4   | 14.49 s        |\n| PRM           | 8.94 s         |\n| SFT           | 4.56 s         |\n| GraphFlow     | 4.73 s         |\n\n**Q4: Is the performance gain of GraphFlow over ToG due to the document cutoff?**\n\n**A4**: When implemented with LLaMA3-8B-Instruct, ToG shares the same context length limit as GraphFlow. Moreover, we cut off the entity document to a maximum of 500 tokens when implementing ToG, SFT, PRM, and GraphFlow, to ensure implementation feasibility and a fair comparison. Thus, we could say that the performance gain of GraphFlow over ToG is not due to the document cutoff. We will add this analysis to our revised manuscripts.\n\n[1]. AvaTaR: Optimizing LLM Agents for Tool Usage via Contrastive Reasoning. NeurIPS 2024.\n\n[2]. MFAR: Multi-field adaptive retrieval. ICLR 2025.\n\n[3]. KAR: Knowledge-Aware Query Expansion with Large Language Models for Textual and Relational Retrieval. NAACL 2025.\n\n[4]. MoR: Mixture of Structural-and-Textual Retrieval over Text-rich Graph Knowledge Bases. NAACL 2025.\n\n[5]. SePer: Measure Retrieval Utility Through The Lens Of Semantic Perplexity Reduction. ICLR 2025."}, {"review_id": "59skDhuNwk", "reviewer": "Reviewer_PkmH", "summary": "The paper explores the use of Knowledge Graphs to enhance Retrieval-Augmented Generation for large language models. The authors propose a novel framework called GraphFlow that aims to efficiently retrieve accurate and diverse knowledge required for complex queries from text-rich KGs. GraphFlow employs a detailed balance objective with local exploration to jointly optimize a retrieval policy and a flow estimator. This approach allows GraphFlow to explore relevant regions of KGs, yielding diverse and accurate results. The framework is evaluated on the STaRK benchmark, demonstrating significant performance gains over existing KG-based RAG methods.", "questions": "1.\tThe supplementary material notes that only a handful of KG-based RAG models have been evaluated on the text-rich STaRK benchmark, whereas most prior work relies on simpler KGQA datasets such as CWQ and WebQSP. Could the authors explain why CWQ and WebQSP were omitted from the experiments? Reporting results on these widely used benchmarks would provide a clearer picture of the framework’s generalizability.\n2.\tCan the authors provide more examples of real-world applications where GraphFlow has been successfully implemented? This would help illustrate the practical benefits of the framework.", "limitations": "Paper discuss the limitations of GraphFlow on complexity and dependence on high-quality data.  Regarding potential negative societal impacts, the paper does not explicitly address this aspect.", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n+ The introduction of GraphFlow, which uses a detailed balance objective and local exploration, is a novel contribution that addresses the limitations of existing KG-based RAG methods. GraphFlow shows a 10% performance gain in both retrieval accuracy and diversity metrics compared to strong baselines, including GPT-4o.\n+ The framework demonstrates strong generalization capabilities by effectively retrieving information from unseen KGs to support new-domain queries. The authors provide extensive experimental results on the STaRK benchmark, covering multiple domains such as e-commerce, academic, and biomedical KGs.\n\nWeaknesses:\n- The commonly used KGVQA datasets CWQ and WebQSP are not included in the experiments. Including results on these standard benchmarks would help better illustrate the generalizability and robustness of the proposed framework.\n- The effectiveness of GraphFlow relies on the availability of high-quality, text-rich KGs, which may not always be accessible or well-maintained.", "was_revised": true, "final_justification": "The rebuttal has addressed most of my concerns.", "rebuttal": "We really appreciate your time and effort in providing valuable comments. We thank the reviewer for acknowledging the novelty of our method, extensive empirical evaluation, and significant performance gain. Our responses to your concerns are summarized as follows:\n\n**Q1: Could the authors explain why CWQ and WebQSP were omitted from the experiments?**\n\n**A1**: Thanks for the constructive suggestions! While CWQ and WebQSP are well-known KG‑based QA datasets, we did not include them in our current experiments due to the following reasons:\n\n- **Scenario Misalignment**: CWQ and WebQSP feature shallow, non-text-rich KGs, where entities are typically associated only with short phrases or labels. In contrast, our focus is on multi-hop retrieval from text-rich KGs, where entities connect to longer documents or passages. These two settings are fundamentally different (see Introduction, Lines 39–62).\n\n- **Different settings**: STaRK datasets are retrieval-centric and provide explicit ground-truth retrieval items for measuring retrieval accuracy and diversity. Meanwhile, CWQ and WebQSP are question-answering (QA) datasets. Since GraphFlow targets retrieval effectiveness, STaRK aligns more directly with our objective.\n\n- **Incomplete KGs in CWQ and WebQSP**: Although we could potentially treat the answer entities in CWQ and WebQSP as the ground-truth retrieval items, we find that many answer entities are missing in CWQ and WebQSP. **Given the dataset quality, it is very difficult to faithfully evaluate the retrieval performance of different KG-based RAG methods on CWQ and WebQSP**.  We show the statistics in Table 1.\n\n**Table 1: Dataset analysis of CWQ and WebQSP**\n| Dataset | Total Answers (test) | Missing Answers (test) | Missing Rate |\n|--------:|----------------------:|------------------------:|-------------:|\n| CWQ     |                6,676  |                 1,907   |       28.56% |\n| WebQSP  |               16,602  |                 7,094   |       42.73% |\n\nWe find that SubgraphRAG [1] uses the shortest-path triplets and GPT4-annotated triplets as the ground-truth retrieval items. Although these ground-truths might not be accurate, we follow their settings and reimplement GraphFlow on WebQSP. The recall-rate are in Table 2 and the performances of baselines are quoted from SubgraphRAG.\n\n**Table 2: The retrieval performance on WebQSP**\n\n| Method          | Shortest Path Triplets | GPT‑4-annotated Triplets | \n|-----------------|------------------------|----------------|\n| Cosine similarity | 0.714               | 0.719          | \n| Retrieval‑rewrite‑answer | 0.058        | 0.062          | \n| RoG             | 0.713                 | 0.388          | \n| G‑Retriever     | 0.294                 | 0.325          | \n| GNN‑RAG         | 0.522                 | 0.405          | \n| SubgraphRAG     | 0.883                 | 0.865          |\n| PRM             | 0.841                 | 0.790          | \n| GraphFlow       | 0.923                 | 0.824          | \n\nSince retrieval from WebQSP mainly leverages structural information without rich textual information, it does not target the motivation of GraphFlow. However, our performance is still competitive, showing good generalization.\n\n**Q2: GraphFlow relies on high-quality and text-rich KGs, which are not easily accessible or well-maintained.**\n\n**A2**: Thank you for pointing this out.  We have illustrated the motivation of developing GraphFlow for diverse and accurate retrieval from text-rich KGs in the Introduction (Line 33-62). To further clarify, we will add more illustrative examples in the Introduction, where text-rich KGs are accessible and well-constructed. We will add a DeepResearch scenario for clarification, where LLMs need to identify relevant scientific literature to a scientific problem to write a report, draw inspiration, and propose a hypothesis. Here, the identification of relevant scientific literature involves retrieving diverse and accurate items from a paper-citation KG. In our future work, we will also explore the application of GraphFlow on the DeepResearch scenario.\n\n**Q3: Can the authors provide more examples of real-world applications that GraphFlow has been successfully implemented?**\n\n**A3**: Thanks for these constructive suggestions! In our manuscripts, we evaluate the proposed GraphFlow and other baselines on the STaRK benchmark, which consists of text-rich KGs from academic, e-commerce, and biology domains. Our experiments on STaRK have shown that the proposed GraphFlow can outperform the baselines by a large margin in terms of retrieval accuracy and diversity on text-rich KGs.\n\nWe agree that exploring real-world applications of the GraphFlow framework is more interesting and promising, since the STaRK benchmark is still curated for research purposes. Though we haven’t yet deployed GraphFlow in these production environments due to time constraints during rebuttal, we believe integrating GraphFlow with AI-powered academic search is very interesting. A possible direction is to incorporate the GraphFlow framework with AI Scientist, which accelerates scientific innovation by retrieving diverse and relevant literature to yield inspiration for scientific problems.\n\n\n[1]. SIMPLE IS EFFECTIVE: THE ROLES OF GRAPHS AND LARGE LANGUAGE MODELS IN KNOWLEDGE-GRAPH-BASED RETRIEVAL-AUGMENTED GENERATION. ICLR 2025."}, {"review_id": "jJiodZYA1J", "reviewer": "Reviewer_jABU", "summary": "The paper proposes GraphFlow, a new framework for improving Retrieval-Augmented Generation (RAG) using knowledge graphs (KGs). Existing KG-based RAG methods often fail to retrieve both accurate and diverse information for complex queries. Unlike prior approaches that require costly process-level supervision, GraphFlow uses a flow-based retrieval policy trained with only final outcome rewards. Inspired by GFlowNets, GraphFlow models retrieval as a multi-step decision process, jointly learning a policy and a flow estimator using a detailed balance objective. This enables effective exploration of high-reward regions in KGs. Experiments on the STaRK benchmark show that GraphFlow outperforms strong baselines (including GPT-4o) by around 10% in both accuracy and diversity, and generalizes well to unseen domains.", "questions": "- Why is GraphFlow deployed using LLaMA3-8B-Instruct while ToG uses LLaMA3-8B?\n- I can't find the training/dev/test dataset statistics used for SFT, PRM and GraphFlow. Please make sure that this information is clearly provided in the final paper?", "limitations": "Yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths:\n- The writing and presentation is clear.\n- Their methodology is novel and well-motivated. Leveraging GFlowNets for this problem is an inspiring prospect.\n- Their empirical results show strong performance over other supervised methods, making it a potentially practical system.\n\nWeaknesses:\n- Although STARK is a reasonably good benchmark for evaluating KG-based RAG systems, it is specifically design to test performance on a very high quality KG coupled with documents. The more general setting of KG-based RAG, in which a KG is automatically extracted from text [GraphRAG, HippoRAG, LightRAG, RAPTOR] and then leveraged for more effective retrieval, would be a more interesting and impactful testing ground for this framework. This is not grounds for rejection but a comment on the potential significance on this method.", "was_revised": true, "final_justification": "All my minor comments were addressed and I maintained my high score.", "rebuttal": "We really appreciate your time and effort in providing valuable comments. **We thank the reviewer for acknowledging our well-presented manuscript, well-motivated methodology, and strong empirical performance**. Our responses to your questions are summarized as follows:\n\n**Q1: [Furture work] Consider more general setting in KG-based RAG systerms where KGs are automatically extracted from raw texts.**\n\n**A1**: Thank you very much for this valuable suggestion. While our current work focuses on retrieval from text-rich, curated KGs, we agree that automatically extracting KGs from raw text is crucial for broader applicability. In future extensions of GraphFlow, we will explore two potential directions:\n\n- We will extend GraphFlow to access external tools, such as entity extraction and web search, to construct high-quality and text-rich KGs from web-sacle academic literature.\n\n- We plan to handle incomplete or noisy KGs by combining KG completion and retrieval. GraphFlow will not only traverse text-rich graphs but also refine entity connectivity and completeness.\n\nThese extensions will enable GraphFlow to operate in more general real-world settings where high-quality KGs don't pre-exist.\n\n\n**Q2: Why is GraphFlow deployed using LLaMA3-8B-Instruct while ToG uses LLaMA3-8B?**\n\n**A2**: Thank you very much for pointing out this typo issue! We implement ToG using LLaMA3-8B-Instruct in our manuscript to keep a fair comparison. And we will correct this typo issue in the revised version. \n\n**Q3: Include the detailed training/dev/test dataset statistics used for SFT, PRM, and GraphFlow in the revised manuscript.**\n\n**A3**: Thank you very much for your valuable suggestions! We initially introduced the training/validation/test dataset split in Table 2 in the supplementary material. We will further include the detailed statistics of processed training/validation/test datasets to implement G-Retriever/SubgraphRAG/PRM/SFT/GraphFlow, the data format for model finetuning, and visualization of data instances in the Experiment section of the revised manuscript.\n\nWe would like to thank you again for your comments and acknowledgement!"}, {"review_id": "5Ork6F0f3f", "reviewer": "Reviewer_yp42", "summary": "This paper proposed to use flow estimation as progress reward models in the training of KG-RAG LLMs.", "questions": "Questions:\n\nPlease refer to Weaknesses.\n\nAlso, since the training process relies on sampled trajectories who cover the final answer, does this means it eventually relies on terminal reward?\nConsidering recent progress of RL4LLM (e.g., the GRPO method) who only depends on terminal rewards , how different between intermediate supervised and terminal supervised in KG-RAG?\n\nSuggestion:\nMaybe finding a new way to model the flow estimation?", "limitations": "yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 2, "originality": 4, "strengths_and_weaknesses": "Strengths:\n1. The idea is interesting: this paper propose to learn a flow function as progress reward models for KG-RAG. I like the idea of introducing intermediate rewards and the idea of using Gflow network/ flow matching.\n\n----\nWeaknesses:\n1. **The learning method is problematic.** In the paper, the authors defined states to be a collection of documents. The flow estimation $F(s_{t+1})$ is calculated by putting all documents of $s_{t+1}$ into a LLM. More specifically, it is calculated by the last  token embedding transformed by a MLP. However, the conditional probability $P(s_{t+1}|s_{t})$ is calculated in a similar way. The only difference between these two is the MLP and the prompt. Within the same documents as input, I doubt how much difference we can have. The learning objective is the MSE between $\\log F(s_{t})-\\log F(s_{t+1})$ and $-\\log P(s_{t+1}|s_{t})$. I don't think this makes sense.\n\n2. **Weak Evaluation** The authors evaluated their method on the STaRK benchmark, which is a less common choice. Using STaRK has no problem, but I would like to see more results since I doubt the effectiveness of methodology. A more common choice of KG-RAG is either Freebase KG (datasets like WebQSP and CWQ), Wikidata KG (2WikiMultihopQA), or even Wikipedia (HotpotQA). The Wikipedia corpus has hyperlinks between entities and has a document for each entity, which exactly matches the description of text-attributed KG in the paper. Moreover, the baselines are weak. The ToG method is the strongest one among the baselines. However, according to my own experience, I cannot reproduce the result reported in the ToG paper. (Its official GitHub has many reproduction issues.) So, I doubt the effectiveness of this method.", "was_revised": true, "final_justification": "1. Well-written paper.\n2. Interesting idea.\n3. Novel and solid work.\n\nThe shortcoming of this paper, in my opinion, is its evaluation. It only evaluate on STaRK, which is a less common choice.\nHowever, its goodness overcomes its shortness. I recommend acceptance.", "rebuttal": "Thank you very much for your time and effort in providing valuable comments and suggestions! Our responses and clarification regarding the concerns are as follows:\n\n**Q1: The Learning method is problematic. The MSE-based learning objective in Eq. 7 does not make sense.**\n\n**A1**: Thank you for raising this concern. Our goal in GraphFlow is to learn a forward policy $P(s_{t+1}|s_{t})$ that generates diverse and high-quality retrieval results over text-rich KGs. To achieve this, we adopt an energy-based formulation (Eq. 5), which provides a principled way to assign scores to retrieval trajectories based on their rewards. To make this objective tractable, we leverage the detailed balance condition from GFlowNet theory [1,2], as shown in Eq. 6. We further introduce detailed balance with local exploration to ensure computational feasibility during optimization. This balance condition enforces consistency between the forward and backward policies and allows us to define a training signal over state transitions as shown in Eq. 7.\nSpecifically, we:\n\n- Take the logarithm of both sides of Eq. 6. Assume a fixed backward policy $P_{B}(s_{t}|s_{t+1})=1$, which is valid under the irreversible trajectory construction used in our sampling process [3].\n\n- Expand the forward policy using the equation in Line 195, and **it finally leads to a mean squared error (MSE)-style objective (Eq. 7)**.\n\n- We also provide a more detailed background of Eq.5-Eq.7 in Section A and Section B of the Supplementary Material.\n\n**In summary, the MSE-like objective is a principled surrogate loss derived from our energy-based objective using well-established assumptions in the GFlowNet literature**.\n\n**Q2: The output between the flow estimation and policy network does not have difference since they receive the same document as input. The only difference between the flow estimation and policy network is the MLP and the prompt.**\n\n**A2**: We would like to clarify that the flow estimation $F(s_{t})$ and policy network $P(s_{t+1}|s_{t})$ receive different inputs and their output are also different. We define the state $s_{t}$ as the $t$-step information-seeking trajectory and the question. $F(s_{t})$ takes $s_{t}$ as input, while $P(s_{t+1}|s_{t})$ takes $s_{t}$ and candidate $a_{t}$ as input. \n\nAlso, If their outputs were the same, then all candidate next states would have equal transition probabilities:$F(s_{t})=P(s^{1}_{t+1}|s_{t})=\\cdots=P(s^{k}_{t+1}|s_{t})$. This would result in a random policy, and unable to distinguish between high-quality and low-quality candidates.\n\n**Thus, $F(s_{t})$ and $P(s_{t+1}|s_{t})$ have different inputs and outputs.** We have introduced the intuition and design of $F(s_{t})$ and $P(s_{t+1}|s_{t})$ in Section 3.2, Section 3.3 and Figure 2, with more details on the input, architecture, and prompts used for $F(s_{t})$ and $P(s_{t+1}|s_{t})$ provided in the Section B of Supplementary material. \n\nWe hope our clarification could answer your question.\n\n**Q3: StaRK Dataset is not a common choice. More evaluations on Freebase KG (CWQ, WebQSP), and Wikidata KG (2WikiMultihopQA), or even Wikipedia (HotpotQA).**\n\n**A3**: STaRK is retrieval-centric and provides explicit ground-truth retrieval items for measuring retrieval accuracy and diversity, making it a good benchmark for our tasks (also pointed out by Reviewer jABU). The other mentioned datasets are Question-Answering (QA) datasets instead of retrieval-centric datasets:\n\n- **CWQ and WebQSP are not included** since: a). scenario misalignment between simple KG retrieval and text-rich KG retrieval, b). different settings between QA and retrieval, c). data quality issue due to incomplete KGs. **We refer reviewer yp42 to our response Q1 to reviewer PKMH for more detailed discussion and experiment analysis**.\n\n- **Wikidata KG (2WikiMultihopQA), or even Wikipedia (HotpotQA)** are not included since: a). 2WikiMultihopQA is also QA dataset and used by a recent arxiv paper [4] (uploaded after NeurIPS ddl). b). HotpotQA is also widely used for QA tasks. In the KG-based RAG domain, HotpotQA is usually used for KG construction [5,6,7], while we focus on the retrieval task.\n\n**Q4: The reproducible issue of ToG on CWQ and WebQSP.**\n\n**A4**: ToG employs CWQ and WebQSP for evaluation in their paper. But we reimplement it on the STaRK benchmark in our manuscript. **The performance divergence is due to the difference in the setting (simple KG v.s. text-rich KG)**. Additional evidence is that SubgraphRAG achieves very good performance on CWQ and WebQSP in their paper, but our reimplementation on STaRK shows that it lags behind baselines such as SFT, ToG, and PRM.\n\n**Q5: Since the training process relies on sampled trajectories who cover the final answer, does this means it eventually relies on terminal reward?**\n\n**A5**: We would like to clarify that the terminal reward in the training set is the one and only supervision signal for GraphFlow. Other baselines, such as SubgraphRAG, also use the final answer as supervision.\n\n**Q6: \"Considering recent progress of RL4LLM (e.g., the GRPO method) who only depends on terminal rewards , how different between intermediate supervised and terminal supervised in KG-RAG?\"**\n\n**A6**: **GraphFlow is quite different from GRPO as GraphFlow is off-policy while GRPO is on-policy**. But it is very interesting to explore GRPO in the KG-based RAG scenario. As for the difference between intermediate supervised and terminal supervised, our experiment shows that using step-wise supervision (SFT) lags behind GrahFlow (implicitly factorizes the terminal reward into step-wise supervision). With the GflowNet-based objective, GraphFlow can learn a policy that leads to diverse and accurate samples guided by factorized step-wise supervision.\n\n**Q7: Maybe find a new way to model the flow estimation?**\n\n**A7**: Thank you very much for the valuable questions. We welcome discussion about any concrete direction you might have in mind for exploring alternative flow estimation methods to enhance the KG-based RAG. \n\n\n\n\n\n\n\n[1]. GFlowNet Foundations, JMLR 2023.\n\n[2]. Trajectory balance: Improved credit assignment in GFlowNet. NeurIPS 2022.\n\n[3]. Amortizing intractable inference in large language model. ICLR 2024.\n\n[4]. KG-Infused RAG: Augmenting Corpus-Based RAG with External Knowledge Graphs. Arxiv: 2506.09542\n\n[5]. Knowledge Graph-Guided Retrieval Augmented Generation. Arxiv: 2502.06864.\n\n[6]. GFM-RAG: Graph Foundation Model for Retrieval Augmented Generation. Arxiv: 2502.01113\n\n[7]. KAG: Boosting LLMs in Professional Domains via Knowledge Augmented Generation. NAACL 2025."}]

Author rebuttal:

Meta-review: This paper introduces GraphFlow, a novel framework for enhancing knowledge-graph-based retrieval-augmented generation (KG-RAG). The work makes a technically solid and empirically validated contribution, offering a creative and interpretable methodology that addresses a recognized challenge in KG-based retrieval.

Reviewers highlight the paper’s strength in novelty and clarity, and commend the authors for the extensive clarifications and new experiments provided during rebuttal, which effectively resolved concerns regarding baselines, retrieval utility, and efficiency.

That said, the evaluation remains somewhat narrow in scope, and evidence of practical deployment on noisier, real-world KGs is still limited. With broader cross-dataset validation and demonstrations in more challenging settings, this line of work could reach an even higher level of impact.

Based on the overall assessment, a spotlight acceptance is recommended.
Decision: Accept (spotlight)