Youtu-GraphRAG: Vertically Unified Agents for Graph Retrieval-Augmented Complex Reasoning
Authors: Junnan Dong
Siyu An
Yifei Yu
Qian-Wen Zhang
Linhao Luo
Xiao Huang
di yin
Yunsheng Wu
Xing Sun
Venue: ICLR 2026 Poster (2026)
Keywords: GraphRAG
Schema
Complex QA
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Graph retrieval-augmented generation (GraphRAG) has effectively enhanced large language models in complex reasoning by organizing fragmented knowledge into explicitly structured graphs. Prior efforts have been made to improve either graph construction or graph retrieval in isolation, yielding suboptimal performance, especially when domain shifts occur. In this paper, we propose a vertically unified agentic paradigm, $\texttt{Youtu-GraphRAG}$, to jointly connect the entire framework as an intricate integration. Specifically, $(i)$ a seed graph schema is introduced to bound the automatic extraction agent with targeted entity types, relations and attribute types, also continuously expanded for scalability over unseen domains; $(ii)$ To obtain higher-level knowledge upon the schema, we develop novel dually-perceived community detection, fusing structural topology with subgraph semantics for comprehensive knowledge organization. This naturally yields a hierarchical knowledge tree that supports both top-down filtering and bottom-up reasoning with community summaries; $(iii)$ An agentic retriever is designed to interpret the same graph schema to transform complex queries into tractable and parallel sub-queries. It iteratively performs reflection for more advanced reasoning; $(iv)$ To alleviate the knowledge leaking problem in pre-trained LLM, we propose a tailored anonymous dataset and a novel 'Anonymity Reversion' task that deeply measures the real performance of the GraphRAG frameworks. Extensive experiments across six challenging benchmarks demonstrate the robustness of $\texttt{Youtu-GraphRAG}$, remarkably moving the Pareto frontier of performance and efficiency with up to 33.6% cost saving and 16.62% higher accuracy over state-of-the-art baselines. The results indicate our adaptability, allowing seamless domain transfer with minimal intervention on the schema.

Published as a conference paper at ICLR 2026
YOUTU-GRAPHRAG:
VERTICALLY UNIFIED AGENTS FOR GRAPH RETRIEVAL-AUGMENTER COMPLEX REASONING
Junnan Dong' Siyu An Yifei Yu², Qian-Wen Zhang', Linhao Luo", Xiao Huang, Yunsheng Wu', Di Vin Xing Sun" Teeneett Youtu Lab, Moonah University, 'The Hong Kong Polytechnic University (hansonjdong, s siyuan] @ @tencent. com
Unifled
Construction-cenrric
Retrieval-centric
Query
</> Graph ConsTruction
Query
Construction
Groph
Retrieval
LLM
Retrieval
Graph Construction
Indexing
Retrieval
LLM
(a)
(b)
(c)
Figure 1: A sketched comparison with existing pipelines. represents non-tailored component, indicat- ing current methods focus on either graph construction (a) or retrieval (b) in isolation, while our proposed Youtu-GraphRAG proposes a unified paradigm (c) for superior complex reasoning performance.
ABSTRACT
Graph retreeval-augmented generation (GraphRAG) has effectively enhanced large language models in complex reasoning by organizing fragmented knowl- edge into explicitly structured graphs. Prior efforts have been made to improve either graph construction graph retrieval in isolation, yielding suboptimal per- formance, especially when domain shifts occur. In this paper, we propose verti- cally unified agentic paradigm, Youtu-GrapaRAG, to jointly connect the entire framework as an intricate integration. Specifically, (2) seed graph schema is introduced to bound the automatic extraction agent with targeted entity types, re- lations and attribute types, also continuously expanded for scalability over unseen domains; (iz) To obtain higher-level knowledge upon the schema, we develop novel dually-perceived community detection, fusing structural topology with sub- graph semantics for comprehensive knowledge organization. This naturally yields hierarchical knowledge tree that supports both top-down filtering and bottom-ur up reasoning with community summaries; (jii) An agentic retriever is designed to interpret the same graph schema transform complex queries into tractable and parallel sub-queries. It iteratively performs reflection for more advanced reason- ing; Extensive experiments across six challenging benchmarks demonstrate the robustness of Youtu GratuurGrraGGAGGGGG remarkably moving the Pareto frontier of per- formance and efficiency with up to 33.60% saving of token costs and 16. 626 higher accuracy over state-of-the-an baselines. The results indicate our adaptabil- ity, allowing seamless domain transfer with minimal intervention on schema.
INTRODUCTION
Graph cetrieva--uugmented generation (GraphRAG) has emerged as promising paradigm to en- hance large language models (LLMs) with structured knowledge (Xiao et al., 2025 Lu al. 2026). particularly for complex multi-hop reasoning tasks across multiple documents (Wang et al. 2024
Zhang et al. 2024). By representing fragmented documents as connected graphs with underlying
relations (He al. 2024 Dong al. 2023), GraphRAG enables LLMs traverse explicit paths
among documents and entities, performing complex reasoning that is otherwise infeasible within flat
retrieval (Peng al. 2024 Han al. 2024). The structured approach effectively addresses critical
"Corresponding author.

---

Published as a conference paper at ICLR 2026
limitations in conventional RAG (Gao et al. 2023 Dong al. 2024c), which often struggles with the coherent relations between discrete pieces of information and multi- reasoning.
The evolution of GraphRAG brings two distinct but equally important trajectories since the foun- dational work of (Edge et al., 2024). First, in terms of retrieval, LightRAG (Guo et al. (0224) pio- neered vector sparsification to improve efficiency. While GNN-RAG and GFM-RAG advanced this direction further by incorporating graph neural networks (Mavromatis & Karypis [224]] Luo et al. 2025) for fine-grained node matching, more recent Hi 1&2 (Jimenez Gutierrez al.) 2024 Gutiérrez et al. 2025) introduced memory and personalized PageRank algorithms for context-aware retrieval. Second, in terms of graph construction, existing methods can be broadly categorized into flat and hierarchical approaches. Early methods, such as KGP (Wang et al., [024], rely on existing hyperlinks or KNN-based graphs, resulting in coarse-graine graieeed relations that fail to capture nuanced hierarchical semantics. More recent advancements, such as GraphRAG (Edge et al., 2024), includ- ing community detection and summarization for multi-leve information. Followed by hierarchical methods like RAPTOR (Sarthi al. 2024) and E GGGaphRAG (Zhao et al. 2025). they further refine the graph using tree-like clustering and recursive summarization to enrich structural representation. However, both pipelines remain constrained by their isolated optimizations, concentrating either construction or retrieval while neglecting their interdependencies. This potentially limits complex reasoning performance where the cohesive components equally important to GraphRAG.
To bridge this gap, we aim to answer a critical question:
How can we effectively unify graph construction and retrieval for robust complex reasoning? This task is challenging for two reasons. First, construction and retrieval are not readily aligned as two distinct components. It remains difficult to organically establish synergy between them, where the constructed graph could effectively benefit retrieval with both structures and semantics. Second, how to properly evaluate the performance remains a tough problem. With the rapid scaling of LLMs, almost all the existing datasets have already been 'seen' before during the pre-training stage of L.MMs. This fails to reflect the real performance of the entire GraphRAG.
In this paper, we propose a vertically unified agentic paradigm, Youtu-GraphRAs to jointly consider both graph construction and retrieval as an intricate integration based on graph schema. To be specific, (i) a graph schema is introduced to bound the extraction agent that ensures the quality and conciseness with targeted entity types, relations and attribute types; The seed schema is continuously and automatically expanded based on the feedback. (it) To obtain higher-level community detection, fusing structural
knowledge upon the schema, we develop topology with subgraph semantics for comprehensive knowledge clustering. This naturally yields a hierarchical knowledge tree that supports both top-down filtering and bottom-up reasoning with community summaries; (iii) An agentic retriever is designed interpret the same graph schema to transform complex queries into parallel sub-queries and perform iterative reflection. The agent iteratively performs both reasoning and reflection for more advanced performance; (iv) To alleviate the knowledge leaking problem in pre-trained LLM, we first propose tailored anonymous dataset with an 'Anonymity Reversion' task. The model required to revert the anonymized entities back to its original form with correct, specific named entities. Extensive experiments across six challeng- ing benchmarks demonstrate the robustness of Youtu-GraphRAG, remarkably moving the Pareto frontier of performance and efficiency with up to 33.60% saving of token consumption and 16.62% higher accuracy over SOTA basclines. The results also indicate our remarkable adaptability which allows seamless domain transfer with minimal intervention on the graph schema, providing insights of the next evolutionary paradigm for real-world applications.
Contributions. In general, our primary contributions are summarized hereunder:
We first propose a vertically unified Agentic GraphRAG framework to integrate graph construction and retrieval for more robust and advanced reasoning, where both construction and retrieval agents are bounded by graph schema for effective extraction and query decomposition, respectively;
A novel community detection algorithm is employed to inject high-level summarization upon graph schema, simultaneously preserving structural and semantic graph properties;
We present a tailored anonymous dataset and 'Anonymous Revertion' task is proposed to prevent L.M knowledge leaking for fair evaluation of the GraphRAG performance;
2

---

Published as a conference paper at ICLR 2026
Graph Schema/ Emity type Relation Attribute Type
Knowledge Tree
Construction
Flanning
Extraction Agent
Query
Retrieval Agent
LLM
Documents
Seed Graph Schoma
Four-Level
Knowledge
Figure toy overview Toutu-Graphin that unifies graph construction and retrieval through a schema-guides agentic paradigm. (i) An extraction agent automatically processes documents into structured knowledge via targeted entity/relation extraction; (ii) A four-level knowledge tree is constructed upon the schema with community detection that fuses topological structures and graph semantics, enabling hierarchical reasoning: (iit) retrieval agent decomposes user queries into parallel sub-queries aligned with the schema, iteratively driving multi-route retrieval.
Extensive empirical experiments are conducted over five challenging benchmarks, showing state- of of-the-art performance across diverse reasoning tasks and domains that moves the Pareto frontier with up to 33.60% saving of token costs and 16.62% higher accuracy.
TASK DEFINITION
In this section, we formally define the general GraphRAG pipeline with standardized notations from scratch, including both graph construction and graph retrieval. We denote scalars as lowercase alphabets (e.g.. a), vectors boldface lowercase alphabets (e.g.. a), matrices boldface uppercase alphabets (e.g., A) and copperplate for a set of elements (e.g., A). We refer to GraphRAG as the task of answering natural language question by first retrieving structured knowledge from corpus and then generating a response.
Given set of documents D, GraphRAG first leverages frozen LLM fLLM(+) to extract im- portant knowledge, connected by a structured graph G as output. To enrich the understanding of G. community detection algorithm foomm(9) (9) is employed to partition G into communities = [C1, C2 Cm to obtain higher-level summarizations. Based on the constructed graph G, given complex query 9 Q, retrieval model fretrieee (4, 9) arg P(Guu q) traverses the graph and retrieves top-k question-specific subgraphs Grub C G that maximize the similarity with given query q. The final performance is evaluated from multiple aspects: (i) graph construction costs including time efficiency and token consumptions; (ii) retrieval accuracy and efficiency; and (itt) final answer accuracy comparing Opeed and ground-trute -ttnnd agold.
2.1 CONSTRUCTION STAGE
Contemporary GraphRAG frameworks process document corpora D through two complementary granularity levels of knowledge organization. At the fine-grained level, a triple-structured graph Grippe (E, R. D) is extracted using frozen L.M agent (LLM(d), which identifies atomic relational triples (h,, r, t) from each document E 'D with entities {h, t E & and relations E R explicitly interconnected capture detailed relational semanties. In parallel, a coarse-graing document graph Gdoe = (D, C) is constructed by clustering entire documents to preserve broader contextual informa- tion. To derive higher-level semantic abstractions, community detection algorithms (e.g.. Louvain, Leiden, GMM) partition & into communities C = {C2, C2..C..... .e2....,c,,, C.. }. each of which is summarized into a meta-node é; = fLIM(C,) via LLsed condensation. The overall construction efficiency is evaluated based on graph build time cconstruct and computational token cost $.
2.2 RETRIEVAL. STAGE
During inference, given a query q E Q, the typical retrieval model fretrieee g) = arg max P(d q) directly returns the top-k similar documents D = {dz, d2 ... d+) as the final answer, while graph- methods provide more explainable subgraph & for multi-hop path traversal, i.c., fretreee (q, 9) arg max P(δ q) where G (eo _ el 22 retrieved subgraph, JuLM(g, 9) is employed generate the final answer. The final performance is evaluated holistically by the retrieval recall comparing Gove and ground truth documents daee and answer accuracy by comparing between apred and agold.
R ex} E G. Based on the
3

---

Published as a conference paper at ICLR 2026
3 APPROACH: YOUTU-GRAPHRAG
In this section, we elaborate on the core methodology of Youtu- Graph-GG designed to answer two fundamental research questions: (i) How achieve unified optimization of graph construction and retrieval for higher robustness and generalizability? (tt) How could we enable effective rea- soning across different knowledge granularities? Correspondingly, our framework integrates three designs in a vertically unified manner based on graph schema. First, graph schema-bounded agent designed ensure construction quality while eliminating noise through automatic expansion. Sec- ond, beyond schema, we present dual-perception community detection that jointly analyzes both topological and semantic similarity to create multi-scale knowledge clusters which form four-level knowledge tree. Finally, agentic retriever is designed effectively decompose questions into schema-aligned atomic sub-queries with parallel retrieval routes and iterative reflection.
3.1 SCHEMA-BOUNDEEE AGENTIC EXTRACTION
Existing GraphRAG methods rely pure LLMs OpenIE for entity and relation extrac-
tion (Jimenez Gutierrez al. 2024; Gutierrez et al. 2025) Luo et al. 2025 Edge et al.] 22024). often
introducing noise and irrelevant information that compromis graph quality. In contrast, we formu-
late graph extraction as a constrained generation process guided by compact, domain-specific seed
schema, defined as follows:
S a S.,
(I)
where S. indicates the targeted entity types (e.g., Person, Disease), S, guides the extraction with condensed relations (e.g., treats, causes), and Sattr lists attribute types that could be at- tached and used describe any corresponding entities (e.g., occupat ion, gender). A frozen LLM-based agent (L D) is bounded to identify matched information that appear in S, effee- tively reducing the open-endes search to a structured space defined by the schema S. This confines the model's s extraction process to identifying instances of the predefined types in Se S,,, and Sauty- Formally, for each document d, obtain set of triples hereunder:
T(d) = { (h, t), (e, Patr, Caur) ) (f(h), f(), f(e)} E Sc, fr, rair J E Sr, Catr Suit
(2)
Therefore, in paper, we define the graph Guiple = (E, D)D where the entity set & Eatr includes both named entities and their attributes, and the relation set R comprises both entity-entity relations and attribute-linke bility and adaptability beyond predefined schemas, introduce adaptive agent that dynamically refines the initial schema S through continuous document interaction. The agent proposes schema expansions by identifying relational patterns in each document d E D via an update function:
relations (e.g., has tha__attriiaaallaaaaaaaaaatalltt aattribuue).......... To enhance scala-
AS (AS., AS., AS Idfux S) @ S]
where ((t) represents the schema at iteration f, μ 0.9 serves as a confidence threshold to control the acceptance of new schema elements. AS contains candidate expansions for entity types, rela- tions, and attributes, respectively. This dynamic adaptation enables the schema to evolve beyond its initial pre-definitions while maintaining controlled growth, the agent selectively incorporates only high-confidence patterns that demonstrate sufficient frequency and contextual consistency across documents in the new domain. This could effectively balance between strict schema guidance and flexible knowledge acquisition for unseen domains.
3.2 UPON SCHEMA: GRAPH INDEXING WITH KNOWLEDGE TREE
The fine- greinrained raw graphs could quickly become extremely dense and noisy. Typically, a comple- mentary community detection algorithm f.comm (G) is employed to summarize the knowledge so as to reorganize the graph in communities C = (C1,C2 C2... ... C. J. Contemporary methods apply Louvain, Leiden, Gaussian Mixture Models (GMM) (Traag et al. 2019 Sarthi et al. (0244),, etc., operates over G with sufficient summaries and abstracts generated by fuL C2 C g is further summarized into a high-level meta-node é; fLLM(C,) by fCL where G.
However, existing community detection methods, which primarily rely on structural connectivity while neglecting semantic information, often yield suboptimal partitions in knowledge graphs. To address this, we propose a novel perceetin framework that simultancously optimizes topo- logical and semantic coherence through three-stage process, illustrated in Figure 3 The output is
4

---

Published as a conference paper at ICLR 2026
[e., Sr,
jacenccy Structure Similority
Triple Embedding
Triple en Sibaroph Sematic Similarity
Input Gnaph
Initial Communities Portitioned triple-level clustering
Community Centers Based on Dual Perception
(b)
Steration
Steration #2
Pair-wise Community Fusion (c)
Final Communities
Figure 3: An overview our communitics via triple embeddings; (b) community center identification through joint consideration of topology connectivity and subgraph semantic similarity; and (c) iterative pairwise community merging form the final hierarchy. Distinet colors represent functionally coherent communities.
community detection. (a) Input graph partitioning into initial
compressed into a depth-L. Knowledge Tree (I, = 4), encapsulating hierarchical levels from coarse community summaries to fine-grained factual leaves, structured as {Community, Keywords, relation Triples, Att Attributes).
Entity Representation. Given graph G = (E, R). we first encode each entity e, E & by harvesting its contextualized embedding e, E R4, aggregating the frozen LM embeddings of all triples within its one-hop neighborhood Ni.
Σ
ftxle:|[rolll..]
[
(eg.r.e))EM;
Specifically, for each triple (e,, e,)) E A,, we form a textual sequence and then encode using frozen LM fiM, e.g., M---M to obtain contextualized embedding for each triple. To this end, the entity representation could effectively preserve both local structural patterns from one-hop structure and semantic relations via neighbors, enabling clustering based on both signals.
Cluster Initialization. To handle large-scale graphs, we first reduce the search space with a initial coarse partition by applying K-means clustering entity embeddings {c; {c,).W. generating initial communities {c(o) .......,))).. c...). The cluster count & is constrained as k = min (max (2, (E/3)), n)) with 3 10 ensuring granularity and r 200 preventing ver-fragmentation. We optimized KMeans (n_ in_init-5, random_stard statonssttttteeee 42) for reproducibility.
Iterative Community Fusion via Dual-Percoption Scoring.
First, to refine the initial clusters, we introduce a dual-perception scoring function o(e;, clu) that quantifies the affinity between node and a community clt) iteration l. This score combines two considerations. (i) topological connectivity overlap (S,) that measures the Jaccard similarity
between the relation incident to e; and those in c(t), (ii) subgraph semantic similarity (S.), which computes the cosine similarity between the entity embedding Fe(T;) and the community centroid Fe (T),),, where Fe is matrix for embedding transformation. C
bles, Cm.) = (ei, @e (e Cm),
(5)
relational
semantic
with
8, (es, Cm
(
(6)
S. - (T.)). (Fa(T))))
jeCm
where S, denotes the Jaccard similarity matrix computed over the multiset of incident relation types (-). (i.j) measures the overlap of relation-speciff neighborhoods between nodes i and j.
At each iteration L. we select the most representative entity e = arg max Φ(e;, C..) for each community based the dual-perception score, which combines both structural connectivity and
5

---

Published as a conference paper at ICLR 2026
semantic representativeness. Communities merged if the divergence between their centroid ex- pectation values, formulated hereunder:
Blo(e;, ci - (ep(ee;,,,)1 <.
(7)
where the merge will only occur when the value is below threshold e, thereby reducing the match- ing search space from mode-communit comparison to node-node comparison, yielding boosted, efficient hierarchical community detection.
3.2.1 KNOWLEDGE TREE
To this end, building upon our schema-bounded ex- traction framework, develop hierarchical knowl- edge organization pipeline that transforms raw graphs into structured Knowledge Tree K. shown in Figure 4 First, the process begins with our novel dual-perception community detection algorithm, which computes entity- community affinity through the combined metric, blend- ing topological connectivity overlap with semantic sub- graph similarity. Second, fLLM(Cm) is then applied to generate brief name and description for the entire com- munity based on the member names. These commu- nity names are treated as community nodes and inserted into the original graph, connecting with each member en- tity with the relation member_of Third, within each detected community C.. identify pivotal keywords by selecting entities maximizing the structural-semantic score arg max,, EC.. Φ(es, Cm).
Community Detection
Level-4 Communities
Keywords Extraction
Level-3 Keywords
Level-2 Triples
Level-1 Attributes
Attributes
Chunks
AT
The resulting hierarchy, together with the schema, collec- tively informs the construction of our four-layer knowl- edge tree K. The tree maximizes bottom-up semantic coherence at each level, simultaneously preserving fine- grained reasoning through granular entity-relation/entiyy
Scheme-bounded Agentic Extraction
Figure 4: Four-level knowledge tree. attribute retrieval (C1) and enhancing high-level community- based filtering (C4). We formally define as K- U Le,
{Cm)
= (Commun tty
farg max Cm), ((h,,,t) h,t E E R}
(Keywords)
Le
(8)
f - (Entity-Relation Triples)
(le,has attr, fell vale })}
L t= (Attributes)
3.3 AGENTIC RETRIEVER
Schema-enhances Query Decomposer. The complexity of multi-hop queries in large-scale knowl- edge tree necessitates an intelligent decomposition mechanism that respects both the explicit schema constraints and implicit semantic relationships. We propose proach illustrated in Appendix A By leveraging the graph schema S S,. we ensure that each generated atomic sub-que -uery strictly adheres to valid patterns in the knowledge tree, filtered by the identified schemas with entity types and attribute types. This thhema-awarenes, prevents the generation of ill-formed queries that would either fail to return results or retrieve irrelevant infor- mation. Therefore, the final Q = fLLM S) {yi, 9.... J. where i is a pre-defined maximum number for total atomic sub-queries and each a, explicitly targets either: (i) node-level retrieval (e, has attr, a). (di) triple-level matching (h, r, t). or (fii) community-lewe verification C,., as de- termined by schema clements S., S,,, and Satr
schema-guides decomposition ap-
Iterative Reasoning and Reflection. Since reasoning and reflection are two core cognitive capabil- ities for the agent, following the standard agent framework of perception--easoninng we formalize our agent as a tuple A = (H. JILM), where H denotes the agent's historical memory containing both former reasoning step and the retrieval results to derive insights for new actions, and the functions fLIM is employed to implement both key operations.
cycles,
A fuLM(9 H (()))))
(9)
Reasoning
Reflection

---

Published as a conference paper at ICLR 2026
This process addresses the compositional generalization challenge in complex QA by (i) maintain- ing explicit symbolic grounding through S during reasoning steps, and (ii) performing continuous self-monitoring via reflection to detect and correct reasoning paths. The agent's operational flow alternates between forward reasoning with schema-guided query decomposition and retrieval and backward reflection for complex scenarios, creating closed-loop framework that progressively converges to optimal solutions. To maximize the strength of different granularities, we equip the retrieval with multiple routes, including entity retrieval, triple matching, community filtering and DFS path traversal. The details could be found in the Appendix A.3
EXPERIMENTS
To systematically evaluate the model's retrieval and generation capabilities across diverse scenarios, we designed unified experimental framework encompassing evaluation metrics, datasets, and base- line models. Specifically, we adopt a dual-mode evaluation protocol with both open mode (allowing the use of parametric knowledge) and reject mode (requiring responses strictly based retrieved evidence) to assess the reliability of GraphRAG methods. Experiments are conducted on multi- ple established and newly constructed anonymous datasets, with comprehensive comparisons across representative baseline methods. Detailed experimental settings are provided in Appendix
A.4 A.4
4.0.1 EVALUATION METRICS
Following the workflow of RAG, the evaluation is typically divided into two stages: (i) assess the accuracy of retrieved evidence and (it) examine the end-to-end performance by evaluating the quality of LLMs responses generated from the retrieved evidence. In practical deployment scenar- ios, where multiple valid retrieval references may exist for identical answers, the latter evaluation paradigm has emerged as the prevailing standard in practical applications. Regarding the assessment of LLMs responses, several character-bass matching protocols, c.g., recall, EM and Fl score were established. To account for semantic deviations caused by minor character variations, where slight textual differences may lead to substantially divergent meanings, we employ DeepSeek- V3-0324 to assess response similarity against ground truth references.
During the reproduction of various GraphRAG frameworks, we observed experimental results ex- hibit significant variations depending the prompts in the LLMs generation stage. Specifically, some frameworks(Zhao et al. (2025)) instruct to explicitly reject to answer when retrieved evidence insufficient, while others(Xiao al. (2025): Sarthi al. (2024)) allow LLMs leverage its para- metric knowledge or ambiguates the instruction in such cases. Given that most LLMs have been exposed to extensive corpora during pretraining, we identify answering questions based on LLMs' knowledge rather than retrieval mechanism as critical factor for fairly evaluation, which we term knowledge leaking. To separately assess two critical capabilities: (i) recognizing knowledge lim- itations, and (ii) leveraging LLMs' parametric knowledge, we therefore implement dual-mode evaluation on three widely-used datasets:
Reject mode. Under this mode, LLMs must reject to answer the question when retrieval fails to provide sufficient evidence from the given graph. This strictly evaluates the retrieval effectiveness and prevent hallucination among existing models.
Open mode. L.MMs are allowed to answer using either retrieved content or its inherently paramet- ric knowledge. This maximally measures the overall capability in real-world practical deployment.
We have reproduced representative baselines and conducted comprehensive evaluations based on the metrics in this work. The corresponding prompts are provided in /subsection A.6) Moreover, the observations further underscore the importance of our proposed AnonyRAG dataset to ensure fair and comprehensive assessment GraphRAG methods.
4.1 COMPARISON OF TIME AND TOKEN CONSUMPTION
For baselines involving graph construction and community detection stages, this section com- pares their token and time consumption. All procedures executed using 32-thread concur- rent inference to ensure both the efficiency of graph construction and the fairness of compar- isons. Figure 5a presents the time and token consumption during the graph construction stage for

---

Published as a conference paper at ICLR 2026
Consumption comparison of graph construction.
(b) Consumption comparison of community detection
Youtu-GraphRAG and five baselines. Our method consistently achieves the lowest token con- sumption across all six datasets and maintains relatively efficient time performance on five of the datasets. In the community detection stage, as shown in Figure 5b we achieve the lowest token con- sumption compared with the other three baselines, consuming no more than 10,000 tokens on any dataset. Meanwhile, method demonstrates consistently efficient performance across all datasets.
Table 1: Overall performance comparisons benchmark datasets in terms of top-20 Accuracy.
Method
HotpotQA
2Wiki
MuSiQue
G-Bench
Annoy-CHS
Annoy-ENG
Open
Reject
Open
Reject
Open
Reject
Open
Open
Deepseek
V3-03344
70.92
Naive RAG
79,90
72.40
70.3
38.9
47.49
30.63
71.81
12.5
43.02
EGGraphRAG
43.20
20.00
8.01
68
16.01
RAPTOR
80.90
73.60
70.10
38.40
48.50
31.10
73.08
12.08
40.2
LighRAG
71.90
56.00
58.00
29.20
38.98
24.57
70.83
9.16
22.14
GrapRRAG
56.10
26.40
41,80
10.00
32.20
16.50
75 75.54
21.66
38.85
G-Retriever
49.00
6.70
35.80
5.00
23.50
1.70
70.63
4,07
5.08
HippoRAG
81.70
73.10
77.90
64.00
48.30
36.20
72.89
36.77
40.68
HippoRAG--RRCOS
81.00
74.60
78.40
66.00
46.70
35.50
73.38
36.05
42.17
IlipoRAG2
81,80
74,90
77.30
48.30
50,80
37.80
79.37
12.92
43.16
Ours w/o Agent
83.70
75.30
72,80
57.80
51.40
40.00
81.53
37.06
40.05
Youtu GraphRAG
86.50
81.20
85.50
77.60
53.60
47.50
86.54
42.88
43.26
Qwen3-32B
Zero-shot LLM
36.40
33.30
13:40
70.04
5.11
6.49
Naive RAG
75.00
69.00
58.50
39.60
40.64
33.03
72.69
7.56
26.84
72.90
22.14
HippoRAG
77.00
71.80
72,80
62.50
40.60
32,10
75
8.58
32.30
HippoRAG-IRCHH
80,30
76.60
74.80
65,40
44.70
37,40
77.11
9. 16
33.15
HippoRAG2
71.30
39.90
37.70
OuES w/o Agent
83,80
73.90
74.90
55,30
52.90
40.10
80.74
34.88
35.13
Youtu-Graphxaa
85.90
78.60
85.70
74.20
54.60
45.30
84.48
39.24
40.05
4.2
MAIN PERFORMANCE COMPARISON
In Table I report the top-20 accuracy across six challenging benchmarks under both open and reject modes, based on strong LLM backbones. i.e., DeepSeek- V3-0324 and Qwen3-32b. Across virtually all datasets and settings. Youtu-GraphRAG attains the high- est performance, reflecting its ability to com- bine precise retrieval with robust reasoning. Besides, we also include an variant with no agent for iterative reasoning and reflection lightweight version, i.e., Ours w/o Agent, fulfilling real-world applications requiring real-time interactive feedback. The distinction between the two evaluation modes provides complementary perspectives on system capability. Open mode unlocks the full reasoning potential of the L.MM synthesize answer regardless of retrieval gaps.
Figure 6: Youtu-GraphRAG effectively moves the Pareto frontier with lower token costs and higher per- formance over six benchmarks.
8

---

Published as a conference paper at ICLR 2026
This mirrors high- real-workd deployments where maximizing end-task accuracy outweighs caution. Youtu-Gr GrahRAG consistently outperforms existing baselines, achieving improvements from to points over the strongest competitor actoss datasets. When augmented with our agent framework, Youtu-Grape arahGG further pushes the performance frontier, reaching top-20 accura- cies of 86.5% 85.5%, and 53.6% on HotpotQA, 2Wiki, and MuSiQue respectively under Deepseek- V3-0324, and 85.9%, 85.7%. and 54.6% under Qwen3-32B, demonstrating clear advantage in multi-hop reasoning and cross-document synthesis. Reject mode, by contrast, imposes stringent criterion if the retrieved context is insufficient, the model must abstain. Youtu-Graphhen at- tains 81.2%, 77.6%, and 47.5% on HoipotQA, 2Wiki, and MuSiQue, outperforming the strongest baseline by 7-14 points. Across all datasets, our method achieves consistently higher top-20 accu- racy, confirming its ability to synergize graph-based retrieval with agent-driven reasoning for both high-coverage and high-precision scenarios. We value this metric since it directly probes retrieval quality, as speculative answers are penalized and the acceptance rate becomes a direct function of retrieval completeness and precision. Our superiority two anonymous datasets also validates the generalizability of routu-Graphhel
beyond standard benchmarks. Specifically, under the open mode, it achieves 42.88% and 43.26% top-20 accuracy Annoy-CHS and Annoy-ENG, respec- tively, surpassing all baselines by a clear margin. These results also reflect our robust reasoning and retrieval integration across diverse languages and domains, demonstrating that our approach could be easily transferred to previously unseen data distributions while maintaining high accuracy.
key objective of Youtu GanttuGGGGRRGGGGG is to jointly optimize performance and efficiency by unify- ing graph construction and retrieval. Figure (6 illustrates the trade-off between token consumption during the construction and overall QA performance across six benchmarks. Our approach con- sistently achieves optimal performance with the least token consumption, effectively shifting the Pareto frontier of both QA performance and costs compared to all baselines.
While existing GraphRAG methods struggle to balance graph construction cost and answer accuracy, Grouu-GraphRAG introduces a vertically integrated way combining achema-guided extraction, dual-percnption community detection, and schema-enhaneee agentic retrieval to build and reason over concise yet informative graphs. Our approach shifts the Pareto frontier, achieving state-of-the- art performance across all benchmarks while reducing graph construction token usage. These results affirm that synergistic integration of schema alignment, hierarchical knowledge trees, and adaptive retrieval significantly enhances the practicality and efficiency of GraphRAG.
Table 2: Overall performance comparisons based on DeepSeek in terms of top-10 accuracy.
HotpotQA
2Wiki
MuSiQue
G-Bench
Annoy-CHS
Annoy-ENG
Method
Open
Reject
Open
Reject
Open
Reject
Open
Open
Open
Naive RAG
79.40
68.00
67.60
33.70
45.58
26.73
71.22
12.08
38.93
RAPTOR
78.20
67.40
45.88
72.79
11.77
G-Retriever
49.90
5.90
38.00
3.80
23.50
1.70
70.24
5.38
5.50
LightRAG
71.98
58.10
65.
38.10
39.40
22.90
69.74
8.58
18.90
GraphRAG
54,30
23.70
40.00
30.20
61.39
21.37
38.36
HippoRAG
78.20
69.40
77.10
61.10
45.20
30.90
70.14
34.01
40.12
IlippoRACa IRCOT
77.70
44.40
41.42
HippoRAG2
79.40
70.40
74.60
45.80
49.10
34,00
77.21
13.52
37.24
Gura w/o Agent
80.50
72.10
72.10
$4.40
49.80
38.30
80.55
35.17
40.54
Youtu-GraphRAG
83.40
78.90
82.30
72.60
52.10
46.90
83.50
38.08
42.57
4.3 ANALYSIS OF GENERALIZABILITY
To examine the domain-transfer capability of Coutu-GraphRas we evaluate across six heterogeneous benchmarks without any task-specific fine-tuning. As shown in Figure 7 Youtu-GraphRAG achieves the best performance both Open Accuracy and Reject Accuracy on all datasets, surpassing state-of ofate-of-the-art GraphRAG baselines by clear margin.
We attribute this strong generalizability to the intrinsic integration of graph construction and retrieval: (i) schema-guided extraction yields consistent, domain-adaptive graphs, while dual- perception community detection builds robust hierarchical structures: (ii) the agentic query decom- poser dynamically adapts to various question types. These results confirm that Youtu-GraphRAG transfers seamlessly to unseen domains, preserving structural and reasoning fidelity.

---

Published as a conference paper at ICLR 2026
Table Ablation studies of our method over six datasets. We evaluate three variants: w/o Commu nity detection, w/o Agent coordination, and w/o Schema guidance.
Variants
HotptQA
2Wiki
MuSiQue
G-Bench
Anony RAG-CRS
AnonyRAG-ENG
Comm.
79.50
44.00
85.02
39,92
w/o Agent
75.30
57.80
40.00
81.53
37.60
40.05
w/o Schema
77.10
73.40
45.60
83.50
35.61
40,32
Youtu GrahhhAG
81.20
47.50
43.26
Furthermore, as shown in Table our method consistently outperforms all base- lines in both open and reject modes un- der top-10 retrieval evaluation. mode, Youtu-GraphRA: achieves accura- cies of 83.4%, 82.3%, and 52.1% on Hot- potQA, 2Wiki, and MuSiQue, outperforming the strongest competitor by 4-8 points. Under reject mode, improvements reach 8-12 points, reflecting stronger retrieval fidelity and reduced speculation. Consistent superiority also ob- served on anonymous datasets, e.g., 38.08% % on Annoy-CHS, and 42.57% on Annoy-ENG. These results underscore the robustness and adaptability our proposed schema-based integration of all components, even under stricter evaluation with limited hints.
Meiibue
open
Roulect
Busteed
Figure 7: We showcase the generalizability six benchmarks terms both open and reject accuracy.
4.4 ABLATION STUDIES
To quantify the contribution of each component, we perform ablations by removing community detection (w/o Comm.), agent reasoning and reflection (w/o Agent), and schema guidance (w/o Schema). Results on six benchmarks are summarized in Table
Specifically, removing community detection leads to a consistent drop across all datasets, particu- larly on multi-hop QA tasks such HotpotQA and 2Wiki around 1.7% and 2.5%, indicating that structuring knowledge into coherent communities facilitates more accurate retrieval and reasoning for global questions. The absence of agentic schema expandion, reasoning and reflection causes the most severe degradation on complex reasoning datasets, especially on 2Wiki and MuSiQue with remarkable 19.8% and 7.5% differences, supporting motivation that the iterative reasoning- feedback loop plays an essential role for resolving ambiguous intermediate steps. Eliminating schema guidance results in noticeable performance drops on wewwlede-inttnsiin settings, espe- cially on AnonYRAG-CHS with 7.27% decreases, highlighting the importance of a high-quality initialization of seed schema for new domains. This further demonstrates advantage that only requires minimum intervention for domain shifts. In conclusion, our model consistently outperforms all ablated variants, demonstrating the importance of each component for multi-hop inference.
CONCLUSIONS
In this paper, we propose Youtu-GraphRAG, vertically unified agentic paradigm that jointly optimizes both aspects through a graph schema. Our framework introduces (i) a schema-guided agent for continuous knowledge extraction with predefined entity types, relations, and attributes: (ii) dually-perceived community and keyword detection, fusing structural topology with subgraph semantics construct hierarchical knowledge tree that supports top-down filtering and bottom- up reasoning: (iiz) an agentic retriever interprets the schema to break complex queries into tractable sub-queries, paired with an iterative reasoning and reflection; and (ir) Anonymity Reversion, a novel task to mitigate knowledge leakage in LLMs, deeply measuring the real performance of GraphR frameworks supported by a carefully curated anonymous dataset. Extensive experiments across six challenging benchmarks demonstrate Youtu Gratu--GappRAG'ss robustness, advancing the Pareto fron- tier with up to 33.60% reduction in token costs and 16.62% higher accuracy than state-of-the-an ant baselines. Notably, framework exhibits strong adaptability, enabling seamless domain trans- fer with minimal schema adjustments. These results underscore the importance unified graph construction and retrieval, paving the way for more efficient and generalizable GraphRAG.
10

---

Published as a conference paper at ICLR 2026
ETHICS STATEMENT
Our research addresses technical problems in the field of information retrieval and knowledge rep- resentation. The work is mainly about the automated processing of publicly available data and questions, and involves no human subjects, animals, or environmentally sensitive materials. We therefore anticipate no direct physical or environmental ethical risks.
REPRODUCIBILITY STATEMENT
Detailed method design, implementation and reference, including dataset curation and sources, base- lines, experimental settings of this paper, provided in experimental settings in Section and Baselines have been carefully implemented to ensure fair comparison. The code and data are open- sourced and could be accessed via the anonymous link: https://githb..cmm htts://ittubbcoomm/eennnnCoCouu SaArecmCCCOOAAAAAAAtttGGGrrhhe
REFERENCES
Yuanchen Bei, Weizhi Zhang, Siwen Wang, Weizhi Chen, Sheng Zhou, Hao Chen, Yong Li, Jia- jun Bu, Shirui Pan, Yizhou Yu, et al. Graphs meet ai agents: Taxonomy, progress, and future opportunities. arXiv preprini arXiv:2506. 18019, 2025.
Junnan Dong, Qinggang Zhang, Xiao Huang, Keyu Duan, Qiaoyu Tan, and Zhimeng Jiang. Hierarchy- awarre multi-hop question answering over knowledge graphs. In The Web Conf, 2023.
Junnan Dong, Zijin Hong, Yuanchen Bei, Feiran Huang, Xinrun Wang, and Xiao Huang. Clr-bench: Evaluating large language models in college-level reasoning. arXiv preprint arXiv:2410.1755: 2024a.
Junnan Dong, Qinggang Zhang, Chuang Zhou, Hao Chen, Daochen Zha, and Xiao Huang. Cost- efficient knowledge-basse question answering with large language models. NeurlPS, 2024b.
Junnan Dong, Qinggang Zhang, Huachi Zhou, Daochen Zha, Pai Zheng, and Xiao Huang. Modality- aware integration with large language models for knowledge-ben visual question answering. In ACL, pp. 2417-2429. ACL, 2024c.
Darren Edge, Ha Trinh, Newman Cheng, Joshua Bradley, Alex Chao, Apurva Mody, Steven Truitt, Dasha Metropolitansky, Robert Osazuwa Ness, and Jonathan Larson. From local to global: A graph rag approach query-focusee summarization. arXiv preprint arXiv:2404. 16130, 2024.
Yunfan Gao, Yun Xiong, Xinyu Gao. Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yixin Dai, Jiawei Sun, Haofen Wang, and Haofen Wang. detrieval-augmeent generation for large language models. A survey. arXiv preprint 23 10997, 2(1), 2023.
Zirui Guo, Lianghao Xia, Yanhua Yu, Tu Ao, and Chao Huang. Lightrag: Simple and fast retrieval- augmented generation. arXiv preprint arXiv:2410.05779, 2024.
Bernal Jiménez Gutiérrez, Yiheng Shu, Weijian Qi. Sizhe Zhou, and Yu Su. From rag memory: Non-parametric continual learning for large language models. ICML, 2025.
Haoyu Han, Yu Wang, Harry Shomer, Kai Guo, Jiayuan Ding, Yongjia Lei, Mahantesh Halap- panavar, Ryan A Rossi, Subhabrata Mukherjee, Xianfeng Tang, et al. Retrieval-augmented gen- eration with graphs (graphrag). arXiv preprint arXiv:2501.0me
2024.
Xiaoxin He, Yijun Tian, Yifei Sun, Nitesh Chawla, Thomas Laurent, Yann LeCun, Xavier Bresson, and Bryan Hooi. G-retriever: Retrieval-augmmentd generation for textual graph understanding and question answering. NeurlPS, 37: 37:132876-132907, 2024.
Xanh Ho, Anh-Khoa Duong Nguyen, Saku Sugawara, and Akiko Aizawa. Constructing multi-hop hop qa dataset for comprehensive evaluation of reasoning steps. arXiv preprint (rXi:2001.00066 2020.
11

---

Published as a conference paper at ICLR 2026
Bernal Jimenez Gutierrez, Yiheng Shu, Yu Gu, Michihiro Yasunaga, and Yu Su. Hipporag: Neuro- biologically inspired long-term memory for large language models. NeurIPS, 37:59532-59569, 2024.
Jiayi Kuang, Ying Shen, Jingyou Xie, Haohao Luo, Zhe Xu, Ronghao Li, Yinghui Li, Xianfeng Cheng, Xika Lin, and Yu Han. Natural language understanding and inference with MLLM in visual question answering: A survey, ACM Comput. Surv. 57(8):190:1-190:
2025. doi: 10.
1145/3711680 URL htthttt:: http:///oo../gg//// 1145/371168)
Junru Lu, Jiarui Qin, Lingfeng Qiao, Yinghui Li, Xinyi Dai, Bo Ke, Jianfeng He, Ruizhi Qiao, Di Yin, Xing Sun, Yunsheng Wu, Yinsong Liu, Shuangyin Liu, Mingkong Tang. Haodong Lin, Jiayi Kuang, Fanxu Meng, Xiaojuan Tang, Yunjia Xi, Junjie Huang, Haotong Yang, Zhenyi Shen, Yangning Li, Qianwen Zhang, Yifei Yu, Siyu An, Junnan Dong, Qiufeng Wang, Jie Wang. Keyu Chen, Wei Wen, Taian Guo, Zhifeng Shen, Daohai Yu, Jiahao Li, Ke Li, Zongyi Li, and Xiaoyu Tan. Youtu-llm: Unlocking the native agentic potential for lightweight large language models, 2026. URL https: http://aaavvvvv.. ozg/ abs /2012. 24618
Linhao Luo. Yuan-Fang Li, Gholamreza Haffari, and Shirui Pan. Reasoning on graphs: Faithful and interpretable large language model reasoning. arXiv preprint arXiv:2310.01061, 2023.
Linhao Luo. Zicheng Zhao, Gholamreza Haffari, Yuan-Fang Li, Chen Gong, and Shirui Pan. Graph- constrained reasoning: Faithful reasoning on knowledge graphs with large language models. arXiv preprint ar arXiv:2410.13080. 2024.
Linhao Luo, Zicheng Zhao, Gholamreza Haffari, Dinh Phung, Chen Gong, and Shirui Pan. Gfm-rag: graph foundation model for retrieval augmented generation. arXiv preprint arXiv:2502.01113, 2025.
Shengjie Ma, Chengjin Xu, Xuhui Jiang, Muzhi Li, Huaren Qu, Cehao Yang, Jiaxin Mao, and Jian Guo. Think-on-graph 2.0: Deep and faithful large language model reasoning with knowledge- guided retrieval augmented generation. arXiv preprint arXiv:2407.10805, 2024.
Costas Mavromatis and George Karypis. Gnn-rag: Graph neural retrieval for large language model reasoning. arXiv preprint arXiv:2405.20139 2024.
Boci Peng, Yun Zhu, Yongchao Liu, Xiaohe Bo. Haizhou Shi, Chuntao Hong, Yan Zhang, and Siliang Tang. Graph retrieval- augmented generation: A survey. arXiv preprint arXiv:2408 08921, 2024.
Libo Qin, Qiguang Chen, Xiachong Feng, Yang Wu, Yongheng Zhang, Yinghui Li, Min Li, Wanxi- ang Che, and Philip S. Yu. Large language models meet NLP: A survey. CoRR, 12881 2024a. doi: 10.48550/ARXIV.240555 12819. URL httpt::tp///oooo. wtw.::/.....01.........0000
2405. 12819
Libo Qin, Qiguang Chen, Yuhang Zhou, Zhi Chen, Yinghui Li, Lizi Liao, Min Li, Wanxiang Che, and Philip S. Yu. Multilingual large language model: A survey of resources, taxonomy and frontiers. CORR, abs/24044009225 2024b. doi: 0044550//RXXI.24444000 04925. URL https:
7/doi 1oooi.org/I0 10. 48544/8588800 /ar.0v/arivvvvvvv 2404. 04925
Parth Sarthi, Salman Abdullah, Aditi Tuli, Shubh Khanna, Anna Goldie, and Christopher D Man- ning. Raptor: Recursive abstractive processing for tree-organized retrieval. In The Twelfih Inter- national Conference Learning Representations, 2024.
Jiashuo Sun, Chengjin Xu, Lumingyuan Tang, Saizhuo Wang, Chen Lin, Yeyun Gong, Lionel M Ni, Yeuug Shum, and Jian Guo. Think-on-graph: Deep and responsible reasoning of large language model knowledge graph. arXiv preprint arXiv:2307.07697 2023.
Vincent Traag, Ludo Waltman, and Nees Jan Van Eek. From louvain to leiden: guaranteeing well-connected communities. Scientific reports, 911::112,, 2019.
Harsh Trivedi, Niranjan Balasubramanian. Tushar Khot, and Ashish Sabharwal. Musique Multihop questions via single-hop question composition. Transactions of the Association Computational Linguistics, 10:539-554 2022.
12

---

Published as a conference paper at ICLR 2026
Yu Wang, Nedim Lipka, Ryan A Rossi, Alexa Siu, Ruiyi Zhang, and Tyler Derr. Knowledge graph prompting for multi-document question answering. In AAAI, volume 38, pp. 19204, 2024.
Yilin Xiao, Junnan Dong, Chuang Zhou, Su Dong, Qianwen Zhang, Di Yin, Xing Sun, and Xiao Huang. Graphrag-bench: Challenging domain-specific reasoning for evaluating graph retrieval- augmented generation. arXiv preprinl urXiv:2506.02404. 2025.
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W Cohen, Ruslan Salakhutdinov, and Christopher D Manning. Hotpotqa: dataset for diverse, explainable multi-hop question answering. arXiv preprint arXiv: arXiv: 1809.8800000 2018.
Michihiro Yasunaga, Hongyu Ren, Antoine Bosselut, Percy Liang, and Jure Leskovec. Qa-gnn: Reasoning with language models and knowledge graphs for question answering. arXiv preprint wrXiv:2/04.06378, 2021.
Qinggang Zhang, Junnan Dong, Hao Chen, Daochen Zha, Zailiang Yu, and Xiao Huang. Knowgpt: Knowledge graph based prompting for large language models. NeurIPS, 37:6052-6080, 2024.
Yibo Zhao, Jiapeng Zhu, Ye Guo, Kangkang He, and Xiang Li. E' 2graphrag: Streamlining graph- based rag for high efficiency and effectiveness. arXiv preprint arXiv:2505.24226 2025.
13

---

Published as a conference paper at ICLR 2026
A APPENDIX
A.1 USAGE OF LLMS
This paper was completely written by human beings and has been proofread and slightly polished with the assistance of LLMs. All LLM-generated content has been thoroughly examined and fact- checked to uphold the accuracy and integrity of the work. We assume full responsibility for any emrors or inaccuracies that may remain in the final version.
A.2 ILLUSTRATION OF THE DECOMPOSER
Consider the query "Where did Turing Award winners study?" Our method automatically maps "Turing Award winner" to the appropriate entity type S. Person with the specific award attribute, while correctly interpreting "study" as an S, educated_at. This semantic precision prevents the common problem of interpretation drift that often occurs in naive decomposition approaches.
did the explorer reach city where the headquarter only group larger than Vilaiyaadu Mankatha' record label is located?
Existing Embedding- based Similarity Matching
Agentic GrophQ
Traditional Agent
Embedding
Query
Agent
Agent
Retrieve
Planning
Werketo lontdii
Type
Feniry
PESSIN
What is The dize of Viliyadda
orploner geva rahournn
Domain
Schoma
rolation
CANINNLDA TONN
Saletiee/
Tho on tw of akkyoodk Wenkolo
headcna issated?
Type
NAME
When explorer cnt?
TIME
Planning
ONTE THE
CRGANEZATION
JORANI FOOTET
el
Vikiponda Markatho
TORBY
Figure 8: The figure contrasts three query-resolution strategies for multi-hop question. While embedding matching retrieves disjointed facts (left) and traditional agents use repetitive templates (right), our agentic decomposer (center) leverages domain schema to plan efficient sub-queries: (1) compare record label revenues, (2) locate the larger group's headquarters, and (3) trace the explorer's visit-achieving precise, with parallel reasoning and outperforming unstructured retrieval and template-based agents.
A.3 MULTI-ROUTE RETRIEVAL
To handle diverse sub-query types, we implement four parallel retrieval strategies with distinct opti- mization objectives:
Entity Matching: :
arg max cos q:) cEE
Triple Matching
arg max cos ro (K.r.))gg
(10)
Community Filtering: : arg max cos @c.. qi) Cm
DES Path Traversal: P(z) 74 22 In en s.t. Vr, E R
In general, the four retrieval paths exhibit distinct specialization patterns: (i) Entity Matching opti- mally handles single-hop simple queries requiring precise node identification, e.g., atomic fact check problem; (di) Triple Matching dominates hhophop reasoning tasks by modeling (h, r) composi- tional semantics, particularly effective for relationship inference; (ii) Community Filtering aims to address global queries, e.g., summarization and cross-domain problems through top-down filtering
14

---

Published as a conference paper at ICLR 2026
in the cluster; (4) DFS Path Traversal scales to complex multi-constraint problems, define the maximum depth = This specialization aligns with the cognitive spectrum from atomic facts to complex reasoning scenarios.
A.4
EXPERIMENTAL ANALYSIS
A.4.1 DATASETS
We firstly evaluate Youtu-GraphRas in dual-mode three widely used multi-hop QA datasets: HotpotQA YYng et al. 2018 ), MuSiQue (Trivedi et al. (2022)) and 2WikiMultHHoOAA (abbrevi-
following the setting in (Jimenez Gutierrez et al. (2024); Gutiérrez et al. ) for fair comparison.
ated as 2 Wiki Ho et
2020
To evaluate the framework's performance across diverse domains, we also employ GraphRAG- Bench(Xiao et al. (2025), shorted as G-Bench, a benchmark dataset constructed from textbook corpora. Additionally, to prevent knowledge leaking, we propose two novel bilingual anonymous datasets, i.e., AnonyRAG-CHS and AnonyRAG-ENG and propose challenging 'Anonymous Re- version' task.
We anonymize specific entity types (e.g., people, locations) in the dataset to break the model's mem- ory shorteuts and prevent it from relying on pretrained knowledge rather than retrieved evidence. Moreover, preserve semantic coherence through entity linking, enabling L.Mss to maintain dis- course comprehension despite anonymized mentions. The construction details of the dataset are documented in subsectioo A.7
A.4.2 BASELINES
We include three pipelines of research as baselines. (i) Naive RAG, the standard RAG approach that retrieves top-k document chunks using vector similarity search without any explicit knowl- edge structuring; (ii) Pure GraphRAG, which builds flat knowledge graphs for retrieval but lacks hicrarchical organization, focusing primarily on relational reasoning through graph traversal algo- rithms, including GraphRAG (Edge et al. (2024)). LightRAG (Guo et al. (2024)). G- R-Retriever (He et al. (2024) and HippoRAG T&2 (Jimenez Gutierrez et al. (2024); Gutiérrez et al. (2025)); (12) Tree-based GraphRAG, represents hierarchical methods that employ recursive clustering and sum- marization to construct multi-level knowledge trees including RAPTOR (Sarthi et al. (2024) and
Ea GGG (Zhao et ))
To ensure a fair performance comparison, we reproduce all the baselines and Youtu-GraphRAG with the same setting and evaluate with consistent metrics. In terms of base models, we maintain DeepSeek-V3-nt V3-0324 and Qwen3-32B as the base LLMs and a lightweight embedding model all- MiniLM--6--v2
A.5 RELATED WORK
While large language models (LLMs) demonstrate remarkable capabilities in language understand- ing and reasoning, they are known to be prone to hallucinations gere--ttnnngggggg confident yet factu- ally incorrect outputs-especiilll when reasoning over complex or multi-hop queries (Dong al. 2024bb[Qin et al. with graph-structured knowledge, therefore, combines the generative flexibility of LLMs with the factual rigor of structured data, enabling more accurate and trustworthy reasoning over complex domains (Luo et al., 2023 Dong et al., 2023 Bei al., 2025 Yasunaga et al.) 202T Luo et al., 2024). The approach of using LLM agents for graph-based reasoning was initiated by ToG (Sun al. 2023), which explores graphs by sequentially expanding reasoning paths. Building upon this, ToG 2.0 (Ma et al.) 2024) significantly refines the retrieval mechanism. It enables interactive access to both knowledge graphs and textual documents, fostering a context-sensitive reasoning process through the integration of multi-source information.
et al.] [025 Dong et al.) 22224a Qin et al. (024a).. Integrating LLMs
Evolving development GraphRAG has progressed along two complementary research trajectories since the seminal work of (Edge al. 2024). The first following approaches have evolved from LightRAG's (Guo et al.) 2024) vector sparsification techniques to more sophisticated graph-awar methods. Subsequent innovations include GNN-RAG and GFM-RAG (Mavromatis & Karypis
15

---

Published as conference paper at ICLR 2026
2024 Luo al. ).25), which employ graph neural networks for enhanced node matching, and Hip- poRAG 1&2 (Jimenez Gutierrez et al. 2024; Gutiérrez et al. 2025) that introduced memory mech- anisms and personalized PageRank algorithms for context-aware retrieval. While another group of methods have focused on improving the quality knowledge organization, hierarchical approaches like RAPTOR (Sarthi et al. 2024) and GrrrhhhRAG (Zhao et al. 2025) employ tree-like clustering and recursive summarization to enhance semantic organization. However, current research remain constrained by their specialized optimizations, either focusing tion, and lack unified design. This fragmentation limits their performance complex reasoning tasks requiring tight integration of knowledge organization and retrieval capabilities, which makes even harder to adjust the entire framework for generalizability especially when domain shifts occur. Our work bridges this gap by developing a holistic framework that jointly optimizes both aspects while maintaining graph foundation model properties.
retrieval or construction in isola-
A.6 PROMPT TEMPLATES LLMS GENERATION
We present the prompt templates in A.6. and A.6.2 which designed evaluate whether permitting L.MSs utilize its parametric knowledge within the RAG system affects performance. To minimize confounding factors, employed minimalistic prompts that solely differentiate between the two modes.
A.6.1 REJECT MODE
Given the question and the extracted knowledge from different retrieval paths, please answer the question below. If the extracted knowledge is not enough to answer, please reject answer.
Question:
{query}
Extracted Knowledge: [context]
Answer:
A.6.2 OPEN MODE
Given the question and the extracted knowledge from different retrieval paths, please answer the question below. If the extracted knowledge is not enough to answer, please answer based on your own knowledge.
Question: {query]
Extracted Knowledge: [context)
Answer:
A.7 DATA COLLECTION AND PROCESSING
All raw data in this study are sourced from the original texts of four classic novels: Water Margin, Dream of the Red Chamber, Moby-Dick, and Middlemarch. The copyrights of all these works have entered the public domain, thus presenting no copyright issues. In selecting data sources, we pursued two key objectives: (1) Ensuring comprchensive multilingual evaluation coverage, while (2) Maintaining sufficient complexity in entity representations (c.g., persons, locations) to rigorously assess model capabilities. The basic statistical information of the dataset is in Table 4
In our data processing methodology, we employed DeepSeek for entity extraction from the corpus, then the data chunks anonymized with the extracted entities. Query-answer pairs structed by DeepSeek using queries from 2Wiki and MuSiQue as seed templates. Upon acquiring the question-answer pairs, performed entity anonymization using the same anonymization dic-
16

---

Published as a conference paper at ICLR 2026
tionary applied to the corpus. This procedure ensures that LLMs cannot effectively leverage parametric memorized patterns from questions. A representative example of anonymized question- answer pairs is presented in Table As clearly demonstrated, while LLMs could handle questions according to common sense knowledge, their performance significantly degrades when confronted with anonymized versions of these questions. This phenomenon forces L.MMs rely retrieved contextual information rather than depending solely on their parametric knowledge.
To avoid the variance in evaluating subjective questions, finally converted the questions into two formats:
Anonymity Reversion. We provide LLMs with anonymized question-answer pairs context, quiring to infer and reconstruct the original entities that were anonymized. This task specifically assesses the model's ability leverage contextual clues for entity recovery.
Multiple Choice. To diversify question types and ensure objective evaluation, subset of questions was converted into multiple-choice format.
We then performed zero-shot filtering to verify model performance on these transformed questions. This design preserves the original assessment objectives of testing the LLM's contextual reasoning capabilities while guaranteeing answer objectivity and uniqueness. Crucially, it mitigates potential unreliability introduced by L.---a-juuge evaluation paradigms. Table 6 presents representative cases of these two question formats.
Table 4: Question Type and Difficulty Distribution Statistics
Chinese Literature
English Literature
Question Type
Difficulty Level
Total
Water Margin
Dream Red Chamber
Moby-Dick
Middlemarch
Objective
Simple (54 hops) Complex (2-4 hops)
26
163
24
34
51
22
131
Subjective
Simple (54 hops) Complex (>-4 hops)
50
51
116
282
(Anonymous Reversion)
146
314
142
Total
264
424
298
1397
Table 5: Comparations of anonymized and Non Non-amonymized questions
Lang
Type
Question
Zero-shot Response
Raw
- # Jo
Correct
CHS
Query
#1 [i] IE
Annoy
LOOCTTIOOSS [PERSON#277]
Wrong
Query
3 [[LOCATION+7599
Nu Q4 T У
#:365 U & #
E $ 6 #
I
Raw
What does the narrator's reac- tion to Queequeg's 's smoking in bed reveal about their relation- ship?
The narrator's acceptance of Queequeg's 's smoking reveals their deepening friendship and shared com- fort.
Correct
ENG
Query
Annoy What does Query SONN1999J's reaction [PERSON#200Y's smoking bed reveal about their relation- ship?
[PER-
analyze what [PERSON#1999]'s reaction to Wrong [PERSONW200J's smoking in reveals about their relationship, we would need more context about specific reaction and...
A.8 PROMPT TEMPLATES DURING EXTRACTION
17

---

Published as a conference paper at ICLR 2026
Table 6: Final Question-Answe Formats
Lang
Question
Ground Truth
Anonymity Reversion
T i: F 1
Q: EIPERSSONSS32 FIOCATIIOS BEE
PERSON#532
1 "PPERSON222 ? HH MI i X
PERSON#277
PERSON #4
18 1
A: PERSONNSS22EEELLCAATTTOO STO
CHS
PERSON#5U3
T
#
LOCATIONHS26
503) |il| P (1
LOCATION# 10
B6EEFT
LOCATION#S35
1E7EL11
+ 2 A
Please read the following QA pairs
Q: What does [PERSON#200Js story about the wedding feast reveal about cultural misunderstandings?
A: The story reveals how cultural misunderstandings, such [PERSON5888] mistaking the punchbowl for a finger-glass, can arise from ignorance of local customs.
PERSON#200 PERSON#588
Queequeg
ENG
captai
then for all anonymized Persons Locations, perform ence to determine the original content that was anonymized.
Multiple Choice
iR J. 15E B i # te fll A5
CHS
A. [LOCATION#340];
[IOOCATION#625] रे
C. C BEER. #9Hh
B. [LOCATIONN+30]]
[L
C. [LOCATION#625]#*) ER; [LOCATION#340]
[LOCATION#340] [LOCATION#625)
Which two physical traits [PERSON#1035] and daughter [PERSON#45] share in common?
ENG
A. Straight hair and round faces Curly hair square faces
B. (Mrs. Garth, daughter Mary)
C. Wavy hair and oval faces
D. Short hair and triangular faces
You are expert information extractor and structured data organizer. Your task is to analyze the provided text and extract as many valuable entities, their attributes, and relations as possible in structured JSON format. Guidelines:
1. Prioritize the following predefined schema for extraction;
Schema: {schema)
2. Flexibility: If the context doesn't 't fit the predefined schema, extract the valuable knowl-
edge needed;
3. Conciseness: The Attributes and Triples you extract should be complementary and no semantic redundancy.
18

---

Published as a conference paper at ICLR 2026
4. Do NOT miss any useful information in the context; 5. Output Format: Return only JSON.
Example Output:
Attributes: Map each entity to its descriptive features.
Triples:
List relations between entities in (entity_mentionl relation, entity_mention2)
format.
Entity types: Map each entity to its schema type based on the provided schema.
Schema Evolution: If you find new and important entity types, relation types, attribute types that are valuable for knowledge extraction, include them in a "new_ ter__shhemmatyeee field. Notably, the strict threshold of adding new schema considering both importance and similarity to the pattern the existing schema is 0.9.
Chunk: {chunk}
Example Output:
"attributes":
"Stephen King": ["profession: author"]
"triples": ["Shawshank Redemption", "based on", "Rita Hayworth and Shawshank
Redemption"]. ("Shawshank Redemption", "directed by", "Frank Darabont"] J.
"entity_types" types":
"Stephen King": "person". "Shawshank Redemption": "creative _ooree,__ "Rita Hayworth
and Shawshank Redemption": "creative_work" "Frank Darabont": "person" ).
"new_scheer scweeaatthheeeeeeeeeeee types":
"nodes": ["Movie"], "relations"; ["starring"], "attributes": ["genre"]
19

---

Published as a conference paper at ICLR 2026
A.8.1 SHCEMA-GUIDED QUERY DECOMPOSITION
You are a professional question decomposition expert specializing in multi-hop reasoning. Given the following ontology and the question, decompose the complex question into 2-3 focused sub-questions and identify involved schema types.
CRITICAL REQUIREMENTS:
1. Each sub-question must be:
Specific and focused single fact or relationship by identifying all entities, relationships, and reasoning steps needed
Answerable independently with the given ontology
Explicitly reference entities and relations from the original question
Designed retrieve relevant knowledge for final answer
2. For simple questions (1-2 hop), return the original question as single sub-question 3. Analyze the question and identify all schema types that might be involved Only return a concise JSON object with sub questions array and involved _tppes object.
Ontology:
[ontology] }
Question:
{question]
Example for complex question:
Original: "Which film has the director died earlier, Ethnic Notions or Gordon Of Ghost City?"
Output: {
luab_questions"
"sub-question": "Who is the director Ethnic Notions?",
"sub-question": "Who is the director of Gordon Of Ghost City?", "sub-question": "When did the director of Ethnic Notions die?".
"sub-question": "When did the director of Gordon Of Ghost City die?"
J.
"involved _ttttttyty=_
"nodes": "'creativ__woff "person"], "relations": ["directed __""].
"attributes": ["name", "date"]
Example for simple question:
Original: "What is the capital France?"
Output:
"sub_ ssub_questions".
"sub-question": "What is the capital of France?"
"involved types":
"nodes": ["location")]
"relations": ['located in")
"attributes": ["name"]
20

---

Published as a conference paper at ICLR 2026
A.8.2 ITERATIVE REFLECTION AND REASONING
You are an expert knowledge assistant using iterative retrieval with chain-of of-thought reasoning.
Current Question: (current_query)
Available Knowledge Context: [context)
Previous Thoughts: )
Step step: Please think step by step about what additional information you need to answer the question completely and accurately.
Instructions:
1. Analyze the current knowledge context and the question
Think about what information might be missing or unclear
3. If you have enough information to answer, in the end of your response, write "So the answer is:" followed by your final answer
4. If you need more information, in the end of your response, write specific query begin with "The new query is:" to retrieve additional relevant information
Be specific and focused in your reasoning
Your reasoning:
A.9 PROMPT TEMPLATES DURING GENERATION
Given the question and the extracted knowledge from different retrieval paths, please answer the question below. If the extracted knowledge is not enough to answer, please answer based on your own knowledge.
Question: {query)
Extracted Knowledge: [context]
Answer:
21


Reviews:
[{"review_id": "C8gScRUgWC", "reviewer": "Reviewer_128g", "summary": "This paper focuses on the problem of RAG on graphs. Specifically, it concerns the problem when a set of documents are converted into a KG prior to performing RAG. Previous methods typically operate in two stages: first they construct the KG and second they can perform RAG on it. The authors argue that the current construction methods are suboptimal for the task of RAG, as RAG may require a different construction process not captured by more traditional KG construction methods. To this point, the authors propose UniGraphRAG, which attempts to unify the construction and retrieval components of GraphRAG. To do this, the authors introduce a graph construction process that is guided by a specific \"seed schema\", allowing for a more targeted final graph. They further use an agent to expand on the graph, by choosing relevant schema elements that are sufficiently similar. They then summarize the knowledge into several communities in a tree structure. They consider 4 types of summaries: communties (i.e., structural), keywords, triples, and attributes (in that order from top to bottom). The communities themselves are created via structural and semantic similarity measures. Retrieval is then done on the final graph, where they further use reflection to refine results. The authors include results across a variety of benchmark datasets.", "questions": "1. I'd appreciate your perspective on my concerns in weakness 2, regarding the limitations of constructing the graph via a seed schema. In particular, I'm concerned that it may omit some information that is important for a subset of queries. [**Note**: This is my main concern and I'd be happy to raise my score depending on the answer] \n\n2.  Can you give more details about the seed schemas used (see weakness 1).", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. I like the motivation, they make a strong point that the construction and retrieval process should ideally be coupled. Because in reality, it's possible that current construction methods may actually harm retrieval.\n\n2. I like their idea for forming communities based on both structural (i.e., where in the graph) and semantic similarity. It's a subtle but good observation, as the structure is really just one piece of information that describes each node. For example, it's possible that two nodes share a high semantic similarity, but due to chance, are not near each other in the documents. As such, they don't appear in any triplets and may have low structural similarity. So this is a welcome idea. \n\n\n3. The overall results are quite good. They also include results on a variety of benchmarks which is good to see.", "weaknesses": "1. It's unclear how the seed shemas were created. The authors only mention that they were made using queries from 2Wiki and MuSiQue (line 863). How was this done? Why is this a good strategy? My main concern is that there is no guidance for how to construct these seed schemas, in terms of their content, size, and other parameters. Furthermore, I imagine that the specific datasets may also play a big role. As such, much more information needs to be given here.\n\n2. A separate, but bigger, concern regarding the seed schemas is that they greatly constrain the resulting graph. This is because that schema guides how the graph is created. I know that the authors introduce an agent to expand on this knowledge, however from my understanding, it should still only really consider related information. However, I'd argue that this is suboptimal in real-world applications for two reasons: **(1)** It's unpredictable what kind of query a user may ask. As such, the constructed graph may have been optimized for queries that some user doesn't ask. In essence, the problem is capturing the long tail of user queries, which I'm unsure this method can handle. **(2)** For multi-hop questions, it is often necessary to take \"detours\" through pieces of information that may not seem relevant. However, by restricting the content in the graph, we are effectively closing off these paths, making it harder to solve these queries. This goes back to my last point, in that while this may be okay for many queries, it could very well hurt the long tail of user queries that are more complex or are on the margins of the seed schema.\n\n3. I don't see why having a more efficient graph construction process is very important, as this is a simply a pre-processing step and only needs to be done once. As such, the benefit is marginal at best.\n\n4. It's unclear to me how the retrieval and construction process are actually \"unified\". At the end of the day, they are still separate processes. Maybe the proposed construction process is better for retrieval, but it nonetheless is it's own thing. What I mean, is for them to really be unified, the construction process would have to be conditional on the retrieval which it isn't (and would be prohibitvely expensive). This is a minor weakness, but I think it would be better for the authors to be more process in their language.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "UwSuvK6Aqq", "reviewer": "Reviewer_oGW9", "summary": "This paper aims to address the suboptimal performance in existing Graph Retrieval-Augmented Generation (GraphRAG) frameworks, which stems from the disjointed and separately optimized \"graph construction\" and \"graph retrieval\" stages. The authors propose **UniGraphRAG**, a \"vertically unified agentic paradigm.\" The core of this framework is the introduction of a shared \"Graph Schema,\" which simultaneously guides (i) a \"construction agent\" to perform controlled, structured knowledge extraction, and (ii) a \"retrieval agent\" to decompose complex queries into schema-aligned sub-queries. Furthermore, the paper proposes a novel \"dually-perceived community detection\" algorithm (fusing topological structure and subgraph semantics) to build a four-level \"knowledge tree\" for hierarchical indexing. The retrieval agent leverages this knowledge tree for iterative reasoning and reflection. To address the \"knowledge leakage\" problem in LLM evaluation, the paper also introduces a new anonymous dataset (AnonyRAG). Experiments demonstrate that UniGraphRAG significantly improves accuracy (up to 16.62%) while substantially reducing token costs during the construction phase (up to 33.60%).", "questions": "None", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. **Novel \"Vertically Unified\" Framework:** The paper's central thesis—using a shared \"Graph Schema\" to simultaneously constrain and align graph construction and retrieval—is highly novel and pertinent. This directly addresses the critical pain point in existing GraphRAG methods, where the two stages suffer from misaligned objectives and informational mismatch. It provides a logically rigorous new paradigm for building more robust GraphRAG systems.\n2. **Advanced Hierarchical Indexing Mechanism:** The paper makes a significant contribution to graph organization. The proposed \"dually-perceived community detection\" (fusing topology and semantics) and the resultant four-level knowledge tree (Community, Keywords, Triples, Attributes) provide the retrieval agent with a powerful, multi-granularity index structure. This enables the agent to efficiently perform both \"top-down\" filtering and \"bottom-up\" fine-grained reasoning.\n3. **Rigorous and Innovative Evaluation Methodology:** The paper excels in its evaluation (Section 4). To address the pervasive \"knowledge leakage\" problem in RAG evaluation (where the LLM answers from memory rather than retrieval), the authors specifically constructed the `AnonyRAG` anonymous dataset and an \"Anonymity Reversion\" task. This approach, combined with the dual \"Reject Mode\" vs. \"Open Mode\" evaluation, substantially increases the reliability of the experimental conclusions and represents an important methodological contribution to RAG evaluation.", "weaknesses": "**1. Strong and Brittle Dependency on Graph Schema:** The entire UniGraphRAG framework is strongly coupled to a shared \"Graph Schema,\" which is both its core strength and its most significant weakness. The framework's effectiveness is critically dependent on the initial quality of the \"seed schema,\" which requires substantial manual expert knowledge for cold-starting, posing a severe practical bottleneck. The paper's claims of \"automatic expansion\" and \"minimal-intervention transfer\" fail to quantify the necessary human-in-the-loop iteration and auditing costs. Furthermore, this \"vertically unified\" design can lead to error amplification: if the seed schema omits critical entity/relation types, the extraction agent will systematically \"miss\" facts, and the retrieval agent, following the same flawed schema, will be trapped in \"valid but ineffective\" sub-query loops. Finally, the schema-controlled extraction (Sec 3.1), while reducing noise, does so at the cost of recall, systematically omitting facts that fall outside the predefined schema.\n\n**2. Confused Contribution Attribution and Missing Key Ablations:** UniGraphRAG is a **strongly coupled** system composed of multiple innovations (Schema, novel community detection, agentic retrieval, reflection). The current ablation study (Table 3) is too coarse (removing only three large modules), making it impossible to attribute the performance gains to their true sources. For instance, the paper's key technical innovation—\"dually-perceived community detection\"—is not directly compared against standard graph clustering algorithms (e.g., Louvain, GMM), leaving its algorithmic value unproven. It is also impossible to discern whether the gains originate primarily from the \"Schema constraint,\" the \"knowledge tree indexing,\" or the \"agentic reflection\" (e.g., how much improvement does iterative reflection provide over single-shot decomposition?). This attribution confusion makes it difficult to pinpoint the marginal benefit of each component.\n\n**3. Heuristic-based Design and Robustness Concerns:** Many core components of the framework rely on brittle heuristic rules and hard-coded thresholds, casting doubt on its robustness. In the **indexing stage**, the \"dually-perceived community detection\" algorithm is itself a chain of heuristics: the k-value selection for K-means, the merging threshold $\\epsilon$, the use of Jaccard similarity for structural comparison (which can be fragile in sparse text graphs), etc. In the **retrieval stage**, the agent is constrained by hard thresholds, such as the maximum DFS depth ($d=5$) and a pre-defined sub-query limit, which may create a performance ceiling on tasks requiring longer reasoning chains. Furthermore, the framework suffers from a **toolchain risk** by using a small, frozen LM (e.g., `all-MiniLM-L6-v2`) to encode triple semantics, whose expressive power may be insufficient for complex domains, leading to poor quality in downstream clustering and matching.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "z6CyRDonbm", "reviewer": "Reviewer_vYRA", "summary": "This paper proposes **UniGraphRAG**, a vertically unified agentic paradigm for Graph Retrieval-Augmented Generation (GraphRAG) that jointly optimizes graph construction and retrieval through a shared graph schema. The framework introduces: (i) a schema-bounded extraction agent with automatic expansion capabilities; (ii) a novel dual-perception community detection algorithm that fuses structural topology with semantic similarity to build a four-level knowledge tree; (iii) an agentic retriever that decomposes complex queries into schema-aligned sub-queries with iterative reflection; and (iv) two anonymous datasets (AnonyRAG-CHS/ENG) with an \"Anonymity Reversion\" task to mitigate knowledge leakage. Experiments across six benchmarks demonstrate improvements of up to 16.62% in accuracy with 33.60% reduction in token costs.", "questions": "Q1. The authors follow the design of HippoRAG1/2, where HippoRAG2 adopts Top-k = 5. What is the rationale for choosing Top-k = 10 and 20 in your experiments? \n\nQ2. As discussed in W3, please supplement comparisons with established agentic RAG baselines. Can you demonstrate that the unified paradigm outperforms applying existing agentic methods directly to GraphRAG? Moreover, can you provide a fair evaluation proving the effectiveness and reliability of UniGraphRAG’s knowledge tree construction under equivalent retrieval setups(without query decomposer or baseline with query decomposer)?\n\nQ3. Can you provide a correlation analysis between the degree of anonymization and the performance of zero-shot LLMs versus RAG models? For example, compare the performance difference of zero-shot LLMs on raw queries versus anonymized queries to quantify how anonymization impacts retrieval dependence.\n\nQ4. How are the hyperparameters λ in Equation 5 and ε in Equation 7 determined or tuned? Please clarify whether they are empirically selected, fixed across datasets, or adapted dynamically.\n\nQ5. How is the initial schema for each domain designed? Is it a general, domain-agnostic schema or a manually curated one per dataset?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "**S1.** The paper addresses a significant practical limitation in GraphRAG systems: the disconnection between graph construction and retrieval optimization. The vertically unified paradigm is a valuable engineering contribution that demonstrates measurable improvements across diverse benchmarks.\n\n**S2.** The schema-guided approach provides a principled framework for aligning extraction, organization, and retrieval. The automatic schema expansion mechanism (Equation 3) enables adaptability to new domains while maintaining quality control through confidence thresholds.\n\n**S3.** The dual-perception community detection algorithm (Section 3.2) combines structural connectivity (Jaccard similarity on relations) with semantic coherence (cosine similarity on embeddings), addressing limitations of purely topology-based or purely semantic clustering methods. The resulting four-level knowledge tree naturally supports multi-granularity reasoning.\n\n**S4.** The anonymous datasets (AnonyRAG-CHS/ENG) and Anonymity Reversion task represent a valuable contribution to fair GraphRAG evaluation. By breaking LLM memory shortcuts through entity anonymization while preserving discourse coherence, these datasets more accurately isolate retrieval effectiveness from parametric knowledge.\n\n**S5.** Comprehensive experimental evaluation across six benchmarks with two strong LLM backbones (DeepSeek-V3, Qwen3-32B). The consistent superiority in both open and reject modes, along with efficiency gains (up to 33.60% token reduction), demonstrates practical value. The dual-mode evaluation protocol properly separates retrieval quality from generation capability.", "weaknesses": "W1. Except for the community detection module, most components largely adapt existing techniques: (i) the schema-guided extraction resembles ontology-based information extraction methods; (ii) the iterative agentic retrieval with reflection closely mirrors the Self-RAG architecture. The main contribution lies in orchestration rather than fundamental innovation. Furthermore, the paper emphasizes issues in unified graph construction and graph retrieval phases, but prior works have already designed retrieval schemes tailored to their our graph-structured databases. The claimed contribution of the schema-guided method is overstated—it functions more like a prompt-level enhancement that improves robustness and reduces noise rather than a genuine methodological innovation.\n\nW2. The authors claim that the proposed method consistently achieves optimal performance with minimal token consumption, yet fail to compare against cost-efficient agentic baselines such as RAPTOR and E2GraphRAG. \n\nW3. While UniGraphRAG is positioned as an agentic approach, most baselines—except for the iterative HippoRAG1/2—are single-turn retrieval models. The paper does not compare against existing agentic RAG methods such as Self-RAG or ReAct-RAG. Moreover, when evaluating “UniGraphRAG w/o Agent,” the model still benefits from a Query Decomposer, while other baselines do not. If the same query decomposition were applied before retrieval for all baselines, their performance—particularly on multi-hop reasoning tasks—might also improve. This experimental bias undermines the reliability of conclusions regarding the superiority of UniGraphRAG’s knowledge tree construction.\n\nW4. Although several datasets may have been seen during LLM pretraining, the zero-shot performance of LLMs remains relatively weak. Using the same base LLM while comparing the accuracy gap between Naive RAG and zero-shot LLM already provides a reasonable measure of retrieval effectiveness. \n\nW5. The provided code URL is inaccessible, and Figures 6 and 7 incorrectly label “UniGraphRAG” as “Youtu-GraphRAG,”.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "YEJ7KJfbmY", "reviewer": "Reviewer_rQJp", "summary": "This paper proposes UniGraphRAG, a vertically unified and agentic framework for graph-based retrieval-augmented generation (GraphRAG). The key idea is to connect the entire GraphRAG pipeline—graph construction, organization, and retrieval—under a single unified paradigm. Specifically, the authors first define a seed graph schema that constrains the automatic knowledge extraction process with specified entity and relation types, which can later be expanded by the LLM for scalability. Then, they introduce a dually-perceived community detection method that combines topological and semantic information to build a hierarchical knowledge tree supporting both top-down filtering and bottom-up reasoning. Finally, an agentic retriever interprets the same schema to decompose complex queries into sub-queries and iteratively reflect on retrieval results for more advanced reasoning. Experiments on six benchmarks demonstrate strong performance and efficiency gains, reportedly achieving up to 33.6% token savings and 16.6% higher accuracy over state-of-the-art baselines.", "questions": "Please refer to the weakness part", "limitations": "", "rating": 2, "confidence": 4, "soundness": 1, "presentation": 2, "contribution": 1, "strengths": "Strengths\n\n**1.** Unified perspective: The paper takes one step toward connecting indexing, retrieval, and generation of Graph-based RAG.\n\n**2.** The proposed dually-perceived community detection that fuses structural and semantic signals is new in this context.\n\n**3.** The reported improvements in both accuracy and token efficiency across six benchmarks suggest the proposed framework is effective in practice.", "weaknesses": "Weaknesses\n\n**1.** A major limitation is that the approach requires a manually designed seed schema for each knowledge domain. Constructing such schema demands expert knowledge and human involvement, which reduces generalizability and scalability, especially for open-domain or evolving corpora.\n\n**2.** The method relies on the LLM to iteratively expand the schema beyond its initial version. This process is computationally expensive and may require substantial hyperparameter tuning (e.g., expansion depth, stopping criteria), making it hard to reproduce and tune. Notably, the authors did not provide ablation or explanation on the confidence $\\mu=0.9$, why not using 0.95 or 0.99 ? Is this value scalable to any open world datasets?\n\n**3.** The framework introduces multiple tunable components—such as seed schema on different datasets, schema expansion confidence, knowledge tree depth, community detection intial cluster number, and community merging threshold—resulting in a large hyperparameter search space that may undermine its practicality and reproducibility.\n\n**4.** The agentic retriever and reasoning process is insufficiently detailed. The paper does not specify how many iterations of reflection are performed, how sub-queries are generated, how many sub-queires will be used, or how the agent’s reasoning quality is ensured. Without these details, the reproducibility and interpretability of the method are questionable.\n\n**5.** Limited ablation and analysis: While performance results are promising, the paper lacks **detailed** ablation studies isolating the contributions of each component and hyper-parameter sensitivity.\n\n**6.** This paper seems to violated the **Anonymous Protocol**. The **youtu-GraphRAG** in the experimental figures tells where the authors are from.\n\n**7. ** Typos: The symbol $S^{(t)}$ mentioned in the text near equation (3) does not appear in the equation.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)