LinearRAG: Linear Graph Retrieval Augmented Generation on Large-scale Corpora
Authors: Luyao Zhuang
Shengyuan Chen
Yilin Xiao
Huachi Zhou
Yujing Zhang
Hao Chen
Qinggang Zhang
Xiao Huang
Venue: ICLR 2026 Poster (2026)
Keywords: large languagde models
retrieval augmented generation
graph retrieval augmented generation
efficiency
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: We propose an efficient linear-scale Graph-based RAG model, which enables token-free (not requiring LLM token) and lossless graph construction.

Abstract: Retrieval-Augmented Generation (RAG) is widely used to mitigate hallucinations of Large Language Models (LLMs) by leveraging external knowledge. While effective for simple queries, traditional RAG systems struggle with large-scale, unstructured corpora where information is fragmented. Recent advances incorporate knowledge graphs to capture relational structures, enabling more comprehensive retrieval for complex, multi-hop reasoning tasks. However, existing graph-based RAG (GraphRAG) methods rely on unstable and costly relation extraction for graph construction, often producing noisy graphs with incorrect or inconsistent relations that degrade retrieval quality. In this paper, we revisit the pipeline of existing GraphRAG systems and propose Linear Graph-based Retrieval-Augmented Generation (LinearRAG), an efficient framework that enables reliable graph construction and precise passage retrieval. Specifically, LinearRAG constructs a relation-free hierarchical graph, termed Tri-Graph, using only lightweight entity extraction and semantic linking, avoiding unstable relation modeling. This new paradigm of graph construction scales linearly with corpus size and incurs no extra token consumption, providing an economical and reliable indexing of the original passages. For retrieval, LinearRAG adopts a two-stage strategy: (i) relevant entity activation via local semantic bridging, followed by (ii) passage retrieval through global importance aggregation. Extensive experiments on four benchmark datasets demonstrate that LinearRAG significantly outperforms baseline models. Our code and datasets are available at https://github.com/DEEP-PolyU/LinearRAG.

Published as a conference paper at ICLR 2026
LINEARRAG: LINEAR GRAPH RETRIEVAL AUG- MENTED GENERATION ON LARGE-SCALE CORPORA
Luyao Zhuang'; ; Shengyuan Chen'r, Yilin Xiao', Huachi Zhou', 1 Yujing Zhang'
Hao Chen', Qinggang Zhang't, Xiao Huang'
'The Department of Computing, Hong Kong Polytechnic University, Hong Kong SAR (luyao. zhuang, yi yilin.... xiao, yu-jing (achanngeccnnneecec polyyneeeettoltpuuuuuu hk; huachi. zhou@connect, polyu. mrru.h, hk; sundaychenhaaleemill.. com; (sheng yuanng--uaa... yuan. chen, qinggang. settaann..aaagaa. zhang, x ac. hpang) yu. edu hk
ABSTRACT
Retrieval-Augmentee Generation (RAG) is widely used mitigate hallucinations of Large Language Models (LLMs) by leveraging external knowledge. While effective for simple queries, traditional RAG systems struggle with large-scale, unstructured corpora where information is fragmented. Recent advances incor- porate knowledge graphs to capture relational structures, enabling more com- prehensive retrieval for complex, multi-hop reasoning tasks. However, existing graph-based RAG (GraphRAG) methods rely on unstable and costly relation ex- traction for graph construction, often producing noisy graphs with incorrect inconsistent relations that degrade retrieval quality. In this paper, we revisit the pipeline of existing GraphRAG systems and propose Linear Graph-based Retrieval-AN Auggnn-ee Generation (LinearRAG). an efficient framework that en- ables reliable graph construction and precise passage retrieval. Specifically, Lin- earRAG constructs a relation-free hierarchical graph, termed Tri-Graph, using only lightweight entity extraction and semantic linking, avoiding unstable rela- tion modeling. This new paradigm of graph construction scales linearly with cor- pus size and incurs no extra token consumption, providing an economical and reliable indexing of the original passages. For retrieval, LinearRAG adopts a two-stage strategy: (i) relevant entity activation via local semantic bridging, fol- lowed by (ii) passage retrieval through global importance aggregation. Exten- sive experiments on four benchmark datasets demonstrate that LinearRAG sig- nificantly outperforms baseline models. Our code and datasets are available https: /tae::///ittuie gig..b.....ggggggg com DDEE/DEEP-. Pol SotelPPoollylleeeaeeeeee nU/
INTRODUCTION
Retrieval-Augmented Generation (RAG) has emerged promising approach to enhance Large Language Models (LLMs) by leveraging external knowledge bases (Gao et al.) 2023 Zhou et al, 2025ah. However, existing RAG systems struggle with the complexities of large-scale, unstructured corpora real-world scenarios, where the relevant information is frequently distributed unevenly across heterogeneous documents. The context retrieved by RAG systems is often voluminous, intri- cate, and lacks clear organization, leading to issues of variability in accuracy and coherence (Sun) al., 2024 Zhang et al., 2024). Although recent advances attempt manage this by segmenting documents into smaller chunks for efficient indexing (Borgeaud et al. 2022 Izacard et al. 2023
Jiang et al. 2002), this strategy often results in the loss of critical contextual details, impairing re-
trieval accuracy and reasoning capabilities for complex tasks (Han et al. 2024 Zhang et al. 2025b).
To address this, Graph Retrieval- Gerrival-AAummntte Generation (GraphRAG) (Zhang et al. 2025b Procko & Ochoal 2024, Xiao et al.] (025a) has recently emerged a powerful paradigm that lever- ages external structured graphs to model the hierarchical structure background knowledge (Han)
2024). Specifically, early work, like RAPTOR (Sarthi al.) 2024) and Microsoft's ct al.
"Equal contribution.
1 "ooresponniig author: Qinggang Zhang, Huachi Zhou.

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GraphRAG (Edge al. 2024), organizes knowledge through recursive summarization and com-
munity detection with LLM-generated synopses, enabling coarse-to to-fent retrieval for comprehen- sive responses. Building this, recent approaches, including GFM-RAG (Luo et al., (025a), G- Retriever (He et al. 2024), and LightRAG (Guo et al. 2024), integrate specialized encoders and ob- jectives, such as query-dependers GNNs, Prize Collecting Steiner Trees, and dual-level indexing, to improve multi-hop generalization, scalability, and efficiency. More recently, HippoRAG (Gutiérrez) al. 2024) and its enhancement HippoRAG2 (Gutiérrez et al., 2025) draw inspiration from cogni- tive processes to utilize personalized PageRank for multi-hop retrieval. These strategies significantly improve retrieval precision and contextual depth, enabling LLMs to address complex, multi-hop queries more effectively.
Despite its conceptual promise and theoretical su- periority, recent studies reveal that GraphRAG models frequently underperform even naive RAG approaches many real-world applications (Han) et al. 2025; Zhou et al. 2025c Zhuang et al.
RAG
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2025). This performance degradation mainly stems from the poor quality of automatically constructed knowledge graphs. While graph-based retrieval increases recall of relevant knowledge, it concur- rently introduces substantial noise and ambiguities into the retrieved contexts, due to errors in graph construction. Specifically, two critical deficiencies undermine graph quality, including (i) local inaccu- racy: relation extraction processes exhibit signifi- cant error rates, resulting in inaccurate semantic relationships between entities. (ii) global inconsis- tency: the absence of mechanisms to enforce hierarchical consistency and global coherence during extraction leads to structurally fragmented graphs with poor connectivity. These deficiencies collec- tively manifest as structural conflicts and semantic ambiguity within the knowledge graph, which subsequently corrupt the retrieval and generation processes. Although recent attempts have been made to refine graph quality via bottom-up clustering-base based community summarization (Edge et al.) 2024 Gutiérrez al. 2025 Wang et al., 2025) or topic modeling (Sarthi et al.) 2024) to offer broader, macro-level view of data, these unsupervised methods are vulnerable to error propagation, where inaccuracies in entity relationships are amplified higher levels of abstraction.
Relotion
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Extraction
Enrity
Kecwledge Graph
Subgrraph
LincorRAS
NER
Semantic
Ratrieual
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Linkin
Extity
Tr-Graph
Passage
Figure 1: Three paradigms of RAG systems.
In this paper, we revisit the pipeline of existing GraphRAG systems and propose Linear Graph- based Retrieval-Augmented Generation (LinearRAG), a framework that enables efficient. reliable graph construction and precise corpus retrieval with multi-hop reasoning, The core idea of Linear- RAG is to simplify the complex relational graph into linear, easy-to-index view by focusing solely on modeling the semantics between target entities and the underlying text passages. Instead of rely- ing on costly relation extraction, LinearRAG constructs a hierarchical graph from entities, sentences, and passages, using only lightweight entity extraction and semantic linking, On top of this graph, LinearRAG introduces a two-stage passage retrieval technique: 1 local semantic bridging for en- tity activation, which identifies contextually relevant entities beyond literal matches by propagating semantic similarity in sentences to mine multihop contextual association; and @ global importance aggregation for passage retrieval, which applies personalized PageRank over the activated sub- graph to aggregate passage importance from a holistic perspective. Together, these modules enable LinearRAG to achieve scalable, accurate, and noise-resilient retrieval for complex queries. Our overall contributions are summarized as follows:
We identify key limitations in existing GraphRAG systems, specifically highlighting how reliance on unstable relation extraction introduces noise and structural inconsistencies. This motivates the design of LinearR novel framework that enables reliable graph con- struction and precise passage retrieval while maintaining linear scalability.
LinearRAG constructs relation-free hierarchical graph, called Tri-Graph, using only lightweight entity extraction and semantic linking, which avoids the instability of tradi- tional relation modeling and reduces indexing time by over 77%.
On top of the constructed graph, we design a two-stage retrieval mechanism that combines local semantic bridging for precise entity activation with global importance aggregation for
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(a) Retrieval and Generation Performance (%) of vanilla RAG EsS. typical GraphRAG baselines.
(b) Two types emror knowledge graphs brought by imperfect relation extraction methods.
Figure 2: (a) Retrieval and generation rerformance (%) of Vanilla RAG v.s. GraphRAG Base- lines. Notably, the evaluation on Medical dataset measures GPT-based accuracy, context relevance, and evidence recall across different RAG baselines.
(b) Case study of relation errors in knowl- edge graph construction from local inaccuracy and global inconsistency perspectives.
passage recall. This integrated strategy enables more accurate, noise-resilient, and single- pass multi-hop retrieval.
We conduct extensive experiments on four benchmark datasets, demonstrating that Lin- earRAG consistently outperforms state-of-the-art baselines in terms of retrieval quality, generation accuracy, and scalability, validating its practicality for real-world applications.
2 PRELIMINARY STUDY
In this section, we conducted a series of preliminary studies to investigate the effect of graphs used in RAG systems. Our findings reveal critical flaws in graph construction that explain the underlying causes of GraphRAG's frequent underperformance compared to naive RAG in traditional tasks.
2.1 PERFORMANCE DEGRADATION IN GRAPHRAG SYSTEMS
GraphRAG models frequently underperform traditional RAG approaches on many real-world tasks. Specifically, our experiments on GraphRAG-Bende (Xiang et al., 2025) demonstrate that while GraphRAG leverages graph structures enhance recall by retrieving a wider array of potentially relevant passages, this gain is offset by significant introduction of noisy and ambiguous contextual information. Specifically, GraphRAG methods such as LightRAG and HippoRAG achieve moder- ate improvements in evidence recall, but exhibit significantly lower context relevance, ranging from 36.86% to 54.61%, %, compared to Vanilla RAG, which attains 62.87% performance. This suggests that although graph-based retrieval expands the scope of contextual information, it introduces sub- stantial noise that compromises the relevance and reliability of generated answers. For example, in anestion-answering task about "climate change impacts", GraphRAG might retrieve passages related to "economic policies" due to tenuous graph links. In contrast, vanilla RAG preserves tighter alignment with the query context, leading to more accurate and stable outputs.
2.2 GRAPH QUALITY AND ERROR ANALYSIS
To diagnose the root cause of performance degradation observed in Figure 2a we performed a fine- grained error analysis on the knowledge graphs used in GraphRAG. Our analysis revealed that this performance degradation stems directly from deficiencies in knowledge graph construction. Tradi- tional GraphRAG pipelines rely on explicit relation extraction construct relational graphs, which usually introduce errors at two levels: (i) Local Inaccuracies: relation extraction models often pro- duce factually incorrect triples. For example, as shown Figure 2b] the sentence "Einstein did not win the Nobel Prize for his theory of relativity" may be misrepresented as (Einstein, won Nobel Prize for, theory of relativity), fundamentally altering factual meaning. existing relation extraction is performed locally individual text passages, with mechanism to validate or reconcile connections across the entire corpus, leading to redundant or contradictory re- lations. For example, "Al" may be linked to "Unsupervised Learning." "NI.P" and "CV" parallel
(ii) Global Inconsistencies:
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The overall pipeline of the proposed LinearRAG framework. Offline Construction. Initially, we construct Tri-graph containing entity, sentence, and passage nodes, with edges con- necting entities to sentences and entities to passages. II. Online Retrieval. We first activate relevant entities via local semantic bridging the entity-sentence subgraph while fixing passage nodes, then using the activated entities to aggregate global importance scores, finally, perform passage retrieval via personalized PageRank on the entity-pant passaae subgraph while fixing sentence nodes.
Figure 3:
subcategories without hierarchical coherence (e.g., g. that NLP and CV are subfields of AI, while "Un- supervised Learning" is technique used within them). This structural ambiguity directly misleads the retrieval process, introducing semantic noise based on these inconsistent connections.
2.3 DISCUSSION
The conventional GraphRAG pipeline relies heavily on explicit relation extraction and triple-based knowledge representation. While this approach aims to summarize passages into structured rela- tional forms, it faces two fundamental issues: First, extracting concise and accurate relational triples is computationally expensive and linguistically challenging, Relations expressed in nat- ural language are often complex and context-dependent, and often nuanced or compositional to be accurately distilled into atomic triples; for example, the sentence "Rachel reluctantly agreed to go running with Phoebe" cannot be cleanly reduced to a single atomic triple without losing critical semantic nuance. Second, explicit relation extraction is unnecessary. Aligned entities, rather than relations, serve as the primary anchors connecting information distributed across passages. The original text preserves relational semanties in full context, which can be interpreted dynamically by large language models during inference, without relying on error-prone extraction.
THE FRAMEWORK OF LINEARRAG
Preliminary findings indicate that explicit relation extraction is not only computationally expensive but also largely unnecessary. These insights inform our revisit of the design of GraphRAG compo- nents, culminating in two central claims: (i) Aligned entities serve the primary anchors connect- ing information distributed across passages. (ii) Contextual relations are best preserved within the original passages, eliminating the need for explicit relation extraction.
Motivated by this idea, we introduce a new GraphRAG paradigm as in Figure 3 It constructs graph that contains three types of nodes: entity nodes, sentence nodes, and passage nodes. Edges connect entities to sentences and entities to passages, represented as two adjacency matrices. Re- trieval proceeds in two stages: entity activation stage, where we fix the passage nodes and use local semantic bridging the entity-sentence subgraph to identify intermediate entities that con- nect different passages; and passage retrieval stage, where we fix the sentence nodes and apply personalized PageRank on the entity- passage subgraph, exploiting the activated entities in the first stage as seeds for global importance aggregation. This framework exhibits linear scalability both graph construction and retrieval (detailed analysis in Appendix and we refer it LinearRAG.
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3.1 TOKEN-FREE GRAPH CONSTRUCTION
We construct a hierarchical graph, called Tri-graph, with multiple granularities that is efficient to maintain and update. Given corpus with a of passages P, first segment each passage into sentences using punctuation (e.g., periods or exclamation marks), obtaining a sentence set S. We then apply lightweight models (c.g., spaCy (Honnibal et al.] (020)) for named entity recognition (NER) to derive an entity set &. The passages, sentences, and entities constitute three types of nodes in the graph, denoted as y, Vs, and Ve, respectively.
Edges are constructed according to the following rules: if passage P: contains an entity ej. we add edge (Vp,, Ve,); likewise, if a sentence s; mentions an entity ej, we add an edge (V.,. Ve,). These relations correspond to two adjacency matrices: the contain malrix C' between passages and entities, and the mention matrix M between sentences and entities.
Formally, the contain matrix C' is defined as an |Vpl x [Vel matrix:
C = =CCyllyys where Ci5 = X (p. contains
(1)
Here, I denotes the indicator function, with Ci 1 if p; contains ej. and Cip otherwise.
Similarly, the mention matrix M is defined as an |V,1 x IVel matrix:
M [Mslpaιi|a where My = V8 (st mentions e
(2)
This design enables efficient graph updates as the corpus grows. When new passages arrive, only those passages undergo sentence segmentation, NER, and edge construction, yielding overall linear complexity. Notably, NER is both precise and efficient compared with OpenlE, and can be per- formed using lightweight language models (e.g., BERT-based models in spaCy) without incurring LLM token costs. addition, the adjacency matrices and M are implemented in sparse form, exploiting their intrinsic sparsity to further reduce memory usage to linear-scalability. Finally, by retaining the original passages as knowledge carriers, the resulting graph preserves all contextual information, thereby ensuring information-losstaee construction.
3.2 PASSAGE RETRIEVAL
With the constructed graphs serving as lossless knowledge carriers, the objective becomes identify- ing the most informative passages effectively. The design graph-based retrieval is critical, as must balance precision and recall of relevant context, particularly in the case of multihop queries. We decompose the retrieval process into two stages: a precise entity activation stage via local se- mantic bridging followed by passage retrieval stage to recall globally important passages.
3.2.1 FIRST STAGE: RELEVANT ENTITY ACTIVATION VIA SEMANTIC BRIDGING
Generally, direct entity matching can miss relevant intermediate entities that bridge multihop re- lations, which are essential for multihop queries. Therefore, it is crucial to identify these latent connectors. However, such intermediate entities cannot be directly mined, and the massive number of entities challenges the precision of identifying them. To tackle this, we propose relevant entity activation via semantic bridging. For an incoming query the process is as follows:
Initial entity activation that identifies entities contained in the query q and represents their activa- tion in the knowledge graph sparse vector. Specifically, we first use SpaCy to extract entities from q, denoted as E.g Then for each extracted entity, we find the most similar entity in the knowl- edge graph. The initial activation score of the matched entity is set to its similarity score:
ag = [
where
ag. V i=arg maxe, eve eim( s sii mimmee es).
Query-sentence relevance distribution that computes the contextual association between the query and each sentence 8; E S, where S = $2.... ( } is the set of sentences the corpus. Represent these similarities as vector:
0g [ where Og.1 = s..gsmmggaa s:).
(4)
Semantic propagation that propagates the similarities in o, through semantic similarity extrac- tion to activate relevant intermediate entities, enabling the bridging of multihop relations in the
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knowledge graph. The entity activation vector a, is updated with the weighted aggregation of their associated neighbors in the sentence-entity bipartite graph using the matrix M from equation 2
a - MAX(MM (o, @ a) ay
where a is the activation vector of the entities in the ten iteration of semantic propagation. With a few iterations, we idemtify set of contextually relevant entities that anchors subgraph in the corpus that aligns with the reasoning structure of the query. This solution mimics the relation-matching process in the GraphRAG algorithms for multi-hop reasoning. Differently, our strategy performs implicit relation matching and does not rely on explicitly constructed relational knowledge graph.
Noteworthy, the vectorized formulation of equation 5 enables n-hop entity activation within only n iterations, where n is generally small (S. 4). Each iteration involves two MatMul (matrix mul- tiplication) and one MAX operation, which be efficiently implemented and executed in parallel on popular machine learning platforms. Furthermore, due to the intrinsic sparsity of the matrix M and the activation vector a we can store sparse format reduce memory consumption, and reduce computation cost by applying SpMM (sparse matrix multiplication) to replace MatMul.
Dynamic pruning. While the above semantic bridging successfully establishes initial associations from query entities to potentially relevant intermediates, it also introduces major challenge: the exponential growth of the search space as propagation deepens. Without proper control, irrelevant entities may repeatedly serve as new seeds, causing the process to expand combinatorially and drift into semantic regions unrelated to the original query intent. To address this issuc, we perform graph pruning that constrains expansion to high-quality semantic paths. Specifically, at each propagation step, we introduce a threshold 8. A newly activated entity is retained for the next iteration only its relevance score exceeds ō; otherwise, it is pruned. Moreover, the process terminates automatically once no new entity surpasses the threshold. This adaptive mechanism ensures that propagation proceeds only along the most relevant semantic paths, while dynamically tailoring the iteration range to the complexity of each query.
3.2.2 SECOND STAGE: PASSAGE RETRIEVAL VIA GLOBAI IMPORTANCE AGGREGATION
In the first stage, relevant entities are extracted via local semantic-bridged subgraph expansion in the sentence-entity graph. These entities are used to initialize importance scores in passage-entity graph, a bipartite graph where nodes represent passages (V₂)) and entities (V2), with edges connecting passages to their contained entities based occurrence. The second stage performs global impor- tance aggregation with hybrid initialization for passage nodes to retrieve important passages using Personalized PageRank (PPR) on the passage-entity graph to compute refined global importance scores for each node v; E Vp U Ve:
1(0,) deg(ry)
I(v;) = (1-d)+d
Σ
(6)
MyEB(n))
Here, d is the damping factor (typically 0.85), B(vs) is the set of nodes linking us and deg(U;) is
the number of outgoing links from node ny. The initial importance score for entity node v; E V. is
ay ....) ) is a vector of entity relevance scores for query computed in the first stage. For passage nodes u E Vp. the initial importance score is:
set to [0g/00 E Ve)) = a. where a, (a. (1) (2)
In(1 + Ne.) )) W,,
Iovlv E VV))= = A + In 1
Σ
(7)
Lee;
C.EE.
where s sim(g, v) is the PPR base score capturing similarity between passage and query g, E, is the set of activated entities from the first stage, No; the occurrence count of entity e; in passage Le; is the hierarchical level of entity e, and W is the passage node weight coefficient, X is trade-off coefficient. Passages are ranked by their PPR scores [(v E Vp). and the top-k passages with the highest scores are selected for retrieval.
4 EXPERIMENTS
In this section, we conduct comprehensive experiments to verify the effeetiveness and efficiency of LinearRAG. Specifically, aim answer the following questions. Q1 (Generation Accuracy):

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Table 1: Result (%) of baselines and LinearRAG on four benchmark datasets in terms of both Contain-Match and GPT-Evaluation Accuracy. The best result for each dataset is highlighted in bold, while the second result indicated with an underline.
HotpotQA
WWiki
MuSiQue
Medical
Method
Contain-Ace GPT-Acc. Contain- Acc, GPT-Acc. Contain-Acc.
GPT-Acc. GPT-Acc.
Direct Zero-shot LLM Inference
Ilama-8B
31.10
27.30
33.60
16.20
7.40
8.10
27.31
Ilama-13B
24.20
16.80
21.90
10.50
3,30
4.40
28.86
Gurr
33.40
43.20
28.70
31.00
10,30
21.90
45.60
GPT-4o-mini
38.90
40.20
36.30
31.40
13.60
15.80
42.10
Vanilla Seaaall-uuuueeeen-GGneeeeeeee
Retrieval (Top-1)
46.30
49.10
36.60
31.70
17.80
21.10
Retrieval (Top-3)
53.00
56.00
44.90
39.70
25.10
27.50
59.07
Retrieval (Top-5)
55.70
58.60
48.60
43.00
26.10
29.60
61.68
RerankRAG
55.90
59.60
49.30
45.30
26.70
31.80
55,43
RCOT
56.30
60.60
49.80
45,30
26.80
30.70
48.69
Graph-based Retrieval-Augmente Annerneeeeeeeeeeeeeeetttionn Generation Methods
KGP
61.50
60.90
31.60
30.00
25.60
30.10
54,22
G-retriever
42.20
40,60
46.60
27.10
14.40
15.50
50.36
RAPTOR
55.90
58.30
50.10
42.10
23.30
27.40
55.75
GrGaphRAG
61.00
63.90
54.30
38.10 10
23.80
26.20
58.00
LightRAG
60.30
59.50
55.20
39.00
27.40
28.60
54.36
HippoRAG
57.00
59.30
66.10
59.90
29.30
24.10
55.04
GFM-RAG
62.70
65.60
66.80
39.50
29.90
34.60
56.07
HippoRAG2
62.90
64.30
62.70
55.00
31.00
35.00
60.77
LinearRAG (Ours)
64.30
66.50
70.20
63.70
33,90
37.00
63.72
How does LinearRAG perform compared to state-of-the-art GraphRAG methods in terms of gener- ation performance? Q2 (Efficiency Analysis): How cost-efficient and time-efficient is LinearRAG relative existing GraphRAG approaches? Q3 (Ablation Study): What contribution does each component of LinearRAG make to the overall performance? Note that for a comprehensive eval- uation of LinearRACC additional experiments retrieval quality, parameter sensitivity, backbone analysis, large-scale efficiency analysis and case studies are presented in Appendix
4.1 EXPERIMENTAL SETTING
Datasets. We first evaluate the effectiveness of LinearRAG three widely-used multi-hop QA datasets and one domain-speciff dataset, including HotpotQA (Yang et al.) 2018). 2WikiMulti- HopQA (2Wiki) (Ho et al. (020), MuSiQue (rivedi et al. 2022). and the Medical dataset from (Xiang et al. (025). We follow the same evaluation method as HippoRAG, us- ing the corpus for retrieval and choosing 1,000 questions from each validation set. This setup allows for a fair comparison between different methods. We also test our approach on the domain- specific Medical dataset from GraphRAG-Bent (Xiang al. 2025), showing that LinearRAG improves both generation results and retrieval quality.
GraphRAG-
Baselines. We categorize all the baselines into three groups: (i) Zero-shot LLM Inference: We eval- uate several foundational models including LLaMA3 (8B) and LLaMA3 (13B) (Dubey et al., 2024), as well as GPT-3.5-turi and GPT-4o-mini (OpenAl 2023). (ii) We deploy Vanilla RAG methodolo- gies across multiple retrieval configurations (retrieving 1,3, or 5 top passages), combining semantic- based document retrieval with fhain-of-though reasoning prompts process. Additionally, we compare against advanced RAG methods, including RerankRAG, which incorporates a reranking model to refine retrieval results, and IRCOT (Trivedi et al. 2023) for it- erative retrieval-reasooingg (iii) State-of-the-art GraphRAG Systems: We compare against lead- ing GraphRAG implementations including KGP (Wang et al. 2024), 2), G-retriever (He et al. 2024), RAPTOR Sarthi et al. 2024), E² GraphRAG (Zhao et al. 2005 LightRAG (Guo et al. (024)) Hip- PoRAG (Gutiérrez et al. 2024). GFM-RAG (Luo et al., 2025a), and HippoRAG2 (Gutiérrez et al.] 2025
guide the L.LM's generation
Among these, RAPTOR organizes the corpus into hierarchical tree graph; G-Retriever,

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LightRAG, HippoRAG, GFM-RAG, and HippoRAG2 extract triples from passages to build struc- tured graphs; while GraphRRAG integrates both strategies.
Evaluation Metrics. We evaluate method using four metries across two categories. For end- to-end QA performance, following existing work (Wang et al., 2025), we use: 1) Contain-Match Accuracy (Contain-Acc.), which checks if the correct answer appears in the generated response, and 2) GPT-Evaluation Accuracy (GPT-ACC.), an LLM-based metric that assesses whether the pre- dicted answer matches the ground truth. For the Medical dataset, since golden answers consist of lengthy descriptive statements, we only evaluate using GPT-ACC. For retrieval quality assessment, we adopt metries from GraphRAG-Bent suring semantic alignment between questions and retrieved passages, and 2) Evidence Recall, which evaluating whether the retrieved contents contain all necessary information for the correct answer.
(Xiang et al. 2025): 1) Context Relevance, which mea-
Implementations. For consistency in implementation, all algorithms use the same embedding model, i.e., all-mpnet--asse-y (Xiao et al.) 2023)). For the RerankRAG baseline, we employ the hge-reranker-large model for reranking. We set k = 5 for top k retrieval in all methods. All RAG approaches employ the same LLM (GPT-4o-mini) for both generation and evaluation tasks. All experiments are conducted on the hardware configuration detailed in Appendix C
4.2 GENERATION ACCURACY (Q1)
To address Q1, we conduct a comprehensive evaluation of generation performance by comparing various baseline methods with LinearRAG across four benchmark datasets. The detailed experimen- tal results are presented in T
Based on our analysis, we derive the following key observations.
Obs. RAG significantly enhances zero-shot LLM performance. Direct prompting of LLMs without external knowledge retrieval yields the poorest performance across all datasets. For instance, advanced models like GPT-44-mini achieve only 15. 55.80% GPT-based accuracy on the MuSiQue dataset when operating without retrieval augmentation. However, integrating relevant corpus con- tent into prompts substantially improves performance, as Vanilla RAG with top-5 retrieved con- texts increases accuracy 99.60% on the same dataset. Such significant performance enhancement demonstrates the essential nature of RAG mechanisms for information-intensin applications.
Obs. 2. GraphRAG methods provide crucial context missed by RAG methods for complex rea- soning. While larger & values improve accuracy, even for advanced RAG methods like RerankRAG and IRCOT that incorporate reranking and iterative retrieval modules, the gains diminish at higher values, revealing vanilla RAG's core limitation in multi-hop reasoning: it tends to overly focus on searching for mentioned entities or keywords within documents, thereby missing logic-related documents essential for the complete reasoning chain. By contrast, methods that model structural dependency the retrieval consistently perform better. Among them, HippoRAG achieves the best results across most datasets, attaining 62.90% Contain- baseed accuracy on HotpotQA and 31 .00% on MuSiQue datasets.
Obs. 3. LinearRAG demonstrates superior performance compared to GraphRAG methods, which suffer from sensitivity to constructed graph quality. Typical GraphRAG methods address semantic misalignment by explicitly structuring knowledge into graphs, but their effectiveness lies heavily on relation extraction. Instead, LinearRAG gets rid of ineffective graph construction and LinearRAG outperforms all baselines by significant margin across all datasets, empirically. Notably, LinearRAG achieves 63.70% GPT-based accuracy on 2Wiki, around 3.80% absolute im- provement over the second best baseline, and boosts Contain- based accuracy to 70.20%.
4.3 EFFICIENCY ANALYSIS (Q2)
To better understand the associated efficiency and cost implications, conduct dedicated analysis on prompt statistics across different GraphRAG models during the indexing and retrieval stages by comparing their token usage and running time 2WikiMultiHopQA dataset. The results presented in Table 2 and we summarize our key observations as follows:
Obs. 4. Complicated graph construction inevitably brings significant time costs during both the indexing and retrieval stages. Models such as G-Retriever and LightRAG employ intricate schema definitions, resulting in notably poor efficiency. For instance, LightRAc AG requires 4,933.22
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Table 2: Efficiency and performance comparison of different GraphRAG methods. Notably, Accuracy represents the average of Contain-Acc. and GPT-Acc. metrics. Prompt tokens represent input LLM, and completion tokens represent LLM output. Best results are highlighted in bold, and second- beoond-besttt results are underlined.
Time (s)
Token Consumption (x109)
Method
Accuracy
Indexing
Retrieval (Avg.)
Prompt
Completion
G-retriever
2745.90
11.487
6.05
2.26
36.85
RAPTOR
1323.57
0,062
0.81
0.03
46.10
GGGraphRAG
534.60
0.053
0.78
0.08
46.20
LightRAG
4933.22
10.963
35.52
51.16
47.10
HippoRAG
936.00
1.461
3.05
0.98
63.00
GFM-RAG
1202.77
1.211
3.05
0.98
63.20
HippoRAG2
1147.01
1.694
4.98
1.22
58.85
LinearRAG (Ours)
249.78
0.093
66.95
seconds for indexing and 10.963 seconds for retrieval. These models define entities and keywords within the graph and perform both high-level and low-level retrieval. Consequently, LLMs are overburdened with processing the large volume of content retrieved from these two information sources, which results in higher fees and latency.
Obs. 5. Reducing LLM token costs does not necessarily have negative impact on perfor- mance. Complicated prompt construction and completion in G-Retriever and LightRAG fail to deliver consistent performance improvements. For instance, HippoRAG2 uses only 3.05M tokens during prompt construction and 0.98M tokens in the completion stage, while HippoRAG2 consumes 4.98M tokens for prompt construction and 1.22M tokens in completion. Despite these lower token costs, both models outperform G- G-Retriever and LightRAG.
Obs. 6. LinearRAG delivers the strongest overall efficiency. Considering the whole pipeline, LinearRAG is the fastest and introduces zero token usage. While E'GraphRAG and RAPTOR more time-efficient than LinearRAG at the retrieval stage, it comes the cost of significantly reduced model performance, as it retrieves only directly related documents with the query with- considering the structural dependencies in the reasoning chain. In comparison, the lightweight pipeline of LinearRAG avoids LLM calls for both indexing and retrieval, which minimizes latency and eliminates token costs. For deployments that demand strong performance along with speed, scalability, and cost control, LinearRAG is the most practical choice
4.4 ABLATION STUDY (Q3)
Effeet of core components in LinearRAG. We conduct systematic ablation studies the core components of LinearRAG across four datasets. We examine key modules: O Rel- evant Entity Activation via Semantic Bridg- ing, i.e., w/o Entity Activation, which directly uses the initial entities extracted from the query activated entities, bypassing relevant entity activation via semantic bridging, and moving directly the passage retrieval stage. @ Pas- sage Retrieval via Global Importance Aggre- gation, iee., w/o Global Importance Aggrega- tion, which skips the global importance and di- rectly uses the scores computed by the entity activation stage to retrieve documents from the corpus, without considering global importance relationships. The experimental results shown in Figure and we have the following findings.
LizzaRRAU
vil basty Actratic
WAO Glibtd Sepurtance Apgregance
(3.5% 61.9%
44.2%
63 00.2%
No
50%
40%
1L.N% 32.9%
30%
20%
HotpoRAA
McSiQc:
Medal
Figure 4: Ablation study on key modules of Linear- RAG under four different datasets. The y-axis repre- sents the average of GPT-Acc. and Contain-Acc.
Obs. 7.
Each module of LinearRAG is critical for optimal performance. The performance gains are primarily attributed to two complementary stages. In the first Stage, LinearRAG identifies contextually relevant entities through semantic similarity propagation, uncovering the hidden logical

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Published as a conference paper at ICLR 2026
relationships in multi-hop questions. In the second Stage, the model uses personalized PageRank algorithms on the activated subgraph to evaluate document importance from a global perspective. different but complementary purposes, allowing LinearRAG to achieve
These two modules both effective and efficient performance.
Impact of Pruning Strategies in LinearRAG. As mentioned in Sec. 3.2.1 LinearRAG employs threshold ò in the entity activation stage to prune entity nodes with weak relevance. In this part, we investigate this strategy by comparing it with two variants: 1) w/o pruming, which retains all activated entities and uses fixed number of iterations (c.g.. iterations), and 2) w. dynamic threshold, which employs threshold that decays with the number of iterations to examine the importance using a fixed threshold. Specifically, we explore two decay schemes: geometric decay (x0.5) and linear decay (-0.1).
Table 3: Performance ( (%) and efficiency comparison of different pruning strategies on 2Wiki dataset. "Acc" means the average scores of GPT-Acc. and Contain-Acce
Method
Acc.
Avg. Retrieval Time
LinearRAC.
66.95
0.093
w/ pruning
64.50
0.186
dynamic threshold (x0.5)
65.15
0.123
dynamic threshold (-0.1)
65.35
0.095
Obs. 8. The fixed threshold pruning strategy in LinearRAG consistently outperforms both no pruning and dynamic threshold approaches. As shown in Table LinearRAG with fixed thresh- old ó achieves the best overall performance. The w/o pruning variant suffers significant performance drops, indicating that retaining all weakly-relevant entities introduces noise and hinders reasoning. The dynamic threshold strategies also underperform compared to the fixed threshold, demonstrating that an explicit distance-decte -ecaying threshold is unnecessary and potentially harmful because could prematurely discard valid long-ran range bridges that are crucial for multi-hop reasoning or introduce more noise, while the selection of the decay factor itself difficult and may negatively affect the model's generalization ability across different datasets.
Impact of using different LLM as evaluators. To address the concern regarding potential evalua- single
Table 4: Generation accuracy (%) across different LLM evaluators. Notably, the values represent GPT-Acc.
tion bias from relying LLM evaluator, we conduct addi- tional experiments using three dif- ferent state-of-the-an LLMSs: GPT- 4o-mini (OpenAl (023), DeepSeek- Chat (Bi et al. 2024). and Claude- Haiku-4.5 and compare LinearRAG against the most advanced baselines (HippoRAG2, GFMRAG, and RAP- TOR) across four benchmarks.
Method
LLM Evaluator
HotpotQA
2Wiki
MuSiQue
Medical
gpt-lo-mini
64.30
55.00
35.00
60.77
HippoRAG2
deepseek chat
69.70
62.30
38,80
64.40
laude-hakku4-5
63.60
58.50
35.60
62.12
apt-fo-mini
65.60
39.60
54.60
36.07
deepseck chat
claude-baiku-4.5
63.60
64,80
34,30
36.84
apt-Ao-mini
58. 30
42.10
27.40
55.75
RAPTOR
deepseek chat
62.90
48.60
30.40
$8.39
claude-haiku-4
57.50
44.90
27.00
56.21
gre 4o-mini
LinearRAG
deepseek chat
72.60
70.20
39,90
66.54
claude hunde-haiku-44.
65.90
66.20
64.11
Obs. LinearRAG consistently outperforms all baseline methods
across all three LLM evaluators, demonstrating robustness against evaluation bias. As shown in Table LinearRAG maintains its performance advantage regardless of which LLM is used the evaluator. While different evaluators exhibit varying scoring patterns (e.g., DeepSeek-Chi tends to assign higher scores overall compared to Claude-Hale Haiku-4.5), the relative ranking remains consistent.
5 CONCLUSION
In this work, we introduce LinearRAG, a novel GraphRAG framework that simplifies graph con- struction by replacing costly and error-prone relation extraction with lightweight entity extraction. It creates a hierarchical graph encompassing entities, sentences, and passages. Upon this, Linear- Pareto frontier by
RAG features a two-stage retrieval mechanism that advances the precision- jointly leveraging both local and global structural information. Extensive experiments demonstrate that LinearRAC consistently surpasses state-of-ther thet--f baselines in retrieval precision, generation accuracy, and scalability, offering robust and efficient solution for handling complex queries.
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ETHICS STATEMENT
The first three datasets used in the experiments including Ho 2WikiMultiHopQA and MuSiQue are widely used datasets. The medical benchmark dataset built from public resources. Our research strictly adheres to the ICLR Code of Ethics, particularly regarding data privacy, trans- parency, and responsible computing practices. And there no participant involved.
REPRODUCIBILITY STATEMENT
We provide detailed information required to reproduce the main experimental results of the pa- per, including data splits and hyperparameter configurations. Additionally, for each experiment, we outline the required computational resources, such as memory usage and exccution time. All baseline models are sourced from public repositories. Our code and dataset are made avail- able. Besides that, the code and implementation details of our model are available at https: 7/github. wtt. httpa//..ccomeaa-PPoooo///aaaaa
ACKNOWLEDGEMENT
The work described in this paper was fully supported by grant from the Innovation and Technology Commission of the Hong Kong Special Administrative Region, China (Project No. GHP/391/22).
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A DATASETS
Our experimental evaluation is conducted on four datasets: three established multi-hop benchmark datasets for multi-hop question answering- HotpotQA (Yang et al., 2018), MuSiQue (Trivedi et al.) 2022). and 2WikiMultiHopQA (2Wiki) eHo al., 2020) and one domain-specific dataset. Below, provide concise overview of each dataset's s key characteristics.
(i) HotpotQA (Yang et al, 2018): comprehensive benchmark comprising 97k question-answer instances designed to evaluate multi-hop reasoning capabilities. Each question requires models to synthesize information from multiple documents, with up to gold-standard supporting passages provided alongside numerous irrelevant documents. This structure challenges systems to perform effective cross-document inference and evidence selection.
(ii) 2WikiMultiHoPQA (2Wiki) (Ho et al. 2020): A multi-hop reasoning benchmark containing 192k questions that necessitate information integration across multiple Wikipedia articles. Each instance requires evidence synthesis from either 2 specific articles, testing models' ability to perform structured cross-document reasoning and maintain coherent information flow.
(iii) MuSiQue (Trivedi et al., 2022): A sophisticated multi-hop QA benchmark featuring 25k question-answer pairs that demand 2-4 sequential reasoning steps. Each question requires coherent multi-step logical inference across multiple documents, challenging systems execute structured reasoning chains while preserving contextual consistency throughout the inference process.
(iv) Medical: A specialized subset derived from TraphRAG-Bende (Xiang et al., 2025). constructed from structured clinical data sourced from the National Comprehensive Cancer Network (NCCN) guidelines. These guidelines provide standardized treatment protocols, drug interaction hierarchies, and diagnostic criteria. The dataset encompasses four tasks of increasing complexity: fact retrieval, complex reasoning, contextual summarization, and creative generation, totaling 4,076 questions across all difficulty levels.
B BASELINE DETAILS
In our experiments, we compare our method against several RAG and GraphRAG approaches.
ii) RerankRAG incorporates a reranking model to refine retrieval results.
(ii) IRCOT (Trivedi al., 2023) employs an iterative retrieval- trasooin framework that interleaves retrieval and of-an--o-tthughhe reasoning for multi-step question answering.
2024 builds knowledge graph over multiple passages with LLMs; at retrieval stage, it introduces an LL.M-driven graph traversal agent to navigate the graph and progres- sively collect supporting passage.
(iii) KGP (Wang
(iv) G-Retriever (He et al. (2024) combines graph neural networks with LLMs by formulating sub- graph retrieval as a Prize-Collecting Steiner Tree optimization problem, enabling effective conversa- tional question answering textual graphs while mitigating hallucination and enhancing scalability.
(v) RAPTOR (Sarthi et al. 2024) develops a hierarchical tree by applying clustering algorithms and abstractive summarization techniques, facilitating representation multiple semantic granularities.
(vi) GrraahRAAG (Zhao et al. 2025) uses spaCy to extract entities and LLMs to summarize pas- sage groups into hierarchical tree with encoded nodes at indexing stage; at retrieval stage, it hits rel- evant entities in k-hop neighborhood and collects associated passages for ranking, otherwise dense retrieval over the whole tree.
(vii) LightRAG (Guo et al. 2024) employs a two-tier framework that incorporates graph-based representations within textual indexing, merging fine-grained entity-relation mappings with coarse- grained thematic structures.
(viii) HippoRAG (Gutiérrez et al. 2024) is training- free graph-enhanced retriever that uses the Personalized PageRank algorithm with query concepts as seeds for single-step or multi-hop retrieval across disparate documents.
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(ix) GFM-RAG (Luo et al. 2025a) implements GraphRAG paradigm by constructing graphs from documents and using a graph-enhanced retriever to retrieve relevant documents.
(x) HippoRAG2 (Gutiérrez et al., 2025) extends HippoRAG with enhanced paragraph integration and contextualization. Optimizes seed node selection and PageRank reset probabilities while main- taining factual memory capabilities and improving associative memory performance. improving associative memory performance.
C MACHINE CONFIGURATION
All experiments in this study were conducted on the hardware configuration detailed in Table
Table 5: Detailed machine configuration used in our experiments.
Component
Specification
GPU
NVIDIA GeForce RTX 4090 D (24GB VRAM) Intel(R) Xeon(R) Gold 6426Y
CPU
D EFFICIENCY ANALYSIS OF GRAPH CONSTRUCTION: ALL-STAGE LINEAR SCALABILITY
We present the efficiency analysis of LinearRAG and demonstrate that it achieves linear scalability in both runtime and memory consumption during graph construction and retrieval, thereby ensuring all-stage linear scalability. Detailed analysis is provided below.
Graph construction stage involves lightweight operations such as sentence segmentation and named entity recognition over the corpus. The resulting computational complexity is O(PPI -T), where T denotes the average length of each passage. Notably, this stage incurs no L.M token con- sumption. Regarding memory usage, the first data structures to be stored are the embeddings of passages, nodes, and sentences, all of which scale linearly with the corpus size, i.e., O(PP)-T). The second set of data structures includes two adjacency matrices, M and C, stored in sparse format by leveraging the inherent sparsity (each sentence contains most ~ entities, and each passage at most ~ 10 entities). Thus, memory consumption is O(PP) + [S), which simplifies O(P)) since the number of sentences is proportional to the number of passages. Therefore, the overall memory complexity is O(PPI T + (Pi) O(IPI T), i.e., linear with respect corpus size.
Retrieval stage involves similarity computations, semantic propagation using SpMM (sparse ma- trix multiplication), and Personalized PageRank (PPR) iterations. Each propagation step requires (nnz)) time, where nnz is the number of non-zero entries in the sparse matrices (OSS) and PPR on the bipartite graph is computable in linear time with respect to the number of edges (O(P))) Overall, the computational complexity is O(n x (S]). where is the number iterations of equa- tion [5] Memory consumption primarily arises from loading sparse graph structures, activation vec- tors (of size O( + |S] + (P()), and temporary query-specific computations.
Acceleration with parallel computation. The above analysis confirms the linear scalability of Lin- earRAG in both graph construction and retrieval. Additionally, due to the vectorized formulation of the graph structures, all data manipulations can be efficiently accelerated through parallel com- putation, further boosting performance in both stages. This is empirically verified in our efficiency experiments in Section Appendix E.4 and Appendix E.3
E ADDITIONAL EXPERIMENTS
RETRIEVAL QUALITY EVALUATION (Q4)
To provide comprehensive assessment beyond generation performance, adopt the settings from SaophRAG-Bendde and utilize four tasks of increasing difficulty: Fact Retrieval, Complex Rea- soning, Contextual Summarization, and Creative Generation. The comparison is conducted on the
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Table 6: Retrieval quality evaluation results (%) of different baselines across four different question categories. The table compares recall and relevance metrics for fact retrieval, complex reasoning, contextual understanding, and creative generation tasks. The best results are highlighted in bold, and second-best results are underlined.
Fact Retrieval
Complex Reasoning
Contextual
Creative Generation
Method
Recall Relevance
Recall Relevance
Recall
Relevance
Recall
Relevance
Vanilla RAG (Top-5)
86.24
63.71
84.97
84.11
84,14
89.94
44.88
58.73
RAPTOR
85,40
69.38
89.70
53.20
88.86
58.73
72.70
52.71
GAG
87.84
69.74
87.08
62.67
89.17
71.63
60.26
35.84
LightRAG
80.32
41.27
82.91
42.79
85.71
43.11
81.34
45.17
HippoRAG
87.25
52.44
83.80
42.19
83.46
49.13
81,66
45,03
GFM-RAG
90.08
57.90
85.03
33.06
78.62
40.14
83,51
22.87
LinearRAG (Ours)
88.86
86,09
87.03
81,58
89.13
87.89
89.08
72.74
Medical dataset, same as GraphRAG-Bench The results comparing LinearRAG with representative GraphRAG methods are presented in Table and we have the following observations.
Obs. 10. GraphRAG models demonstrate significant improvements in recall metries com- pared to RAG baselines, particularly in creative generation tasks, but fall behind in relevance metries. We infer that the reasons are twofold. On the one hand, for simpler tasks such as fact retrieval, models are not required to analyze reasoning chains to recall all the necessary supporting facts. Direct semantic retrieval is enough, reducing the risk of retrieving irrelevant or redundant information during graph traversal. On the other hand, for more complex tasks such as creative generation, the model needs continuous documents with strong logical dependencies to produce sat- isfactory outputs. RAG struggles to capture such complex relationships due to underlying graph structure of gold answers. Specifically, -FM improves recall from 44.88% to 83.51% in cre- ative generation, but relevance drops from 58.73% to 22.87%.
Obs. 11. LinearRAG demonstrates tremendous performance improvements over GraphRAG models in relevance metries, owning the ad advantages of both high recall and strong relevance. is challenging to improve recall and relevance simultaneously because increasing recall often introduces more irrelevant documents, lowering relevance, while increasing relevance usually misses relevant results and lowering recall. LinearRAG achieves the best performance across four tasks in recall while achieves better than RAG in relevance metrics most cases. We infer that the reason lies in our construction of high-tuulity graph with minimal redundant connections, ensuring that irrelevant documents are not retrieved. Specifically, in complex reasoning tasks, LinearRAG achieves 87.03% recall and 81.58% relevance, significantly outperforming GFM-RAG's 85.03% recall but only 33.06% relevance, demonstrating LinearRAG's ability to maintain high precision while preserving comprehensive information coverage.
E.2
SENSITIVITY (Q5)
We conduct ablation studies to in- vestigate the impact of key hyperpa- rameters in LinearRAG. All experi- ments evaluated using the aver- age scores of GPT-Acc. and Contain- Acc. on 2WikiMultHopQAA.
67-6
66556
Impact of threshold 6. The threshold 8 determines whether an entity's score is retained during dynamic pruning and whether to con- tinue expansion. As shown in Fig- ure 5a when is too small, it intro- duces excessive noise and reduces ef- ficiency in the retrieval stage. How- ever, when is too large, it prevents
Obs. 12.
(a) The impact of threshold 6. (b) The impact of coefficient A.
Figure 5: Parameter analysis LinearRAG performance in the 2WikiMultiHopQA dataset. (a) shows the dependency of LinearRAG performance on threshold å in dynamic pruning. (b) examines the effect of trade-off coefficient X.
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Table 7: Comparison of different sentence embedding models used in LinearRAG. The best
results are highlighted in bold.
HotpotQA
2Wiki
MuSiQue
Medical
Model
Contain-
GPT-Acc.
Contain-Aoo
GPT-Acc.
Contain-Ace.
GPT-Acc.
GPT-Ace.
all- all-mppet-base-v2
64.30
66.50
70.20
63.70
33,90
37.00
63.72
all-MiniLM--L6--2
64.20
64.90
69.50
62.60
32.80
36.90
62.32
bge-large-en-v1.55
66.20
67.60
69.80
63,90
31.80
35.20
64.45
c5-large-v2
66.80
68.30
69.90
63.10
31.70
36.50
65.42
Table 8: Indexing efficiency comparison of GraphRAG methods on large-scale ATLAS-Wiki corpus. Results are evaluated on WTLAS-Wiki subsets of 5M and 10M tokens. Notaly, prompt tokens represent input to LLM, and completion tokens represent LLM output.
Token Consumption (x (x109)
Dataset
Method
Indexing Time (s)
Prompt
Completion
RAPTOR
18033.75
7.43
0.96
5M
HG LinearRAG (Ours)
6032.46
13.94
3.68
1409.95
0
RAPTOR
46430.96
16.62
2.82
10M
HippoRAG
13815
28.04
7.53
LinearRAG (Ours)
3084.38
0
the incorporation of more relevant entities, limiting the model's ability to capture comprehensive contextual information. Therefore, selecting appropriate value for å is crucial for balancing trieval quality and computational efficiency. And, we set 5 = 4 based on our experimental setting.
Obs. 13. Impact of trade-off coeflicient A. The parameter À controls the balance between DPR- based passage similarity and entity-level information during passage initial scoring. As observed in Figure 55b] optimal performance is achieved when À takes a relatively small value (e-g.. 0.05), ndicating that entity information serves as the primary component while DPR similarity score acts as an auxiliary enhancement.
E.3 EFFECTIVENESS OF DIFTERENT SENTENCE EMBEDDINGS (Q6)
In this section, we study the effectiveness of different sentence embeddings in the LinearRAG. We compare the all aill-mpet--ee -2 (Song et al.) 2020). bge- barglerge-en (Xiao et al. (003).. all-MiniLM- 16-v2 (Wang al. 2020) and e5 la-lerge-v2 (Wang al. 2022). We download the official pre-trained model from the Huggingface2.
Obs. 14. Based on the findings presented in Table we observe that performance variations across
different sentence embedding models remain relatively modest, with all-mpnet-base-v2 demonstrat- ing superior results on the majority of datasets. This suggests that LinearRAG exhibits robustness to the selection of sentence embedding models. Consequently, we adopt all-mpnet-v2 as our default embedding models in all experiments due to its computational efficiency.
E.4 LARGE-SCALE EFFICIENCY ANALYSIS (Q7)
To evaluate the scalability and effectiveness of LinearRAG on large corpora, we conduct compre- hensive efficiency analysis using the ATLAS-Wik. dataset (Bai et al. (2025). We create two subsets containing 5M and 10M tokens respectively for indexing phase evaluation, as shown in Table g
The results demonstrate that LinearRAG achieves significant efficiency advantages over typical GraphRAG methods. Specifically, LinearRAG eliminates token consumption entirely (0 prompt and completion tokens) while achieving 12.8x and 15.1x speedup compared to RAPTOR on 5M and 10M datasets respectively. This zero-cost indexing approach makes LinearRAG particularly suitable for large-scale enterprise deployments without API dependencies.
Obs. 15.
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E.5 RETRIEVAL EFFICIENCY ACROSS GRAPH SIZES AND DOMAINS (Q8)
In this section, we select four benchmarks of varying scales: Medical, 2WikiMultHHopOA, Hot- potQA, and Novel (Xiang et al., 2025). to evaluate the average retrieval latency across different graph sizes and domains. As shown in Table | where Medical is from the medical domain, Novel is from literary texts, and 2WikiMuuliHopQA and AotpotQA are from Wikidata knowledge.
the corpus sizes range from 202K to 1.06M tokens,
Obs. 16. We observe that retrieval latency increases with graph size, ranging from 0.08 Is for the small- dataset (Medical, 202K nodes) to 0.107s for the largest (HotpotQA, 1.06M nodes). Notably, the re- trieval process remains highly efficient across dif- ferent sizes and domains, taking approximately 0.1s even on the largest graph, which demonstrates the practical scalability of LinearRAG.
Table 9:
Graph size and retrieval latency statistics across datasets.
Dataset
Graph Size
Avg. Retrieval Time (s)
Medical 2Wiki
202,126
517,370
0.093
Novel
1,027,013
0.102
HotparQA
1,062,480
0.107
E.6 CASE STUDY (Q9)
Table 10: Case study of LinearRAG. LinearRAG successfully captures implicit relationships with- out requiring explicit relation extraction, enabling correct multi-hop reasoning.
Question
"What nationality is Beatrice Countess Of Burgundy's 's husband?"
Ground Truth
Germany
Support Context
l'Beatrice I, Countess of Burgundy" - "spouse" + "Frederick Barbarossa")
('Frederick Barbarossa" "country of citizenship" "Germany"")
HippoRAG2
Retrieved context:
1) / "William Duncan (actor)": fr.ddeerick barharossa was elected king of germany at frankfurt on 4 march 1152...
2) X "Dulce of Aragon": "Dulce of Aragon (or of Barcelona; I 116 2013 September...
3) X "Gerdon Flemyng": Gordon William Flemyng( 7 March 1934 2013 12 July 1995)
4) X "Marian Hillar": Marian Hillar is an American philosopher, theologian, linguis...
5) X "Semi- "Scmi-Tough", Semi-Tough 1977 American film directed by Michael...
Prediction:
French.
LinearRAG
Activated Entities:
Tier O-Entity: Beatrie
Tier O-Sentence: Beatrice I (1143 2013 15 November 1 1184) was Countess of Burgundy from 1448 until her death, and was also Holy Roman Empress by marriage to Frederick Barbarossa.
Tier I-Fititt: Frederick Barharossa Tier I- 1-Sentence: Frederick Barbsrossa was elected King of Germany at Frankfurt on 4 March 11552 and erowned
on March 1152.
Tie 2- E-Entity: Germany
Retrieved context:
1) V "Wiliimm Duncun (actor)": ...he was elected king germany frunkfurt march 1152...
2) / "Beatrice I, Countess of Burgundy": .oll roman empress by marriage to frederick barbarossa...
3) X "Richilde, Countess of Hainaut": Richilde, Countess of Mons and Hainaut 2013 15 March 1086)...
4) X "Sophic of Pomerunia, Duchess of Pomerania": Sophia Pomerania- Stolpí 1435 2013 24...
5) x ""DD0e lire Drechlethan": "DôOelire DrechlethanD00e the Broad Faceis a...
Prediction:
/ Germany.
To clearly contrast typical GraphRAG baselines with our LinearRAG framework, we include de- tailed case analysis in Table 10, comparing results from the strong baseline HippoRAG2 and our model on a multi-hop question from the 2WikiMultihopAA dataset.
The case demonstrates that HippoRAG2 fails to retrieve relevant evidence due to its dependence on explicitly pre-extracted relations (such as husband) that missing in this context. In comparison, our LinearRAG method effectively captures implicit relationships through entity activation and contextual chaining, without dependence on pre-extracted relational tuples.
Obs. 18.
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F RELATED WORK
2024 Jiang et al. 2025 Fang et al, 2025 Zheng et al., 2025 Hong et al. 2024 Yuan et al.) 2025 Zhong et al.) 2024), while RAG is a promising solution by grounding the reasoning process on contextual evidence from knowledge bases (hang et al.] 2025al Zhou et al. 2025c; Zhang et al. 2025b Yang et al. 2026 Luo et al.) [
Large language models are prone to hallucination (Fang et al.
20256 Xia et al. |025 Luo et al. 2024). However, real-world knowledge is often distributed across documents, organizing them effectively for answering complex questions has always been chal- lenging yet promising research topic in RAG. In the following, we discuss two major lines (0, @) of graphR research that closely relate our work, which construct explicit graphs for organizing external knowledge sources. Then discuss reasoning-enhanced RAG (0) which leverages L.M's inherent reasoning ability for answering complex questions without explicitly structuring corpora.
Clustering- based hierarchy construction. One line of research employs clustering-based com- munity detection, a bottom-up method that applies algorithms like Louvain or Leiden to identify densely connected clusters of entities within the initial graph (Edge et al. 2024 Sarthi et al. 2024] Gutiérrez et al., 2024). By grouping related entities, creates a hierarchical structure that abstracts passages into topic-hased communities, reducing redundancy and offering broader, macro-level view of the data /hou et al. (2024). However, as an unsupervised technique, it vulnerable to error propagation Zhou et al.] (2025b), where inaccuracies in entity relationships are amplified at higher levels of abstraction. Morcover, applying these clustering algorithms to large-scale graphs presents significant scalability issues, making real-time applications infeasible in practice.
cration-extractioos based knowledge graph construction. This line of research (Gutiérrez et al., 2024: Guo et al. 2024 Xiao et al. 2025c) adopts the idea of knowledge graphs to organize know Poowledeeooo across different passages. The basic idea is to extract triples from each text chunk (pas- sage) as atomic summarization of the knowledge within the passage. The triples are connected via entity alignment (Chen et al.] 2025a 2024). ultimately forming a unified knowledge graph, which directly carries structured knowledge and serves the index of passages, where off off-the-shelf graph reasoning algorithms (Shengyuan et al. 2024| Qu & Tang 2019; Liu et al. (023)) can be applied to perform reasoning (Sun al. 2024] LUO et al. 2024). However, due to context window limits, OpenIE on each passage is processed independently, so the generated triples may be inconsistent.
Recent work (Liang et al.] 2024) (Sharma et al. 2024) mitigates this via top-down graph construction guided by globally defined schema, but it relies on manual expert annotation to create and maintain the domain-specifue schema, which is not only expensive, time-consuming. and does not generalize well across domains.
Reasoning-enhance RAG. The key to answering complex questions in RAG is to effee- tively identify multiple distributed documents that support multihop logical dependencies. While GraphRAG methods explicitly construct graphs with human priors to organize corpora for efficient structured retrieval, reasoning- enhanned RAG directly leverages the inherent reasoning ability of LLMs to decompose complex queries into simpler subquestions that can be addressed by dense re- trieval. LogicRAG (Chen al.. 20256) and LAG (Xiao al., 2025b) formalize this idea by first decomposing a query into a set of subqueries, then constructing a directed acyelic graph based on the logical dependencies among them, and solving subproblems one by one by following the topologi- cal order. Similarly, Chain-of-Nor notes that break down complex queries into intermediate reasoning steps, retrieving relevant doc- uments for each step to build coherent answer. Another approach, SelfRAG (Asai et al.) 22024), integrates self-reflection into the RAG process, where the LLM iteratively evaluates and refines sub- queries to ensure retrieved documents align with the logical flow of the reasoning process. These methods collectively highlight the potential of LLM-driven reasoning to improve retrieval efficiency and answer quality for complex, multihop queries.
(CoN) (Yu et al. (2024) enhances RAG by generating sequential
G FREQUENTLY ASKED QUESTIONS (FAQS)
G. HOW DOES LINEARRAG HANDLE MULTI-HOP TASKS?
Our stasst design, which consists of local semantic bridging followed by global importance ag- gregation, is built to capture long-range dependencies without requiring explicit relation triples. The first stage functions progressive fole-wollowing mechanism that performs iterative entity activa-
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tion the sentence centence-entity bipartite graph, exploring n-hop subgraph to anchor relevant context passages. The second stage then scores each passage by evaluating its relevance to the activated entities well as its semantic similarity to the query. PPR is then applied on the passage-entily bi- partite graph to propagate importance from activated entities and rank passages globally. concrete example illustrating how this mechanism works in practice is provided in Table TO]
G.2 How DOES LINEARRAG ADDRESS THE POLYSEMY PROBLEM?
We acknowledge that polysemy is a common challenge in information retrieval systems, where different passages may be incorrectly connected through entities sharing the same name, such as "Apple" referring to both the fruit and the company. However, our design can largely alleviate this issue from the following aspects:
By deferring relation understanding to the LLM during generation, our approach leverages the LLM's semantic understanding ability to distinguish conceptual differences between entities by analyzing the original context, effectively filtering noise information from irrelevant passages.
LinearRAG employs sentence-level semantic bridging during entity activation, judging entity rel- evance through the context of sentences. Homonymous entities with different meanings are natu- rally distinguished by the semantic differences in their surrounding sentences.
LinearRAG can naturally integrate self-reflection and query-rewrite strategies as post-refinement for the RAG process.
G.3 WHY ADOPTS BINARY CENTENCE-ENTIVE EDGE WEIGHTS IN LINEARRAG?
Some may argue that using binary weights for sentence-entity edges is a simplification that fails to capture the varying semantic importance of different entities, potentially amplifying the influence of noisy or peripheral entities during propagation. We address this concern from three perspectives:
Binary sentence-entity edges are adopted to preserve efficiency guarantees, specifically, fully LLM-free indexing and linear-time construction during graph building. Introducing richer pre- computed weights, such as TF-IDF and thus break these guarantees.
scores, would require additional computation
Assigning fixed importance weights without knowing the incoming query risks down-weighting peripheral entities that may serve critical intermediate anchors in particular query. Binary weighting treats all mentioned entities equally and therefore preserves the possibility of activating less- frequent but query relevant bridges, favoring recall especially on multi-hop benchmarks.
A typical sentence contains only two to three entities, naturally limiting the fan-out of noisy en- tities and making binary weighting far less harmful. Additionally, dynamic pruning as described in Section 3.2.T requires entities to exceed relevance threshold to be carried forward, further filtering peripheral or noisy entities.
G.4 HoW DOES LINEARRAG HANDLE QUERIES WITH NO EXTRACTABLE ENTITY?
When no entities can be extracted from the query, the retrieval process does not halt. LinearRAG automatically falls back to standard similarity-based dense passage retrieval, ensuring the system re- mains robust even for entity-free or highly abstract questions. Furthermore, when extracted entities are ambiguous and cannot be disambiguated without the full query context, our design mitigates this issue through two mechanisms. During entity activation, compute the query-sentence relevance distribution, which is used for semantic propagation, so ambiguously activated entities receive low support and do not effectively propagate relevance to their neighbors, while correctly activated en- tities dominate the propagation. the global importance aggregation stage, as described in Section 3.2.2 passage importance scores are initialized with direct paessage similarity. This additional signal incorporates the complete query context and further suppresses passages reached only through ambiguous or incorrect entities.
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H THE USE OF LARGE LANGUAGE MODELS
We employ LLMs primarily for writing polish, including correcting spelling errors, fixing gram- matical issues, and rewriting non-native expressions improve clarity and fluency, And LLMs used only for writing polishing of our manuscript and appendix. It does not generate research ideas, results, or claims. We ensure that all scientific contributions and implementations are original.
23


Reviews:
[{"review_id": "wLcoCdEr9u", "reviewer": "Reviewer_oM4K", "summary": "The paper proposes LinearRAG, an efficient GraphRAG framework that constructs a relation-free hierarchical graph (Tri-Graph) using lightweight entity extraction and semantic linking over entities, sentences, and passages. For retrieval, LinearRAG adopts a two-stage strategy: (1) local semantic bridging to activate contextually relevant entities beyond literal matches, and (2) global importance aggregation via personalized PageRank to retrieve salient passages. This construction scales linearly with corpus size and incurs no extra token consumption, providing economical and reliable indexing for large corpora. Experiments on four benchmarks show consistent improvements over strong baselines in retrieval quality, generation accuracy, and scalability.", "questions": "Q1: What exact linear retrieval procedure is used (single-pass BFS-like scan, bounded-degree expansion, heuristic queue ordering)? How are thresholds chosen and adapted?\n\nQ2: How are node and edge attributes computed (embedding similarity, co-occurrence, LLM scoring)? Are edge weights normalized and thresholded consistently?\n\nQ3: What fallback exists for multi-hop or compositional queries requiring evidence aggregation? Can limited expansions be temporarily enabled without breaking linearity guarantees?\n\nQ4: Are there systematic trade-off curves (recall vs. latency/memory) across different graph sizes and domains? How robust are results to parameter changes?\n\nQ5: Do comparisons include equal-resource settings against beam search or greedy path selection to isolate algorithmic gains from compute differences?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "S1: The approach fits production constraints and integrates with standard embedding + reranking pipelines.\n\nS2: The linear retrieval design emphasizes bounded expansions and pruning, offering predictable runtime and memory behavior on large graphs.\n\nS3: The method encourages simple deployment: light graph construction, single-pass adjacency, and top-k selection, reducing system complexity compared to full GraphRAG stacks.", "weaknesses": "W1: Linear constraints risk under-retrieving long-range dependencies; multi-hop reasoning and compositional queries may suffer without fallback mechanisms.\n\nW2: Limited theoretical analysis (e.g., recall/latency trade-offs, worst-case behaviors) could make the contribution primarily engineering rather than principled.", "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": "Z0URwFZw1k", "reviewer": "Reviewer_qxoV", "summary": "This paper proposes LinearRAG, a novel GraphRAG framework designed to address key challenges in existing methods: the difficulty and noise associated with knowledge graph construction, and low retrieval efficiency. The core innovation is constructing a three-layer hierarchical graph (Tri-Graph) comprising entities, sentences, and paragraphs. During retrieval, the model first activates query-relevant entities via local semantic propagation and then uses these as seeds in a Personalized PageRank algorithm on a paragraph-entity bipartite graph to aggregate global importance for retrieval in a single pass. Both the time and space complexity scale linearly with the corpus size, and the entire indexing/retrieval process requires zero LLM API calls. Experimental results on four public datasets demonstrate that LinearRAG significantly outperforms existing GraphRAG methods.", "questions": "Could the framework benefit from more informative weights for sentence-entity edges?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper presents a strong, well-argued case that relation extraction errors are a primary bottleneck for GraphRAG performance. The decision to \"de-relationalize\" the graph and defer relation understanding to the LLM during generation is a conceptually clear and impactful design choice. \n\n2. The proposed Tri-Graph structure is a novel and potentially groundbreaking simplification of the GraphRAG paradigm. The retrieval mechanism, leveraging sparse matrix operations for semantic propagation and PPR, is computationally elegant. \n\n3. The framework's linear scalability, facilitated by update-friendly appends and the complete absence of LLM calls in indexing/retrieval, is a major strength. The demonstrated linear growth trend on a million-scale corpus underscores its high practical value for real-world deployment.\n\n4. The paper provides an exceptionally thorough evaluation, going beyond standard QA accuracy to include critical efficiency metrics like token cost and latency. This holistic comparison makes the results highly persuasive and relevant for computation-budget-sensitive scenarios.", "weaknesses": "1. The Tri-Graph merges entity mentions based on string matching, which ignores polysemy (different entities with the same name). This is a significant limitation, as it can introduce erroneous co-occurrence edges, potentially leading to the retrieval of irrelevant paragraphs. While high NER accuracy is claimed, the paper does not quantify the impact of this inherent ambiguity on retrieval performance, leaving a major potential source of error unanalyzed.\n\n2. Using binary weights (0/1) for sentence-entity edges fails to capture the varying semantic importance of different entities within a sentence. This uniform weighting strategy is a simplification that may amplify the influence of \"noisy\" or peripheral entities during the propagation step, potentially harming result relevance and interpretability. \n\n3. The parameters, particularly the activation threshold and the trade-off coefficient, are noted to be highly sensitive. The paper lacks an analysis establishing a functional relationship between these parameters and query characteristics or graph properties.", "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": "ZYhBO4KhgH", "reviewer": "Reviewer_5Por", "summary": "This paper proposes a new framework for RAG, LinearRAG, =which overcomes the limitations of existing GraphRAG systems. The proposed LinearRAG replaces complex relation modeling with a relation-free hierarchical Tri-Graph, built using lightweight entity extraction and semantic linking. This design scales linearly with corpus size and avoids extra token consumption. The retrieval process follows a two-stage mechanism: First, local semantic bridging activates relevant entities beyond literal matches through semantic propagation. Second, global importance aggregation applies personalized PageRank to rank passages holistically.\n\nExperiments on HotpotQA, 2WikiMultiHopQA, MuSiQue, and a Medical benchmark show that LinearRAG consistently outperforms both vanilla RAG and advanced GraphRAG methods in retrieval quality, generation accuracy, and efficiency. It achieves strong multi-hop reasoning performance with minimal computational overhead.\n\nLinearRAG offers a practical and scalable solution for integrating structured retrieval with large-scale text corpora, avoiding the instability of relation extraction and achieving linear scalability in both construction and retrieval.", "questions": "- The token-free graph construction process appears similar to the context-graph construction in the paper *\"Synthesize-on-graph: ...\"*, as both utilize the position relationships between entities and texts as graph relationships. Could you please explain the key distinctions of your method?\n- Other questions refer to Weaknesses.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- This paper proposes a efficient and lightweight graph construction method, Tri-Graph, which avoids error-prone relation extraction in GraphRAG, maintains linear scalability, and achieves 0 token consumption in retrieval.\n- This paper proposes a novel GraphRAG framework that combine local semantic bridging and global importance aggregation, whose effectiveness is valided through extensive experiments.\n- This paper conducted extensive experiments to validate the effectiveness of LinearRAG, including sufficient main experiments against GraphRAG and vanilla RAG.", "weaknesses": "- The ablation study seems coarse-grained. For example, when constructing Tri-Graph, the current method simultaneously incorporates co-occurrence relationships between entities and both passages and sentences. How would the final performance be affected if only passages, only sentences, or paragraphs instead?\n- The main experiment only compared with vanilla RAG. What about comparisons with some advanced RAG methods, such as LightRAG, Lexical Diversity-aware RAG, and others?", "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": "IZbJ8fn52N", "reviewer": "Reviewer_df73", "summary": "This paper introduces LinearRAG, a GraphRAG framework that streamlines graph construction by employing lightweight entity extraction instead of complex relation extraction. It constructs a hierarchical graph comprising entities, sentences, and passages and utilizes a two-stage retrieval strategy that integrates both local and global structural information to enhance precision and recall.", "questions": "1. Entity activation is a crucial initial step in LinearRAG. However, the authors do not clarify how the framework handles queries from which no entities can be extracted. Does the retrieval process halt in the absence of seed entities? Additionally, if extracted entities are ambiguous and cannot be disambiguated without the query’s full context, could this lead to activation of incorrect entities and degraded retrieval performance? An explanation addressing these scenarios would strengthen the paper.\n\n2. In Equation 5, the matrix M is not defined. Moreover, since σ_q is a ∣S∣×1 vector (per Equation 4), its transpose σ_q^T is 1×∣S∣, which mismatches the dimension ∣V_e∣ of α_q^(t-1) (from Equation 3). A more detailed explanation of this equation and its dimensions is necessary.\n\n3. LinearRAG’s dynamic pruning currently applies a fixed threshold across all expansions. Given that dynamic pruning aims to produce high-quality semantic paths, empirical evaluation of this fixed-threshold approach is warranted but absent from the manuscript. Furthermore, since distant entities are generally less important than closer ones, justification for using a uniform threshold would be valuable.\n\n4. For evaluation using LLMs as judges, it is recommended to include multiple state-of-the-art LLMs from different providers to reduce bias stemming from reliance on a single model.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "S1: Addresses a timely and significant challenge relevant to both industry and academia.\nS2: The proposed method is straightforward, achieving the fastest indexing speed alongside very low retrieval latency.\nS3: Demonstrates competitive performance in token efficiency, content accuracy, and evaluation accuracy using large language models (LLMs) as judges.", "weaknesses": "W1: Some equations lack clarity and contain unexpected errors.\nW2: The effectiveness of the dynamic pruning mechanism is not empirically evaluated.\nW3: Since LLMs serve as the evaluators, it would be beneficial to incorporate multiple state-of-the-art LLMs from diverse vendors to mitigate potential evaluation bias.", "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)