NeuroPath: Neurobiology-Inspired Path Tracking and Reflection for Semantically Coherent Retrieval
Authors: Junchen Li
Rongzheng Wang
Yihong Huang
Qizhi Chen
Jiasheng Zhang
Shuang Liang
Venue: NeurIPS 2025 poster ()
Keywords: Retrieval-Augmented Generation
Large Language Models
Multi-hop Question Answering
Knowledge Graphs
Primary area: deep_learning
TL;DR: NeuroPath is a RAG framework inspired by brain path navigation. It tracks semantic paths and refines retrieval via LLM reasoning, enhancing semantic coherence and reducing noise, achieves state-of-the-art performance on multi-hop QA.

Abstract: Retrieval-augmented generation (RAG) greatly enhances large language models (LLMs) performance in knowledge-intensive tasks. However, naive RAG methods struggle with multi-hop question answering due to their limited capacity to capture complex dependencies across documents. Recent studies employ graph-based RAG to capture document connections. However, these approaches often result in a loss of semantic coherence and introduce irrelevant noise during node matching and subgraph construction. To address these limitations, we propose NeuroPath, an LLM-driven semantic path tracking RAG framework inspired by the path navigational planning of place cells in neurobiology. It consists of two steps: Dynamic Path Tracking and Post-retrieval Completion. Dynamic Path Tracking performs goal-directed semantic path tracking and pruning over the constructed knowledge graph (KG), improving noise reduction and semantic coherence. Post-retrieval Completion further reinforces these benefits by conducting second-stage retrieval using intermediate reasoning and the original query to refine the query goal and complete missing information in the reasoning path. NeuroPath surpasses current state-of-the-art baselines on three multi-hop QA datasets, achieving average improvements of 16.3\% on recall@2 and 13.5\% on recall@5 over advanced graph-based RAG methods. Moreover, compared to existing iter-based RAG methods, NeuroPath achieves higher accuracy and reduces token consumption by 22.8\%. Finally, we demonstrate the robustness of NeuroPath across four smaller LLMs (Llama3.1, GLM4, Mistral0.3, and Gemma3), and further validate its scalability across tasks of varying complexity. Code is available at [https://github.com/KennyCaty/NeuroPath](https://github.com/KennyCaty/NeuroPath).

:euroPath: Neurobiology-Inspire Path Tracking and Reflection for Semantically Coherent Retrieval
Junchen Li', Rongzheng Wang', Yihong Huang', Qizhi Chen', Jiasheng Zhang', Shuang Liang"
Institute of Intelligent Computing.
University of Electronic Science and Technology of China, Chengdu, China junchenl aatheenl@stada uestc. edu. en, shuangliang@ues edu. cn
Abstract
Retrieval-augmenned generation (RAG) greatly enhances large language models (LLMs) performance in knowledge-intensive tasks. However, naive RAG methods struggle with multi-hop question answering due to their limited capacity to capture complex dependencies across documents. Recent studies employ graph-based based RAG to capture document connections. However, these approaches often result in a loss of semantic coherence and introduce irrelevant noise during node matching and sub- graph construction. To address these limitations, we propose NeuroPath, an I L..M- driven semantic path tracking RAG framework inspired by the path navigational planning of place cells in neurobiology. It consists of two steps: Dynamic Path Tracking and Post-retrieval Completion. Dynamic Path Tracking performs goal- directed semantic path tracking and pruning over the constructed knowledge graph (KG), improving noise reduction and semantic coherence. Post-retrieval Comple- tion further reinforces these benefits by conducting second-stage retrieval using intermediate reasoning and the original query to refine the query goal and complete missing information in the reasoning path. NeuroPath surpasses current state-of- the-art baselines on three multi-hop hhpp QA datasets, achieving average improvements of 16.3% recall @ 2 and 13.5% recall over advanced graph-based RAG methods. Moreover, compared to existing iter-based RAG methods, NeuroPath achieves higher accuracy and reduces token consumption by 22.8%. Finally, we demonstrate the robustness of NeuroPath across four smaller LLMs (Llama3. 1, GLM4, Mistral0.3, and Gemma3), and further validate its scalability across tasks of varying complexity. Code is available at
Introduction
Humans and other animals possess fundamental spatial navigation and episodic memory capabilities that enable accurate target searching. These abilities originate from cognitive maps formed through the coordinated activity of multiple neural systems within the hippocampus and entorhinal cortex. Place cells, as the cornerstone of spatial memory [29], work in concert with grid cells and boundary vector cells to support not only physical spatial navigation but also the construction of abstract memory spaces, organizing episodic memory [28] g These neurons exhibit a continuous and interconnected activation pattern, transitioning from longle-location encoding to path integration and broader cognitive map formation, dynamically supporting gial-directed behaviors [.]
Corresponding Author
This work is supported by National Natural Science Foundation of China No.62406057, Fundamental Research Funds for the Central Universities No.ZYGX202XXJ44, and the Sichuan Science and Technology Program under Grant No.2024ZDZX0011.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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In knowledge- -enleddg-intensive tasks, current large language models (LLMs) resemble a navigator without a cognitive map. Despite their strong textual embedding capabilities for local contextual under- standing (2], they struggle to form global associations within open-domain knowledge. Naive auugenedd generation (RAG) adopts a strategy of individually embedding document chunks and retrieving relevant content based on similarity [21]. It is essentially a linear search for discrete knowledge. This strategy cannot capture the associations between knowledge, causing information silos when faced with complex queries that require multi-hop reasoning. Although iter-based RAG sennerate-rerriee-rreeenee
expands the knowledge coverage through modeling of the associations between knowledge and is still difficult to handle complex queries. Recently, researchers have proposed a new paradigm for knowledge organization and retrieval called graph-based RAG, which captures explicit associations across documents. However, existing graph- based methods have limitations: (1) the loss of semantic coherence clues and (2) irrelevant noise in node matching or subgraph construction. HippoR AG [14] leverages a knowledge graph (KG) and the Personalized PageRank (PPR) algorithm to propagate node importance. However, it does not explicitly use relationship semantics, leading to retrieval results that tend to structural relevance rather than path semantic coherence, as shown in Figure (igure[F(a). LightRAG [3] adopts dual-level retrieval to match nodes and construct subgraphs, enhancing integrated information retrieval capabilities. However, collecting direct neighbors in the subgraph construction brings considerable noise, as shown in Figure (b).
iteration, it lacks explicit
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Figure 1: Comparison between graph-baser based and path-based RAG methods. To the query: Which company acquired the phone brand created by the Android founder? (a) HippoRAG uses the PPR algorithm to propagate node importance but ignores edge semantics, increasing the risk of retrieving incorrect nodes such as 2008; (b) LightRAG's subgraph construction tends to introduce considerable noise; (c) Our method leverages coherent semantic paths for goal-directed tracking, progressively eliminating noise and tracking the correct answer Nothing.
We argue that an important advantage of modeling with graph structure is its explicit semantic reasoning paths formed through entity association. However, current graph-based methods focus more on structural relevance rather than semantic coherence, which weakens the semantic alignment with the query and introduces irrelevant noise. Existing methods rely static topological model- ing of knowledge associations, fundamentally different from the goal-directed dynamic encoding mechanisms of the human brain in complex environments episodic memory. As shown in Figure 2 studies have shown that the brain't 's hippocampal place cells activate in specific regions as animals explore their environment 29 like neural map. During navigation, the hippocampus preplays these cell sequences to support goal-directed path planning. These sequences can be dynamically reorganized based on task goals and can even predict unknown paths [30]. In addition, hippocampus replays them during rest to consolidate memory.
Inspired by these neurobiological mechanisms, we propose NeuroPath, RAG framework based on semantic path tracking. Unlike methods that simply aggregate discrete knowledge nodes, our approach dynamically constructs goal-directed semantic paths, as illustrated in Figure (c). Based on the KG
2

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extracted from source documents, NeuroPath models each entity as a place cell in semantic space and each knowledge triple as place field in spatial navigation. Guided by the goal of the query, NeuroPath simulates the hippocampal preplay and replay mechanisms: during preplay, dynamically constructs semantic paths from seed nodes and filters out noisy information; during replay, it revisits prior reasoning chain to support path information completion aligned with the query goal. Specifically,, we propose Dynamic Path Tracking, a strategy that lever- ages LLM to automatically filter and expand se- mantic paths. This strategy continuously marks valid paths, selectively expands paths, and pre- dicts potential path directions for pruning. To further improve retrieval quality, we also pro- pose a Post-retrieval Completion strategy that performs a second-stage retrieval using intermediate reasoning generated by the LLM to complete missing information along the reasoning path.
Poplay & Replay
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passing spocific place field to reprosent cureet leatioon
Place cells mechanism. Place cells repre- sent specific spatial locations. During navigation, they preplay upcoming sequences, and during rest, they replay these to support memory consolidation.
Figure 2:
In summary, the contributions of this work are as follows: (I) Inspired by neurobiology mechanisms, novelly map the hippocampal place cell navigation and memory consolidation mechanisms into RAG, enhancing path reasoning and cross-document knowledge integration. (2) We propose NeuroPath, RAG framework that dynamically tracks semantic paths align with the query goal by leveraging LLMs and integrates intermediate reasoning to fill in missing information, thereby enriching semantic coherence and reducing noise. (3) NeuroPath achieves a new state-of-the-an performance on multi-hop QA, demonstrates robustness across LLM scales and tasks of varying complexity, and outperforms closed-source models using only a fine-tuned 8B open-source model.
2
Related Work
From single-step to multi-step RAG. The RAG framework greatly enhances LLMs' performance in knowledge-intensive tasks [TO D Naive RAG methods retrieve top-k documents based on vector similarity between queries and document embeddings [21]. but they overlook associations between documents. Due to the flat structure of vector databases, these methods cannot support multi-hop reasoning. As queries grow more complex and relevant knowledge becomes finer-grained, their effectiveness declines. Some methods refine queries to better capture complex intent and improve similarity matching (25) 6). yet they still rely on flat knowledge organization and struggle with multi-hop reasoning. Iterative retrieval RAG methods (iter-based RAG) interleave retrieval and generation to optimize queries for the next retrieval step [31 35 39]. but they do not explicitly model relationships among knowledge entities, making it hard to connect information across documents.
From flat to structured retrieval. Recent works have advanced the ability of LLMs to reason over To better capture document dependencies, researchers have proposed Unlike flat structures, these methods represent text as nodes and edges, enabling dependency propagation and multi-hop reasoning. Some methods model global and abstract information. For example, GraphRAG 77] uses community detection to summarize each group, while LightRAG [13 extracts local and global keywords to retrieve relevant nodes and edges. However, these methods focus on sense-making tasks, requiring global understanding of knowledge and providing abstract summarics or diverse answers, which can introduce significant noise during the retrieval process. Some other methods focus factual multi-hop QA, such as HippoRAG 14] and KG-Retriever (5). However, these works pay more attention to structural relevance and collect documents by retrieving important nodes without explicitly utilizing edge semantics. Although HippoRAG [5] improves query matching by replacing node matching with triple matching, it still suffers from the random walk nature of the PPR algorithm during the retrieval phase, which can lead the search to focus on incorrect nodes. These methods ignore path semantic coherence, resulting in retrieval errors noise.
graph structures 332
40
graph-based RAG methods 7
13
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From subgraph to path. To avoid redundancy from subgraph construction, recent work extracts key paths directly through bath-based methods. PathRAG [3] retrieves query-related nodes, constructs
3

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paths using flow-based pruning algorithm for resource allocation and scoring, and selects reliable paths. However, it allocates resources equally across outgoing edges, ignoring edge importance and semantics, which can misalign scores with actual meaning. As PathRAG focuses on sense-making and uses a different approach, we consider our method based on dynamic semantic path construction fundamentally distinct and better suited for multi-hop QA.
3 Methodology
3.1
Overview
The workflow of our framework consists of three specific steps, shown in Figure 3 namely Static Indexing, Dynamic Path Tracking, and Post-retrieval Completion. We use an LLM to extract entities and relations from each document at once to build KG index. Each entity builds coreference set, and each triple segment is used as the smallest unit of the path receptive field. When retrieving, we simulate the preplay and replay mechanisms of place cells to perform path navigation and memory consolidation. Specifically, the LLM starts from seed nodes and selects valid paths from candidates based on their relevance to the query. while deciding whether to expand additional paths to track deeper information. If expansion is needed, it generates expansion requirements to guide the direction and prune irrelevant paths. Once the final paths are determined, we collect the source documents along the paths as context for question answering. This process is similar to place cells generating mreaactivation sequence during navigation and tracking the target through this sequence. We extract the reasoning generated by the LLM during path tracking and perform a second-stage retrieval by combining this generation and the original query. This simulates the replay mechanism in place cells, where sequences are reactivated during rest to consolidate memory.
Statie Indexing
Dynamic Path Tracking
Post-retrieval Completion
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Figure 3:
The overview of NeuroPath's workflow: (1) Static Indexing. Use an LLM to extract entities and relationships to build KG, and build a coreference set for each entity. (2) Dynamic Path Tracking. Using an LLM for goal-directed path tracking. The expansion requirements will be used for pruning. (3) Post-retrieval Completion. Collect documents along the path and leverage intermediate reasoning for second-stage retrieval to complete missing information in the reasoning path.
3.2 Static Indexing
Given a set D = [d1, dx) containing N documents, we use an LLM as generator G to extract the KG, aiming to capture information from the documents as detailed as possible. Specifically, we let G' extract the entity set E and the relation triple set T for each document d; once.

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In order to reduce the risk of missing references between semantically similar entities in the knowledge graph, we introduce pseudo coreference resolution mechanism based on similarity. For each entity in KG, we construct its potential coreference set R; by including similar candidate entities whose vector cosine similarity exceeds the threshold 0.8. We use an embedding model to uniformly encode entities and calculate similarity. The specific similarity calculation formula and coreference association set are as follows:
Sim(i,)) CosSim(Ene((),
R, = PegtopkySimii..) i,j E E,
(2)
where Ene(.) is an embedding model, and the argtopk operation retrieves the top-k entities based on similarity. By default, we retrieve five most similar entities as a coreference set.
3.3 Dynamic Path Tracking
Seed nodes filtering rules. Given query q, we use an LLM as a generator to extract set of key entities Q = Gent (g), and select set of the most similar nodes by Sim(..-) function from the indexing graph as initial seed nodes S. Then expand the entities the corresponding coreference association set R. become new set S {s + R. E S), to better cover the path starting nodes.
In the subsequent path expansion, we directly use the expandable nodes of the paths and follow the above steps to add coreference nodes as seeds.
Sh = (a + R. a Link(P
where Link(-) a function return the expandable nodes that connect the path. the current expanded hop and Peep is the set of paths that need expansion.
Path expansion rules. the specific expansion step, we retrieve the triple segments Ph sub T
connected to the seed nodes 5h and concatenate the current path set under expansion eap- We then combine the current valid paths Ph to form the candidate paths for the next pruning and tracking.
Phut
P Cat(Peepp Plus)
Po Pezp
P val 0.
(4)
where Cat(-,)) -) denotes the operation of concatenating the triple segment to the end of the path under expansion. We merge the previous valid path set and the expanded path set into a new candidate path set. LLM can choose to keep or delete these paths to improve fault tolerance.
Tracking by LLM. We design a prompt guide the LLM for path tracking, as shown in Appendix At each step of the tracking. we send the current candidate paths to LLM for filtering. LLM forms an intermediate reasoning chain according to the current valid information and mark the valid paths, determine whether expansion is needed and generate specific expansion requirements.
In order to make path expansion more directional and avoid exponential growth, we use expansion requirements generated by the LLM in the previous hop to prune paths before tracking.
p cur
argtopk,, Sim ,p), E PA cur
(5)
c Ph vatt g", Ph Plap. Gtracker (Ph
(6)
where e is the reasoning chain organized under the current valid paths. y is the specific requirement or goal of the expansion, Ph. is the set of valid paths marked by the current hop, and PA is the set of paths that need to be expanded at the current hop. ct is the flag for whether to continue to expand. The default setting for pruning top-k is 30. If ct is 0, it means that the answer is found or no additional information can be added, otherwise continue to expand.
3.4 Post-retrieval Completion
After determining the final paths, we collect source documents along the paths as candidate documents Dp- In addition, we adopt enhanced second-stage retrieval step. Specifically, we concatenate the intermediate generation from the last hop of tracking and the original query as a new query to perform second-stage retrieval to complete the candidate documents, in order to refine the query
5

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goal and avoid the missing of path information. We define the new q Clast Plast where Clast and 9tast represent the reasoning chain and expansion requirements generated by the LLM in the last expansion hop (If the paths doesn't it find an answer or reaches the maximum expansion hop count, expansion requirements will still be generated). They are concatenated with the original query g for second-stage retrieval based on text similarity to improve the completeness of the information. The final set of source documents Deet= = Dp U De. where Dret represents the final candidate document set, and De represents the document set for second-stage retrieval.
4 Experiments
We conducted extensive experiments to evaluate NeuroPath and answer the following research questions (RQ): RQ1: How effective NeuroPath? RQ2: How does NeuroPath demonstrate its advantages in semantic coherence and noise reduction? RQ3: Do all parts of our framework work? RQ4: What its scalability other small open-source models and to tasks of varying complexity?
4.1 Setup
Datasets. We selected three challenging multi-hop question answering datasets to evaluate our method: MuSiQue [38], 2WikiMultHopQA [I6] and HotpotQA [3]. All three datasets are used to evaluate open-domain multi-hop reasoning tasks, ranging from 2-hop to longer-hop reasoning scenarios. We followed the settings HippoRAG [14], selected 1. 1,000 questions from each dataset for evaluation, used all the documents from each selected dataset as the retrieval corpus. For each question, only a small number of supporting documents are involved verify the retrieval performance. Additionally, evaluate the model's ' robustness on tasks of varying complexity for RQ4, we utilize two simple QA datasets (PopQA (24) Natural Questions [9] and a more challenging long-document multi-hop QA dataset (MultiHop-Ran
(6)).
Baselines. We construct comparisons from (1) naive RAG methods BM25 33 BGE-m3 4 and
Contriever [18; (2) iter-based methods IRCoT [39, Iter-RetGen [51; (3) graph- based methods
HippoRAG [4, HippoRAG 2 [5]. LightRAG [3: (4) path- based method PathRAG [.)
Metrics. We report retrieval performance on @ and r @R@2 and R @@5 below), and question answering performance on exact match (EM) and F1 score.
Implementation Details. In our experiments, all graph indexing is performed using GPT-4o- mini [17]. To evaluate the performance and robustness of using large language models for path tracking, we further replace the retrieval component with a variety of open-source models. In the main experiment,
GPT-4o-mini and Qwen-2.5-14B [22] and additional experiments with smaller-scale models are presented in Section 4.5 For NeuroPath, we set the maximum number of reasoning hops to 2 and use Zero-Shot prompting setup. For IRCoT and Iter-RetGen we set the maximum number of iterations to and follow the original paper settings, with the number of documents retrieved per iteration set to 2, 4, 6 and 5, respectively. We evaluate all baselines using Contriever and BGE-M3 as retrievers. Additionally, since LightRAG and PathRAG do not directly return source documents, we do not assess its retrieval metrics. More details can be found in Appendix|C
4.2 Main Results (RQ1)
As shown in Table NeuroPath outperforms all naive and graph-based baselines, including the state-of-the-art HippoRAG 2, with average improvements of 16.3% in Recall @ 2 and 13.5% in R 5, Compared to iter-based baselines, it also achieves average improvements of 8.6% in Recall @ 2 and 10.2% in Recall demonstrating more stable and competitive performance. Additionally, our method reduces the average token consumption by 22.8% compared iter-based baselines, indicating more efficient use of resources. Detailed cost and efficiency comparison can be found in Appendix [ Our relatively lower performance on HotpotQA primarily due its lower knowledge integration requirements. This limits the advantages of the multi-hop reasoning methods. We further discuss this Appendix [E.3 In contrast, the MuSiQue dataset is more complex in its construction, designed for more difficutt multi-hop reasoning. and our method achieves the best performance on Notably, our method maintains stable performance across different retrievers, while base- methods and HippoRAG are highly sensitive to retriever choice, showing up to
Retrieval Results.

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20% gap on 2WikiMuliHHoQQ. The above results demonstrate NeuroPath's superior performance in handling complex multi-hop queries. To further validate NeuroPath applicability across different scenarios, we further evaluate its effectiveness on simple queries, see the Section 4.5 The results show that NeuroPath maintains competitive performance even on simpler tasks.
Table 1: Retrieval performance.
MuSiQue
2Wiki
HotpotQA
Average
Category
Method
R@2
R@5
R@2
R@5
R@2
R@5
R@2
R@5
BM25
32.2
41.2
51.7
61.9
55.4
72.2
46.4
58.4
Naive
Contriever
34.8
46.6
46.6
57.5
57.1
75.5
46.2
59.9
BGE-M3
40.4
54.2
64.9
71.8
71.8
84.7
59.0
70.2
GPI--4-mini (Contriever)
Iter-RetGen
46.0
59.8
62,1
76.5
78.3
90.6
62.1
75.6
Iter-based
IRCoT
42.2
55.8
54.2
70.0
68.6
83.3
55.0
69.7
HippoRAG
41.6
54.2
71.6
89.6
61.0
78.5
58.1
74.1
Graph-based
HippoRAG2
41.8
55.5
62.5
74.2
65.3
83.4
56.5
71.0
Path-based
NP(Zero-Shot)
48.0
62.7
77.2
92.5
75.6
90.4
66.9
81.9
Qwen-2.5-14B
(Contriever)
Iter-RetGen
45.8
58.8
62.2
75.6
78.2
90.2
62.1
74.9
Iter-based
IRCoT
40.2
52.7
56.2
72.0
65.9
76.9
54.1
67.2
HG
41.3
53.8
67.1
85.6
59.3
76.7
55.9
72.0
Graph-based
HippoRAG2
40.3
52.7
56.7
67.7
63.6
81.3
53.5
67.2
Path-based
NP(Zero-Shot)
51.4
65.3
76.6
92.1
76.0
90.9
68.0
82.8
GPTT-4ommin
(BGEE-3)
Iter-RetGen
46.3
60.6
74.1
87.1
81.0
90.4
67.1
79.3
Iter-based
IRCoT
48.7
62.7
79.0
92.0
81.3
90.8
69.7
81.8
HippoRAG
41.5
52.3
70.6
87.7
62.0
77.7
58.0
72.6
Graph-based
HippoRAG2
43.6
61.2
72.2
88.8
71.1
89.4
62.3
79.8
Path-based
NP(Zero-Shot)
47.7
64.7
78.1
95.1
78.0
92.7
67.9
84.1
1wwn-22.5-14
(BGE-M3)
Iter-RetGen
49.9
64.8
78.3
93.4
85.4
93.6
71.2
83.9
Iter-based
IRCoT
45.0
56.4
76.6
91.8
78.5
84.7
77.6
HippoRAG
41.2
51.5
65.6
82.5
60.0
75.5
55.6
69.8
Graph-based
HippoRAG2
44.7
59.9
70.5
85.1
72.8
89.1
62.6
78.0
Path-based
NP(Zero-Shot)
50.6
67.4
78.3
94.6
77.9
91.9
68.9
84.6
QA Results. As shown in Table 2] We report the QA performance using GPT-4o-mini and Contriever, results with BGE are similar and can be found in Appendix [ Our method outperforms all baselines on the MuSiQue and 2WikiMultiHopQA datasets, and performs slightly below HippoRAG 2 on HotpotQA. This is because Hotpot allows shortcuts answers through guessing 33 We discuss the reasons for this observation performed poorly on QA tasks. Despite leveraging graph structures capture knowledge connections, they failed on multi-hop factual QA, performing worse than even naive methods. Appendix Detaiis their limitations and explains why our path-based method is more effective than PathRAG.
Appendix [3 In addition, we found that LightRAG and PathRAG
Table 2: QA performance with Contriever as the Retriever.
MuSiQue
2Wiki
HotpotQA
Average
Category
Method
EM
F1
EM
F1
EM
F1
EM
F1
BM25
20.7
30.7
44.2
48.1
43.3
55.6
36.1
44.8
Naive
Contriever
22.3
32.1
38.5
43.7
44.4
57.5
35.1
44.4
BGE-M3
27.8
39.7
48.2
54.5
47.8
61.8
41.3
52.0
Iter-RetGen
29.9
43.6
51.5
62.2
48.7
62.2
43.4
56.0
Iter-based
IRCoT
30.0
42.1
47.7
57.0
45.2
59.9
41.0
53.0
LighRRAG
4.2
13.7
10.8
19.4
14.7
27.7
9.9
20.3
Graph-based
HippoRAG
27.8
40.5
58.6
69.1
43.3
57.8
43.2
55,8
HippoRAG 2
27.4
39.1
46.0
55.3
50.7
65.5
41.4
53.3
PathRAG
8.4
20.8
21.0
31.6
23.8
41.2
17.7
31.2
Path-based
NeuroPath
31.4
44.3
63.4
73.2
50.5
64.7
48.4
60.7

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4.3 Case Studies (RQ2)
To illustrate how NeuroPath addresses semantic incoherence and noise in graph-baser methods, we compare it with several representative baselines, including HippoRAG 2 and LightRAG. As shown in Figure we conduct case studies using a question from the MuSiQue dataset evaluate the retrieval processes and answers of each method.
NeuroPath answers the question correctly by selectively expanding paths that preserve semantic coherence at each hop, effectively aligning with the question and avoiding irrelevant content. The documents retrieved rank the supporting evidence at the top, highlighting the quality of its retrieval process. In contrast, HippoRAG 2 applies PPR over both document and entity nodes but overempha- sizes irrelevant content due ignoring edge semanties. LightRAG retrieves a large subgraph (60 entities, 169 relations) but still fails to answer correctly, as much of the information is irrelevant to the question.
Query: Who was charge of the place where Castricum located?
Gold answer: Johan Remkes
Method: NeuroPath
Keywords: [Castrieum]
Path: eastricum town in north holland; north holland has king's commissioner johan remkes
Retrieved documents: [Castricum, North Holland, Lordship of Frisia, De Baarsjes, Laurens Reael]
Answer. Johan Remkes
Method: HippoRAG2
Keywords: [Castricum]
Retrieved documents (Top ranked documents): [Castricum, Theo van den Boogaard, The Beach (film), Glass
Beach, Roman Republic]
Answer: King's Commissioner of North Holland
Method: LightRAG
High level keywords: [Leadership, Castricum, Geographical location).
Low level keywords: [Historical figures, Goverannee, Local authority, Municipality] Query uses entities, 169 relations
Answer: Castricum is governed as part modern municipal structures
Figure 4: Case studies. Comparison between NeuroPath and the graph-based baselines.
4.4 Ablation Studies (RQ3)
In this section, we ablate various components NeuroPath, analyzing the impact pruning, Post- retrieval Completion, hop counts, and prompts retrieval performance.
Table Dissecting NeuroPath. p denotes the number of paths retained after pruning. expansion_ and purreni_chain correspond to the expansion requirements and current chain in the prompt.
MuSiQue
2Wiki
HotpotQA
R @
R@5 w/ Post-retricval Completion
Token (k) R@2
R@5
Token (k)
R@2
R@5
Token (k)
w/o pruning
48.7
63.8
2891
76.8
92.0
1883
75.7
90.8
2504
default (p=30)
48.0
62.7
4 1.5.7%
77.2
92.5
4 8.4%%
75.9
90.1
4 1.3988%
p=20
47.3
62.2
52.6%
76.5
90.8
J 17.3%
74.9
89.4
J. 47.0%
w/o Post-retrieval Completion
(ontriever(baseline)
34.8
46.6
46.6
57.5
57.1
75.5
max hop=1
35.5
35.5
61.0
62.3
61.3
66.4
max hop=2
41.8
45.3
73.6
84.1
67.5
71.4
max hop=3
42.1
45,4
73.9
84.5
67.7
72.2
w/o sspansion_regy req*
40.9
44.2
69.9
75.9
65.8
70.1
w/o current_chain
39.9
44.2
71.5
82.9
65.7
71.0
As seen in Table lines 4-6, we conduct a comparison performance and token consumption before and after pruning, demonstrating the efffcctiveness of the proposed pruning method. With 30 paths

---

retained after pruning, the retrieval performance is nearly equivalent and even higher in some metrics, while token consumption decreases by 45.7%, 8.4%, and 39.8%, respectively. With 20 paths retained after pruning. the performance reduction is not obvious, but token consumption is reduced by almost half on the MuSiQue and HotpotQA datasets. the same time, this result also indirectly verifies that there is lot of noise in the graph structure that is irrelevant to answering queries, and our method can greatly reduce this noise.
As seen in Table 3 lines 9-11, we report the performance of path tracking using only the LLM after removing the Post-retrieval Completion strategy. Removing this strategy leads significant drop in @ecall@5, indicating that this strategy helps compensate for missing path information and improves retrieval performance. The retrieval performance improves progressively as the maximum expansion hop parameter increases from 1 to 2 to 3, with particularly notable gain observed when increasing from to 2. Given that most of the questions in the three datasets are 2-hop questions, the improvement from 2 to 3 is relatively small. It is important to note that the hop parameter does not directly correspond to the number of reasoning steps in multi-hop QA. In our method, even with a hop value of 1, multi-hop reasoning across documents can be achieved through the linking of head and tail entities in triples. Notably, even with a maximum hop count of I, our method outperforms the naive RAG baseline using the same retriever on the recall @ 2 metric. This demonstrates that the direction-awar aware path filtering enables higher-quality retrieval under fewer document conditions.
As seen in Table 3 lines 12-13, we conduct experiments by removing the prompts that instruct the LLM to generate the current reasoning chain and the expansion requirements (i.e., using only the query for pruning instead of expansion requirements), respectively. The results show performance drops, indicating two key insights: (1) Explicitly prompting the L.M to generate reasoning chains enhances its ability to assess path filtering and expansion; (2) Pruning with expansion requirements better captures semantic information related to path expansion, enabling more accurate direction calibration and noise suppression compared to query-based pruning.
4.5 Robustness to Model Scales and Task Complexity (RQ4)
In this section, we verify NeuroPath's 's robustness different scales of LLMS and task complexity.
For LLMs, we evaluate performance on various smaller LLMs, including Llama-3. 1-BB- Instruct 11), GLMM-499B-0114 [II, Histral-7B-Insttruet-
[27]. and Gemma-3-4b-it (37). We
also fine-tune 3.ama-3.1-8B3 I-St-Instt [I2] to evaluate potential performance gains. All experiments
use Contriever the retriever and adopt a One-Shot setting for NeuroPath.
Table 4: Retrieval performance of alternative small open-source LLMs on MuSiQue.
Llama-3.1-81
GLM---9B
Mistral-7B
Gemma-3-4B
Average
Method
R@2
R@5
R@2
R@5
R@2
R@5
R@2
R@5
R@2
R@s
Iter-RetGen
44.0
58.4
45.1
58.9
41.8
54.1
42.1
55.3
43.3
56.7
IRCoT
40.3
53.7
40.2
49.1
38.5
50.9
36.8
42.4
39.0
49.0
HippoRAG
39.7
52.0
41.6
53.8
38.4
50.8
36.0
46.6
38.9
50.8
HippoRAG
40.5
54.9
41.9
55.2
40.3
53.7
39.5
54.7
40.5
54.6
NeuroPath 45.5
58.4
46.6
59.3
40.1
53.9
43.4
58.1
43.9
57.4
We compare all the methods on the most challenging MuSiQue dataset, as shown in Table 4 Neu- roPath consistently outperforms all baselines across the LLMs, except for Mistral-7B. Table shows the performance improvement after fine-tuning on Llama-3. 1---3.8----nttuuue The results demon- strate that our method achieves a significant improvement through fine-tuning, even outperforming GPT-4o-mn 4o-omii...... See the Appendix H for fine-tuning details.
Table 5: Retrieval performance after fine-tuning on LLama-3.1 1-8---3..----B-tttttt
MuSiQue
2Wiki
HotpotQA
Model
R@2
R@5
R@ 10
R@2
R@5
R@
10
R@2
R@5
R@ 10
GPT-4o-mini (default)
48.0
62.7
70.8
77.2
92.5
94.1
75.6
90.4
94.3
Llama-3. 1.nma-3.1-88 3.B (SFT)
50.5
64.9
72.1
77.2
92.5
94.2
76.4
90.8
94.5
To verify the adaptability of our method to simple queries (standard tasks), we conduct additional experiments on two simple QA datasets: PopQA (24] and NaturalQuestions [19], which mainly

---

consist of Mingle-entity--e centty-eentered questions. Following the setup of the main experiments, we randomly select 1000 queries from each dataset.
Table 6: Retrieval performance standard tasks with Contriever and GPT-4o-mini.
PopQA
NQ
Average
Method
R@2
R@5
R@2
R@5
R@2
R@5
Contriever
27.0
43.2
29.1
54.6
28.1
48.9
Iter-RetGen
35.4
46.6
40.9
68.6
38.1
57.6
IRCoT
30.2
38.2
35.1
55.8
32.6
47.0
HippoRAG
36.5
52.7
22.5
45.7
29.5
49.2
HippoRAG 2
34.2
47.4
32.1
59.5
33.2
53.4
NeuroPath 40.5
49.3
35.8
65.2
38.2
57.2
As shown in Table 6 our retrieval performance is still better than the graph-based method, and even better than the iter-based method on the PopQA dataset. The PopQA dataset is particularly entity-centric, with questions constructed around specific entities. Graph structure enhanced methods, which represent both entities and their relations, can quickly locate relevant entities for retrieval, giving them natural advantage on this type of data.
Our main experiments rely on short, clean paragraphs, which may not reflect real-world scenarios involving long, noisy documents. To address this and further test NeuroPath's robustness, we evaluate it on the MultiHop-RAG [36] dataset. We sample 1000 questions for evaluation. Given that documents in this dataset are substantially longer (each document has become almost 20 times longer, see Appendix C). we chunk them into 512 token segments. This setup inherently challenges retrieval performance with fragmented evidence and noise, providing a more stringent evaluation. We compare NeuroPath against baselines from prior experiments.
Table 7: Retrieval performance MultiHop-RAn RAG Dataset with GPT-4o-mini.
MultiHop-RAC (Contriever)
MultiHop-RAG (BGE)
Method
R@2 /@@5
R@2 / /R@5
Contriever
9.5 20.4
-/-
BGE-M3
-/- 17.8 / 30.9
25.4 44.7
Iter-RetGen
28.8 45.8
IRCoT
8.8 17.1
25.3 39.8
HippoRAG
16.5 / 26.6
18.2 / 30.2
HippoRAG 2
15.7 29.5
21.4 40.8
NeuroPath
23.7 / 39.0
26.1 46.8
As shown in Table 7 our method outperforms typical baselines, especially when using the smaller Contriever. Notably, other methods still exhibit sensitivity to the choice of embedding model, whereas NeuroPath shows relatively stable performance differences between Contriever and BGE-M3.
Conclusions
In this paper, introduced NeuroPath, novel RAG framework inspired by hippocampal place cell mechanisms to address the critical limitations of semantic incoherence and noise in existing graph-based methods. Our contributions, Dynamic Path Tracking and Post-retrieval Completion, empower LLMs to dynamically construct and refine reasoning paths that are semantically aligned with the query. Extensive experiments demonstrate that NeuroPath achieves new state-of-the-ar performance on challenging multi-hop QA datasets. Furthermore, our findings confirm its robustness across various L.MMs and task complexities. We underscore key insight: for multi-hop reasoning, semantic coherence is a more effective retrieval principle than simple structural relevance. This work not only provides a perfomeefoem solution for multi-hop QA but also opens a promising direction for developing more faithful, explainable, and neurobiologically inspired reasoning systems within the RAG paradigm.
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References
[1] T. E. Behrens, T. H. Muller, J. C. Whittington S. Mark, A. B. Baram, K. L. Stachenfeld, and
Z. Kurth-Nelson. What a cognitive map? organizing knowledge for flexible behavior. Neuron, pages 490-509, 2018.
[2] B. Brown, Mann, N. Ryder, M. Subbiah, Kaplan, P. Dhariwal, A. Neelakantan, Shyam,
G. Sastry, A. Askell, et al. Language models are few-shot learners. In NeurlPS, 2020.
[3] B. Chen, Guo, Z. Yang, Y. Chen, J. Chen, Z. Liu, C. Shi, and C. Yang. Pathrag: Pruning graph- based retrieval augmented generation with relational paths. arXiv preprint 1:Xxv:2502.14902 2025.
[4] J. Chen, S. Xiao, P. Zhang, K. Luo, D. Lian, and Z. Liu. M3-embedding: Multi culti-lingguality, funtiiiunncionalitt,,. multi tatiigrrauuatttyy text embeddings through oofflnnowedgge distillation. In Findings of ACL, pages 2318 22335. Association for Computational Linguistics, 2024.
[5] W. Chen, T. Bai, J. Su, J. Luan, W. Liu, and C. Shi. Kg-retriever: Efficient knowledge indexing for retrieval-augmenttdd large language models. arXiv preprini arXiv:2412.05547, 2024.
[6] Y. Chuang, W. Fang, S. Li, W. Yih, and J. R. Glass. Expand, rerank, and retrieve: Query reranking for open-domant question answering. In Findings of ACL, pages 12131-12147. Association for Computational Linguistics, 2023.
[7] D. Edge, H. Trinh, N. Cheng, Bradley, A. Chao, Mody, S. Truitt, and J. Larson. From local to global: A graph RAG approach to query-focused summarization. arXiv preprint arXiv:2404. /6130, 2024.
[8] H. Eichenbaum, P. Dudchenko, E. Wood, M. Shapiro, and H. Tanila. The hippocampus, memory, and place cells: is it spatial memory or a memory space? Neuron, pages 209-226, 1999.
[9] W. Fan, Y. Ding, L. Ning. S. Wang, H. Li, D. Yin, T. Chua, and Q. Li. A survey RAG meeting Ims: Towards aetreee large language models. In KDD, pages 6491-6501. ACM, 2024.
[10] Y. Gao, Y. Xiong, X. Gao, K. Jia, J. Pan, Y. Bi, Y. Dai, J. Sun, Q. Guo, M. Wang, et al. Retrieval- augmented generation for large language models: A survey. arXiv preprint arXiv:2312.10997, 2023.
[11] GLM, A. Zeng, Xu, B. Wang, C. Zhang, Yin, D. Zhang, D. Rojas, G. Feng, H. Zhao, et al. Chatglm: A family of large language models from glm-130b to glm-4 all tools. arXiv preprint arXiv:2406.1220 12793, 2024.
[12] A. Grattafiori, A. Dubey, A. Jauhri, A. Pandey, A. Kadian, A. Al-Dahle, A. Letman, A. Mathur, A. Schelten, A. Vaughan, et al. The Ilama herd of models, arXiv preprint 2rx1v:240..21783, 2024.
[13] Z. Guo, L. Xia, Y. Yu, T. Ao, and C. Huang. Lightrag: Simple and fast aarrivval-augmentee generation. arXiv preprint arXiv: 2024.
[14] B. J. Gutiérrez, Y. Shu, Y. Gu, M. Yasunaga, and Y. Su. Hipporag: Neurobiologically inspired long-term memory for large language models. In NeurIPS, 2024.
[15] B. J. Gutiérrez, Y. Shu, W. Qi, S. Zhou, and Y. Su. From RAG to memory: Non-parametric continual learning for large language models. arXiv preprint arXiv:2502. 14802, 2025.
[16] X. Ho, A. D. Nguyen, S. Sugawara, and A. Aizawa. Constructing A multi-hop QA dataset for comprehensive evaluation of reasoning steps. In COLING, pages 6609-6623 International Committee on Computational Linguistics, 2020.
[17] A. Hurst, A. Lerer, P. Goucher, A. Perelman, A. Ramesh, A. Clark, A. Ostrow, A. Welihinda, A. Hayes, A. Radford, et al. Gpt-4o system card. arXiv preprint arXiv:2410.21276, 2024.
11

---

[18] G. Izacard, M. Caron, L. Hosseini, S. Riedel, P. Bojanowski, A. Joulin, and E. Grave. Un- supervised dense information retrieval with contrastive learning. Trans, Mach. Learn. Res., 2022.
[19] T. Kwiatkowski, J. Palomaki, O. Redfield, M. Collins, A. P. Parikh, C. Alberti, D. Epstein, I. Polosukhin, J. Devlin, K. Lee, et al. Natural questions: a benchmark for question answering research. Trans. Assoc. Comput. Linguistics, 7:452-466, 2019.
[20] W. Kwon, Z. Li, S. Zhuang, Y. Sheng, L. Zheng, C. H. Yu, J. Gonzalez, H. Zhang, and I. Stoica. Efficient memory management for large language model serving with pagedattention. SOSP, pages 61 1-1662.. ACM, 2023.
[21] P. Lewis, E. Perez, A. Piktus, F. Petroni, V. Karpukhin, N. Goyal, H. Küttler, M. Lewis, W. Yih, Rocktäschel, et al. augmenee generation for knowledge-intensive NLP tasks, In NeurlPS, 2020.
[22] A. Liu, B. Feng, B. Xue, B. Wang, B. Wu, C. Lu, C. Zhao, C. Deng. C. Zhang, C. Ruan, et al. Deepseek-v3 technical report. arXiv preprint arXiv:2412. 19437, 2024.
[23] L. Luo, Li, G. Haffari, and Pan. Reasoning on graphs: Faithful and interpretable large language model reasoning. In ICLR, 2024.
[24] A. Mallen, A. Asai, V. Zhong, R. Das, D. Khashabi, and H. Hajishirzi. When not to trust language models: Investigating effectiveness of parametric and non-maramttric memories. In ACL, pages 9802-9822. Association for Computational Linguistics, 2023,
[25] Y. Mao, P. He, X. Liu, Y. Shen, J. Gao, J. Han, and W. Chen. Generation-augmenter retrieval for open-domain question answering. In ACI/IJCNLP, pages 4089-4100. Association for Computational Linguistics, 2021.
[26] S. Min, E. Wallace, S. Singh, M. Gardner, H. Hajishirzi, and L. Zettlemoyer. Compositional questions do not necessitate multi-hop reasoning. In ACL. pages 4249 4257, Association for Computational Linguistics, 2019.
[27] Mistral AI. Mistral-7b-insttrut--o... model card. http:://bugiinfaacc.ccmmiaaaaaae Mistral-78- Innt---ttIInttttt-oo.o 2024.
[28] E. I. Moser, E. Kropff, and M.-B. Moser. Place cells, grid cells, and the brain's spatial representation system. Annual Review of Neuroscience, pages 69-89, 2008.
[29] J. O'efee and J. Dostrovsky. The hippocampus as spatial map. preliminary evidence from unit activity in the freely-moving rat. Brain Research, pages 171-175, 1971.
[30] B. E. Pfeiffer and D. J. Foster. Hippocampal place-cell sequences depict future paths to remembered goals. Nature, pages 74-79, 2013.
[31] O. Press, M. Zhang, S. Min, L. Schmidt, N. A. Smith, and M. Lewis. Measuring and narrowing the compositionality gap in language models. In Findings of EMNLP, pages 5687-5711. Association for Computational Linguistics, 2023.
[32] X. Ren, J. Tang, D. Yin, N. V. Chawla, and C. Huang. A survey of large language models for graphs. In KDD, pages 6616-6626. ACM, 2024.
[33] E. Robertson and H. Zaragoza. The probabilistic relevance framework: BM25 and beyond. Found. Trends Inf. Retr. 3(4):333-38, 2009.
[34] P. Sarthi, S. Abdullah, A. Tuli, S. Khanna, A. Goldie, and C. D. Manning, RAPTOR: recursive abstractive processing for tree-organized retrieval. In ICLR, 2024.
[35] Z. Shao, Gong, Shen, Huang, N. Duan, and W. Chen. Enhancing retrieval- cerreval-augmeeneed large language models with iterative retrieval-generattion synergy. In Findings of EMNLP, pages 9248-9274. Association for Computational Linguistics, 2023.
[36] Y. Tang and Yang. Multihop-RAG: Benchmarking aetrivval-uugmented generation for hupti-hop queries. In COLM, 2024.
12

---

[37] G. Team, A. Kamath, J. Ferret, S. Pathak, N. Vieillard, R. Merhej, S. Perrin, T. Matejovicova,
A. Ramé, M. Rivière, al. Gemma technical report. arXiv preprint arXiv:2503.19786 2025.
[38] H. Trivedi, Balasubramanian, T. Khot, and A. Sabharwal. Musique Multihop questions via single-hop question composition. Trans. Assoc. Comput. Linguistics, 10:539-554, 2022.
[39] H. Trivedi, N. Balasubramanian, T. Khot, and A. Sabharwal. Interleaving retrieval with chain-of- thought reasoning for knowledge-intene multi-step questions. In ACL, pages 10014-10037. ACL, 2023.
[40] R. Wang. S. Liang, Chen, Y. Huang, M. Li, Y. Ma. D. Zhang, Qin, and M.-F. Leung. Graphcogent: Mitigating Ilms' working memory constraints via multi-agent collaboration in complex graph understanding. arXiv preprint arXiv:2508. 12:37999.8...7, 2025.
[41] R. Wang, S. Liang, Q. Chen, Zhang, and K. Qin. Graphtool-instructinn Revolutionizing graph reasoning in Ilms through decomposed subtask instruction. In KDD, pages 1492-1503. ACM, 2025.
[42] A. Yang. B. Yang. B. Zhang, B. Hui, B. Zheng, B. Yu, C. Li, D. Liu, F. Huang, H. Wei, et al. Qwen2.5 technical report. arXiv preprint arXiv:2412.15115, 2024.
[43] Z. Yang, P. Qi, S. Zhang. Y. Bengio, W. W. Cohen, R. Salakhutdinov, and C. D. Manning. Hotpotqa: A dataset for diverse, explainable multi-hop question answering. In EMNLP, pages 2369-2380 Association for Computational Linguistics, 2018
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network composed of nodes A Neurobiology-imspired Mechanism
In this section, we will briefly introduce the mechanism of hippocampal place cells and cognitive maps supplement to explain our motivation.
Episodic Memory
Preplay Replay
Episodic Memory
Activation (Firing)
A
A
A
2
A
A
+
Place
Knowledge
(5) Represemting Physical Spece
(b) Repecaentnng Abstect Memory Sprce
(c) Reprosmning Krowkedge Spraet
Figure 5: Representation forms of cognitive maps in physical and memory space, and their analogy to knowledge representation.
Place cells in the hippocampus. Place cells are found in the CA1 and CA3 regions of the hip- pocampus and show place-specific firing when animals explore their environment (29]. They play an important role in spatial positioning and navigation, shown in Figure (a). When an animal reaches a specific location during movement, a corresponding place cell becomes active. The area represented by each place cell is called place field. Groups of place cells work together with grid cells and boundary vector cells the entorhinal cortex to form a cognitive map and support spatial replay previously experi- enced paths during rest or sleep. This phenomenon is believed be related to memory consolidation and episodic memory [8]. Pfeiffer et al. confirmed that the preplay of place cell sequences plays an important role in predicting future paths [30). During navigation, the hippocampus generates a sequence of place cell activity in chronological order, like neural map. Even when the combination of start and target positions is completely new. Place cell sequences show flexibility and adaptability, allowing dynamic path planning based the current environment and goals.
processing (28). Studies have shown that place cell activity sequences
Place cells and cognitive map. Fichenbaum al. believe that hippocampus should participate in the representation of wide range of memory spaces through place cells and collaborative cells, and the representation physical space only a special case of memory space representation [8]. This theory believes that cognitive maps are essentially episodic memories composed of sequences of different events or location nodes, and then different episodic memories are linked together through repeated common elements (also called nodes) to establish memory space. As shown in Figure (b).
Graph-structured knowledge organization. Knowledge in the real world does not exist in isolation, but interconnected through complex associative relationships. This organization of knowledge resembles the structure of cognitive maps, which can be represented and edges, as illustrated in (c) Both path navigation in ncurobiology and knowledge retrieval in multi-hop QA task, the core mechanism involves the integration of multi-source information and progressively locate the target. Therefore, inspired by the role of place cell sequences in navigational planning of future paths [30,, simulate the preplay and replay mechanisms of place cells, thereby enabling goal- core-directed knowledge retrieval and memory consolidation.
14

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B NeuroPath Workflow
Joutlines the NeuroPath workflow, which comprises three main stages: Static Indexing. Dynamic Path Tracking, and Post-retrieval Completion. Moreover, Figure I0 and Figure [I] provide illustrative examples of the Dynamic Path Tracking and Post-retrieval Completion processes, respectively.
Algorithm 1 NeuroPath Framework Workflow
Require: Source documents D and query
Ensure: Candidate documents 'reet (Final retrieved)
1: Static Indexing:
2: Extract entities and relation triples from documents D using LLM
See Fig. 12 for prompt
3: Construct knowledge graph (KG) from extracted facts
4: Construct coreference sets based on embedding similarity of entities
5: Dynamic Path Tracking:
6: Use LLM to extract key entities from query Retrieve initial seed nodes via entity similarity
See Fig. for prompt
8: Expand seed nodes with coreference set
9: repeat
10: if hop h = 0 then
Gather initial seed nodes connected triple segments from KG
12:
Concatenate these triple segments to form candidate paths
13:
Prune candidate paths using similarity with query !
14:
else
15:
Identify expandable nodes linked current path and expand them with coreference set as new seed nodes
16:
Gather seed nodes connected triple segments from KG
17:
Concatenate these triple segments to form candidate paths
18:
Prune candidate paths using similarity with previous expansion goal g²-1
19:
end if
20:
Retain top-30 candidate paths
21:
Use LLM to:
See Fig. 14 for prompt
22:
Select valid paths
23:
Mark paths that need be expanded
24:
Generate reasoning chain based on valid paths c²
25:
Generate next-hop expansion requirements gd
26:
Decide whether to continue, represented by the generated flag ct
until or maximum hop reached
28: Post-retrieval Completion:
29: Collect all source documents from selected valid paths as Dp
30: Concatenate the and g from the final hop with the original query to form the new query g'
Retrieve additional documents D. using q
32: return final set Dret D, D.
C Detailed Experimental Settings and Results
All open-source L.MMs our experiments are deployed using vL..M 20 on NVIDIA GeForce RTX 4090. For GPT-4o-mini, use the official OpenAI API.
We show the detailed data of entity and relationship extraction when NeuroPath performs static indexing in Table For MultiHop-RAGG we selected 1,000 questions from the original dataset and merged all supporting documents for each question into a single corpus. We then chunked all documents within this corpus into blocks of 512 tokens and retained the original title field. Table g shows the comparison of the average number of tokens in documents across different datasets.
Experiments for HippoRAG & HippoRAG 2, LightR and PathRAG are conducted using the official code provided by the authors. LightRAG uses version 1.3.4 with the specified mode set to 'local', and all other baselines use default settings. Except for LightRAG and PathRAG,
15

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Table 8: Comparison of the average number of tokens per document.
MuSiQue
2Wiki
HotpotQA
PopQA
NQ
MultiHop-RAG 2,289 (chunk to 512)
Avg tokens doc
110
105
128
129
136
standardize the QA prompts across all models. Additionally, LightRAG and PathRAG do support retrieval performance evaluation, we follow their original configuration by merging each dataset into a single document and chunking. Morcover, ensure consistency in QA output style with other methods, we add standardized output prompts to LightRAG and PathRAG, enabling fairer comparisons in terms of EM and Fl metrics. The detailed prompt is provided in Appendix
Table 9: Statistics of KG data extracted by GPT-4o-mini.
MuSiQue
2Wiki
HotpotQA
PopQA
NQ
MultiHop-RAG
Entities
104,442
49,362 15,049
89,947
87,233
92,852
39,281
Relations
40,861
33,914
22,163
40,273
18,564
Triples 120,226
60, 188
108,621
106,061
116,689
43,638
addition to the QA performance with Contriever, we supplement QA performance comparison using BGE-M3 as the retriever in Table [T] Similar to the results with Contriever, our method consistently outperforms the baselines.
Table 10: QA performance with GPT-4o-mini and BGE-M3.
MuSiQue
2Wiki
HotpotQA
Average
Category
Method
EM
F1
EM
F1
EM
F1
EM
Fl
BM25
20.7
30.7
44.2
48.1
43.3
55.6
36.1
44.8
Naive
Contriever
22.3
32.1
38.5
43.7
44.4
57.5
35.1
44.4
BGE-M3
27.8
39.7
48.2
54.5
47.8
61.8
41.3
52.0
Iter-RetGen
31.7
46.3
56.0
66.7
49.8
64.5
45.8
59.2
Iter-based
IRCoT
30.8
44.6
61.5
72.1
50.3
64.4
47.5
60.4
AG
5.2
15.8
16.8
30.3
18.0
32.7
13.3
26.3
Graph-based
HippoRAG
25.8
36.7
57.4
66.7
43.7
57.2
42.3
53.5
HippoRAG 2
31.6
44.5
57.9
67.8
55.0
70.1
48.2
60.8
PathRAG
7.1
19.8
18.5
28.9
21.7
38.5
15.8
29.1
Path-based
NeuroPath
33.0
46.4
63.7
74.1
50.3
65.2
49.0
61.9
Compare to PathRAG
In the main experiment, NeuroPath demonstrates markedly different performance compared to PathRAG, due to two key factors: differences in path construction methods and task objectives.
Difference in path construction methods:
NeuroPath leverages LLMs dynamically filter and expand paths starting from selected seed nodes. Our approach emphasizes semantic coherence during path expansion and alignment with the query objective. Throughout the process, the expansion direction is continuously adjusted based on the query to minimize noise. In essence, the entire path expansion chain is dedicated answering the query, retaining only semantically relevant and mutually reinforcing information that contributes to the answer.
In contrast, PathRAG first selects key nodes and then constructs paths among them. Aside from the key nodes being related to entities in the query, the path construction process does not ensure semantic relevance to the query. Moreover, its path pruning strategy distributes resources evenly based on node connectivity, which often fails build effective reasoning paths.
Difference in task objectives:
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NeuroPath is designed for multi-hop reasoning tasks that require strictly factual answers. This typically demands a precise alignment between the reasoning path and the query requirements.
In contrast, PathRAG-likk LightRAG- tailored for sense-making tasks, which often involve answering global abstract questions. These tasks generally benefit from collecting comprehensive and diverse information to support broad understanding. While PathAGG reduces the information redundancy introduced during subgraph construction in LightRAG, this optimization only targets redundant paths between nodes, It does not fundamentally address the issue of introducing query-irrelevant noise.
E Error Analysis
E.1 Retrieval Results Distribution
We present the distribution of retrieval results in Figure 6 which focuses on the top- retrieved documents for each question, where the gray portion represents those that were not successfully retrieved.
10%
25%
18%
23%
32%
43%
85%
59%
MuSiQue Documents retrieved by path tracking
ZWikiMulliHopOAA Documents retrieved by completion
HotpotQA Documents failed to retrieved
Figure 6: Retrieval results distribution.
E.2 Nodes Mismatch
We analyze the questions in the MuSiQue dataset where the supporting documents were not perfectly retrieved. Among these, 31.5%, 40.8%, and 26.7% categorized as 2-hop, 3-hop, and 4-hop questions, respectively. We attempted to increase the maximum length of path expansion, but found that it did not significantly improve performance. Therefore, we proceed to analyze aspects such as entity extraction and nodes matching.
Table 11:
The degree of mismatch between the initial seed nodes and the nodes along the path with respect to the supporting document. 2Wiki* indicates randomly deleting one key entity from the query-extracted entity list (if the list has more than one entity).
MuSiQue
Mismatch (%) 2Wiki
Mismatch ( (%) 2Wiki*
mismatch ( (%
Seed nodes
49.6
Seed nodes
36.1
Seed nodes
57.7
Path nodes
39.1
Path nodes
8.9
Path nodes
36.7
We analyzed
wwwpssuppootindocuuue mismatch on the MuSiQue dataset (Table 11.. A match is defined by the intersection between the node and the set extracted from the supporting documents. Results show that 49.6% of initial seed nodes fail to match any supporting documents. While path expansion reduces the mismatch rate some extent, many documents remain uncovered. For comparison, the initial seed nodes mismatch rate is 36.1 % on the 2WikiMultHHoPAA dataset, and path expansion reduces it further to 8.9%. This difference likely due to higher complexity of MuSiQue questions. Figure 7 shows an example of the extracted entities from MuSiQue. The model failed identify the core event Tripartite discussions, leading to mismatch in path tracking. Similarly, when randomly remove key entity from each query in 2WikiMullHHoPA. the
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mismatch rate of initial seed and path nodes increases significantly, highlighting the importance of accurate initial key entities extraction.
Query: What month did the Tripartite discussions begin between Britain, France, and country where, despite being headquartered in the nation called the nobilities commonwealth, the top-ranking Warsaw Pact operatives originated?
Extracted key entities: ['Britain', 'Fancc', Warssaw Pact', 'nobilities commonwealthh]
Figure 7: Example of query key entities extraction in MuSiQue dataset.
E.3 Discussion of Underperformance on HotpotQA
In this section, we discuss the reasons why both the retrieval and generation processes did not achieve optimal performance on the HotpotQA dataset.
38 26 have shown that many HotpotQA questions can be answered correctly without complete multi-hop reasoning, often via naive RAG approach. This is primarily because: (1) HotporQA documents are short and information-dense. (2) most questions are simple synthetic two-hop queries requiring minimal reasoning; and (3) its distractors are generally weak, which simplifies the retrieval of target passages. While iter-based methods perform well on HotpotQA by leveraging these characteristics to match dense chunks and refine queries, they are less efficient, consuming 22.8% more tokens (see Appendix F). Furthermore, their sensitivity to embedding models leads to inconsistent performance.
Previous studies
We observed a notable discrepancy in our experiments: HippoRAG 2 underperformed NeuroPath on retrieval metrics yet surpassed it on QA metrics. We attribute this to HotpotQA's allowance for shortcuts answers derived through guessing [38]. As shown in Figure [8] HippoRAG 2 retrieved only one of the two required supporting documents but was still able to infer the answer by spotting the mention of "Gal Gadot". This because HippoRAG matches triples and passages using the entire query rather than intermediate nodes, a method that favors information-dent documents and thus facilitates such guessing. In contrast, Table demonstrates that NeuroPath is less reliant on this guessing. It achieves 9.3% higher proportion of correct retrievals among its correct answers compared HippoRAG This a direct result NeuroPath's 's design: it expands paths one hop at time, with each expansion being influenced by the semantic coherence of the previous path. This process inherently prevents sudden jumps to random nodes, ensuring a more faithful reasoning process.
Table 12: Proportion of correct retrievals among correct answers.
Correct retrievals
Correct answers
Proportion (%)
HippoRAG 2
408
507
80.4
NeuroPath
453
505
89.7
F
Cost and Efficiency Comparison
Token Cost. We compared the token consumption of different methods, which is closely related to LLMs, at each stage (on GPT-4o-mini), as shown in Figure 9 Our method consistently achieves the lowest token usage across all stages. It reduces token usage by 31.1% average during graph indexing stage (LLM-based graph indexing), and by 22.8% on average during retrieval stage (LLM- based iterative methods). In the QA stage, method (along with other methods) reduces token usage by 89.2% compared to LightRAG, indicating that LightRAG introduces significant irrelevant noise through subgraph construction. Our method filters this noise during path tracking, improving both retrieval and QA performance.
Time Cost. Table 13 compares the graph indexing time of NeuroPath with other methods requiring graph indexing on 2WikiMuliHHopQA. Since we perform NER and relation extraction for each document within single LLM call, the time consumption is reduced by nearly half compared to
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Query: Which post DC Extended Universe actress will also play role in what is intended to be the fifth installment of the DC Extended Universe?
Gold answer: Gadot
Justice League (film)
...It is intended to be the fifth installment in the DC Extended Universe (DCEU). The film is directed by Zack
Snyder and written by Chris Terrio and Joss Whedon, from story Snyder and Terrio, and features ensemble that includes Affleck, Henry Cavill, Gal Gadot, Jason Momoa, Ezra Miller, Ray Fisher, Ciarán Hinds, Amy Adams, Willem Dafoe, Jesse Fisenberg, Jeremy Irons, Diane Lane, Connic Nielsen, and K. Simmons...
Aquaman (film)
List of DC Extended Universe cast members
Batman Superman: Dawn of Justice
DC Extended Universe
Supporting documents not retrieved
Wonder Woman (2017 film)
It the fourth installment in the DC Extended Universe (DCEU). The film is directed by Patty Jenkins, with screenplay by Allan Heinberg, from story Heinberg, Zack Snyder, and Jason Fuchs, and Gal Gadot, Chris Pine, Robin Wright, Danny Huston, David Thewlis, Connie Nielsen, Elena Anaya...
Figure 8: Example where HippoRAG answered successfully but retrieval failed.
x10'
x10
20000
x10"
2500
18000
1000
2000
14000
6000
12000
6000
1300
10000
8000
4000
6000
2000
500
4000
2000
Graph Indexing
Retrieval
QA
LighRRAG
HippoRAG
[lipooRAG2
PathRAG
NeuroPath
Figure 9: Comparison of Token Consumption across three stages. In QA, NeuroPath represents all methods other than LightRAG and PathRAG (all using the top documents for answering).
other methods. The main bottleneck of the retrieval time cost of our method lies in the call waiting of LLM. The call time of method L.M is almost the same that of other methods based itcrative retrieval using LLM. Table 14 presents comparison of retrieval time consumption between NeuroPath and other iterative retrieval methods. Our slightly higher time cost mainly comes from an extra LLM call for extracting query's 's key entities. In addition, the pruning strategy requires the use of embedding model calculate the embedding of each path and the expansion requirement in parallel. It takes about 0.2s for each question on Contriever and BGE-M3, which is much shorter than the waiting time for LLM calls. Therefore, we believe that the time complexity the pruning strategy is acceptable.
Table 13: Comparison of graph indexing time with graph-based based methods.
HippoRAG
HippoRAG2
LightRAG
PathRAG
NeuroPath
Time (minutes)
64
62
52
72
39
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Table 14: Comparison of average retrieval time per question with iter-based methods.
IRCoT
NeuroPath
Time (seconds)
5.6
4.7
6.7
G
Limitations
Although dynamic construction of semantic paths significantly improves performance multi- hop QA tasks, several limitations remain. First, NeuroPath heavily relies on LLM calls for path filtering and expansion, which introduces a time bottleneck. Second, due to the low granularity of the triple segments expanded in each step, some simple questions may be unnecessarily split into multi- hop problems. Finally, do not optimize the retrieval corpus itself (e.g., via summarization), the method may face limitations in sense mansing tasks that require abstraction or summarization.
H Fine-tuning Details
We additionally select 1,500 questions from the original 2WikiMultiHopQA dataset and follow the same procedure as in the main experiments using DeepSeek- V3 [22] The outputs are then used to fine-tune 1--mm--.1-8B-Insiiii
[12].
Specifically, we fine-tune 2 for efficiency. LoRA is applied to all layers with rank of 8, 16, and dropout We train for epochs using the Adam W optimizer and a cosine learning rate schedule with a base
1-8--trrrrtttt using QLoRA with 8-bit quantization and
learning rate of 5e-5.
I LLM Prompts
Figure 12 and Figure 13 show the prompts when NeuroPath performs static indexing and extracts key entities from the query.
Figure 14 shows the prompt when NeuroPath performs path tracking, and Figure 15 shows the example of One-Shot.
Figure shows the restrictive prompt words used by NeuroPath and other models for QA question answering to unify the model output style.
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Query:
Which has the director born first, Sensation In Savoy or Devasuram? Key entities: [Sensation In Savoy, Devasuram]
Hopl:
Paths:
sensation in savoy-Pwas directed by-reduard von borsody; <expandablce;; [eduard von borsody]
I: sensation in savoy-2was made by->bavaria film; <expandable>; [bavaria film]
2: gevasuram->direcee by->i sasi; <expandable>: [i sasi]
sensation in savoy-Pwas made the year-> 1950; <expandable>; [1950]
ddvasuram->consideee as-Pone the best films of sasi carcor; <expandable>; [one of the best films v sasi carcer]
5: sensation in savoy-Pis a film of genre->comedy crime; <expandable>; [comedy crime]
6: sensation in suvoy->starring-2pan klinger; <expandable>> [paul klinger]
Output:
"current_chaeee "The director of Sensation In Savoy is Eduard Von Borsody, The director Devasuram is Sasi.",
"valid ids": [0, 2].
"expansion requirements": "Find when Eduard Von Borsody and V Sasi born.", "need expand ids": [0, 2]. "continue":
Hop2:
Paths:
sensation in savoy-2was directed by-reduard von borsody;
I: dovasuram--dirocted by->i v sasi;
2: sensation in savoy->was directed by-reduard von borsody; eduard von borsody-=born on->13 june 1898; <expandable>: [13 june 1898]
devasuram->dirette by->i sasi; sasi->was born on->28 march 1948; <expandable>> [28 march 1948]
4: sensation in savoy->was directed by-Seduard von borsody; eduard von horsody->died on->1 january 1970; <expandable>: [] january 1970]
sensation in savoy->was directed by->eduard borsody; eduard borsody.>was-rausttin <expandable>: [austrian]
sensation in savoy-2was directed by-reduard von borsody; eduard von borsody->was a->film editor;
<expandable>; [film editor]
Output:
"cureett chain": "The director Sensation Savoy is Eduard von Borsody, who was born on June 1898.
The director of Devasuram is I. V. Sasi, who was born on 28 March 1948.".
"valid ids": [2. 3).
"expansion requirements"! m9
"need expand ids":
"continue":
Figure 10: Example of Dynamic Path Tracking.
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Query: Which film has the director born first, Sensation In Savoy or Devasuram?
Valid Path:
I: sensation in savoy-Swas directed by-Seduard von borsody; eduard von borsody-2hom on-213 june 1898;
2: devasuram-~dirrctted by-Si v sasi; sasi-Pwas born on->28 march 1948;
The source documents that the paths pass through (the following the document titles):
Docl: Sensation Savoy; Doc2: Eduard von Borsody; Doc3: Devasuran; Doc4: Sosi
The chain of reasoning in the last hop:
The director of Sensation In Savoy is Eduard von Borsody, who was born on 13 June 1898. The director of Devasuram is V. Sasi, who was born on 28 March 1948.
Prepare a second retrieval to augment candidate documents compensate for potential missing information the paths:
New Query: Which film has the director born first, Sensation In Savoy or Devasuram? The director of Sensation In Savoy is Eduard von Borsody, who was born 13 June 1898. The director of Devasuram is V. Sasi, who born 28 March 1948.
New documents retrieved (the following are the document titles): Doc5: Boaz Davidson; Doc6: Jacques Deray: ...
Supporting Document Titles:
Doc1: Sensation in Savoy; Doc2: Eduard von Borsody; Doc3: Devasuran; Doc4: V. Sasi
Figure 11: Example of Post-retrieval Completion.
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Your task is to extract named entities from the given passage and construct an KG (Knowledge Graph) from the passage and the entities you extracted. Requirements:
The entity type can be [organization, person, object, location, time, event, term], etc. the should describe the information contained in the text as detailed as possible.
The format of KG (Knowledge Graph) triple is ("head node", "relation", "tail node "J, and each part must have value.
Coreference and Pronoun Resolution:
Specific names should explicitly resolved maintain clarity.
Respond with JSON Object.
# Example Begin
Passage:
Teutherga
Teutberga( died 11 November 875) was queen of Lotharingia by marriage to Lothair II. She was daughter of Bosonid Boso the Elder and sister of Hucbert, the lay- abbot of St. Maurice's Abbey.
Output:
"named entitiesnt: ["Teutberga", "11 November 875", "Lotharingia", "Lothair II", "Bosonid Boso the Elder", "Hucbert", "St. Maurice's Abbey"). "triples": [
("Teutberga", "died on", "11 November 875"),
("Teutberga", "was queen of". "Lotharingia").
("Teutberga", "married to", "Lothair I1").
("Teutberga", "is daughter of". "Bosonid Boso the Flder"), ["Teutberga", sister of", "Hucbert"],
("Hucbert", "is the lay-abot ; "St. Maurice's Abbey"] J.
).
Example End
Passage: [Passage] Output:
Figure 12: Prompt for entities and relationships extraction.
Please extract all named entities that important solving the questions below, Place the named entities json format.
# Example
Question:
Who wrote the book that was published carlier, "The Great Gatsby" "To Kill Mockingbird"? Output:
"named entities": ["The Great Gatsby", "To Kill a Mockingbird"]
# Example
Question
Are the cities of Paris and Berlin located in the country?
Output:
entitteen: ["Paris", "Berlin"]
Question:
[Question]
Output:
Figure 13: Prompt for extracting key entities from query.
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To answer given query, you need select of valid clue paths form chain of reasoning and determine whether some paths need to be expanded with more information help answer query.
# Explanation
Valid paths can provide intermediate reasoning steps evidence help answer the query. Paths be redundant, filter them out. path that contains the sexpandable> tag an expandable path, which identifies phrase that can be expanded with additional information.
information is sufficient or no more valid information can be added, stop expanding. Return the required JSON object.
JSON object format
"curent "current_chain" chain": "A string. You should think step by step. Try to form the current chain of reasoning.".
"valid ids": [List of valid path IDs (int) (sort by helpfuiness to query)), "expansion requirements". "A string. If need expand, provide the specific requirements of the expansion. Otherwise empty string.",
expand ids": [List of path IDs (int) that need to be expanded. (if any)].
"continue": or (0 stop expanding, continue expanding)
Figure 14: Prompt for path tracking.
# Example
Query: Which film has the director who was born later, Extrano Viaje Love In Pawn?
Paths:
Extrano Viajo->released in-> in->1964;
I: El Extrano Viaje->directed be---eeo Fernan Gomez; <expandable>; [Fernando Fernan Gomez]
2: El Extrano Vinje->starring--loss Isbert; <expandable>: [Josc Isbert]
Love Pawn-rreleased in-=1155;;
Love Pawn->directed bh-rChsss Saunders; <expandable>: [Charles Saunders] Output:
current chain": "The director of El Extrano Viaje Fernando Fernan Gomez. And the director of Love In Pawn is Charles Saunders",
"valid ids": [1,4],
"expansion_ iopaasioonrrquirenee requirements". "Find when were Fernando Feman Gomez and Charles Saunders born.".
nned expand ids": [1,4].
continue":
Figure 15: One-Shot example for path tracking.
NeuroPath other methods
You an assistant who is good at reasoning and question answering. You need to analyze the given information according the questions. Response start after "Thought: ", where You will go through the reasoning process step by step explain how you to conclusion. Conclude with "Answer: present concise, definitive response, devoid additional elaborations.
Individual Settings of LightRAG and PathRAG
(The following prompt needs appended QA prompt LightRAG PathRAG.) At end your output, conclude "Answer: to present concise, definitive response, devoid of additional elaborations.
Figure 16: Prompt for QA.
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NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
The contributions of this work are clearly articulated in the abstract and introduction. Specifically, the paper proposes a novel path-based RAG framework NeuroPath aimed resolving the semantic incoherence and redundant noise problems found in existing graph-baser approaches, leading to improved retrieval performance in multi-hov question answering tasks. Our experimental results in Section 4 are consistent with the claimed contributions.
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It is fine to include aspirational goals as motivation as long as it is clear that these goals not attained by the paper.
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Question:
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Answer: [Yes]
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3. Theory assumptions and proofs
Question: For each theoretical result, does the paper provide the full set of assumptions and complete (and correct) proof?
Answer: [Yes]
Justification: The hippocampal place-cell-basce mechanisms for navigation and episodic memory serve as a biological inspiration for our RAG framework for large language models. These neural mechanisms have been validated in the field of neurobiology. We provide a clear and properly cited in Section [ and a more detailed explanation is included in Appendix
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(d)
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Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
Answer: [Yes]
Justification: All main experiments reported in Section are conducted using an average 1,000 test queries per dataset for retrieval and question answering. Error analysis provided in Appendix B
We evaluate the average performance of different methods using
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two distinct LLMs and two different retrievers. To assess the adaptability of our method different tasks, we additionally include experiments on simplified datasets and a more complex dataset in Section 4.5 Furthermore, to evaluate the robustness of our method, we introduce multiple smaller-scale LLMs in the experiments reported Section 4.5
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Reviews:
[{"review_id": "JvU27ynEm5", "reviewer": "Reviewer_y7M1", "summary": "The paper proposes NeuroPath, a biological inspired RAG for knowledge reasoning over multi-hop (multi-document) questions. The paper is a good followup of how thinking is organized into semantic units that are then connected based on semantic traversal towards a goal. A query drives the logical connections the brain follows to derive the answer. Based on this, the paper extends earlier works short-comings 1)  topological (structural) aggregation of subgraph in PathRAG/LightRAG 2) Page rank based subgraph selections in HippoRAG, which are noisy and not as semantically aligned to the goal target. The proposed NeuroPath achieves this with expanding the search from seed nodes to the target through semantic reasoning paths using prompt-based LLMs and retrieval in this iterative (multi-step) RAG framework. \n\nThey also compare against topological baselines (HippoRAG, PathRAG, LightRAG) and iterative based retrieval baselines (Iter-RAG, and IterCoT) covering the full space of methods relevant to the comparison here. Their work provides two strong retrievers (Contriever and BGE-m3) along with 4 LLM models of varying sizes, making the study comprehensive.", "questions": "None", "limitations": "yes", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 4, "originality": 4, "strengths_and_weaknesses": "Strengths:\n1. **Motivation and contextualization with the latest research in RAG**: The neurologically inspired framework is inspiring, and it is contextualized very effectively with the recent advances on RAG for multi-document reasoning. It provides a valuable solution to the recent research short-comings 1)  topological (structural) aggregation of subgraph in PathRAG/LightRAG 2) Page rank based subgraph selections in HippoRAG, which are noisy and not as semantically aligned to the goal target. The proposed NeuroPath achieves this with expanding the search from seed nodes to the target through semantic reasoning paths using prompt-based LLMs and retrieval in this iterative (multi-step) RAG framework, with properly motivated and analyzed cases (e.g. appx F.3).\n2.  **Experimental evaluations and analysis**: The evaluations are comprehensive and robust. They compare against topological baselines (HippoRAG, PathRAG, LightRAG) and iterative based retrieval baselines (Iter-RAG, and IterCoT) covering the full space of methods relevant to the comparison here. Their work provides two strong retrievers (Contriever and BGE-m3) along with 4 LLM models of varying sizes, making the study comprehensive. The evaluation and analysis of the failures are insightful and make it clear how different methods behave rather than relying solely on numerical results (including retrieval recall, and EM/F1).\n3. **Reproducibility** - Clear evaluations, details of the algorithm in the appendix and prompts, make it easily reproducible.\n\nWeakness:\n1. **Graph construction offline scale**: IterRet-Gen (Iterative RAG) is strong as a baseline method and with a good retriever and generator, it can expand the context and get the multi-documents needed to derive the answer iteratively. It has lower complexity than the proposed method - which requires Graph Construction (with LLM) to pre-structure all the documents (at a large scale this is costly). The proposed method results outperform IterRet-Gen with some margin, but the graph construction complexity at scale might mitigate the practical benefit over Iter-RAG methods. Yet, the neurologically inspired formulation and contextualization with recent research in RAG, and the detailed evaluations and insights make the proposed paper worthwhile for advancing the field or summarizing these solutions to multi-document RAG reasoning.", "was_revised": true, "final_justification": "Ack the rebuttal. Same review/score as before.", "rebuttal": "Thank you for recognizing the novelty and experimental rigor of NeuroPath. We provide the following responses and perspectives regarding the weakness you pointed out.\n\n***Weakness: Graph construction offline is costly.***\n\nWe agree that offline KG construction incurs upfront cost. However, NeuroPath and other RAG systems are designed for scenarios requiring repeated queries over static corpora. Here, one-time indexing amortizes cost, while online retrieval achieves 22.8% lower token consumption vs. iter-based methods (Appendix G, Page18).\n\nTable 1: Comparison of graph indexing time with graph-based methods. (Page 19)\n\n|                | HippoRAG | HippoRAG 2 | LightRAG | PathRAG | NeuroPath |\n| -------------- | -------- | ---------- | -------- | ------- | --------- |\n| Time (minutes) | 64       | 62         | 52       | 72      | 39        |\n\nOur KG construction is 1.5× faster than graph/path-based RAG baselines (39 min vs. 52–72 min for HippoRAG/LightRAG/PathRAG on 2WikiMultihopQA, Table 11, Page 19) through:\n\n1. Single-pass KG extraction: LLM generates entities and relations per document in one call (vs. multi-stage pipelines in baselines).\n2. Embedding-based coreference resolution and similarity computation: Avoids LLM calls.\n\nAlthough the graph construction stage inevitably incurs some cost, experiments show that NeuroPath brings significant gains over graph-based and iter-based methods under different LLMs and embedding models, demonstrating the effectiveness of our framework.\n\nIt is worth noting that finer-grained path-based retrieval is less sensitive to the performance of the embedding model (Section 4.2). Even small embedding models (e.g., Contriever) can effectively capture path semantics. In contrast, document-level retrieval in iter-based methods is highly sensitive to embedding model performance, with up to a 20% performance gap observed across different embedding models on the 2WikiMultiHopQA dataset (Table 1, Page 7).\n\nAdditionally, we further validated that NeuroPath can be extended beyond multi-hop retrieval to other tasks:\n\n1. Standard retrieval tasks (PopQA and NaturalQuestions)\n\n   We validated the generalizability of NeuroPath on standard retrieval tasks in Appendix D (Page 15). The results demonstrate that our NeuroPath still outperforms graph-based methods (achieving 15% and 7% improvements over HippoRAG2 in recall@2 and recall@5, respectively) and remains highly competitive even when compared to iter-based methods like Iter-RetGen (with 14% and 6% gains on the PopQA dataset, respectively). \n\n   Table 2: Retrieval performance on standard retrieval tasks. (Page 15)\n\n   |               | PopQA    |          | NQ       |          | Avg.     |          |\n   | ------------- | -------- | -------- | -------- | -------- | -------- | -------- |\n   | Method        | R@2      | R@5      | R@2      | R@5      | R@2      | R@5      |\n   | Contriever    | 27.0     | 43.2     | 29.1     | 54.6     | 28.1     | 48.9     |\n   | Iter-RetGen   | 35.4     | 46.6     | **40.9** | **68.6** | 38.1\\*   | **57.6** |\n   | IRCoT         | 30.2     | 38.2     | 35.1     | 55.8     | 32.6     | 47.0     |\n   | HippoRAG      | 36.5\\*   | **52.7** | 22.5     | 45.7     | 29.5     | 49.2     |\n   | HippoRAG 2    | 34.2     | 47.4     | 32.1     | 59.5     | 33.2     | 53.4     |\n   | **NeuroPath** | **40.5** | 49.3\\*   | 35.8\\*   | 65.2\\*   | **38.2** | 57.2\\*   |\n\n2. More complex, uncertain retrieval scenarios such as sense-making tasks (NarrativeQA). \n\n   We follow the experimental setup and baselines from the HippoRAG 2 paper, using NV-Embed-v2 as the embedding model. The results show that our method surprisingly outperforms the other baselines. Although our method was not originally designed for sense-making tasks, we seem to be able to demonstrate scalability. We attribute this to LLM actively choosing possible paths during path tracking when facing ambiguous or uncertain context.\n\n   Table 3: QA performance on NarrativeQA (sense-making tasks)\n\n   | Method           | NarrativeQA (F1 Score) |\n   | ---------------- | ---------------------- |\n   | NV-Embed-v2 (7B) | 24.2                   |\n   | GraphRAG         | 20.9                   |\n   | LightRAG         | 9.0                    |\n   | HippoRAG         | 16.1                   |\n   | HippoRAG 2       | 25.2                   |\n   | **NeuroPath**    | **32.7**               |\n\nWe appreciate your recognition of these strengths and hope this clarifies the balance between efficiency, scalability, and performance."}, {"review_id": "PhiLZ0boei", "reviewer": "Reviewer_hmsH", "summary": "The paper introduces NeuroPath, a RAG framework inspired by the path navigation mechanism in neurobiology, specifically hippocampal place cells. The framework aims to improve multi-hop QA by dynamically tracking semantic paths within a KG, enhancing semantic coherence and reducing noise. The method consists of two main components: Dynamic Path Tracking, which selectively filters and expands semantic paths, and Post-retrieval Completion, which refines paths through additional retrieval after the initial path tracking. NeuroPath is shown to outperform existing RAG frameworks, achieving state-of-the-art results on multiple QA datasets with notable improvements in retrieval performance and efficiency.", "questions": "1. Could the authors provide further insights into the scalability of NeuroPath for more diverse and large-scale datasets, especially those with noisy or incomplete knowledge graphs?", "limitations": "Yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "### **Strengths**:\n\n1. The use of neurobiological inspiration (specifically hippocampal place cells) in designing the path-tracking mechanism is a creative and original approach. This neurobiology-inspired method introduces a dynamic, goal-directed way to improve multi-hop reasoning, addressing the issues of noise and semantic incoherence prevalent in traditional RAG methods.\n\n2. The experiments demonstrate that NeuroPath outperforms SOTA models on several multi-hop QA datasets (MuSiQue, 2Wiki MultiHopQA, HotpotQA), achieving substantial improvements in recall and retrieval accuracy, while also reducing token consumption by 22.8%.\n\n3. The paper provides a detailed description of the methodology, including the rationale behind the neurobiological inspiration, the steps of path tracking, and post-retrieval completion, making the framework easy to understand.\n\n### **Weaknesses:**\n\n1. While the framework performs well across different LLMs and models, its scalability and generalization to much larger, more diverse datasets (e.g., noisy, incomplete, or domain-specific data) could be explored further.\n\n2. The reliance on coreference resolution for entity linking and path expansion could lead to errors when dealing with less structured or unclean data, which the authors do not fully address in the paper.\n\n3. While the method reduces token consumption compared to iter-based methods, it may still be resource-intensive due to the dynamic nature of the path-tracking and post-retrieval steps, potentially limiting its application in large-scale, real-time systems.\n\n4. My main concern of the usefulness of this method is the reliance on high-quality KGs, which limits the value of the method in real-world scenarios.", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for appreciating the clear motivation, detailed description, and substantial improvements of NeuroPath. Below, we will address the weaknesses you point out.\n\n***Weakness 1: Should explore the framework's scalability and generalization***\n\n1. We have already evaluated the scalability of NeuroPath on standard retrieval tasks (PopQA and NaturalQuestions) in Appendix D (Page 15). The results demonstrate that our NeuroPath still outperforms graph-based methods and remains highly competitive compared to iter-based methods.\n\n   Table 1: Retrieval performance on standard retrieval tasks. (Page 15)\n\n   || PopQA||NQ|| Avg.||\n   | - | - | - | - | - | - | - |\n   | Method | R@2 | R@5  | R@2 | R@5| R@2 | R@5 |\n   | Contriever| 27.0 | 43.2 | 29.1 | 54.6 | 28.1| 48.9  |\n   | Iter-RetGen   | 35.4| 46.6| **40.9** | **68.6**  | 38.1 | **57.6** |\n   | IRCoT | 30.2 | 38.2 | 35.1 | 55.8| 32.6| 47.0|\n   | HippoRAG | 36.5\\* | **52.7** | 22.5 | 45.7 | 29.5 | 49.2 |\n   | HippoRAG 2 | 34.2 | 47.4| 32.1| 59.5  | 33.2 | 53.4|\n   | **NeuroPath** | **40.5**  | 49.3\\* | 35.8\\* | 65.2\\* | **38.2** | 57.2\\* |\n\n\n\n2. We conducted additional experiments on the MultiHop-RAG benchmark, a challenging dataset specifically designed for evaluating multi-hop retrieval and reasoning. MultiHop-RAG contains 2,556 questions requiring 2 to 4 hops of reasoning. It features longer candidate documents and noisier supporting evidence.\n\n   Table 2: Comparison of the average number of tokens per document.\n\n   || MuSiQue | 2Wiki | HotpotQA | MultiHop-RAG|\n   | - | - | - | - | - |\n   | Average tokens per document |110|105|128| 2,289 (chunk to 512) |\n\n   We selected 1,000 questions for retrieval, and the results are as follows:\n\n   Table 3: Retrieval results on MultiHop-RAG.\n\n   || MultiHop-RAG (Contriever) | MultiHop-RAG (BGE) |\n   | - | - | - |\n   | Method| R@2 / R@5| R@2 / R@5|\n   | Contriever| 9.5 / 20.4 | - |\n   | BGE-M3| - | 25.4 / 44.7 |\n   | Iter-RetGen| 17.8 / 30.9| **28.8** / 45.8 |\n   | HippoRAG 2| 15.7 / 29.5| 21.4 / 40.8 |\n   | **NeuroPath** | **23.7** / **39.0** | 26.1 / **46.8**|\n\n   Our method outperforms typical baselines, especially when using the smaller Contriever. Notably, other methods still exhibit sensitivity to the choice of embedding model, whereas NeuroPath shows relatively stable performance differences between Contriever and BGE-M3 (consistent with the conclusions in Section 4.2, Page 6).\n\n\n\n3. In addition to multi-hop and standard tasks, we also evaluated the scalability for the sense-making task (the ability to interpret larger, more complex, or uncertain context). We followed the setup and baselines from the HippoRAG 2, using the NarrativeQA dataset. This dataset requires understanding and reasoning over large-scale narratives, such as long novels.\n\n   Table 4: QA performance on NarrativeQA.\n\n   | Method| NarrativeQA (F1 Score) |\n   | - | - |\n   | NV-Embed-v2 (7B) | 24.2|\n   | GraphRAG | 20.9|\n   | LightRAG | 9.0 |\n   | HippoRAG | 16.1|\n   | HippoRAG 2| 25.2 |\n   | **NeuroPath** | **32.7**|\n\nThe results show that NeuroPath can be extended to more complex or uncertain tasks. We attribute this capability to the dynamic path tracking process of the LLM, where the model can autonomously choose the most likely path under uncertain context.\n\n\n\n***Weakness 2: Reliance on coreference resolution may cause errors on less structured or unclean data.***\n\nWhile fundamentally improving graph construction is not the goal of our work, we mitigate coreference-related errors at runtime.\n\nThe quality of generated KG data depends on the LLM's performance. While existing methods depend on multi-step LLM extraction, we simplify this with one-pass KG extraction and efficient embedding-based coreference resolution. \n\nAlthough we simplified the process to achieve faster indexing, we still adopt a two-stage solution to address errors in coreference linkage:\n\n1. Path tracking: For nodes with the same surface form but different referents, each path expansion step collects a series of triple fragments that carry relational context. The LLM uses these fragments to disambiguate and filter irrelevant coreferences.\n2. Post-retrieval completion: If seed nodes or their coreference sets fail to capture key nodes during expansion, the final path may lack information. We address this with an additional embedding-based retrieval step using the output of the last expansion, which includes requests for missing information. (Ablation results: Section 4.4, Page 8.)\n\nDespite the simplified KG construction, our NeuroPath still outperforms other graph-based baselines, demonstrating the strength of our retrieval framework.\n\nWe also verify whether LLMs can benefit from identifying phrases in the coreference set by changing the number k of expanded coreferent phrases (0, 1, 5).\n\nTable 5: Impact of the number k of expanded coreferent phrases on retrieval.\n\n|| MuSiQue|| 2Wiki || HotpotQA ||\n|-|-|-|-|-|-|-|\n|      | R@2     | R@5  | R@2   | R@5  | R@2      | R@5  |\n| k=5  | 48.0    | 62.7 | 77.2  | 92.5 | 75.6     | 90.4 |\n| k=1  | 47.8    | 63.7 | 76.2  | 91.3 | 74.0     | 89.2 |\n| k=0  | 47.2    | 63.1 | 75.5  | 90.6 | 73.3     | 88.7 |\n\nIn our entity extraction prompts (Figure 12, Page 22), we explicitly instruct the LLM to use noun phrases with explicitly referents. As a result, it tends to extract consistent phrases for the same entity, which explains why the performance gains from k = 0, 1, and 5 are relatively small. Nonetheless, the results show that using coreference sets still brings improvements, indicating that the LLM can recognize and leverage them.\n\n \n\n***Weakness 3: Method may still be resource-intensive due to the dynamic nature of the path-tracking and post-retrieval steps.***\n\nThis insightful comment aligns with our goal of balancing performance and cost. We respectfully clarify that the main source of resource consumption (i.e., LLM calls) lies in the path tracking stage. The post-retrieval completion stage simply extracts the output from the final step of path tracking and performs fast vector retrieval. Its purpose is to compensate for any missing information that may have been overlooked during the path tracking process. In fact, it was our intentional resource control that resulted in a 22.8% reduction in token consumption over the iter-based methods.\n\nIn Dynamic Path Tracking stage, we use 2 strategies to control the token cost:\n\n1. Candidate paths are pruned before being fed into the LLM. We use an embedding model to compute the similarity between each candidate path and the query, retaining only the top 30 paths. (default setting)\n2. The LLM is used to further select valid paths, in order to prevent the expansion of irrelevant paths in the next step (if any).\n\nTable 6: Ablation study of the pruning strategy. (Section 4.4, Page 8)\n\n|| MuSiQue ||| 2Wiki ||| HotpotQA |||\n|-|-|-|-|-|-|-|-|-|-|\n|| R@2| R@5| Token (k)|R@2|R@5|Token (k)| R@2|R@5|Token (k)|\n| w/o pruning    | 48.7    | 63.8 | 2891| 76.8  | 92.0 | 1883              | 75.7     | 90.8 | 2504|\n| default (p=30) | 48.0 | 62.7 | $\\downarrow$45.7% | 77.2  | 92.5 | $\\downarrow$8.4%  | 75.9 | 90.1 | $\\downarrow$39.8% |\n| p=20           | 47.3 | 62.2 | $\\downarrow$52.6% | 76.5  | 90.8 | $\\downarrow$17.3% | 74.9 | 89.4 | $\\downarrow$47.0% |\n\nOur ablation studies (Section 4.4, Page 8) systematically evaluate the impact of path pruning on computational efficiency. Specifically, we compare three configurations: 1) no pruning, 2) retaining top-20 paths, and 3) retaining top-30 paths (default). The results demonstrate that:\n\n1. Pruning to 20 paths reduces token consumption by 17.3%-52.6%\n2. Our default setting (30 paths) achieves a 8.4%-45.7% reduction in token usage\n    Notably, both pruning configurations maintain comparable recall performance, with the 30-path setting even showing slight improvements in some cases.\n\nIn real-world applications, the maximum number of expansion hops and pruning thresholds can be flexibly adjusted. \n\nAs noted in Appendix H (Page 19), LLM calls are the runtime bottleneck. Nevertheless, our method demonstrates the feasibility of leveraging LLMs for path tracking to address semantic incoherence and noise issues, leading to substantial improvements in retrieval quality for multi-hop queries. It also shows scalability on different tasks, such as standard factual retrieval task and sense-making task. Based on the above, we consider the tradeoff between performance and cost to be reasonable.\n\n\n\n***Weakness 4: The main concern is its reliance on high-quality KGs, which may limit real-world applicability.***\n\nWe appreciate the reviewer’s concern about how KG quality may affect NeuroPath in real-world scenarios. To enhance real-world applicability, we will extend NeuroPath in two directions:\n\n(a) Task-adaptive structured information processing. While the current KG directly reflects raw document content, higher-level tasks (e.g., abstraction or summarization) may require restructuring knowledge granularity. This flexibility will broaden task scalability.\n\n(b) Efficient high-quality path expansion. Building upon optimized KGs, we will explore accelerated path-tracking mechanisms to alleviate LLM bottlenecks while maintaining retrieval precision.\n\n\n\n***Question: Explore the framework's scalability and generalization for more diverse and large-scale datasets.***\n\nAs we detailed in our response to Weakness 1, our framework not only extends to noisier and more challenging multi-hop retrieval tasks, but also generalizes to standard retrieval tasks and more complex, uncertain sense-making tasks. Experimental results demonstrate its strong scalability and performance. We attribute this to the autonomy of the LLM in dynamic tracking."}, {"review_id": "pYMPd32a0C", "reviewer": "Reviewer_GVGa", "summary": "In this work, the authors propose NeuroPath, a retrieval framework that draws inspiration from place cells and their path integration capabilities to improve the performance of current graph-based RAG systems such as HippoRAG, LightRAG and PathRAG on multi-hop retrieval benchmarks. NeuroPath uses a combination of Dynamic Path Tracking and Post-Retrieval Completion in order to accomplish more faithful traversal of an automatically extracted knowledge graph. Dynamic Path Tracking involves using an LLM to decide which paths to continuously expand into across the KG from an initial set of of entities. In contrast, Post-Retrieval Completion filters irrelevant paths, thus avoiding an explosion in the subgraph visited during search. This methodology accomplishes strong performance over other advanced graph-based RAG methods and iterative retrieval based methods on three multi-hop QA datasets.", "questions": "N/A", "limitations": "Yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 4, "significance": 4, "originality": 3, "strengths_and_weaknesses": "Strengths:\n- Neurobiological inspiration is interesting and insightful\n- Their methodology is well-motivated and the problem they are addressing is important and timely.\n- The approach, although relatively simple, addresses a well known problem with graph-based RAG systems in a principled way.\n- Their discussion is comprehensive, strong ablation studies and case studies demonstrate why and how their method performs well.\n\nWeaknesses:\n- The experimental setting is relatively sound, however, there are two somewhat serious issues:\n1) In the abstract and introduction, the authors explain that their method \"surpasses the state-of-the-art\" but I do not find this statement convincing given that simple large-scale retrieval embeddings are known to perform better than the reported results on the three benchmark datasets (HippoRAG 2). Given that the authors use several 6-9B parameter LLMs for generation, it seems that they could have used large-scale embedding models which are simple and very powerful. \n2) Although strong performance on multi-hop retrieval is important, it would make for a much stronger empirical analysis if other RAG benchmarks were used that test standard retrieval as well as multi-hop.", "was_revised": true, "final_justification": "The authors have addressed all of my concerns. This paper should be accepted.", "rebuttal": "Thank you for appreciation of our motivation, method, and discussions. We address the issues you raised below.\n\n***Issue 1: Missing comparison to strong large-scale embedding models (e.g., baselines in HippoRAG 2).***\n\nOur NeuroPath aims to address the issues of semantic incoherence and noise in graph-based RAG by leveraging a goal-directed, dynamic semantic path tracking mechanism, which is independent of the embedding model size. Experimental results demonstrate that the LLM-driven path tracking mechanism itself brings significant performance gains when compared under the same embedding model conditions (e.g., Contriever and BGE-M3). Moreover, it exhibits scalability across LLMs of different sizes.\n\nWe appreciate the reviewer’s suggestion to conduct experiments with larger-scale embedding models. Following the baselines used in HippoRAG2, we selected three 7B-scale embedding models for evaluation, and set the embedding models for both HippoRAG2 and NeuroPath to NV-Embed-v2. \n\nTable 1: Retrieval results using NV-Embed-v2 as the embedding model and GPT-4o-mini as the LLM for both HippoRAG 2 and NeuroPath.\n\n| Method                      | MuSiQue  |          | 2Wiki    |          | HotpotQA |          |\n| --------------------------- | -------- | -------- | -------- | -------- | -------- | -------- |\n| (use GPT-4o-mini)           | R@2      | R@5      | R@2      | R@5      | R@2      | R@5      |\n| gte-Qwen2-7B-instruct       | 48.2     | 63.6     | 66.7     | 74.8     | 75.9     | 89.9     |\n| GritLM-7B                   | 49.6     | 65.8     | 67.2     | 76.3     | 79.1     | 93.3     |\n| NV-Embed-v2                 | 52.7     | 69.3     | 69.1     | 76.5     | **83.7** | 95.3     |\n| HippoRAG 2 (NV-Embed-v2)    | 53.5     | 74.2     | 74.6     | 90.2     | 80.5     | 95.7     |\n| **NeuroPath (NV-Embed-v2)** | **54.1** | **76.0** | **79.3** | **97.3** | 81.3     | **97.0** |\n\nThe results show that NeuroPath remains at a leading level and achieves significant gains compared to NV-Embed-v2.\n\n\n\nWe also conducted experiments using open-source LLMs Qwen3-8B and Qwen3-4B.\n\nTable 2: Retrieval results after replacing the LLM with Qwen3-8B and Qwen3-4B.\n\n| Method                      | MuSiQue  |          |  2Wiki   |          | HotpotQA |          |\n| :-------------------------- | :------: | :------: | :------: | :------: | :------: | :------: |\n| (use Qwen3-8B)              |   R@2    |   R@5    |   R@2    |   R@5    |   R@2    |   R@5    |\n| HippoRAG 2 (NV-Embed-v2)    |   55.0   |   73.1   |   74.7   |   88.8   |   83.2   |   96.0   |\n| **NeuroPath (NV-Embed-v2)** | **57.7** | **78.2** | **81.5** | **97.5** | **83.6** | **97.0** |\n| (use Qwen3-4B)              |   R@2    |   R@5    |   R@2    |   R@5    |   R@2    |   R@5    |\n| HippoRAG 2 (NV-Embed-v2)    |   55.1   |   73.5   |   74.9   |   88.9   | **83.3** |   96.0   |\n| **NeuroPath (NV-Embed-v2)** | **57.6** | **78.6** | **81.7** | **97.4** |   82.5   | **96.8** |\n\nThe results show that NeuroPath still outperform HippoRAG 2 on small-scale open-source models.\n\nWe believe smaller embedding models better reflect the performance gap between different RAG methods, particularly graph-based methods, and their sensitivity to embedding quality. In our experiments, other methods showed up to 20% gap on 2WikiMultiHopQA when changing embeddings. In contrast, our method remains stable across all three datasets (Section 4.2, Page 6).\n\nAdditionally, Contriever and BGE-M3 require only 1–5 GB of GPU memory to support most tasks, while larger models like NV-Embed-v2 (7B) need up to 26 GB in full precision. In our experiments, even with float16, it still required at least 20 GB, making it impractical for real-world use. We agree that using diverse embedding models helps evaluate robustness, but in future agentic applications, allocating resources to LLMs is likely more beneficial. Our method already achieves substantial gains with lightweight embedding models.\n\n \n\n \n\n***Issue 2: Does the method generalize beyond multi-hop retrieval?***\n\nWe have verified that NeuroPath exhibits strong scalability on both standard retrieval and sense-making tasks: \n\n(i) We have already evaluated the scalability of NeuroPath on standard retrieval tasks (PopQA and NaturalQuestions) in Appendix D (Page 15). The results demonstrate that our NeuroPath still outperforms graph-based methods (achieving 15% and 7% improvements over HippoRAG2 in recall@2 and recall@5, respectively) and remains highly competitive even when compared to iter-based methods like Iter-RetGen (with 14% and 6% gains on the PopQA dataset, respectively). \n\nTable 3: Retrieval performance on standard retrieval tasks. (Page 15)\n\n||PopQA||NQ||Avg.||\n|-|-|-|-|-|-|-|\n|Method|R@2|R@5| R@2| R@5| R@2| R@5|\n|Contriever| 27.0               | 43.2               | 29.1               | 54.6                | 28.1               | 48.9               |\n| Iter-RetGen| 35.4               | 46.6               | **40.9**           | **68.6**            | 38.1 | **57.6**           |\n| IRCoT| 30.2               | 38.2               | 35.1               | 55.8                | 32.6               | 47.0               |\n| HippoRAG| 36.5\\* | **52.7**           | 22.5               | 45.7                | 29.5               | 49.2               |\n| HippoRAG 2| 34.2               | 47.4               | 32.1               | 59.5                | 33.2               | 53.4               |\n| **NeuroPath** | **40.5**           | 49.3\\* | 35.8\\* | 65.2\\* | **38.2**           | 57.2\\* |\n\n(ii) In addition to standard and multi-hop tasks, we also validated the scalability of NeuroPath on the sense-making task (the ability to interpret larger, more complex, or uncertain context). Our experiments followed the setup and baselines from the HippoRAG 2, using the NarrativeQA dataset. This dataset requires understanding and reasoning over large-scale narratives, such as long novels.\n\nTable 4: QA performance on sense-making task.\n\n| Method           | NarrativeQA  (F1 Score) |\n| ---------------- | ----------------------- |\n| NV-Embed-v2 (7B) | 24.2                    |\n| GraphRAG         | 20.9                    |\n| LightRAG         | 9.0                     |\n| HippoRAG         | 16.1                    |\n| HippoRAG 2       | 25.2                    |\n| **NeuroPath**    | **32.7**                |\n\nAlthough not originally designed for sense-making, NeuroPath performs best on NarrativeQA. However, our focus was on factual multi-hop retrieval tasks. Experiments show that NeuroPath can be extended to more complex or uncertain tasks. We attribute this capability to the dynamic tracking process of the LLM, where the model can autonomously choose the most plausible path under uncertain context."}, {"review_id": "pBvfivTjFe", "reviewer": "Reviewer_QcTf", "summary": "The authors propose a method for improving RAG methods' ability to answer multi-hop questions involving dependencies across documents. The authors propose a neurobiology-inspired method. The authors validate the method on three multi-hop benchmarks MuSiQue, 2WikiMultiHopQA, and HotPotQA using 1k questions from each. The authors compare their proposed method against standard RAG, iter-based methods, and graph-based methods (HippoRAG).", "questions": "please see above.", "limitations": "I believe a more explicit discussion of the proposed method's limitations are warranted. As noted above, computational overhead, method complexity, and mixed results on HotpoQA should be discussed and presented in more detail.", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 1, "significance": 2, "originality": 3, "strengths_and_weaknesses": "* Motivation for what structural relevance is and why it's insufficient needs to be clarified in the introduction. I'd recommend including an example of the queries the users have in mind or some definitions with intuition to motivate the approach.\n* Method claims to learn semantic paths that form a knowledge graph. It would be nice to provide additional analysis or evidence to support this claim, even if qualitative. \n* Method involved several steps\n    * 1) Static indexing: build set of entity, relations, documents. Use cosine similarity of embeddings to ensure quality and retain the five most similar entities., 2) Dynamic path tracking, and 3) Post-retrieval completion.\n* I found exposition of the method and its many steps difficult to follow. In particular, the high level descriptions of steps were not easy to translate into algorithmic implementations for me. \n* Similarly, as a researcher with little knowledge of biology and cognitive science, I found the motivation while admirable to be inaccessible. I imagine other readers without the necessary background would find it equally challenging and would benefit from a more accessible exposition. \n* Iter-based methods seem to perform better on HotpotQA in many settings. Can the authors comment on why this may be the case? The proposed method need not outperform existing baselines across all benchmarks to be a worthwhile contribution, but an acknowledgement of the this limitation beyond \"HotpotQA ... has lower knowledge integration requirements\" and discussion of tradeoffs is missing in this current version. \n* For question-answering, the authors call into the question the quality of HotpotQA noting it's prone to shortcut learning. If that's the case, I recommend the authors simply include HotpotQA in the appendix and note this limitation  and instead evaluate other benchmarks of quality. \n* Given the mixed performance on HotpotQA, I also recommend teh authors expand the list of benchmarks to include additional multi-hop reasoning datasets. For example, the original IRCot paper includes several other suggestions https://arxiv.org/abs/2212.10509 \n* Missing baseline Multihop RAG https://arxiv.org/pdf/2401.15391 that seems particularly relevant to compare against (and is well cited). \n* Nice to see the additional study using other model families. \n* Compared to existing methods, how computationally intensive is the proposed method? It seems to involve several computations of similar and refinement that would be important to quantify any computational tradeoffs  compared to existing methods.", "was_revised": true, "final_justification": "I believe this manuscript would benefit from substantial revision to improve its accessibility, particularly for the general community at NeurIPS. In terms of validation, I find the method to invovled multiple complex components must be weighed against the performance gains (that are in some setting mixed). I'd encourage the authors to consider revisions to clarify the need for such complexity and improve the accessbility of the work.", "rebuttal": "Thank you for the detailed and insightful comments. We address the issues you raised below.\n\n***Issue 1: Motivation clarification: structural relevance vs. semantic coherence.***\n\nWe have already provided a detailed query example and textual explanation in Figure 1 (Page 2), using comparisons across different methods to illustrate our motivation. We understand that further clarification of certain concepts can help better convey the motivation.\n\n1. We define **structural relevance** as methods that retrieve graph nodes based on the graph topology (e.g., Personalized PageRank in HippoRAG, subgraph construction in LightRAG). However, such approaches risk retrieving nodes that are structurally close but semantically irrelevant. \n\n2. In contrast, **semantic coherence** requires that the path formed by nodes and edges aligns with the logical intent of the query(e.g.,[Android]->(createdBy)->[Andy Rubin]->(founded)->[Essential Products]->(acquiredBy)->[Nothing] as illustrated in Figure 1 (c)).\n\n \n\n***Issue 2: Lack of analysis or evidence for semantic path learning***\n\n1. The case study (Section 4.3, Pages 7–8) shows example path sequences selected by Neuropath. Compared to HippoRAG and LightRAG, NeuroPath contain richer relational information and show clear logic connections to the query.\n\n2. Figure 10 and Figure 11 (Appendix B, Page 20) provide concrete examples of Dynamic Path Tracking and Post-retrieval Completion\n\n3. The ablation study (Section 4.4, Page 8) compares retrieval performance using only path tracking (i.e., without post-retrieval completion). The table below is based on Table 3 (Page 8) in the main paper and highlights the relative improvement (%) from path tracking alone under the recall@2 metric.\n\nTable 1: Ablation study of the path tracking without post-retrieval completion. (Section 4.4, Page 8)\n\n|| MuSiQue| 2Wiki  | HotpotQA |\n| - | - | - | - |\n| Contriever | 34.8| 46.6 | 57.1|\n| max hop=1  | 35.5 ($\\uparrow$2.0%)  | 61.0 ($\\uparrow$30.9%) | 61.3 ($\\uparrow$7.4%)  |\n| max hop=2  | 41.8 ($\\uparrow$20.1%) | 73.6 ($\\uparrow$57.9%) | 67.5 ($\\uparrow$18.2%) |\n\n \n\n***Issue 3-4：The methods and motivation background are unclear and inaccessible.***\n\n* Methods：\n\nWe describe our framework in detail in the following three aspects:\n\n1. **Algorithm 1 (Appendix B, Page 14)** formalizes workflow with step-by-step pseudocode. \n2. **Figure 3 (Page 4)** visually maps the interplay between static indexing, dynamic path tracking, and post-retrieval completion. Extended examples in **Figure 10-11 (Pages 20-21)** demonstrate concrete LLM outputs during path expansion. **Appendix J** (Page 19) provides detailed prompts.\n3. **Mathematical specifications** (e.g., coreference resolution in Eq.1-2, path concatenation in Eq.4, pruning via Eq.5) anchor abstract concepts to computable operations.\n\nWe argue this multi-angle presentation meets reproducibility standards for complex systems, and we have also released our code and detailed reproduction steps on an anonymous GitHub repository (linked in the abstract).\n\n\n\n* Motivation: \n\nTo aid readers unfamiliar with neuroscience, we summarize the relevant biological mechanisms and their parallels to retrieval, with further details in Appendix A (Page 13).\n\n1. Limitation of current graph-based RAG: Methods like HippoRAG (PageRank-based) and LightRAG (subgraph expansion) construct static topologies, often introducing semantically incoherent or noisy results. In contrast, brain retrieval is dynamic and goal-directed.\n2. In the brain, *place cells* support two key mechanisms: **preplay** for path planning and **replay** for memory consolidation.\n    NeuroPath simulates this via LLM-based **dynamic path tracking** (navigation) and **post-retrieval completion** (consolidation).\n3. Brain/Bio-inspired mechanisms have recently sparked new ideas in the LLM era. Our work aims to shift the RAG paradigm from **“brain-like storage”** (e.g., HippoRAG’s graph-based indexing) to **“brain-like reasoning”**, by enabling dynamic retrieval over structured knowledge graphs.\n\n\n\n***Issue 5-6: Lack of trade-off discussion for the method’s underperformance on HotpotQA.***\n\nThank you for pointing out this limitation. We discuss it from three perspectives: 1. Shortcuts in HotpotQA, 2. Iter-based methods and tradeoffs, and 3. NeuroPath’s tradeoffs.\n\n1. Shortcuts in HotpotQA: Prior work (e.g., HippoRAG [1], MuSiQue [2]) has shown that HotpotQA often allows shallow shortcut-based answering without full multi-hop reasoning. We have analyzed it in Appendix F.3 (Page 17). HippoRAG 2 achieves 9.3% more correct answers not backed by full evidence compared to NeuroPath (Table 10, Page 17). In fact, many HotpotQA questions can be effectively retrieved and answered using naive RAG methods [3], due to several inherent limitations:\n   1. HotpotQA documents are short and dense.\n   2. Most questions are synthetically constructed 2-hop queries with limited dependency complexity [2].\n   3. Prior work [1] shows that distractor documents are often weak, making target passages easier to retrieve.\n\n2. Iter-based methods and tradeoffs: These perform well on HotpotQA by matching dense chunks and refining queries via LLMs. However, they consume 22.8% more tokens (Appendix G) and are more sensitive to embedding models (Table 1, Page 7), resulting in inconsistent performance. \n\n3. NeuroPath’s tradeoffs: Due to the limitations of HotpotQA, NeuroPath may underperform iter-based methods on this dataset. However, its path-based, fine-grained retrieval is better suited for complex multi-hop reasoning (e.g., MuSiQue and 2WikiMultiHopQA). \n\n   It is also more robust to embedding model changes due to shorter and cleaner path token. It is worth noting that we evaluate the scalability of NeuroPath on other tasks:\n\n   1. We demonstrate that our NeuroPath can retain the impressive performance of simple retrieval on the standard retrieval datasets PopQA and NaturalQuestions (Appendix D, Page 15).\n   2. We also extended our evaluation to the more complex and uncertain sense-making task. (follow the settings and baselines in HippoRAG 2 paper) Our test results on the NarrativeQA dataset are as follows, and NeuroPath still maintains good performance.\n\nTable 2: QA performance on sense-making task.\n\n|Method| NarrativeQA (F1 Score) |\n| - | - |\n| NV-Embed-v2 (7B) | 24.2 |\n| GraphRAG         | 20.9 |\n| LightRAG         | 9.0  |\n| HippoRAG         | 16.1 |\n| HippoRAG 2       | 25.2 |\n| **NeuroPath**    | **32.7**  |\n\nGiven HotpotQA’s known limitations and NeuroPath’s broader scalability, we believe this trade-off is acceptable and will elaborate further in revised Appendix F.3.\n\n \n\n***Issue 7-8：Suggest using higher-quality datasets such as MultiHop-RAG.***\n\nThank you for the suggestion. We conducted additional experiments on this dataset.\n\nSetup: We selected 1,000 questions and split the documents into 512-token chunks. Contriever and BGE were used as embedding model, respectively.\n\nTable 3: Retrieval results on MultiHop-RAG.\n\n|| **MultiHop-RAG (Contriever)** | **MultiHop-RAG (BGE)** |\n| - | - | - |\n|Method| R@2 / R@5 | R@2 / R@5 |\n| Contriever | 9.5 / 20.4  | -                      |\n| BGE-M3   | -  | 25.4 / 44.7  |\n| Iter-RetGen   | 17.8 / 30.9     | **28.8** / 45.8 |\n| HippoRAG 2    | 15.7 / 29.5                   | 21.4 / 40.8            |\n| **NeuroPath** | **23.7** / **39.0**           | 26.1 / **46.8** |\n\n Results show that NeuroPath remains in the leading position. It is worth noting that other baselines show high sensitivity to the performance of the embedding model, whereas NeuroPath remains more robust in comparison.\n\n\n\n***Issue 9：The computational cost needs clearer quantification and comparison to existing methods.***\n\nWe rigorously evaluated the cost and efficiency of NeuroPath against baselines (Appendix G, Page 18-19), and our results demonstrate a favorable tradeoff between overhead and performance gains. \n\nTable 4: Comparison of graph indexing time with graph-based methods. (Page 19)\n\n|                | HippoRAG | HippoRAG 2 | LightRAG | PathRAG | NeuroPath |\n| - | - | - | - | - | - |\n| Time (minutes) | 64       | 62         | 52       | 72      | 39        |\n\nTable 5: Comparison of average retrieval time per question with iter-based methods. (Page 19)\n\n|                | Iter-RetGen | IRCoT | NeuroPath |\n| - | - | - | - |\n| Time (seconds) | 5.6         | 4.7   | 6.7       |\n\n1. NeuroPath’s indexing is 1.5× faster than graph/path-based methods due to single-pass KG extraction. It reduces token usage by 31.1% on average (Appendix G, Figure 9).\n\n2. NeuroPath’s retrieval is 1~2s slower per query than iter-based methods, this is mainly due to the additional query processing. Path pruning allows embeddings to be computed in parallel on the GPU, with an average runtime of just 0.2 seconds per query. By calling LLM twice for each query, our token cost is reduced by 22.8% (Appendix G, Figure 9). \n\n\n***Limitation: Need more discussion of the proposed method's limitations and cost.***\n\nAs we detailed in our response to Issues 5-9, we discussed the limitations of NeuroPath on HotpotQA, as well as the trade-offs between performance and scalability. We further validated the superiority of our method on a more challenging datasets MultiHop-RAG. Finally, we compared the computational cost of different methods and clarified the source of our method’s complexity.\n\n[1] HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Models\n\n[2] MuSiQue: Multihop Questions via Single-hop Question Composition\n\n[3] Compositional Questions Do Not Necessitate Multi-hop Reasoning"}]

Author rebuttal:

Meta-review: The paper introduces NeuroPath, a biologically inspired retrieval-augmented generation (RAG) framework for multi-hop (multi-document) question answering. The approach is motivated by how human reasoning organizes knowledge into semantic units and connects them through goal-directed semantic traversal. A query guides the construction of logical connections, mimicking how the brain derives answers.

Building on this intuition, NeuroPath addresses the limitations of prior work: (1) the purely topological subgraph aggregation used in PathRAG and LightRAG, and (2) the PageRank-based subgraph selection in HippoRAG, which is often noisy and less semantically aligned with the query. Instead, NeuroPath expands the search from seed nodes toward the target via semantic reasoning paths, implemented through prompt-driven LLMs and iterative retrieval in a multi-step RAG framework.

The evaluation compares NeuroPath against both topological baselines (HippoRAG, PathRAG, LightRAG) and iterative retrieval baselines (Iter-RAG, IterCoT), covering the full spectrum of relevant methods. The study is made comprehensive by combining two strong retrievers (Contriever and BGE-m3) with four LLMs of different scales.

In the rebuttal, the authors have provided comprehensive experiments to alleviate the reviewers' concerns. Three reviewers present positive ratings on the paper; the other reviewer gave a score of 3. The negative reviewer did not provide further feedback after the authors' responses, and I believe the weak points can be easily solved in the final version. I tend to recommend acceptance.
Decision: Accept (poster)