Title: NeuroPath: Neurobiology-Inspired Path Tracking and Reflection for Semantically Coherent Retrieval
Venue: NeurIPS 2025
Accepted: yes
Keywords: Retrieval-Augmented Generation

Large Language Models

Multi-hop Question Answering

Knowledge Graphs
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).


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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).\n\nSTRENGTHS AND WEAKNESSES:\n* 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.\n\nQUESTIONS:\nplease see above.\n\nLIMITATIONS:\nI 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.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 1, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-20"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nIn 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\nN/A\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 4, "IMPACT": 4, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\n### **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.\n\nQUESTIONS:\n1. 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?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-05"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\nNone\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 6, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-07-07"}