BrowseNet: Graph-Based Associative Memory for Contextual Information Retrieval
Authors: PAVAN KUMAR S
Kiran Kumar Nakka
C Vamshi Krishna Reddy
Divyateja Pasupuleti
Prakhar Agarwal
Harpinder Jot Singh
Anshu Avinash
Nirav Pravinbhai Bhatt
Venue: ICLR 2026 Poster (2026)
Keywords: retrieval augmented generation
graph-of-chunks
continual learning
large language models
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: graph based method for information retrieval task that requires associative memory

Abstract: Associative memory systems face significant challenges in efficiently retrieving semantically related information from large document collections, particularly when queries require traversing complex relationships between concepts. Traditional retrieval-augmented generation (RAG) approaches often struggle to capture intricate associative patterns and relationships embedded within textual data. To address this limitation, we propose BrowseNet, a novel associative memory framework that leverages query-specific subgraph exploration within a named-entity-based graph for enhanced information retrieval. Our method transforms unstructured text into a graph-of-chunks representation, where nodes encode document chunks with semantic embeddings and edges capture lexical relationships between content segments. By dynamically traversing the graph-of-chunks based on query characteristics, BrowseNet emulates content-addressable memory systems that enable efficient pattern matching and associative recall. The framework incorporates both structural similarity derived from lexical relationships and semantic similarity based on embedding representations to optimize retrieval performance. We evaluate BrowseNet against established RAG baselines and state-of-the-art (SOTA) pipelines using publicly available datasets that require associative reasoning across multiple information sources. Experimental results demonstrate that BrowseNet achieves SOTA performance in exact match score over both the graph-based RAG approaches and the dense retrieval methods. The two-pronged approach combining structural graph traversal with semantic embeddings enables more effective associative memory retrieval, particularly for queries requiring the integration of disparate but related information. The code and datasets are open-sourced at: https://github.com/bisect-group/BrowseNet

Published as a conference paper at ICLR 2026
BROWSENET: GRAPH-BASED ASSOCIATIVE MEMORY FOR CONTEXTUAL INFORMATION RETRIEVAL
Pavan Kumar S Kiran Kumar Nakka", C Vamshi Krishna Reddy, Nirav P Bhatt Department of Data Science and Artificial Intelligence Indian Institute of Technology Madras, India (spavaniitae tm, me20b117, 12, piravbhatt iell.iitm www.immmmm.. an..ac....
Divyateja Pasupuleti, Prakhar Agarwal, Harpinder Jot Singh, Anshu Avinash
DevRev, India {di (divyateja. [disyateja..asuuuleti,, agarhar. harpinder. mnsh..vvinashh@ddeere
ABSTRACT
Associative memory systems face significant challenges in efficiently retrieving mantically related information from large document collections, particularly when queries require traversing complex relationships between concepts. Traditional retrieval- -uggeeted generation (RAG) approaches often struggle to capture intri- associative patterns and relationships embedded within textual data. To address this limitation, we propose BrowseNet, a novel associative memory framework that leverages query-specific subgraph exploration within Gamed-entity-bue bbsed graph for enhanced information retrieval. Our method transforms unstructured text into a graph-of-chunss representation, where nodes encode document chunks with semantic embeddings and edges capture lexical relationships between content segments. By dynamically traversing the graph-of-chune off-chunkssss based on query charac- teristics, BrowseNet emulates Content-addressall efficient pattern matching and associative recall. The framework incorporates both structural similarity derived from lexical relationships and semantic similarity based on embedding representations to optimize retricval performance. We eval- uate BrowseNet against established RAG baselines and state-of-the -hetthrrrt (SOTA) pipelines using publicly available datasets that require associative reasoning across multiple information sources. Experimental results demonstrate that BrowseNet achieves SOTA performance in exact match score over both the graph-based RAG approaches and the dense retrieval methods. The two-pronged approach combin- ing structural graph traversal with semantic embeddings enables more effective associative memory retrieval, particularly for queries requiring the integration of disparate but related information. The code and datasets are open-sourced at: http://gtttubbooom///ccggggg///u
memory systems that enable
INTRODUCTION
The emergence of Large Language Models (LLMs) such GPT-5 (OpenAL.) 2025b Achiam et al., 2023), Gemini (Google.) 2025 Reid et al. 2024), and LLaMA (Dubey et al. (2224) has marked a transformative in artificial intelligence. These models excel at pattern recognition and coherent text generation, but face inherent limitations in incorporating new information without costly retraining. This challenge has motivated research into frameworks that allow LLMs to update and associate knowledge in more flexible, human-like ways. Retrieval Retrieval-Augmented Generation (RAG) (Lewis et al.,
2020 Gao et al. 2023) represents key advance in this direction. By separating knowledge storage from reasoning, RAG systems enable models to access external repositories that can be updated independently of the model parameters. This modular design improves adaptability and scalability, yet current RAG pipelines often focus on retrieving documents based on literal similarity, overlooking
"These authors contributed equally to this work.
1 "orresponding author

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richer associative relationships. As a result, they struggle with queries that require reasoning across multiple contexts or integrating disparate pieces of information.
Associative memory offers natural solution to this limitation. In human cognition, associations between concepts enable flexible recall that extends beyond surface similarity, allowing connections across contexts and domains (Suzuki 2005 Yao al. 2023). These bidirectional associations form semantic networks that facilitate knowledge transfer and generalization, capabilities that remain challenging for current AI systems. Incorporating such associative mechanisms into retrieval systems is therefore a crucial step toward more robust memory architectures for LLMs (Wu et al. 2025). Multi- hop question answering (MHQA) exemplifies this challenge. Standard RAG approaches typically retrieve isolated chunks without modeling their interconnections, which limits their effectiveness for multi-step reasoning. To compensate, existing methods rely on iterative prompting strategies that require multiple interactions with LLMs (Trivedi et al.) 2022a Wei et al. 2022] Yao et al.] 2023 Wang et al. 2024). While effective, these approaches increase latency and inference costs (Gutiérrez
al. 2024
To address these gaps, we propose BrowseNet, a graph-based associative memory framework that unifies lexical and semantic retrieval approaches. BrowseNet transforms unstructured text into lexically connected graph-off-chnkee where edges capture entity co-occurrence and syntactic relations, and nodes enriched with semantic embeddings. By framing MHQA as a traversal problem, BrowseNet constructs query-subgraphs that reflect the structural and semantic requirements of complex questions (refer to Fig. (a)). This approach enables the system to link decomposed single-hop queries into coherent reasoning chains, retrieving information more efficiently and effectively than conventional retrievers.
graph
The contributions of this work are as follows: (1) BrowseNet dynamically adapts traversal paths through the graph-of-chunks according to the query's structural and semantic features, (2) The framework integrates lexical and semantic relationships, allowing more nuanced and context-aware retrieval than singic-modality methods, (3) Retrieval achieved with a single LLM interaction, guided by pre-gend generated decomposed queries, reducing cost and latency, and (4) Empirical evaluation shows that BrowseNet achieves tfte-offthhe-art performance in multi-hop question answering, outperforming both dense retrievers and graph-based RAG systems.
2 RELATED WORKS
Retrieval Augmented generation (RAG): RAG was introduced by Lewis et al. (020) to overcome the limitations of traditional fine-tuning for LLMss By enabling dynamic access to external knowledge, RAG improves factual accuracy and adaptability. Its framework comprises three stages: Indexing, where documents or document chunks are encoded and stored in vector database; Retrieval, where relevant text chunks are fetched; and Generation, where the LLM produces responses using an augmented prompt Gao et al. (2023). BrowseNet departs from this pipeline every stage. In indexing, it constructs a graph-of- offphh-o--chuks that integrates both lexical relationships and semantic embeddings. In retrieval, instead of isolated chunks, BrowseNet extracts a query-specifee subgraph that preserves reasoning dependencies among information units. Finally, in generation, the augmented prompt incorporates decomposed sub-queries, enabling the LLM to perform structured reasoning over the retrieved content.
Graph Informed Retrieval Augmented Generation: Naive RAG pipelines often fall short when queries require integrating information across multiple documents. To address this, several graph-based extensions have been proposed. GraphRAG (Edge et al. 2024) constructs hierarchical knowledge graphs (KG) to improve reasoning over complex relationships. RAPTOR (Sarthi et al.) 2024) employs recursive clustering and summarization to build tree-structured document represen- tations that support multi-level retrieval. LightRAG (Guo et al., 20224) enhances text indexing with graph structures and introduces dual-level retrieval mechanism for greater efficiency and contextual accuracy. While these methods improve cross-context reasoning. they rely heavily on LLMs during indexing to generate expand the retrieval corpus. This reliance increases costs and introduces noise from LLM- -eM----eetttedd text. In contrast, BrowseNet minimizes such dependence: generative -LMMs are used only for graph link inference during indexing and for query decomposition during retrieval. Our best- performing setup requires no LLM involvement in offline indexing, making it both cost-efficient and less prone to noise.
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Brain-Inspired RAG: HippoRAG (Gutiérrez et al. 2024) and HippoRAG (Gutiérrez al. 2025) advance RAG by introducing brai--isspired mechanisms that emulate associative memory for improved integration of retrieved knowledge. HippoRAG 2 currently achieves state-of-the-ar performance in MHQA. However, these approaches require both named entity recognition (NER) and relation extraction (RE) for KG construction. In contrast, BrowseNet requires only NER, simplifying the pipeline while maintaining high retrieval effectiveness.
3 METHODOLOGY
The overall workflow of the proposed approach, BrowseNet, is illustrated in Fig. (b). is structured into three phases: (1) Graph-of Graph-of-chunks construction, (2) Context retrieval, and (3) Answer generation.
Q1
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Q2
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fire, Tasto or The Heart
127
Nede Initiater
Imads
Q3
sntrievee
LLM generator
membor
culled
Query
Quary-utgrraph
Qugry-h
Indexing
Retrieval
Generation
Figure a) Decomposition of the multi-hop query: The multi-hop queries are decomposed into single-hop queries, structurally linked according answer dependencies. b) Overview of the BrowseNet workflow: The indexing phase includes chunking, embedding encoding, and keyword extraction to construct the graph-of-chunks. During retrieval, BrowseNet employs LLMs to extract the query-subgraph and identifies structurally similar and semantically relevant subgraphs within the traph-of-chunke Finally, the generation phase leverages the retrieved subgraphs and queries to generate answers.
3.1 GRAPII-OF-CIUNSS CONSTRUCTION
Let G = -V, E) graph-of-chunks constructed from corpus documents D, where denotes set of nodes corresponding passages (or chunks) of documents, and E represents the set of edges between these nodes. Each node E is associated with three key attributes: a unique index that identifies the passage, the text of the passage along with its title, and semantic vector that captures its contextual meaning. The NV-Embed-v2 model (Lee et al. 2024) is used to encode the entire corpus into vector embeddings. Let M denote the embedding of the chunk c. Also, ablations are done on the encoders, GTE-Qwen2 (7B) eL al. 2023) and Granite- Miiiee-125---nngiii (Awasthy) al. 2025) (refer Section 5.4). An edge eig- E exists between two nodes (c,, Cj E V) if they share a common or synonymous entity, thereby capturing lexical relationships between passages. The construction of the graph-of-chunks involves
main steps: 1) Named Entity Recognition (NER): Identifying named entities within each passage, and 2) Entity Linking: Detecting synonymous or semantically related entities establish additional edges.
NER is performed using GLiNER (a BERT-based model) (Zaratiana et al. 2023), which supports zero-shot entity extraction based on provided label sets. We observed that results are largely consis- tent when using generative models based on GPT-like architectures (e.g., GPT-40 (OpenAI. (025a) and Claude-3.7-Somme defined labels as input. Given that the benchmark datasets used are general-purpose rather than domain- speecii, we adopt broad category labels such as event, facilities, language, location, money, nationality, religious, political, organization, person, product, work of art, occupation, time, ordi- nal, and date. For GPT-like models, a one-shot demonstration is used for entity extraction (refer Appendix for prompts). All extracted entities are post-processed retain only alphanumeric
(Anthropic, (024)) in place GLINER. For GLiNER, NER requires pre-
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characters and spaces. Entity linking is performed using the ColBERTv2 model (Santhanam et al. 2021] to identify synonymous entities (e.g.. TV host and TV presenter). Entities extracted from the NER step are imput into ColBERTv2 to compute pairwise similarity scores. Numerical entities and dates are excluded from this step as they are less informative for establishing semantic relationships. Pairs of entities with cosine similarity score greater than 0.9 are considered synonymous (refer to Section 5.4 for ablation studies on other thresholds). This allows passages that contain equivalent entities to be connected in the graph, enriching the structure. A snippet of the graph-of-chunks with seven nodes from the 2 2WikiMuliHooQAA corpus is shown in Fig. 2 in Appendix.
3.2 CONTEXT RETRIEVAL
Context retrieval for a given question consists of two essential steps: 1) Query-subgraph extraction, and 2) Graph-of-chunks traversal.
3.2.1 QUERY-SUBGRAPH EXTRACTION
Each multi-hop query, Qorig can be decomposed into a sequence of single-hop queries, where each single-hop query builds upon the answer to the previous one (Fig. D. We model this multi-hop query as a directed graph, referred to as the query-subgraph, where nodes correspond to individual single-hop queries, and directed edges represent the dependency between them, linking queries through their intermediate answers. This directed subgraph has to be acyclic and can have more than one connected component (Example question used Fig. (b) has two connected component), reflecting the inherent structure of multi-hop question answering. Circular dependencies would imply that subquestion requires its own answer as a prerequisite, leading to ill-defined and non-terminating reasoning, Consistent with this design choice, all gold query decompositions in standard benchmarks (HotpotQA, 2WikiMQA, MuSiQue) are acyclic. Restricting decomposition to Directed Acyclic Graphs (DAGs), therefore, ensures semantic validity, guaranteed termination, and tractable reasoning in practical multi-hop QA settings. We have employed the GPT-4o model (OpenAI. 2025a) to generate the query-subgraph (refer Appendix A. for prompts). Furthermore, ablations are done on DeepSeek Reasoner (Guo et al. 2025), GPT-4o-mini (OpenAT. 2023c) and Claude-3.7 3.-de-ee---Sneet (Anthropic. 2024).
3.2.2 GRAPH-OF-CHUNKS TRAVERSAL FOR CONTEXT RETRIEVAL
Once the subry-subgraph is identified, the retrieval process involves traversing the Graph-of-chunks G to extract a subset of chunks from V that provide contextual information for answer generation the query Qorig- Formally, let Q (Vg. Eg) denote the query-subgraphe where Vg represents set of nodes corresponding to the decomposed single-hop queries, and Eg denotes the set of directed edges capturing dependencies between them. The retrieval begins by identifying the connected components,
Qi+) Q. each of which is processed separately for subgraph extraction. Within each connected component, nodes are sorted in topological order, with initiator nodes (those with no incoming edges) the beginning and terminal nodes (those with no outgoing edges) at the end. Consider the example query shown in Fig. (b). Q1 and Q3 are the initiator nodes, and Q2 and Q4 are the terminal nodes. Retrieval is performed for each node in the order of the topological sort to extract its respective candidate chunks. Our approach accounts for noise and errors in query decomposition and lexical relationship extraction, and accordingly designs robust retrieval algorithm. Two distinct approaches are employed for initiator nodes and non- non-initiator nodes.
Retrieval for Initiator Nodes:
For each initiator node, the top-k candidate chunks (denoted .I...., Ca E V) with the highest similarity scores are retrieved by treating all nodes (chunks) in the graph-of-chunks as the corpus. The similarity score for a chunk c, denoted SS., is computed as the maximum cosine similarity between its embedding (c.)), the embeddings of the original multi-hop query (M (Qrri)) and the corresponding single-hop query (M())). using Equation
SS.. = max(cos (ci), M (Qorgg)) cos cos M(;)))
This approach is designed to mitigate the effects of noise or errors introduced by incorrect query decomposition. The intuition is that if a single-hop query accurately captures the needed information, the corresponding chunk will exhibit higher similarity to it than to the multi-hop hop query. Conversely, the single-hop query is poorly formulated by the I.M,, the original multi-hop query may still retrieve
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the relevant chunk effectively. Also, note that when query cannot be decomposed into subqueries, the retrieval procedure falls back to the single initiator node case, which is semantic search over the entire corpus.
For a non-initiator node with p predecessor nodes, P = (V V is a predecessor of } in the query subgraph (retrieval proceeds in topological order), each predecessor has k retrieved candidate chunks. All possible combinations formed by selecting one chunk from each predecessor's retrieved set are considered, resulting in a total of k2 combinations. For each such combination, the candidate chunks for the non-initiator node are defined as the union of the neighbors of the selected predecessor chunks in the graph-of-chunks This approach is based on the assumption that the graph-of-chunks captures meaningful relationships between the single-hop queries of the predecessor nodes and the target non-initiator node. By considering neighbors in the graph-of of chuukks,s, the method leverages structural information to guide multi-hop retrieval. The implementation details are follows.
Retrieval for Non-Initiator Nodes:
A modified query is created by concatenating a chunk from the combination to the current single- hop query, if the query is semantically more similar to the chunk than to any of the neighbors. Subsequently, a semantic search is conducted by assigning each chunk in the neighbors of each combination similarity score, defined as the maximum of its cosine similarity with the current single-hop query, the modified query, and the original multi-hop query. Now, for each combination, the top-k chunks with the highest scores are selected, resulting in k(++1) subgraphs. Each subgraph is then scored using a weighted average of the similarity scores of its chunks, where the weight for each chunk is the inverse of the index of the subquery in the topological order, as shown in Equation 2 SSc,
weightsc = - depthe;
Where deptha; refers to the minimum depth of the corresponding subquery from an initiator node. where i refers to the index of the subquery in the topological order. The weighting scheme places greater emphasis on initial nodes compared to later ones, as incorrect retrieval at imitial nodes can result in entirely erroneous neighbors and, consequently, incorrect subgraph retrieval. The top- k subgraphs with the highest scores are retained, and the corresponding chunks for the current node within these subgraphs are selected. This approach is analogous to beam search applied over subgraphs, where fixed number of high-scoring candidate subgraphs are maintained (top-k), enabling efficient exploration of multiple reasoning hypotheses while focusing retrieval on the most promising paths. The example retrieval for a query with two-hops is shown in Appendix Fig.
Although the theoretical space of possible subgraphs is hP+1, this upper bound rarely realized in practice. Realistic query structures are shallow, typically with at most four predecessors (p 4), and even p- = is already uncommon. With a practical choice of A = 5, the full combinatorial space would contain at most 5 3125 candidate subgraphs. However, because only the top-k subgraphs are retained at each expansion step, the effective search complexity is drastically reduced, making the approach computationally feasible.
Context Curation:
Once retrieval is performed for every node in the query subgraph following the topological order, the context is formed by the chunks from the top-k subgraphs retained the end of the retrieval process. Here, k is a hyperparameter referred to as subsrgaaphs hereafter. The structured algorithm, along with its pseudo-code, for traversing the graph-of-chunis to retrieve the most relevant subgraphs is presented in Algorithm T in the Appendix.
3.3 ANSWER GENERATION
Once the relevant context is retrieved for a given question, is provided input to the LLM. Along with the retrieved context, we incorporate an instruction prompt. This prompt guides the model in generating a well-structured response using the generated subqueries that provide the final answer and includes a detailed explanation of the reasoning process that led to the derived conclusion. This ensures that the model's response is both interpretable and transparent, thereby enhancing the system's 's overall reliability in snowledge-intensiin along with the reasoning that led to the answer. The prompt used is provided in the Appendix A. To align with the previous methods, the LLM used to generate the answer is gpt-4o-mini. However, GPT-4.1 -inii, Seepseck-Chat-CS.
tasks, which enables the generation of an answer
ablations are done for answer generation using and Gemini-2.0 0lash..
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4 EXPERIMENTAL SETUP
4.1 DATASETS
We evaluate our approach on three benchmark multi-hop question answering datasets: HotpotQA Yang et al., 2018). 2WikiMQA (Ho al. 2020), and MuSiQue (Trivedi al. 2022bp. For each dataset, we randomly sample 1,000 questions from the validation split to construct the set of queries and the associated corpus, as performed previously (Press et al. 2022 Trivedi et al., 2022a Gutiérrez et al. 2024). To better reflect real-world use cases, we have modified the benchmark datasets by including all passages from other questions as candidate distractors. The number of passages for the benchmarks are 9,221, 6,1 19 and 1 11655 respectively for HotpoiQA, 2WikiMQA and Musique. This effectively enlarges the candidate corpus, simulating a more realistic retricval setting where numerous irrelevant documents must be filtered.
4.2 BASELINES
We compare BrowseNet with a range of retriever-based approaches, including: (i) simple retrievers
such as BM25 (Robertson & Walker 1994), Contriever (Izacard et al.) 2021), and GTR (Ni et al.)
202T); (ii) dense retrievers such as NV-Embed-v2 (Lee et 20244), GTE- Owen2 (Li et al. (023).
Granite- 225--Enlishh (Awasthy et al., 2025), and Proposition (Chen et al. (2224; and (iii Graph- augmented RAG methods, including RAPTOR (Sarthi et al. 2024), GraphR (Edge et al.
2024).
LightRAG (Guo et al.) 2024), HippoRAG (Gutiérrez et al.) 2024). SiReRAG (Zhang al. and HippoRAGi2 (Gutiérrez et al. (025).
2024
To ensure a fair comparison across all baselines, we
we employ the same LLM, gpt-4o-mini, for all stages that rely on a generative-LLM, including indexing, retrieval, and question answering. This applies to methods such as RAPTOR, HippoRAG, HippoRAG-2, LightRAG, GraphRAG, and SiReRAG. HippoRAG-2 additionally requires an embedding model, for which we use Embedd2-22 the same embedding model employed in BrowseNet. For BrowseNet, we conduct extensive experiments with multiple LLMs and report the best performance obtained. Ablation studies further demonstrate that BrowseNet remains robust across different choices of generative-LL.Ms.
4.3 EVALUATIONS
BrowseNet is evaluated at three stages: graph-of-chunks construction, context retrieval, and answer generation.
The constructed graph-of-chunks is evaluated based on its ability to capture the edges necessary to answer multi-hop queries. These required edges are derived from the gold evidence paths provided in the benchmark datasets (refer Appendix A.2). Specifically, MuSiQue and 2WikiMQA include annotated reasoning paths that link the chunks involved in answering each question. The quality of the constructed graph-of-chunks is assessed by measuring the fraction of its edges that correctly represent these gold reasoning paths, thereby reflecting its effectiveness in supporting multi-hop reasoning.
Graph-of-chunks Evaluation:
Evaluation: To evaluate the quality of the generated query-subgraph, we define metric called isomorphic accuracy (refer Appendix A.3). which captures the structural similarity between the generated subgraph and the gold reasoning pathway provided in the 2 2WikiMQA and MuSiQue datasets. Two graphs, G1 and G2. are considered isomorphic if there exists bijective function ff that maps the vertices of G1 to the vertices of G2, such that adjacency is preserved. In other words, an edge exists between vertices u and e in G1 if and only if an edge exists between f(u) and f(v) in G2. In our setting, G1 corresponds to the generated query-subgraph, and G2 is the gold graph derived from the annotated reasoning path or query decomposition provided the benchmark datasets. Isomorphism checking is carried out using NetworkX's 's is issmmrppicc( function, which implements the exact VF2 algorithm (Cordella et al. 2001). While graph isomorphism is hard in general, the query- subrrsubsss in our evaluation are very small (maximum four nodes), making exact isomorphism testing computationally feasible across all benchmarks. The results for isomorphic accuracy are presented in the Appendix A.3
Context Retrieval Evaluation: We evaluate context retrieval performance using the Recall@
metric. For each question, @ecall@k k is defined as: R@k [Tap-k Retrieved Passagos n Gold Passagsil Here, Gold Pavsages]
Gold Passages refer to the set of chunks required to answer the given multi-hop query, specified

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in the benchmark datasets. The final Recall @k score is obtained by averaging the recall across all questions in the evaluation set.
Answer Generation Evaluation: We evaluate the quality of generated answers using two standard metrics: Exact Match (EM) and F1 score.
Exact Match (EM): The Exact Match metric measures whether the generated answer matches the ground truth answer exactly, word for word. It returns score of 1.0 for an exact match and 0.0 otherwise. The final EM score reported in the results section is the average of EM scores across all evaluation questions.
F1 Score: The Fl score evaluates the overlap between the generated and ground truth answers the token level. Both answers are tokenized by splitting whitespace. Precision is defined as the ratio of overlapping tokens to the total number of tokens the generated answer, while Recall is the ratio of overlapping tokens to the total number of tokens in the ground truth answer. The Fl score is then computed as the harmonic mean of precision and recall: Fl = 2 x Precisionx The final Fl score reported as the average of the overall questions in the evaluation set.
Precision Recall
RESULTS AND DISCUSSIONS
5.1 EVALUATION OF GRAPH-OF-CHHNNSS
The graph-of-chunks constructed for BrowseNet are evaluated based on their ability to retrieve the relevant edges necessary for answering multi-hop queries. This assesses the effectiveness of the graph-of-chunks in identifying key relationships and entities in the corpus. Table shows that, in the 2WikiMQA dataset, the graph-of-chunk, achieves an edge accuracy nearing 99.86%, indicating that nearly all essential subgraphs required for reasoning are successfully captured. In contrast, for the MuSiQue dataset, the GLiNER model achieves an edge accuracy of 91 .00%. Additionally, the number of entities extracted remains almost consistent across the different NER models used in the study (refer Table 14 in Appendix). The synonymity threshold in COIBERT, used for rrah-oof-hhunk construction, has minimal effect edge accuracy for the 2WikiMQA dataset across all NER models, as shown in Table I in the Appendix. However, for the MuSiQue dataset, edge accuracy is sensitive to the threshold used. The smaller the threshold, the greater the graph's 'ss density (refer Appendix (A.4). which introduces noise edges (false positives). Also, the ablation studies on ColBERT synonymity threshold (refer to Table 4 show little
variation in the retrieval. Hence, a larger threshold is chosen to reduce the latency period. We also evaluate retrieval and latency improvements on using graph-of-chunks in the Appendix A.55 Results in Table in Appendix |A.5|show: shows that Recall @ improves by approximately 5% compared to the baseline (BrowseNet without graph-of-chunke while the computation time is reduced by about 1.5 times compared the baseline. These results underscore the importance of a cell-constructed graph-of-chunks in enhancing both the accuracy and efficiency of multi-hop question answering systems.
Table 1: Graph-of-chunks statistics and performance across different datasets. No. of Nodes No. of Entities Graph Density Edge Accuracy
NER Model
Dataset
HotPotQA
9,221
60,862
0.0641
NA
GLiNER
2WikiMQA
6,119
44,907
0.0978
99.86
MuSiQue
11,656
67,332
0.0498
91.03
5.2 RETRIEVAL RESULTS
The retrieval performance of BrowseNet is assessed against state-of-the-art (SOTA) models and base- line methods. GraphRAG, SiReRAG, and LightRAG do not follow the retrieve-and-reat paradigm; hence, their results are not included in this comparison. Retrieval efffectiveness, measured by Re- call@2 (R @2) and Recall@5 (R@5), demonstrates that BrowseNet achieves the highest average performance across the three benchmark datasets, shown in Table 2, thereby establishing new SOTA results, while HippoRAG-2 is the second-best performing pipeline.
In the HotpotQA dataset, the NV-Embed-v2 retriever achieves slightly better performance R @@2. Although HotpotQA requires two-hop reasoning, prior studies (Gutiérrez et al., [024]] Trivedi et al. 2022b) have identified it as weaker benchmark for multi-hop retrieval due the presence of
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spurious signals. Nevertheless, BrowseNet outperforms all baselines R@5 in this dataset. In the 2WikiMQA dataset, the query subgraphs typically contain connected components of maximum length two, allowing keywords alone serve as effective linking mechanisms between passages. Here, BrowseNet surpasses HippoRAG-22 (the previous SOTA) by 2% in both R@2 and R @. Conversely, the MuSiQue dataset presents more challenging setting. with query-subgraph components extending up to four hops. This necessitates traversing multiple connections to retrieve relevant context. While NV-Embed-v2 relies solely on semantic similarity, BrowseNet integrates both keyword-bascdr linking and semantic proximity. This hybrid strategy improves its performance in complex multi- hop scenarios, enabling capture both explicit and implicit relationships between information sources. As result, BrowseNet outperforms HippoR AG-2 in recall evaluations. Latency evaluations (refer Appendix A.6) indicate that BrowseNet exhibits slightly higher lateney than HippoRAG-2. Nonetheless, the increase is marginal (0.49 seconds on average) and is justified by the substantial improvements in retrieval recall.
Table 2: BrowseNet pipeline outperforms other baselines in retrieval performance in all three benchmarks tested. The best score is written in Bold and the second best is underlined. A detailed statistical analysis of closely competing metrics is provided in Appendix [A.II
HotpotQA
2WikiMQA
MuSiQue
Average
Retriever
R@2 R@5 R@2 R@5
R@2 R@5
R@2 R@5
Simple baselines
BM25
55.40
72.20
51.80
61.90
32.20
41.20
46.50
58.43
Contriever
57.20
75.50
46.60
57.50
34.80
46.60
46.20
59.87
GTR
59.40
73.30
60.20
67.90
37.40
49.10
52.33
63.43
Dense retrievers
NV-Embed-v2 (7B)
83.95
95.65
69.05
76.72
53.30
69.85
68.77
80.74
GTE-Qwen2 (7B)
72.70
87.50
65.22
73.25
48.78
63.45
62.23
74.73
Granite-125M-Ennn Engllsh
70.40
85.30
64.12
70.62
44.54
59.37
59.69
71.76
Proposition
58.70
71.10
56.40
63.10
37.60
49.30
50.90
61.17
KG-augmented RAGs
RAPTOR
58.10
71.20
46.30
53.80
35.70
45.30
46.70
56.77
HippoRAG
60.05
78.10
70.40
87.87
41.86
53.57
57.44
73.11
HippoRAG-2
81.80
96.20
74.60
90.20
53.50
74.20
69.97
86.87
BrowseNet
83.85
96.40
76.77
93.30
55.12
73.91
71.91
87.87
Table 3: BrowseNet pipeline outperforms other baselines in answer generation in all three benchmarks tested. The best score is written Bold and the second best is underlined. BrowseNet's 's performance over the second best method is statistically significant based on paired bootstrap test (p 0.05).
HotpotQA
2WikiMQA
MuSiQue
Average
Retriever
EM
F1
EM
F1
EM
F1
EM
F1
NV-Embed-v2 (7B)
59.80
75.52
52.50
62.57
36.90
49.80
49.73
62.63
LightRAG
9.90
20.20
2.50
12.10
2.00
9.30
4.8
13.87
GraphRAG
51.40
67.60
45.70
61.00
27.00
42.00
41.37
56.87
SiReRAG
48.30
63.17
41.30
48.05
26.00
39.59
38.53
50.27
HippoRAG-2
59.30
76.90
60.50
69.70
35.00
49.30
51.60
65.30
BrowseNet
62.20
77.69
63.90
74.50
41.60
54.08
55.90
68.76
5.3 QUESTION ANSWERING RESULTS
In the QA evaluation, BrowseNet demonstrates superior performance all other methods across all benchmarks, achieving higher exact match and Fl scores as reported in Table The performance gains can be attributed the inclusion of sub-queries in the prompt alongside the original question,
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enabling the language model to perform intermediate reasoning steps that reduce ambiguity and enhance contextual understanding. Furthermore, the overall LLM cost of HippoRAG-2 33x higher than that of the BrowseNet pipeline, from indexing through to question-answering (refer Appendix A.77)
5.4 ABLATIONS STUDIES
We performed ablations three stages of answer generation: Graph-of-chunks construction, retrieval, and answer generation to understand the contribution and importance of different components in BrowseNet. Results for retrieval ablations are shown in Table with the others Appendix [A.8] BrowseNet shows stable performance across synonymity thresholds and keyword generation models. Increasing the number of subgraphs (n__sul (n_subgrephs) improves context retrieval but increases latency, making larger values preferable when context size is not limiting and latency is less critical. Retrieval effectiveness was consistent across query decomposition models (DeepSeek Reasoner and gpt-4o- mini), indicating robustness to this choice. On the other hand, a significant drop in performance was observed when BrowseNet was evaluated with a different encoder, underscoring the critical role of encoder choice in the overall effectiveness of the system.
Table 4: Ablation studies on retrieval performance of BrowseNet.
HotpotQA
2WikiMQA
MuSiQue
Average
Alternatives
R@2
R@5 R@2 R@5 R@2 R@5 R@2
R@5
BrowseNet
83.85 96.40 76.77 93.30
55.12 73.91
71.91
87.87
Synonymity 0.8
83.60 96.30
76.77
93.00
55.01
73.82
71.79
87.71
threshold
0.7
83.60 96.20
76.77
92.97
55.19
73.99
71.85
87.72
Keyword generation
Claude-3.--Soneet GPT-4o
83.80 96.40 76.77
93.10
55.16
74.21
71.91
87.80
83.45 96.05 76.50
92.75
55.21
74.04
71.72
87.61
10
83.65 96.20
76.77
93.00
55.26
73.92
71.89
87.71
n_subgraphs
15
83.55
96.00 76.77
92.95
55.24
73.94
71.85
87.63
DeepSeek Reasoner
83.85
95.80
76.60
93.05
56.04
74.33
72.16
87.73
Subquery
GPT-4o-mini
82.85
95.40
76.05
92.60
56.77
74.41
71.89 87.47
decomposer
Claude-3.7-Sonner
80.90
94.05
75.85
92.12
53.86
72.95
70.20 86.37
Encoder model
GTE-Qwen2 (7B)
75.40
89.80
73.25
91.10
48.69
64.81
65.78
81.90
Granite-125MM 125M-Eng.
73.95
88.85
73.47
89.95
49.69
65.38
65.70
81.39
5.5 ROBUST RETRIEVAL GAINS ACROSS ENCODERS
To check if the performance gains of BrowseNet over the NaiveRAG are robust across all the encoders, we performed the robustness analysis on recall improvements. Table 5 illustrates that BrowseNet consistently outperforms NaiveRAG across all tested encoder models in terms of retrieval performance. This consistent improvement underscores that the strength of BrowseNet lies not in reliance particular encoder, but in the robustness of its retrieval methodology. The substantial gains observed up to 9.63 points in average Recall@5 demonstrate the effectiveness of BrowseNet's structured query decomposition and graph-based context selection, regardless of the underlying encoder used.
5.6 ERROR ANALYSIS
The downstream performance of our question-answering pipeline, specifically answer generation, relies the effectiveness of the entire workflow. To analyze the source of errors, we sampled 100 questions where BrowseNet failed from the MuSiQue dataset and classified the errors into four categories: praph-of-chunks construction, query subrr-hubg extraction, retrieval, and answer generation. Table pressens the number of questions corresponding to each category. Note that a single question be associated with multiple sources of error. The analysis reveals that nearly

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Table 5: Retrieval performance comparison of NaiveRAG and BrowseNet with different encoders
using R The last column shows the gain in average Recall for BrowseNet over NaiveRAG.
Method
Encoder
HotpotQA
2WikiMQA
MuSiQue
Average
Gain
NaiveRAG
NV-Emmed-v2(7B)
95.65
76.72
69.85
80.74
BrowseNet
NV-Embed-v2(7BB
96.40
93.30
73.91
87.87
+7.13
NaiveRAG
GTE-Qwen2(7B)
87.50
73.25
63.45
74.73
BrowseNet
GTE-Qwen2(7B))
89.80
91.10
64.81
81.90
+7.17
NaiveRAG
85.30
70.62
59.37
71.76
BrowseNet
Granite- 1amite-125M-Englisss
88.85
89.95
65.38
81.39
+9.63
half of the errors originate from the semantic retrieval stage, followed by query decomposition, with smaller contributions from Graph-of-chunks construction and final answer generation. Detailed examples and case studies are provided in the Appendices/A.99 A.9-A.10
Table 6: Error source for 100 sampled questions from the MuSiQue dataset where BrowseNet produced an F1 score of zero. ME: Manual Evaluation, II: Isomorphic inaccuracy
Stage
Graph Query-subgraph Query-subgraph construc- extraction (ME) extraction (II) tion
Semantic retrieval generation
Answer
Error % 9
33
35
49
9
CONCLUSIONS
We presented BrowseNet, graph- associative memory framework for multi-hop question answering. Unlike standard RAG pipelines that retrieve isolated text chunks, BrowseNet formulates retrieval query-specific subgraph exploration. By combining lexical relationships with semantic embeddings, it constructs reasoning pathways that mirror the structural and semantic requirements of complex queries. Our experiments demonstrate that BrowseNet outperforms both dense retrieval and graph-enhanced RAG baselines, achieving state-of-the-an results on multi-hop question answering tasks. The framework reduces reliance on repeated LLM interactions by leveraging reegeeerrteed sub-queries, making retrieval both more efficient and cost-effective. Future work will focus on expanding the range of semantic relationships encoded in the graph and extending the approach to more diverse and heterogeneous information sources, further strengthening BrowseNet as scalable associative memory system for LLMs.
REPRODUCIBILITT STATEMENT
All code and datasets required to reproduce our results are included the supplementary material. A detailed README.md file is provided, outlining step-by-step instructions replicate every experiment and result presented in the paper. We have made every effort to ensure reproducibility by independently verifying that all reported results can be faithfully reproduced using the provided resources. The benchmark dataset used in our analysis is also included in the supplementary material. Since the evaluation involves randomly sampled questions from the validation split, we have uploaded the entire corpus along with the corresponding questions to enable exact replication. Details of the computational environment and resource requirements are provided Appendix A.12
ACKNOWLEDGMENTS
The authors thank Dr. Alka Bhushan for critically reviewing and commenting on the manuscript. This project is supported by funding provided by DevRev, India.
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REFERENCES
Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, lige Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et Gpt-4 technical report. arXiv preprint arXhv:2303.08774 2023.
Anthropic. Claude-3-family. 2024. URL
Parul Awasthy, Aashka Trivedi, Yulong Li, Mihaela Bornea, David Cox, Abraham Daniels, Martin Franz, Gabe Goodhart, Bhavani Iyer. Vishwajeet Kumar, al. Granite embedding models. arXiv preprint arXiv:2502.20204 2025.
Tong Chen, Hongwei Wang. Sihao Chen, Wenhao Yu, Kaixin Ma, Xinran Zhao, Hongming Zhang, and Dong Yu. Dense x retrieval: What retrieval granularity should we use? In Proceedings of the 2024 Conference on Empirical Methods Natural Language Processing, pp. 15159-15177, 2024.
Luigi Pietro Cordella, Pasquale Foggia, Carlo Sansone, Mario Vento, al. An improved algorithm for matching large graphs. In 3rd IAPR-TC15 workshop on graph-based representations in pattern recognition, pp. 149-159, 2001.
Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Amy Yang. Angela Fan, et al. The Ilama herd of models. arXiv preprint arXiv:240..27783, 2024.
Darren Edge, Ha Trinh, Newman Cheng, Joshua Bradley, Alex Chao, Apurva Mody, Steven Truitt, and Jonathan Larson. From local global: graph rag approach query foccued summarization. arXiv preprint arXiv:2404. 16130, 2024.
Yunfan Gao, Yun Xiong, Xinyu Gao, Kangxiang Jia, Jinliu Pan, Yuxi Bi, Yi Dai, Jiawei Sun, and Haofen Wang. Retrieval-augmenter generation for large language models: A survey. arXiv preprint arXiv:2312 10997, 2023.
Google. Gemini 2.5: Our most intelligent ai model, 2025. URL https: google//
Daya Guo, Dejian Yang. Haowei Zhang, Junxiao Song, Ruoyu Zhang, Runxin Xu, Qihao Zhu, Shirong Ma, Peiyi Wang, Xiao Bi, et al. Deepseek-rl: Incentivizing reasoning capability in llms via reinforcement learning, arXiv preprint arXiv:2501.12248, 2025.
Zirui Guo, Lianghao Xia, Yanhua Yu, Tu Ao, and Chao Huang. Lightrag: Simple and fast retrieval- augmented generation. arXiv preprint arXiv:2410.05779, 2024.
Bernal Jiménez Gutiérrez, Yiheng Shu, Yu Gu, Michihiro Yasunaga, and Yu Su. Hipporag: Neurobi- ologically inspired long-term memory for large language models. In The Thirty- Annual Conference on Neural Information Processing Systems, 2024.
Bernal Jiménez. Gutiérrez, Yiheng Shu, Weijian Qi, Sizhe Zhou, and Yu Su. From rag to memory: -on-parametric continual learning for large language models. arXiv preprint arXiv 14800 2025.
Xanh Ho, Anh-Khoa Duong Nguyen, Saku Sugawara, and Akiko Aizawa. Constructing multi-hop qa dataset for comprehensive evaluation of reasoning steps. In Proceedings of the 28th International Conference on Computational Linguistics, pp. 6609-6625, 2020.
Gautier Izacard, Mathilde Caron, Lucas Hosseini, Sebastian Riedel, Piotr Bojanowski, Armand Joulin, and Edouard Grave. Unsupervised dense information retrieval with contrastive learning. Transactions on Machine Learning Research, 2021.
Chankyu Lee, Rajarshi Roy, Mengyao Xu, Jonathan Raiman, Mohammad Shoeybi, Bryan Catanzaro, and Wei Ping. Ny-embed: Improved techniques for training llms as generalist embedding models. arXiv preprint 2405 /7428, 2024.
11

---

Published as a conference paper at ICLR 2026
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al. Retrieval-uugmented genera- tion for knowledge-intee -iteggivinnsieveeeee nlp tasks. Advances in neural information processing systems, 33: 9459-9474, 2020.
Zehan Li, Xin Zhang, Yanzhao Zhang, Dingkun Long, Pengjun Xie, and Meishan Zhang. Towards general text embeddings with multi-stage contrastive learning. arXiv preprint urXiv23308.0328/ 2023.
Jianmo Ni, Chen Qu, Jing Lu, Zhuyun Dai, Gustavo Hernández Ábrego, Ma, Vincent Y Zhao, Yi Luan, Keith B Hall, Ming-Wa Wei Chang, al. Large dual encoders are generalizable retrievers. arXiv preprint arXiv:21 2021.
OpenAI. Gpt-4o, 2025a. URL htt.tt/tce/aaatttrm.eee..c.///oe
OpenAl. Gpt-5, 2025b. URL. http://ss/bbaattoo.ooooo.///////
OpenAl. Gpt-o4-mini, 2025c. h /ttp:t/tte////aacoooooo.o//////t
Ofir Press, Muru Zhang, Sewon Min, Ludwig Schmidt, Noah A Smith, and Mike Lewis. Measuring and narrowing the compositionality gap in language models. arXiv preprint 10.05:2200003350, 2022.
Machel Reid, Nikolay Savinov, Denis Teplyashin, Dmitry Lepikhin, Timothy Lillicrap, Jean-baptiste Alayrac, Radu Soricut, Angeliki Lazaridou, Orhan Firat, Julian Schrittwieser, et al. Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context, arXiv preprint arXiv:2403.05550, 2024.
Stephen E Robertson and Steve Walker. Some simple effective approximations the 2-poisson model for probabilistic weighted retrieval. In SIGIR 99: Proceedings of the Seventeenth Annual International
Conference on Research and Development Information Retrieval, organised by Dublin City University, pp. 232-241 Springer, 1994.
Keshav Santhanam, Omar Khattab, Jon Faldo,,,oon, Christopher Potts, and Matei Zaharia, Colbertv2: Effective and efficient retrieval via lightweight late interaction. arXiv preprint arXiv:2112.01488. 2021.
Parth Sarthi, Salman Abdullah, Aditi Tuli, Shubh Khanna, Anna Goldie, and Christopher D Man- ning. Raptor: Recursive abstractive processing for tree-organized retrieval. arXiv preprint arXiv:2401.18059 2024.
Wendy Suzuki. Associative learning and the hippocampus. Psychological Science Agenda, 2005.
Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, and Ashish Sabharwal. Interleaving retrieval with chain-of-thought reasoning for knowledge-intensive multi-step questions. arXiv preprint 1rXiv:2212.10500 2022a.
Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, and Ashish Sabharwal. Musique Multihop questions via single-hop question composition. Transactions of the Association for Computational Linguistics, 10:539-554, 2022b.
Yu Wang, Nedim Lipka, Ryan A Rossi, Alexa Siu, Ruiyi Zhang, and Tyler Derr. Knowledge graph prompting for multi-document question answering. Pruceedings of the AAAI Conference on Artificial Intelligence, volume 38, pp. 19206-19214, 2024.
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou et al. Chain-of -faiugh prompting elicits reasoning in large language models. Advances in neural information processing systems, 0124824-248337 2022.
Yaxiong Wu. Sheng Liang, Chen Zhang, Yichao Wang, Yongyue Zhang, Huifeng Guo, Ruiming Tang, and Yong Liu. From human memory to Al memory: A survey on memory mechanisms in the era of lmss. arXiv preprint arXiv:2504.15965 2025.
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Published as a conference paper at ICLR 2026
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio. William W Cohen, Ruslan Salakhutdinov, and Christopher D Manning. Hotpotqa: A dataset for diverse, explainable multi-hop question answering. arXiv preprint arXiv:1809.09600) 2018.
Yao Yao, Zuchao Li, and Hai Zhao. Beyond chain-of-thought, effective graph-of-thoghtt reasoning in language models, arXiv preprinl arXiv:2305. 16582, 2023.
Urchade Zaratiana, Nadi Tomeh, Pierre Holat, and Thierry Charnois. Gliner: Generalist model for named entity recognition using bidirectional transformer. arXiv preprint arXiv:2311.08526, 2023.
Nan Zhang, Prafulla Kumar Choubey, Alexander Fabbri, Gabriel Bernadett-Shapiro, Rui Zhang, Prasenjit Mitra, Caiming Xiong, and Chien-Sheng Wu. Sirerag: Indexing similar and related information for multihop reasoning. arXiv preprint 4rXiv:2412.06206, 2024.
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A APPENDIX
A.1
LLM PROMPTS USED IN THE STUDY
The LLM prompt used for NER using GPT-like models is shown in Fig- 4 Prompts used for query- subgraph extraction are shown in Figs. 5 Prompt used for answer generation after the context retrieval is shown in Fig. g
A.2 GRAPI-OF-CHUNKS EVALUATION BASED ON PRESENCE OF EDGES
For each multi-hop question in the MuSiQue dataset and 2WikiMQA, the evidence track the reasoning path across chunks is investigated and the exact approach to track the reasoning path is discussed in this section.
A.2.1 MUSIQUE DATASET:
In the MuSiQue dataset, for each of the questions, question decomposition is provided, and hence, the reasoning path can be traced to get the edges between the chunks. For example, for the question, "When was the person who Messi's goals in Copa del Rey compared to getting signed by Barcelona?" The question decomposition is provided as
Q1 To whom was Messi's goal in the first leg of the Copa del Rey compared? [Chunk ID: 1] Q2 When was <ANS-1> signed by Barcelona? [Chunk ID: 2]
Using the details of Chunk IDs for the respective chunks, it can be inferred that Chunk IDs 1 and 2 should have an edge between them. In similar fashion ground truth chunk edges (True edges) are inferred for each of the questions in the MuSiQue dataset. The edge accuracy of question is calculated follows
True edges n Edges in the graph-of-chunks
Edge accuracy
True edges
For the column 'Edge Accuracy' shown in Table the average of all the edge accuracies across the questions is shown.
A.2.2 2W1KIMQA DATASET:
In the 2WikiMQA dataset, all questions are categorized into four classes: comparison, inference, composition, and bridge comparison. Comparison, inference, and composition questions involve two-hop reasoning. Inference and composition questions require a connecting edge between them, whereas comparison questions do not establish any edge between the two retrieved chunks. In contrast to them, bridge comparison questions involve four-hop reasoning with two connecting edges. Given the predefined question types, the true edges in this dataset can be inferred. The edge accuracy computed using the formula provided in Equation 3
A.3 ISOMORPHIC ACCURACY
Isomorphic accuracy calculated for the query-subgraphs is discussed in this section. Consider the following multi-hop query from the MuSiQue dataset:
"What month did the Tripartite discussions begin between Britain, France, and the country where, despite being headquartered in the nation called the nobilities commonwealth, the top-ranking Warsaw Pact operatives originated?"
The gold query decomposition is:
Q1: What was the nobilities commonwealth?
Q2: Despite being headquartered in #1, the top-ranking operatives the Warsaw Pact were from which country?
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Q3: What month did the Tripartite discussions begin between Britain, #2, and France?
This decomposition can be represented as graph: Q1 + Q2 - Q3.
In contrast, the query decomposition generated by GPT-4o is:
Q1: What is the nation called the nobility's 's commonwealth?
Q2: <Q1> Where are the top-ranking Warsaw Pact operatives headquartered?
Q3: <Q2> In which country did the top-ranking Warsaw Pact operatives originate?
Q4: <Q3> What month did the Tripartite discussions begin between Britain, France, and the country where the top-ranking Warsaw Pact operatives originated?
This decomposition forms graph: Q1 + Q2 + Q3 Q4.
Since the generated and gold decompositions differ in structure, they are not isomorphic, resulting in an isomorphic accuracy score of O.
similar evaluation procedure is applied to the 2WikiMQA dataset. The average isomorphic accuracy across models is summarized in Table 7
Table 7: Isomorphic accuracy of generated query decompositions.
LLM Model
2WikiMQA MuSiQue
GPT-4o
0.973
0.685
Claude-3.7-Somee
0.967
0.487
The results indicate that isomorphic accuracy is consistently higher on the 2WikiMQA dataset than on MuSiQue for both models. GPT-4o performs slightly better than Claude-3.7 Sonnet across both datasets, achieving 0.973 on 2WikiMQA and 0.685 on MuSiQue, compared to 0.967 and 0.487, respectively.
A.4 GRAPH DENSITY CALCULATION
The density of an undirected graph is measure of how many edges are in the graph compared the maximum possible number of edges. Let G = (V, E) be a graph. The density GD of the graph is given by:
2 E
GD
-
where
| |indicates the cardinality of the given set.
A.5 IMPORTANCE OF GRAPH-OF WEEH-OF-CHUNKK
The graph-of-chunde plays vital role in BrowseNet, not only in capturing dependencies but also in enhancing latency. In BrowseNet, is used for the dynamic modification of the corpus, which constitutes the foundation of the proposed approach. This dynamic refinement significantly improves both retrieval effectiveness and latency. Table shows the improvement in retrieval performance when the graph-of-chunks is utilized, compared bascline that considers all nodes the corpus at every retrieval step using the subquery. The results the table are reported on a sample of 100 questions from the MuSiQue dataset.
Table 8: Impact of using Graph-of-chunks on retrieval performance and latency for 100 questions from the MuSiQue dataset.
Setting
Time Taken (sec)
Recall@5
Without graph-of-chunks
177
70.75
With graph-of-chunks
115
75.08
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A.6 LATENCY ANALYSIS
To evaluate latency, we sampled 50 questions from the MuSiQue dataset. Table 9 presents comparison between BrowseNet and HippoRAG-2 based on the Average Time Per Query (ATPQ) metric, which measures the average time in seconds taken to process each query up to the retrieval stage. shown Table BrowseNet incurs slightly higher latency compared to HippoRAG- 2. However, the additional latency is marginal (0.49 seconds average) and is justified by the significant gains in retrieval accuracy and overall QA performance, as demonstrated in previous sections.
Table 9: Latency comparison between BrowseNet and HippoRAG- based on the Average Time Per Query (ATPQ) for 50 sampled questions from the MuSiQue dataset.
Method
ATPQ (seconds)
BrowseNet
2.70
HippoRAG-2 2.21
For BrowseNet, query decomposition accounts for 1.50 seconds of the total latency, primarily due
the overhead of API-based LLM calls. This component could be significantly improved by deploying the language model locally, which would reduce network latency. In contrast, the retrieval stage relatively efficient, taking only 1.19 seconds on average.
A.7 COST ANALYSIS
We provide quantitative comparison of LLM-related costs between BrowseNet and the state-of-the- art retrieval baseline, HippoRAG-2, using the HotpotQA benchmark dataset in Table TO The analysis considers the full pipeline cost, from indexing retrieval, using gpt-4o-mini in both systems. The pricing model follows OpenAl's current API rates (As 24th September 2025); $0.15 per 1M input tokens and $0.6 per IM output tokens.
Table 10: Token-level comparison between HippoRAG-2 and BrowseNet on HotpotQA.
HippoRAG-22 BrowseNet
Input tokens
5,880.618
249,503
Output tokens
2,110,007
44,641
Total tokens
7,990,625
294,144
The corresponding LLM costs can be estimated as follows:
Input tokens
Output tokens
Cost
0.15 +
x 0.6
106
106
Using this formula:
HippoRAG-2: $2.15
BrowseNet: $0.064
Thus, BrowseNet achieves roughly 33x higher cost efficiency while maintaining state-of-the-art retrieval.
A.8 ABLATIONS
We performed ablation studies the graph-of-chunks construction by varying the NER model for keyword extraction and the ColBERT synonymity threshold for connecting nodes. As shown in Table 11 edge accuracy the 2WikiMQA dataset remains near 100% across all thresholds,
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indicating robustness to synonymity settings. In contrast, for the MuSiQue dataset, higher edge accuracy is observed at lower synonymity thresholds across all NER models.
We also conducted ablations answer generation using different LLMs (Table 12). The results indicate substantial variation in performance, with average exact match scores differing by ap- proximately 10% between models, highlighting the influence of the LLM choice final retrieval quality.
Table Graph-of-chunte performance with varying ColBERT synonymity threshold. Here, GD
refers to Graph density, and EA refers to Edge accuracy.
Synonymity HotpotQA Threshold
2WikiMQA
MuSiQue
NER Model
GD
EA
GD
EA
GD
EA
0.9
0.0641
NA 0.0978
99.86
0.0498
91.03
GLiNER
0.8
0.2309
NA 0.2653
99.86
0.1732
95.18
0.7
0.3781
NA 0.4018
100
0.3155
97.43
0.9
0.0844
NA 0.0663
98.74
0.0371
97.83
GPT-4o
0.8
0,2533
NA 0.2163
98.94
0.1652
97.94
0.7
0.3976
NA 0.3282
99.20
0.2934
98.71
0.9
0,0893
NA 0.0673
99.74 0.0550 94.33
Hlaud-3.7-Soomc
0.8
0.2667
NA 0.2325
99.87
0.1848 98.26
0.7
0,4086
NA 0.3399
100 0.3143 98.54
Table 12: Performance comparison of different LLMs for answer generation on HotpotQA, 2WikiMQA, and MuSiQue. The best score is written in Bold and the second best is underlined.
HotpotQA
2WikiMQA
MuSiQue
Average
LLM
EM
F1
EM
Fl
EM
F1
EM
F1
gpt-4o-mini
62.20
77.69
63.90
74.50
41.60
54.08
55.90
68.76
gpt-3.5-turbo
58.80
73.81
47.70
59.57
37.40
49.77
47.97
61.05
gpt-4.
63.20
79.21
64.50
74.43
42.70
55.07
56.80
69.57
deepseck-chat-
62.20
78.91
66.10
75.86
43.50
56.25
57.27
70.34
gemini-2.0-ffast
63.40
78.00
62.10
70.30
38.10
47.37
54.53
65.22
A.9
CASE STUDY
Using example questions, demonstrate how BrowseNet improves retrieval performance compared to the NaiveRAG approach. Table [B]leenn]reeentt a comparison of the articles retrieved by each method. As see for NaiveRAG, the retrieved corpus is related to the keywords present in the query rather than the underlying reasoning path required to answer the multi-hop question, which often leads to the inclusion of spurious contextually irrelevant passages.
For a detailed understanding of the BrowseNet approach, consider the question, 'What is the Till dom ensamma performer's birth date?'. The query decomposition produces the subqueries:
-Q1) Who is the performer of "Till dom ensamma"?
Q2) <QI> What is the birth date of the performer of "Till dom ensamma"?
The subgraph to be retrieved from the graph-of-chunks has the structure, Q1 Q2. Retrieval begins with the initiator node QI, treating all nodes in the graph-offchunks as the initial corpus. Using semantic similarity search, the top most similar nodes (passages) are retrieved. Among them, the passage containing "Till dom ensamm" is found to be most similar to Q1.
Next, for the query node Q2, the candidate contexts is restricted to the neighbors of the previously retrieved passage "Till dom ensamma". Then Q2 is used to search the new corpus, from which the passage "Mauro Scocco" is identified most relevant and subsequently retrieved.
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Table 13: Top-5 articles retrieved by NaiveRAG and BrowseNet for each question. The underlined entries indicate the golden articles required to answer the corresponding question.
Top-5 Retrieved Documents
Question
NaiveRAG
BrowseNet
1. Till dom ensamia
Till dom ensamma
Langa Natter
2. Mauro Scocco
What is the Till dom ensamma performer's birth date?
3. Nar hela varlden ser pa
Hits (Mauro Scocco album)
Roxy Recordings
4. Langa natter
5. Du far gora du vill
5. Nar hela varlden ser pa
1. The Bag or the Bat
1. The Bag the Bat
2. List of Orange Is the New Black episodes
2. List of Ray Donovan episodes
How many episodes are in season of the series with "The Bag or the Bat"?
3. List of Orange Is the New Black episodes
3. Cheatty Cheatty Bang Bang
4. Cheatty Cheatty Bang Bang
4. Samurai Jack (season 5)
5. Arrested Development (season 5)
5. Samurai Jack (season 5)
Table 14: Number of entities extracted using distinct NER models.
NER Model
No. of Entities
HotPotQA
2WikiMQA MuSiQue
GLiNER
60,862
44,907
67,332
GPT-4o
61,959
41,219
66,795
Claude-3.7
62,671
41,921
67,216
A.10
ERROR ANALYSIS: DETAILS
These errors arise because of missing edges in the con- structed graph. Our analysis reveals that 9% of relevant edges are missing from the graph.
Graph-of-chunks construction:
Query-subgraph extraction: This stage is evaluated through two approaches: manual analysis to determine whether each question is correctly decomposed, and isomorphic accuracy, as defined in Section 4.3 Human evaluation shows that 33% of the questions are either incorrectly decomposed contain redundant sub-queries (i.e., multiple sub-queries retrieving the same information). Isomorphic accuracy reveals that 35% of the questions result in query-subgrajj errors or redundancies in the decomposition process.
that not isomorphic to the ground-truth structure, indicating
Here, we present an example where query-gran eeery-graph generation failed to produce accurate outputs.
Query: Are both businesses, Google and Banco De Ponce, located in the same country? The generated single-hop queries with dependencies are:
Q1: In which country Google located?
- Q2: In which country Banco De Ponce located?
-3:: <ANS- <ANS-2> Are both Google and Banco De Ponce located in the same
country?
In this case, only the first two questions are necessary to traverse the graph. The third question is redundant because the answer to the third question cannot be inferred from any the chunks
graph-of-chunks.
Semantic retrieval: terms of overall recall (Recall@5) distribution across all evaluated questions, 1% of the questions resulted in zero recall, while 48% exhibited recall of less than To understand the influence query-subgraph extraction on retrieval effectiveness, analyze recall performance based on both human evaluation and isomorphic accuracy.
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According to human evaluation, among the 33 incorrectly decomposed questions, only 11 (33.33%) achieved full recall. In contrast, for the 67 correctly decomposed questions, 40 (59.70%) achieved full recall. This highlights the critical role of accurate query decomposi- tion in successful retrieval. A similar trend is observed using isomorphic accuracy. Out of the 35 incorrectly decomposed questions (based non-isomorphic subgraphs), 12 (34.28%) achieved full recall. Among the 65 correctly decomposed questions, 40 (60.00%) achieved full recall. These results further reinforce that effective query decomposition significantly improves retrieval performance.
Answer Generation: For questions where the retrieval stage achieved full recall, the final answer generation step still produced incorrect answers in 9% of the cases. This indicates that even when all relevant information is successfully retrieved, the model may still fail generate the correct answer. Such failures can be attributed to challenges in reasoning. interpreting the evidence, or inherent limitations of the generative model itself.
Importantly, despite enrors in decomposition, BrowseNet's adaptive retrieval strategy was still able to achieve full recall in approximately one-third of the incorrectly decomposed cases. This demonstrates the robustness of the system in handling imperfect inputs. The utilization of multi-query approach, as discussed Section 3.2.2 plays key role in alleviating the impact decomposition errors and enhancing retrieval robustness.
A.11 STATISTICAL SIGNIFICANCE
Confidence intervals for retrieval results are presented in Table 15 computed using paired bootstrap test with 10,000 resamples and 0.05, yielding 95% confidence intervals. We also performed significance analysis across all retrieval comparisons. Most of the BrowseNet retrieval results are statistically significant (p 0.05), except in three cases: HotpotQA-Recallle vs. NV-Embed- v2, HotpoiQA-Recall @5 5 vs. HippoRAG-22 and MuSiQue-Recallee @5 vs. HippoRAG-2, where the improvements are not statistically significant. However, even when retrieval differences are small, BrowseNet still achieves stronger overall -anweerin performance because the query- subgraph guides the LLM more effectively toward the correct final answer.
Table 15: Confidence Intervals (CIs) of recall metric for context retrieval on HotpotQA, 2WikiMQA, and MuSiQue. The best score is written in Bold and the second best is underlined. The confidence intervals are represented in the superscript (upper bound) and subscript (lower bound) of the mean value.
HotpotQA
2WikiMQA
MuSiQue
Retriever
R@2
R@5
R@2
R@5
R@2
R @
NV-Embed-v2 (7B)
83.95
1.60
95.64
+0.86
69.05
76.72
53.31
69.86
+1.70
1.60
0.89
1.53
1.47
1.65
1.71
HippoRAG-2
81.79
96.20+0.88
75.45+1.62
90.62
9..62+1.11
53.63+182
73.70
1.69
0.90
1.68
1.09
1.81
1.68
BrowseNet
83.83
1.52
96.40
+0.80
76.77+1.60
93.29
+0.91
55.12
73.92
1.58
0.85
1.64
0.91
1.68
1.67
12
COMPUTATIONAL RESOURCES USED:
To get the embeddings from the encoder, NV-Embed-v2. we have used NVIDIA A100 GPU and 512GB RAM.
A.13 LARGE LANGUAGE MODEL (LLM) USAGE:
During the preparation of this work, the author(s) used Large Language Model application to improve the paper's 's organizational flow and eliminate emrors by providing the draft. After using this tool or service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the published article.
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Borkzer Han Berizur bom 1350) Americnn
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Figure 2: A small snippet (seven nodes) of graph-of-chunde obtained from the 2WikiMultiHopA.. The color of the edge denotes whether the keyword obtained from an exact match or derived from the Colbert similarity mapping.
Query Subgraph:
Q1
Q2
Retrieval Strategy for n_subgrrapsss2222 2
0.80
0.80
score:
1
20
cos
0.80
0.80
0.70
cos = 0.80
C
score:
1
2
1-15
cos
0.70
Consider top-2 subgraphs!
0.75
0.80
score:
1-15
1
C27
cos
0.80
0.75
0.90
cos = 0.75
+
1 20
cos 0.90
Figure Traversal over the graph-of-chunks analogous the beam search where the top-K candidates are retained at every iteration. Here cos/ refers to the maximum of cosine similarity obtained from Equation] & refers the candidate chunks to retrieved from the graph-of-chunks.
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Prompt:
Your task is to extract named entities from the given paragraph. Respond with JSON list of entities
Dhe-shot demonstration:
Input:
Radio City India's first private radio station and was started July 2001. plays Hindi, English and regional songs. Radio City recently lorayed into New Media in May 2008 with the launch of music portal PlaanetRadoocit.ccomm that offers music related news, vidcos, songs, and other musie-related features.
Output:
("naned antities": Intttiessntiiis'
("Radio City", "India", July 2001", "Hindi" "English", "May 2008", "aaactRadioccitycco "]]
Figure 4: LLM prompt used for named entities extraction. This prompt adapted from the HippoRAG Gutiérrez et al. 2024)
Prompt:
You are a heiptul assistant designed to split a multi-hop query into set of single-hop queties. These smaller queries will be used Jater to retrieve relevant information from the previous except the comparison automated manner Each query (Q). Q2. etc.) should progressively build upon the answer questions. The follow-up questions shoald have indicator previous question they are building upon (like =Q1>). Few-shot examples are provided below reference and similar format.
Fen-shot demonstration:
INPUT: 'ho married the publisher of abolitionist newspaper The North Star" OUTPUT: Q1) Who publisher abolitionist newspaper The North Star? <Q1> Who married the publisher abolitionist
newsgaper The
INPUT: 'In what state is the district where the man who wanted to reform the religion practiced by Innocenzo Ferrieri preached a sermon
Mazian devotion."
OUPUT: Q1) What religion Innocenzo Ferrieri Q2) <Q1> Who wanted reform religion practiced by Innocenzo lerrier?? the man who Marian devotion?(4) <Q3> what state is the district where the man who wamted to reform the religion practiced by Innocenzo Ferrieri
Q3) <Q2>
religion practiced by Innseenzo
preached sermon
preached sermon Marian devotion?
OUTPUT: QI) What the birth city of Siddhi Savessila? Q2) <Q1> In which country Sicdhi Savetsila"
INPUT: The Beach fllmed what
country that
city of Siddhi Savetsila located" Q3)
<Q2> The Beach was filmed what location of the country that contains the bärth city Siddhi Savetsila?"
Figure 5: LLM prompt used for query-subgraph extraction in the Musique dataset. Few shot example questions taken from Musique dataset
Prompt:
You are a helpfal ascistant designed split multi-hop query into set single-hop queries These smaller queries will be used later to retraeve relevant information from in an automated manner. Each query (QI. Q2, etc.) should progressively build upen the answer to the previous ones for the comparison kind of questions The follow-up questions should have indicator the previous question they building upon (like <QIs). l'ew-chot examples provided below for reference and follow the smilar format.
Fen-shot demonstration:
INPUT: Which film released finst, Aas Ks. Panchhi Phoolwari? Panchin rekused!?
OUTPUT: Q1)
(7) When the Phoolwar released!
INPUT: Which film has the director who died first. The Goose Woman You Can No Longer Ramain Silen!!
OUTPUT: Q1) Who the director of The nim Gioose Woman!
Q2) Who the diractor of the film You No Woman of Q4) <Q2>> When did the devector the Gil You Can No Longer Remain Silent die?
Remain Silent?
Q3)
INPUTT: Who lived longer, Lndwig Elsbett or Pamela Ann Rymer?
OUTPUT: Q1) How long did Ludwig Elsbett live? (2) long Pumela
Rymer
INPUT: What the place of hirth of the director film Gaby: True Story""
OUTPUT: Q1) Who the director of film Gaby; A Tree Story?
(2) =Q1> What is place of birth the director of dilm Geby: A True Story?
INPUT:
Sisowath Kossimak?
OUTPUT: QL) Who hasband wile of Sisowath Kossamakk?
Q2) <Q1>> Who the father-in-lan law of Ssowatll Kussunak?
Figure 6: LLM prompt used for query-subgraph extraction in the 2WikiMQA dataset. Few shot example questions are taken from 2WikiMQA dataset
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Prompt:
single-hop queries These smaller queries will be later to retriove relevant infermation from curpus in an automated muumerr Each yuay (QI, Q2. etc)) should progresivcly build upon the answer to the previous ones except for the comparison kind of quedions The follow up questions should have indicator presious question they bailding upon (like <QI>). Few-shot stamples provided below reference and follow the smilar lormat.
helpful assistant designed to split multi-hop query
Few-shot demonstration:
INPUT: 'What was the other name for the war between the Cherokee people and white settlers in 1793? OUTPUT: QI) What
between the Cherokee people und white settlers 1793 culled1Q2) <Q1> What the other name the war between Cherokee people and white settlers in 1793!
INPUT: 'Which university has campuses, Dalhousie University California State Polytechnic University, Pomona""
OUIPUT: Q1) How many campuses does Dalhousic University have?Q22) How many campuses does California State Polytechnic University, Pomona have?
Figure 7: LLM prompt used for query-subgraph extraction in the HotpotQA dataset. Few shot example questions are taken from HotpotQA dataset
Prompt:
advauced aeuding comprchension assistant, your meticulonsly arrve the final answer. use the subqueries provided to you given order. If you cannot answer subquery, then try to answer the question to the best of your ability. based on the information avxilable the retrieved context. Start your response with "Though:: where you systematically explain your reasoning process, hreaking down the steps leading to the unswer: Conclude with explanations In (or) United States
aualyze retrieved
wikipedia passages
"Answer:
followed by concise, definitive response limited just the essential words, multiple possible answers, provide answers (or) between, as "American (0r)
NOTE:
1. hope answer matches the answer exuctly so ENSURE E that the answer Gollowngg "Answer"
such as 14 May, 1832
THE SHORTER THE BETTER:
the answer is date, please provide full date as much as possible, soch May, 1932.
3. the answer is place. please provide the full name of the place as much as possible, such as "Oxford, England" instead of "Oxford"
Figure 8: LLM prompt used for answer generation after relevant context retrieval
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Algorithm I Graph Traversal and Context Retrieval for Query-Subgraph
1: Input: Original Query Qorigs Query-subgraph Q = (Vg. Eg) with connected components
{Q(), , }, Graph-of-chunks G = (V, E), number of subgraphs k: Encoder M
2: Output: Top-k scored subgraphs per connected component retrieved context
3: for each connected component Q in Q do
4: [Vy V22 3 < Topological_sor((((")
5: weight(e) 0 for each e V
6:
for each subquery V (V V22 V?....) do
7:
if Vi has no predecessors then Retrieve top-k chunks cı, C2,..., Ck E V based Equation] | add top-k selected chunks C1, 02,..,... C in CondidateSel(( for each CandidateSet(())) do
8:
9:
10:
11:
weight(c) = SS.
end for else
13:
14:
Let - V/ is predecessor of V Generate all combinations C by selecting candidate from each predecessor in P for each combination C, do
15:
16:
17:
Neighbours(C.)) Uc,ec, Ueighbours(c)) C.,)
18:
Set V =Vi for each Gj C, do if cos, max(fcos<(W)) onk) : cnk E UsJec, Neighbours(e.))
19:
20:
then
21:
V + concat(c,, V
22:
end if
23:
end for for each E Neighbors(C.) do
24:
25:
Compute SS, max(cos/ (V/ v), cos /V;',, u), (Qori/ 1)) end for
27:
Select top-k chunks from (eighhors(Cnn) hased on SS,
28:
for each c E K do
29:
wr((c)) = de for cach c, C, do update weight of wt((x) = wt((c)) + weight((.) end for
85.,
30:
31:
32:
33:
weight((s)) < wl(ck) then
34:
weight (((c))
35:
update predecessors of Cn and their predecessor list add cu CandidateSet((C) with weighte, end if end for
37:
38:
39:
Select top-k chunks from CondidateSett(C) and include in CondidateSedt(?) if vi is terminal node then
40:
41:
Selected chunks the terminal nodes along with their predecessors are the selected
top-k subgraphs for Q
42:
end
43:
end for
44:
end if
end for
46:
end for
23


Reviews:
[{"review_id": "Zy3QkKUay5", "reviewer": "Reviewer_Jw6M", "summary": "This paper proposed a method for gathering context for retrieval-augmented generation (RAG): first process the candidate documents into a form of graph (where nodes are chunks and edges exist when there are shared entities), and explore according to a query-specific subgraph generated by LLMs. The method achieves state-of-the-art performance across a wide range of question answering tasks that require multi-hop reasoning.", "questions": "- L195: $cos$ has a single argument: to denote cosine similarity, please write $\\cos \\angle (x, y)$.\n- L202-L220: Please clarify this search process on how this is related to beam search.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The preprocessing of the corpus is intuitive and based on entities. The idea is widely applicable to other NLP tasks\n - Evaluation is done at all stages of the RAG pipeline: at the construction of the graph, subgraph planning, retrieval, and final answer generation. The evaluations presented are convincing.", "weaknesses": "- The graph constructed probably should not be called a \"knowledge graph\" -- usually in the context of information extraction a KG has entities as nodes and predicates as edges. The proposal here is a graph on chunks of text (which I agree that is a better format for downstream RAG than a traditional 3-tuple KG)\n - The context is gathered with one shot -- maybe one could execute retrieval multiple times as the LLM executes the query subgraph it generates? As in a beam-search like process, the retrieved context could evolve as the LLM tries to solve the question (as in the style of https://aclanthology.org/2023.acl-long.557/)? \n - A snippet subgraph of the constructed graph would be illuminating-- I suggest the authors present one in the final version.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "x9pd2127P4", "reviewer": "Reviewer_oVjo", "summary": "This paper proposes BrowseNet, a knowledge graph–based associative memory framework for multi-hop contextual information retrieval. Unlike traditional RAG systems that rely solely on semantic similarity, BrowseNet constructs a knowledge graph where document chunks are nodes enriched with embeddings and lexical entity links form edges. For each query, BrowseNet decomposes it into sub-queries and performs structured graph traversal to retrieve relevant subgraphs that better capture reasoning chains. Experiments on HotpotQA, 2WikiMQA, and MuSiQue show state-of-the-art performance in recall and exact match compared to dense and graph-based RAG baselines, while reducing LLM interaction cost through more efficient retrieval.", "questions": "See weakness", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "Proposes a knowledge graph–based traversal approach that decouples queries for more accurate and context-aware retrieval, achieving competitive results in both passage retrieval and answer generation.\n\nGraphRAG is an important domain for facts-required questions answer.", "weaknesses": "The core idea focuses on context retrieval, but the novelty is limited since query decomposition and graph-based iterative retrieval have been explored previously; comparison to similar methods (e.g., SiReRAG, ArchRAG, GraphRAG) is missing.\n\n\nZhang, N., Choubey, P. K., Fabbri, A., Bernadett-Shapiro, G., Zhang, R., Mitra, P., ... & Wu, C. S. (2024). Sirerag: Indexing similar and related information for multihop reasoning. arXiv preprint arXiv:2412.06206.\nWang, S., Fang, Y., Zhou, Y., Liu, X., & Ma, Y. (2025). ArchRAG: Attributed Community-based Hierarchical Retrieval-Augmented Generation. arXiv preprint arXiv:2502.09891.\nHan, H., Wang, Y., Shomer, H., Guo, K., Ding, J., Lei, Y., ... & Tang, J. (2024). Retrieval-augmented generation with graphs (graphrag). arXiv preprint arXiv:2501.00309.\n\nScalability to large, real-world corpora and real-time use cases is not clearly addressed, as experiments rely on controlled corpora with gold evidence and distractors.\n\nFairness of comparison is unclear, particularly whether the same backbone models (generation, NER, embeddings) were used across baselines, which may confound reported improvements.\n\nThe current framework is optimized for structured multi-hop reasoning and may not directly generalize to open-domain retrieval or tasks with unstructured context dependencies.\n\nThe approach depends on LLM-based query decomposition, which may introduce structural or semantic errors when sub-queries are misgenerated, leading to cascading retrieval failures.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "FErzBbtEGi", "reviewer": "Reviewer_9MFQ", "summary": "The paper introduces BrowseNet, a framework for retrieval-augmented generation (RAG) that represents document collections as graphs of semantically related text chunks. Nodes correspond to document segments, while edges represent lexical or semantic associations between them. During query processing, BrowseNet decomponses a question into directed acyclic graphs and then performs query-specific subgraph exploration to retrieve relevant information, combining both structural similarity (through graph traversal) and semantic similarity (through embeddings). The system is evaluated on multiple multi-hop QA datasets, showing competitive or superior performance to existing RAG, GraphRAG, and dense retrieval baselines.\n\nOverall, this is an interesting and well-executed paper that tackles a relevant challenge in retrieval-based question answering: how to retrieve information that is indirectly connected through multiple associative steps. The approach is well-motivated, and the experimental setup is thorough. However, some of the claims, particularly regarding LLM independence and the nature of the “knowledge graph”, feel overstated or insufficiently substantiated.", "questions": "1. Could you clarify in what sense the dependence on LLMs is reduced, or whether the key benefit lies more in structuring the outputs of LLMs rather than avoiding them altogether? Have you quantified the computational or monetary cost of these LLM-dependent components relative to baseline RAG methods?\n2. The retrieval process for non-initiator nodes appears to involve scoring $k^p$ candidates. Could you provide more details about how this complexity behaves in practice?\n3. How would BrowseNet handle queries that involve cycles, mutual dependencies, or other forms of recursive reasoning?\n4. The isomorphic accuracy is an interesting way to measure the generated subgraphs. I assume that since the graphs are likely small, you used an exact algorithm for isomorphism check. Could you please elaborate on this?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Representing document chunks as graph nodes connected by lexical or semantic relations is a clear and intuitive way to capture associative structures in text.\n2. The paper provides ablation studies and analyses of multiple components (e.g., graph generation, retrieval mechanisms, and QA results), which help the reader understand what drives performance.\n3. The proposed method performs competitively, and in some cases surpasses, both dense retrieval and graph-based RAG baselines.\n4. The paper is well-written and easy to follow, with good structure and figures that illustrate the key ideas effectively.", "weaknesses": "1. While the results are competitive, other claims such as BrowseNet minimizing dependence on LLMs are not really backed by how the method is desgined. It relies on LLMs at several stages, for creating the KG, embedding chunks, decomposing queries, etc. It is therefore not clear what the actual cost is in comparison with other methods.\n2. An important source of cost is in retrieval for non-initiator nodes. This requires considering a total of $k^p$ chunks that need to be scored, increasing the cost of the method.\n3. The query decomposition is based on a directed acyclic graph, which might lead to a limitation for queries that do involve cycles or cannot be expressed as a DAG. Whether this is a source of issues is not discussed.\n4. Calling the constructed graph a “knowledge graph” seems somewhat misleading, since the edges primarily encode textual similarity or shared entity mentions, rather than semantic relations between well-defined concepts. It might be more accurate to refer to it as a semantic chunk graph or entity-linked similarity graph. Clarifying this terminology would prevent confusion for readers coming from the KG community.\n5. Some improvements reported (e.g., Table 2, HotpotQA in Table 3) are small, and it’s unclear whether they are statistically significant. Including confidence intervals or significance tests (e.g., paired bootstrap) would increase confidence in the reported gains.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "a9uVq60cyO", "reviewer": "Reviewer_Dpwg", "summary": "This paper presents an implementation of GraphRAG. The idea is to decompose the query into a graph of sub-queries using LLM, create a graph of passages based on common enities, and use the query graph to guide the exploration of the passage graph to select chunks to be fed into LLM for generation.", "questions": "Q1. Can you compare your approach with early multi-hop QA methods that also build a graph of passages based on their similarity (e.g., common entities)?\n\nQ2. How do you demonstrate the generalizability of your heuristic graph traversal?", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "S1. The paper is generally easy to read.\n\nS2. The experiments are well designed, providing retrieval and generation performance as well as extensive ablation studies.", "weaknesses": "W1. Novelty is limited. KG construction (Section 3.1), query decomposition (Section 3.2.1), and answer generation (Section 3.3) are standard practice in the literature. The rest of the approach, KG traversal (Section 3.2.2), is heuristic and its generalizability is not well justified given experiments on only three datasets.\n\nW2. Baselines are focused on recent RAG solutions, but the proposed approach is closely related to early multi-hop QA solutions, which are not compared empirically.\n\nW3. The constructed KG is actually not a KG but a graph of passages connected based on common entities. This is a common practice in conventional multi-hop QA research. It is not a KG that should represent relations between entities.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)