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arxiv: 2510.11541 · v2 · submitted 2025-10-13 · 💻 cs.LG · cs.AI

Question-Adaptive Graph Learning for Multi-hop Retrieval Augmented Generation

Yuchen Yan , Peiyan Zhang , Zhihua Liu , Hao Wang , Yatao Bian , Weiming Li , Xiaoshuai Hao This is my paper

Pith reviewed 2026-05-18 07:08 UTC · model grok-4.3

classification 💻 cs.LG cs.AI
keywords multi-hop retrievalretrieval-augmented generationgraph neural networksquestion-adaptive learningknowledge graphssynthesized data pre-trainingmulti-level information modeling
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The pith

A question-adaptive graph neural network on multi-level knowledge graphs improves retrieval accuracy for multi-hop questions in RAG systems.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes a framework for multi-hop retrieval-augmented generation by first building a Multi-information Level Knowledge Graph that represents questions at varying levels of detail. It then introduces a Question-Adaptive Graph Neural Network whose intra- and inter-level message passing is guided by the question itself to aggregate relevant facts and suppress noise. Pre-training on two kinds of synthesized multi-hop examples further strengthens the learned representations. A sympathetic reader would care because current RAG pipelines frequently miss or mix up the multiple distinct facts required by complex questions.

Core claim

The authors show that question-guided message passing across intra- and inter-level edges on a multi-information-level knowledge graph produces representations that capture complex semantic structure and reduce the effect of irrelevant retrieval noise, with the largest gains appearing after pre-training on synthesized multi-hop data and especially for high-hop questions.

What carries the argument

Quest-GNN, which performs question-guided intra- and inter-level message passing on the Multi-L KG to enable multi-granular aggregation while limiting noise.

If this is right

  • Question-guided aggregation reduces noise in multi-target retrieval.
  • Pre-training on synthesized multi-hop examples transfers to real multi-hop scenarios.
  • Performance gains are largest on high-hop questions, reaching 33.8 percent improvement.
  • Multi-granular information is aggregated more effectively than in standard graph or embedding approaches.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same question-guided passing idea could be tested on single-hop or non-retrieval reasoning tasks where noise is also a problem.
  • Alternative graph-construction heuristics that do not rely on fixed information levels might be compared to the Multi-L KG design.
  • Pairing Quest-GNN with existing reranking or query-rewriting modules could produce additive improvements in full RAG pipelines.

Load-bearing premise

The synthesized data generation strategies produce pre-training examples whose distribution matches real multi-hop questions closely enough for the Quest-GNN to learn robust, generalizable representations that transfer to downstream retrieval tasks.

What would settle it

A controlled experiment that measures retrieval accuracy on a set of real high-hop questions when the model is pre-trained on the paper's synthesized data versus trained from scratch or on mismatched synthetic data would test whether the distribution match is necessary for the reported gains.

Figures

Figures reproduced from arXiv: 2510.11541 by Hao Wang, Peiyan Zhang, Weiming Li, Xiaoshuai Hao, Yatao Bian, Yuchen Yan, Zhihua Liu.

Figure 1
Figure 1. Figure 1: Performance on multi-hop questions. Existing methods struggle to perform well as hop number increases while QSGNN can achieve better performance on high-hop questions. search strategies (Edge et al., 2024; Guo et al., 2024; Gutierrez et al., 2025) or utilize Graph Neural ´ Networks (GNNs) (Fang et al., 2019; Mavromatis & Karypis, 2024; Luo et al., 2025) to identify relevant information. By using KGs, these… view at source ↗
Figure 2
Figure 2. Figure 2: Framework overview. It first constructs Multi-L KG to model the multi-level relation￾ships within corpora. QSGNN is designed to aggregate information from different levels, all the aggregations are guided by query. After pre-training and fine-tuning, it can generate representations for multi-hop questions. entity-chunk Eoc, entity-document Eod, chunk-chunk Ecc, chunk-document Ecd. The construction of Multi… view at source ↗
Figure 3
Figure 3. Figure 3: Influence of pre-training scale on various model dimension. Pre-training Scale and Information Dimension. We pre-train QSGNN on the sythesized QA pairs from 0 to 150k across information dimension from 32 to 512 respectively, the results are shown in [PITH_FULL_IMAGE:figures/full_fig_p017_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Good case 1 for QSGNN. Query: What county is the city that shares a border with the state capital of the state where Andrew Deveaux was born located in? QSGNN Retrieval： Andrew Deveaux\nAndrew Deveaux (30 April 1758 – 11 July 1812) was an American Loyalist from South Carolina who is most famous for his recapture of the Bahamas in 1783. Charleston, South Carolina\nAlthough the city lost the status of state … view at source ↗
Figure 5
Figure 5. Figure 5: Good case 2 for QSGNN. 18 [PITH_FULL_IMAGE:figures/full_fig_p018_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Good case 3 for QSGNN. in 1786”), and iii) geographic relationships (“Forest Acres borders Columbia”). The successful re￾trieval is due to the query-alignment and comprehensive understanding of contextual information. However, HippoRAG2 incorrectly retrieves Savannah county information due to over-reliance on “county” and “located on” seed nodes. Good Case 3 [PITH_FULL_IMAGE:figures/full_fig_p019_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Bad case 1 for QSGNN. Query: In which province is San Clemente, from the country where Fuser and Alberto meet the indigenous couple who were traveling to look for work? Answer: Talca Province Gold docs： The Motorcycle Diaries (film)\nDuring their expedition, Guevara and Granado encounter the poverty of the indigenous peasants, and the movie assumes a greater seriousness once the men gain a better sense of … view at source ↗
Figure 8
Figure 8. Figure 8: Bad case 2 for QSGNN. Bad Case 2 [PITH_FULL_IMAGE:figures/full_fig_p020_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Bad case 3 for QSGNN. both pre-training and fine-tuning corpora. The model deflect to general terms like “province” and “county”, retrieving irrelevant documents. Bad Case 3 [PITH_FULL_IMAGE:figures/full_fig_p021_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Sentence extraction prompt for OpenIE. Goal: Your task is to extract named entities from the given paragraph. Respond with a JSON list of entities. Example: - Output -: {"named_entities": ["Radio City", "India", "3 July 2001", "Hindi", "English", "May 2008", "PlanetRadiocity.com"] } - Input -: Radio City\nRadio City is India's first private FM radio station and was started on 3 July 2001. It plays Hindi, … view at source ↗
Figure 11
Figure 11. Figure 11: Entity extraction prompt for OpenIE. A.11 PROMPTS FOR SYNTHESIZED PRE-TRAINING DATA The prompts used for generate pre-training data are shown in [PITH_FULL_IMAGE:figures/full_fig_p022_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Triple extraction prompt for OpenIE. 23 [PITH_FULL_IMAGE:figures/full_fig_p023_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: One hop question generation prompt for OpenIE. 24 [PITH_FULL_IMAGE:figures/full_fig_p024_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Two hop question generation prompt for OpenIE. 25 [PITH_FULL_IMAGE:figures/full_fig_p025_14.png] view at source ↗
read the original abstract

Retrieval-augmented generation (RAG) has demonstrated its ability to enhance Large Language Models (LLMs) by integrating external knowledge sources. However, multi-hop questions, which require the identification of multiple knowledge targets to form a synthesized answer, raise new challenges for RAG systems. Under the multi-hop settings, existing methods often struggle to fully understand the questions with complex semantic structures and are susceptible to irrelevant noise during the retrieval of multiple information targets. To address these limitations, we propose a novel graph representation learning framework for multi-hop question retrieval. We first introduce a Multi-information Level Knowledge Graph (Multi-L KG) to model various information levels for a more comprehensive understanding of multi-hop questions. Based on this, we design a Question-Adaptive Graph Neural Network (Quest-GNN) for representation learning on the Multi-L KG. Quest-GNN employs intra/inter-level message passing mechanisms, and in each message passing the information aggregation is guided by the question, which not only facilitates multi-granular information aggregation but also significantly reduces the impact of noise. To enhance its ability to learn robust representations, we further propose two synthesized data generation strategies for pre-training the Quest-GNN. Extensive experimental results demonstrate the effectiveness of our framework in multi-hop scenarios, especially in high-hop questions the improvement can reach 33.8\%. The code is available at: https://github.com/Jerry2398/QSGNN.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper introduces a Multi-information Level Knowledge Graph (Multi-L KG) to represent multi-hop questions at varying granularities and a Question-Adaptive Graph Neural Network (Quest-GNN) that performs intra- and inter-level message passing guided by the input question. Two strategies for synthesizing pre-training data are proposed to improve representation robustness, and the framework is evaluated on multi-hop retrieval tasks with a reported peak improvement of 33.8% on high-hop questions.

Significance. If the reported gains prove robust to baseline choice, statistical testing, and pre-training distribution shift, the work would offer a concrete graph-based mechanism for reducing noise in multi-hop retrieval while preserving multi-granular information. The public release of code at https://github.com/Jerry2398/QSGNN is a positive contribution to reproducibility.

major comments (3)
  1. [Abstract / Experiments] Abstract and Experiments section: the headline claim of a 33.8% improvement on high-hop questions is presented without naming the strongest baseline, reporting dataset splits, or providing statistical significance, which prevents direct assessment of whether the gain is attributable to the Multi-L KG + Quest-GNN design rather than experimental setup.
  2. [Pre-training / §4] Pre-training subsection: no distributional diagnostics (e.g., hop-count histograms, embedding-space overlap, or KL divergence between synthetic and real multi-hop queries) are supplied to support the assumption that the two synthesized data strategies produce examples whose semantic structure and noise profile transfer to downstream real-world questions; this assumption is load-bearing for the central effectiveness claim.
  3. [Ablation studies] Ablation studies: the contribution of the question-guided aggregation is not isolated from the effect of pre-training data; an ablation that trains Quest-GNN from scratch on the target task (or with random guidance) is needed to establish that the adaptive message-passing mechanism, rather than data artifacts, drives the reported gains.
minor comments (2)
  1. [Method] Notation for the Multi-L KG levels and the intra/inter-level aggregation functions could be clarified with an explicit diagram or additional equations showing how question embeddings modulate the message-passing weights.
  2. [Figures] Figure captions and axis labels in the experimental plots should explicitly state the metric (e.g., recall@K or exact-match) and the number of runs used for error bars.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback. We agree that additional clarifications and analyses will strengthen the manuscript and address the concerns about robustness and attribution of gains. We respond to each major comment below and indicate planned revisions.

read point-by-point responses

  1. Referee: [Abstract / Experiments] Abstract and Experiments section: the headline claim of a 33.8% improvement on high-hop questions is presented without naming the strongest baseline, reporting dataset splits, or providing statistical significance, which prevents direct assessment of whether the gain is attributable to the Multi-L KG + Quest-GNN design rather than experimental setup.

    Authors: We agree that the abstract and experiments would benefit from greater precision to allow direct assessment. In the revised manuscript we will explicitly name the strongest baseline (the best-performing prior method on each dataset), report the exact dataset splits and high-hop question subsets used, and include statistical significance (means and standard deviations over multiple runs together with paired t-test p-values). These additions will make clear that the reported 33.8% improvement on high-hop questions is measured against the strongest available baseline under the same evaluation protocol. revision: yes

  2. Referee: [Pre-training / §4] Pre-training subsection: no distributional diagnostics (e.g., hop-count histograms, embedding-space overlap, or KL divergence between synthetic and real multi-hop queries) are supplied to support the assumption that the two synthesized data strategies produce examples whose semantic structure and noise profile transfer to downstream real-world questions; this assumption is load-bearing for the central effectiveness claim.

    Authors: We acknowledge that explicit distributional diagnostics would provide stronger support for the transferability assumption. While downstream task gains constitute our primary evidence, we will add hop-count histograms comparing the two synthetic pre-training distributions to the real multi-hop queries in the evaluation sets. We will also briefly discuss how the synthesis strategies were designed to preserve semantic structure and noise characteristics. If space permits we will include a simple embedding-space overlap statistic; otherwise the histograms and design rationale will be included in the revised §4. revision: partial

  3. Referee: [Ablation studies] Ablation studies: the contribution of the question-guided aggregation is not isolated from the effect of pre-training data; an ablation that trains Quest-GNN from scratch on the target task (or with random guidance) is needed to establish that the adaptive message-passing mechanism, rather than data artifacts, drives the reported gains.

    Authors: This is a fair request for isolating the source of improvement. We will add two new ablation settings in the revised experiments: (1) Quest-GNN trained from scratch on the target multi-hop retrieval task without any pre-training, and (2) the same architecture using random (non-question-adaptive) guidance during intra- and inter-level message passing. These results will be reported alongside the existing ablations to demonstrate that the question-adaptive aggregation mechanism contributes gains beyond those attributable to the synthesized pre-training data alone. revision: yes

Circularity Check

0 steps flagged

No significant circularity; empirical framework with independent experimental validation

full rationale

The paper introduces a Multi-L KG and Quest-GNN with question-adaptive message passing, plus two synthesized data strategies for pre-training, then reports empirical gains on multi-hop retrieval tasks. No equations, fitted parameters renamed as predictions, or self-citation chains are present in the provided text that would reduce the claimed improvements to inputs by construction. The derivation chain consists of architectural design choices and data generation heuristics whose effectiveness is tested externally via experiments rather than being tautological. This qualifies as a self-contained empirical contribution against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 2 invented entities

The framework rests on the untested premise that question-guided aggregation on a multi-level graph reduces noise more effectively than standard retrieval or GNN baselines, plus the assumption that synthetic pre-training data generalizes.

invented entities (2)
  • Multi-information Level Knowledge Graph (Multi-L KG) no independent evidence
    purpose: Model various information levels for comprehensive understanding of multi-hop questions
    New structure introduced to capture multi-granular information
  • Question-Adaptive Graph Neural Network (Quest-GNN) no independent evidence
    purpose: Perform representation learning with intra/inter-level message passing guided by the question
    Core novel component for noise reduction and multi-granular aggregation

pith-pipeline@v0.9.0 · 5800 in / 1173 out tokens · 30350 ms · 2026-05-18T07:08:43.061908+00:00 · methodology

discussion (0)

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    and GPT-4o-mini (OpenAI., 2024)) for QA task. 14 Under review as a conference paper at ICLR 2026 A.4 QA PERFORMANCE ONGPT-4O-MINI Table 8: QA performance on GPT-4o-mini. Average means the average performance across all the datasets. We highlight the best results withboldand the second best results with under line . MuSiQue 2Wiki HotpotQA Average Method EM...

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    A.5 MULTI-HOPPERFORMANCE ON2WIKI Table 9: Performance of different hop numbers on 2Wiki. The best result is shown inbold. 2Wiki(Recall@5) 2Wiki(F1) 2Wiki(EM) Method 2-hop 4-hop 2-hop 4-hop 2-hop 4-hop NV-Embed-v2 81.40 63.52 63.21 42.19 59.28 23.35 GFM-RAG 83.37 56.09 64.36 33.11 54.44 18.82 RAPTOR 85.91 64.75 66.13 40.14 53.71 20.12 GraphRAG - - 67.99 39...

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    The movement started with the first election for the Reichstag; those elected were called "les députés protestataires\", and until the fall of Bismarck in 1890, they were the only deputies elected by the Alsatians to the German parliament demanding the return of those territories to France. At the last Reichstag election in Strasbourg and its periphery, t...

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    Figure 9 shows a typical bad case for QSGNN. In this case the term like “Messi” and “Barcelona” dwarf the key evidence “Diego Maradona, who Messi can bring comparison to”. The sequential dependency between finding “Diego Maradona” and subsequent evidence (“June 1982 transfer record”) further complicates retrieval. It is difficult for query alignment to id...

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    22 Under review as a conference paper at ICLR 2026 Goal: Example: - Output -: {"triples": [ ["Radio City", "located in", "India"], ["Radio City", "is", "private FM radio station"], ["Radio City", "started on", "3 July 2001"], ["Radio City", "plays songs in", "Hindi"], ["Radio City", "plays songs in", "English"], ["Radio City", "forayed into", "New Media"]...

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    Entity list two: ['December 2012', 'May 2013', 'Lionel Messi', 'Bayern Munich', 'Champions League', 'Copa del Rey', 'FC Barcelona', 'Pep Guardiola', 'Spanish', 'Tito Vilanova', 'Real Madrid', 'July'] Common entity list: ['Real Madrid', 'Champions League', 'Copa del Rey', 'FC Barcelona'] - Output -: {"question-answer-doc triples": [ { "question": "Which co...

    work page 2012