Title: DuetGraph: Coarse-to-Fine Knowledge Graph Reasoning with Dual-Pathway Global-Local Fusion
Venue: NeurIPS 2025
Accepted: yes
Keywords: Knowledge Graph Reasoning
Abstract: Knowledge graphs (KGs) are vital for enabling knowledge reasoning across various domains. Recent KG reasoning methods that integrate both global and local information have achieved promising results. However, existing methods often suffer from score over-smoothing, which blurs the distinction between correct and incorrect answers and hinders reasoning effectiveness. To address this, we propose DuetGraph, a **coarse-to-fine** KG reasoning mechanism with **dual-pathway** global-local fusion. DuetGraph tackles over-smoothing by segregating—rather than stacking—the processing of local (via message passing) and global (via attention) information into two distinct pathways, preventing mutual interference and preserving representational discrimination. In addition, DuetGraph introduces a **coarse-to-fine** optimization, which partitions entities into high- and low-score subsets. This strategy narrows the candidate space and sharpens the score gap between the two subsets, which alleviates over-smoothing and enhances inference quality. Extensive experiments on various datasets demonstrate that DuetGraph achieves state-of-the-art (SOTA) performance, with up to an **8.7\%** improvement in reasoning quality and a **1.8$\times$** acceleration in training efficiency.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper presents DuetGraph, a dual-pathway architecture for knowledge graph reasoning that integrates local and global information via a coarse-to-fine strategy. Experiments show consistent gains over strong baselines across multiple benchmarks.\n\nWhile the design is novel and results are promising, concerns remain. The inference efficiency and model size are not evaluated, despite the system’s complex two-stage structure. The motivation for the parallel fusion mechanism is unclear and not empirically supported. Notably, removing the coarse-to-fine module results in performance worse than KnowFormer, raising questions about the necessity of this design. Additionally, the paper contains several reference errors.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n- The dual-pathway architecture is a valuable contribution that effectively combines local and global information, addressing a common limitation in existing models.\n- The empirical results are impressive, showing significant improvements over baseline methods in various datasets, which indicates the practical utility of the proposed approach.\n\nweaknesses:\n\n- Despite the demonstrated training efficiency and time complexity analysis, I remain concerned about the inference efficiency and model size due to the algorithm's complex two-stage and parallel structure. This concern is not addressed experimentally in the paper.\n- The motivation behind the parallel design for fusing global and local information is insufficiently explained. Moreover, this aspect is not supported or verified through targeted experiments.\n- There are several errors in the references listed in the paper.\n\nQUESTIONS:\n- The proposed model combines both global and local knowledge graph information. However, KnowFormer also adopts a hybrid strategy. The paper does not clearly articulate the novelty or necessity of introducing a parallel structure in this context. According to the ablation study, removing the Coarse-to-Fine reasoning module leads to performance even worse than that of KnowFormer, which raises further questions about the justification and effectiveness of this structural design.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces DuetGraph, a new method for knowledge graph reasoning that solves the over-smoothing problem in deep models. The authors point out two main issues: combining local and global reasoning in one path causes over-smoothing, and single-stage models lack enough reasoning power. DuetGraph uses a dual-pathway design to separate local and global reasoning and combines their results with a learnable weight. It also uses a coarse-to-fine strategy, where a simple model first selects candidates, and a stronger model refines the results. A score gap-based method helps choose the final answer. The paper includes theory showing DuetGraph reduces over-smoothing better than older models, and experiments prove it achieves strong performance on multiple datasets.\n\nSTRENGTHS AND WEAKNESSES:\n# Strengths\n\n* The paper proposes a novel dual-pathway architecture that decouples local (message passing) and global (transformer) reasoning. This design mitigates over-smoothing and enables parallel computation, leading to both improved performance and training efficiency.\n* The paper provides rigorous theoretical analysis, including score gap bounds and convergence behavior, to support the proposed architecture and its claimed benefits.\n* The experiments cover diverse datasets, include key ablation studies, and provide comparison on LLM based methods (performance and data leakage), offering solid empirical support for the proposed method.\n\n# Weaknesses\n\n* The hyperparameter ∆ used in the coarse-to-fine stage appears important to the final decision, yet its selection strategy is not discussed. There is no ablation study to show its impact on performance.\n* The contribution of the coarse ranking model to overall performance is unclear. An ablation experiment isolating the effect of modifying the coarse stage would strengthen the claims.\n\nQUESTIONS:\n* Q1: In Line 81, the authors state that other KG completion tasks can be reformulated as tail entity completion. Could the authors explain how the relation prediction task (h, ?, t), which is structurally distinct, can be transformed into tail entity prediction task?\n* Q2: The manuscript introduces the hyperparameter ∆ in the coarse-to-fine reasoning stage. Could the authors clarify how ∆ is set in the experiments? Additionally, it would be valuable to include an ablation study analyzing the sensitivity of model performance to different values of ∆, to better understand its impact.\n* Q3: The coarse ranking model plays a crucial role in the DuetGraph framework. Could the authors provide an ablation study or analysis to quantify how much the coarse ranking component contributes to the overall performance?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposed a framework named DuetGraph for knowledge graph reasoning designed to overcome the over-smoothing problem that plagues existing methods. Duet Graph achieves this through two key techniques, dual-pathway global-local fusion and coarse-to-fine reasoning optimization. The core contributions include:\n- Targets the over-smoothing issue: By decoupling and adaptively fusing information, DuetGraph mitigates the problem of incorrect answers receiving scores similar to the correct ones.\n- Theoretical justification: The paper provides theoretical proofs for the effectiveness of the proposed model and optimization, providing a strong foundation for its claims.\n- Performance improvement: Extensive experiments on various datasets and tasks validate the effectiveness of the proposed method.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n- The paper targets a fundamental task and a critical issue, i.e., the over-smoothing problem.\n- The paper provides theoretical proofs for the effectiveness of the proposed model and optimization.\n\nWeaknesses:\n- The presentation needs improvement. I can hardly understand the task when I first read the abstract, especially the concept of KG reasoning. It seems the task is the same with KG representation learning.\n- The text description of the local and global pathways is inconsistent with Figure 1.\n- In Equation (1), $\\alpha$ is used to balance the local and global information. Please clarify the difference between learning the control parameter and using the graph attention network to learn different attention weights.\n- In the coarse-to-fine reasoning, what is the standard of the coarse model? It seems all the baseline models could act as coarse models, and the proposed fine reasoning conducts re-ranking.\n- In the evaluation, 1) What is the task definition of inductive and transductive reasoning? 2) In inductive reasoning, why divide the datasets? 3) In the transductive reasoning, the paper claims the baseline results are directly from the original papers, but there are some inconsistencies, e.g., the RotatE. If the results are reproduced based on publicly available code, please clarify where the gaps exist. 4) Section 2 claims the paper primarily focuses on the tail entity completion. Triple classification is also a typical task. Can the framework be applied to it?\n\nQUESTIONS:\nNo further questions. Please refer to the weaknesses.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 2, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces DuetGraph, a novel knowledge graph (KG) reasoning framework designed to mitigate the problem of \"score over-smoothing\"  which often blurs the distinction between correct and incorrect answers in existing KG reasoning methods. DuetGraph addresses this through two main innovations: a dual-pathway global-local fusion mechanism and a coarse-to-fine reasoning optimization.\n\nSTRENGTHS AND WEAKNESSES:\nOriginality: The core idea of segregating local and global information processing into dual pathways, rather than stacking them, is novel and directly addresses the identified problem of score over-smoothing. The coarse-to-fine optimization further enhances this by refining the candidate space\n\nGeneralizability of Coarse Models: While DuetGraph shows consistent outperformance with various coarse-grained models, deeper analysis into why certain coarse models might perform better or worse within the DuetGraph framework could provide more insight.\n\nQUESTIONS:\nAdaptive Fusion Parameter (α): The paper mentions that the adaptive fusion approach \"drives α below the theoretical threshold in Theorem 1 via parameter update with gradient descent\". Could the authors elaborate on how this parameter α is initialized and empirically how its value changes during training across different datasets? Are there any cases where α does not converge as expected, and what might be the implications?\n\n\nImpact of Coarse Model Choice: Table 4 shows DuetGraph's performance with different coarse models. While the results are consistently good, can the authors provide more insight into the characteristics of a \"good\" coarse model for DuetGraph? For instance, does the performance of the coarse model directly correlate with the final DuetGraph performance, or does DuetGraph's coarse-to-fine mechanism mitigate weaknesses of less effective coarse models?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 2, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-10"}