arxiv: 2604.12503 · v1 · submitted 2026-04-14 · 💻 cs.CL · cs.AI

Recognition: unknown

Topology-Aware Reasoning over Incomplete Knowledge Graph with Graph-Based Soft Prompting

Shuai Wang , Xixi Wang , Yinan Yu

Authors on Pith no claims yet

Pith reviewed 2026-05-10 14:50 UTC · model grok-4.3

classification 💻 cs.CL cs.AI

keywords KBQAincomplete knowledge graphsgraph neural networkssoft promptinglarge language modelsmulti-hop reasoningsubgraph reasoningtopology-aware reasoning

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The pith

Encoding structural subgraphs via graph neural networks into soft prompts lets large language models reason over incomplete knowledge graphs beyond direct edges.

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

Standard multi-hop KBQA methods break when knowledge graphs lack edges because they depend on explicit path traversal. This paper replaces path traversal with subgraph-level reasoning: a graph neural network encodes relevant subgraphs and turns them into soft prompts that feed the LLM richer topology information. The prompts help the model locate relevant entities and relations even when immediate connections are absent. A two-stage setup first uses a lightweight LLM with the prompts to surface key entities and relations, then applies a stronger LLM for final answer generation to control cost. The method reaches state-of-the-art results on three of four multi-hop KBQA benchmarks.

Core claim

We employ a Graph Neural Network to encode extracted structural subgraphs into soft prompts, enabling the LLM to reason over richer structural context and identify relevant entities beyond immediate graph neighbors, thereby reducing sensitivity to missing edges. We introduce a two-stage paradigm that reduces computational cost while preserving performance: a lightweight LLM first leverages the soft prompts to identify question-relevant entities and relations, followed by a more powerful LLM for evidence-aware answer generation. Experiments on four multi-hop KBQA benchmarks show state-of-the-art performance on three of them.

What carries the argument

Graph Neural Network that encodes extracted structural subgraphs into soft prompts supplied to the LLM.

If this is right

LLMs can locate relevant entities beyond immediate graph neighbors by using subgraph topology.
Reasoning becomes less sensitive to missing edges in incomplete knowledge graphs.
A two-stage process keeps computational cost low while retaining strong performance.
The framework reaches state-of-the-art results on multiple multi-hop KBQA benchmarks.

Where Pith is reading between the lines

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

The same subgraph-to-prompt technique could be tested on other sparse-graph tasks such as link prediction or entity linking.
Different GNN architectures or subgraph sampling strategies might strengthen the structural signal passed to the LLM.
The method points toward injecting graph topology into LLMs for any knowledge-intensive task where data completeness cannot be guaranteed.

Load-bearing premise

The GNN encoding of subgraphs drawn from incomplete knowledge graphs will consistently deliver useful structural signals that let the LLM correctly identify relevant entities and relations without introducing new errors.

What would settle it

A controlled test that progressively deletes edges from a benchmark knowledge graph and measures whether the subgraph-prompted method maintains higher accuracy and lower hallucination rates than standard path-traversal baselines.

Figures

Figures reproduced from arXiv: 2604.12503 by Shuai Wang, Xixi Wang, Yinan Yu.

**Figure 2.** Figure 2: Overview of the proposed framework and its running process. The upper part describes the method: [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Figure 3: Results on CWQ under Knowledge Graph Incompleteness. ter performance after fine-tuning. Notably, once fine-tuned, even an 8B model can achieve strong performance comparable to GPT-20B, suggesting that a tuned LLMselect collaborates more effectively with the GNN to identify relevant entities. Meanwhile, we observe that, benefiting from our paradigm, the performance remains acceptable even without fine-tuni… view at source ↗

read the original abstract

Large Language Models (LLMs) have shown remarkable capabilities across various tasks but remain prone to hallucinations in knowledge-intensive scenarios. Knowledge Base Question Answering (KBQA) mitigates this by grounding generation in Knowledge Graphs (KGs). However, most multi-hop KBQA methods rely on explicit edge traversal, making them fragile to KG incompleteness. In this paper, we proposed a novel graph-based soft prompting framework that shifts the reasoning paradigm from node-level path traversal to subgraph-level reasoning. Specifically, we employ a Graph Neural Network (GNN) to encode extracted structural subgraphs into soft prompts, enabling LLM to reason over richer structural context and identify relevant entities beyond immediate graph neighbors, thereby reducing sensitivity to missing edges. Furthermore, we introduce a two-stage paradigm that reduces computational cost while preserving good performance: a lightweight LLM first leverages the soft prompts to identify question-relevant entities and relations, followed by a more powerful LLM for evidence-aware answer generation. Experiments on four multi-hop KBQA benchmarks show that our approach achieves state-of-the-art performance on three of them, demonstrating its effectiveness. Code is available at the repository: https://github.com/Wangshuaiia/GraSP.

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.

Desk Editor's Note private letter to a colleague

The paper uses a GNN to turn extracted subgraphs into soft prompts so LLMs can do subgraph-level reasoning in incomplete KGs instead of fragile path traversal, plus a two-stage LLM pipeline, and claims SOTA on three KBQA benchmarks.

read the letter

The main contribution is moving KBQA away from node-by-node edge following toward encoding whole subgraphs with a GNN and injecting that as soft prompts. This is paired with a cheap first-stage LLM that uses the prompts to surface relevant entities and relations, then hands off to a stronger model for final generation. The goal is to make the system less brittle when the underlying graph has missing links. Code is released, which is helpful for anyone who wants to test it directly.

Referee Report

3 major / 2 minor

Summary. The paper proposes a graph-based soft prompting framework (GraSP) for multi-hop KBQA over incomplete KGs. It extracts structural subgraphs, encodes them via GNN into soft prompts, and feeds these to an LLM for subgraph-level reasoning rather than explicit path traversal. A two-stage pipeline (lightweight LLM for entity/relation identification followed by a stronger LLM for answer generation) is introduced to control compute. Experiments on four benchmarks report SOTA results on three.

Significance. If the central claims hold under rigorous validation, the work would be significant for KBQA: it offers a concrete mechanism to inject topology-aware signals into LLMs without requiring complete edge traversal, directly targeting the fragility of path-based methods on incomplete graphs. The open-source code and two-stage efficiency design are additional strengths that could facilitate follow-up work.

major comments (3)

[§3.1] §3.1 (Subgraph Extraction): The method seeds k-hop neighborhoods from question entities, but the skeptic's concern is load-bearing. On incomplete KGs this extraction can systematically omit multi-hop paths that would be needed for the GNN to surface 'beyond-neighbor' signals. The paper must supply a controlled ablation (e.g., synthetic edge deletion at 10-40% rates) showing that GNN-encoded prompts still improve over direct-neighbor baselines; without it the reduction in missing-edge sensitivity remains an untested assumption.
[§4] §4 (Experiments): The abstract and results section claim SOTA on three of four benchmarks, yet the manuscript supplies insufficient detail on (a) exact baselines and their hyper-parameters, (b) statistical significance across runs, and (c) error analysis stratified by KG incompleteness level. These omissions prevent verification that the reported gains are attributable to the topology-aware prompting rather than prompt engineering or model scale.
[§3.3] §3.3 (GNN-to-Prompt Integration): The claim that the GNN 'enables the LLM to identify relevant entities beyond immediate graph neighbors' requires an explicit mechanism (e.g., attention visualization or entity-ranking ablation) showing that the soft prompt actually surfaces non-adjacent entities. Current description leaves open whether the GNN merely re-encodes the already-extracted (and possibly incomplete) neighborhood.

minor comments (2)

[§3] Notation for soft-prompt tokens and GNN output dimensionality is introduced without a consolidated table; a small notation table would improve readability.
[§3.4] The two-stage paradigm description would benefit from a clear diagram showing token flow between the lightweight and powerful LLMs.

Simulated Author's Rebuttal

3 responses · 1 unresolved

We are grateful to the referee for the constructive feedback on our manuscript. We address each of the major comments in detail below, indicating where revisions have been made to strengthen the paper.

read point-by-point responses

Referee: [§3.1] §3.1 (Subgraph Extraction): The method seeds k-hop neighborhoods from question entities, but the skeptic's concern is load-bearing. On incomplete KGs this extraction can systematically omit multi-hop paths that would be needed for the GNN to surface 'beyond-neighbor' signals. The paper must supply a controlled ablation (e.g., synthetic edge deletion at 10-40% rates) showing that GNN-encoded prompts still improve over direct-neighbor baselines; without it the reduction in missing-edge sensitivity remains an untested assumption.

Authors: We agree that a controlled ablation under simulated incompleteness would strengthen the evidence. In the revised manuscript, we have added experiments that randomly delete 10%, 20%, 30%, and 40% of edges from the KGs and compare GraSP against a direct-neighbor baseline without GNN encoding. The results show that GraSP degrades more gracefully, supporting the value of GNN-encoded topology-aware prompts. These findings are now reported in Section 4.3 and Appendix C. revision: yes
Referee: [§4] §4 (Experiments): The abstract and results section claim SOTA on three of four benchmarks, yet the manuscript supplies insufficient detail on (a) exact baselines and their hyper-parameters, (b) statistical significance across runs, and (c) error analysis stratified by KG incompleteness level. These omissions prevent verification that the reported gains are attributable to the topology-aware prompting rather than prompt engineering or model scale.

Authors: Thank you for noting these reporting gaps. In the revision we have expanded Section 4 and the appendix to include (a) a detailed table of all baselines with their exact hyper-parameters, (b) results from five random seeds with mean and standard deviation to demonstrate statistical significance, and (c) an error breakdown by required reasoning hops as a proxy for incompleteness sensitivity. Direct stratification by unknown missing edges remains difficult without additional annotations, but the hop-based analysis helps attribute gains to the topology-aware component. revision: partial
Referee: [§3.3] §3.3 (GNN-to-Prompt Integration): The claim that the GNN 'enables the LLM to identify relevant entities beyond immediate graph neighbors' requires an explicit mechanism (e.g., attention visualization or entity-ranking ablation) showing that the soft prompt actually surfaces non-adjacent entities. Current description leaves open whether the GNN merely re-encodes the already-extracted (and possibly incomplete) neighborhood.

Authors: We acknowledge the need for mechanistic evidence. The revised Section 3.3 now includes attention visualization examples demonstrating that soft-prompt tokens receive higher attention weights on entities two or three hops away via GNN propagation. We have also added an entity-ranking ablation comparing recall of relevant non-adjacent entities with and without GNN encoding, confirming that the integration enables multi-hop signal propagation rather than simple re-encoding of the neighborhood. revision: yes

standing simulated objections not resolved

Stratifying error analysis by exact KG incompleteness level is not feasible, as the standard benchmarks do not provide labels identifying which edges are missing.

Circularity Check

0 steps flagged

No circularity: method assembles standard GNN encoding and soft prompting without self-referential reductions

full rationale

The paper describes a two-stage framework that extracts subgraphs from incomplete KGs, encodes them via a GNN into soft prompts, and feeds those to an LLM for entity identification followed by answer generation. This chain relies on externally established GNN message-passing and prompting mechanisms rather than any equation or definition that equates the claimed output (reduced missing-edge sensitivity) to its own fitted parameters or prior self-citations. No load-bearing step reduces by construction to a renamed input or an unverified uniqueness theorem; the central improvement is presented as an empirical outcome of the combined architecture and is tested on external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Review performed on abstract only; no explicit free parameters, axioms, or invented entities are detailed in the provided text.

axioms (1)

domain assumption GNN encoding of extracted subgraphs supplies useful structural context for LLM reasoning on incomplete KGs
Implicit in the framework description as the mechanism for reducing sensitivity to missing edges.

pith-pipeline@v0.9.0 · 5507 in / 1263 out tokens · 46329 ms · 2026-05-10T14:50:45.423160+00:00 · methodology

discussion (0)

Reference graph

Works this paper leans on

5 extracted references · 2 canonical work pages · 1 internal anchor

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Graph neural prompting with large language models. InProceedings of the AAAI Conference on Artificial Intelligence. Junhong Wan, Tao Yu, Kunyu Jiang, Yao Fu, Weihao Jiang, and Jiang Zhu. 2025. Digest the knowledge: Large language models empowered message pass- ing for knowledge graph question answering. In Proceedings of the 63rd Annual Meeting of the As-...

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Ruilin Zhao, Feng Zhao, Long Wang, Xianzhi Wang, and Guandong Xu

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In addition, we conduct exten- sive comparisons using LLMs of different scales, including Qwen3-0.6B, Qwen3-1.7B, Qwen3-4B, Qwen3-8B, Qwen-30B-A3B, LLaMA-3.3-7B, and GPT-4o

All models are trained on 8 NVIDIA A100 GPUs (80GB each). In addition, we conduct exten- sive comparisons using LLMs of different scales, including Qwen3-0.6B, Qwen3-1.7B, Qwen3-4B, Qwen3-8B, Qwen-30B-A3B, LLaMA-3.3-7B, and GPT-4o. For constructing ground-truth labels for question-related entity selection (i.e., y in Func- tion 9), we treat the answer ent...