Papers by Fu Ning
DLTKG: Denoising Logic-based Temporal Knowledge Graph Reasoning (2025.findings-emnlp)
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| Challenge: | Current approaches to temporal knowledge representation face limited generalization to unseen facts and insufficient interpretability of reasoning processes. |
| Approach: | They propose a framework that uses a denoising diffusion process to complete reasoning tasks . they propose introducing a noise source and historical conditionguiding mechanism to improve interpretability . |
| Outcome: | The proposed framework outperforms state-of-the-art methods on three benchmark datasets. |
Not All Modalities at Once: Dynamic Dropout and Bidirectional Fusion for Robust Multi-modal Knowledge Graph Completion (2026.findings-acl)
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| Challenge: | Existing MKGC methods train with all modalities available, implicitly assuming consistent complementarity . however, this often induces modality dependence and modality competition under heterogeneous noise, which can hinder robust multi-modal fusion and limit overall performance. |
| Approach: | They propose a framework to infer missing links in multimodal knowledge graphs by leveraging structured triples together with auxiliary modalities such as text and images. |
| Outcome: | The proposed framework outperforms baselines and achieves new state-of-the-art results. |
Query-Aware Graph Attention for Precise Subgraph Retrieval in Knowledge-Augmented Reasoning (2026.findings-acl)
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| Challenge: | Existing Knowledge Graph (KG)-based Retrieval-Augmented Generation (RAG) systems insufficiently model the interaction between query semantics and relation types, resulting in imprecise subgraph retrieval and unstable reasoning. |
| Approach: | They propose a retrieval framework that integrates query semantics and relation embeddings directly into the attention mechanism. |
| Outcome: | Experiments on WebQSP and CWQ establish new state-of-the-art results in both Triple Recall and Answer Recall. |
How to Make LMs Strong Node Classifiers? (2026.findings-eacl)
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Zhe Xu, Kaveh Hassani, Si Zhang, Hanqing Zeng, Michihiro Yasunaga, Limei Wang, Dongqi Fu, Ning Yao, Bo Long, Hanghang Tong
| Challenge: | Language Models (LMs) are increasingly challenging the dominance of domain-specific models, such as Graph Neural Networks (GNNs) and Graph Transformers (GTs). |
| Approach: | They propose a novel approach that empowers off-the-shelf LMs to achieve performance comparable to state-of-the art (SOTA) GNNs on node classification tasks without requiring any architectural modifications. |
| Outcome: | The proposed approach outperforms existing GNNs on node classification tasks and is open-source upon publication. |
MIO: A Foundation Model on Multimodal Tokens (2025.emnlp-main)
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Zekun Moore Wang, King Zhu, Chunpu Xu, Wangchunshu Zhou, Jiaheng Liu, Yibo Zhang, Jessie Wang, Ning Shi, Siyu Li, Yizhi Li, Haoran Que, Zhaoxiang Zhang, Yuanxing Zhang, Ge Zhang, Ke Xu, Jie Fu, Wenhao Huang
| Challenge: | Existing models lack multimodal understanding capabilities, resulting in closed-source model that does not support multimodal interleaved sequences. |
| Approach: | They propose a foundation model built on multimodal tokens capable of understanding and generating speech, text, images, and videos in an end-to-end, autoregressive manner. |
| Outcome: | The proposed model is able to understand speech, text, images, and videos in an end-to-end, autoregressive manner. |
Text Editing as Imitation Game (2022.findings-emnlp)
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| Challenge: | Text editing is an important domain of processing tasks to edit the text in a localized fashion, such as text simplification. |
| Approach: | They propose a nonautoregressive decoder for state-to-action demonstrations that parallels the decoding while retaining the dependencies between tokens. |
| Outcome: | The proposed model outperforms the autoregressive baselines on a suite of Arithmetic Equation benchmarks in terms of performance, efficiency, and robustness. |
Critic Rule Induction: Improving Temporal Knowledge Graph Forecasting with Generator-Critic Language Models (2026.findings-acl)
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| Challenge: | Existing methods for predicting future facts from time-evolving graphs rely on statistical co-occurrences and extensive path enumeration. |
| Approach: | They propose a Critic-Guided Rule Induction method which treats temporal rules as rule hypotheses to be examined and adopts a decoupled Generation-Discrimination pipeline to induce rules that are high-coverage and high-precision. |
| Outcome: | The proposed method outperforms strong baselines on three benchmarks and achieves state-of-the-art performance. |
Investigating and Enhancing the Robustness of Large Multimodal Models Against Temporal Inconsistency (2025.acl-long)
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Jiafeng Liang, Shixin Jiang, Xuan Dong, Ning Wang, Zheng Chu, Hui Su, Jinlan Fu, Ming Liu, See-Kiong Ng, Bing Qin
| Challenge: | Large Multimodal Models (LMMs) have demonstrated impressive performance on general video comprehension benchmarks, but their robustness needs to be thoroughly investigated for broader applications. |
| Approach: | They propose a temporal robustness benchmark which introduces temporal inconsistency perturbations separately at the visual and textual modalities to assess the robustness of models. |
| Outcome: | The proposed method improves the model’s robustness and reliability in temporal analysis. |