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Dongyuan Li

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7 papers

2 author rows

AAAI Conference 2026 Conference Paper

EssayBench: Evaluating Large Language Models in Multi-Genre Chinese Essay Writing

Fan Gao
Dongyuan Li
Ding Xia
Fei Mi
Yasheng Wang
Lifeng Shang
Baojun Wang

Prompt-based essay writing is an effective and common way to assess students' critical thinking skills. Recent work has evaluated the impressive capabilities of Large Language Models (LLMs) on this task. However, most studies focus primarily on English. Those examining LLMs' performance in Chinese often rely on coarse-grained text quality metrics, overlooking the structural and rhetorical complexities of Chinese essays, particularly across diverse genres. We therefore propose EssayBench, a multi-genre benchmark specifically designed for Chinese essay writing, along with a fine-grained, genre-specific scoring framework that hierarchically aggregates scores to better align with human preferences. The dataset comprises 728 real-world prompts across four major genres (Argumentative, Narrative, Descriptive, and Expository), and includes both Open-Ended and Constrained types. Our evaluation protocol is validated through a comprehensive human agreement study. The results show that our protocol aligns well with human judgments, achieving a highest Spearman's correlation of 0.816 and outperforming coarse-grained evaluation methods by an average of 8.6\%. Finally, we benchmark 15 large LLMs, analyzing their strengths and limitations across genres and instruction types. We believe EssayBench offers a more reliable framework for evaluating Chinese essay generation and provides valuable insights for improving LLMs in this domain.

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AAAI Conference 2025 Conference Paper

BIG-FUSION: Brain-Inspired Global-Local Context Fusion Framework for Multimodal Emotion Recognition in Conversations

Yusong Wang
Xuanye Fang
Huifeng Yin
Dongyuan Li
Guoqi Li
Qi Xu
Yi Xu
Shuai Zhong

Considering the importance of capturing both global conversational topics and local speaker dependencies for multimodal emotion recognition in conversations, current approaches first utilize sequence models like Transformer to extract global context information, then apply Graph Neural Networks to model local speaker dependencies for local context information extraction, coupled with Graph Contrastive Learning (GCL) to enhance node representation learning. However, this sequential design introduces potential biases: the extracted global context information inevitably influences subsequent processing, compromising the independence and diversity of the original local features; current graph augmentation methods in GCL cannot consider both global and local context information in conversations to evaluate the node importance, hindering the learning of key information. Inspired by the human brain excels at handling complex tasks by efficiently integrating local and global information processing mechanisms, we propose an aligned global-local context fusion framework for sequence-based design to address these problems. This design includes a dual-attention Transformer and a dual-evaluation method for graph augmentation in GCL. The dual-attention Transformer combines global attention for overall context extraction with sliding-window attention for local context capture, both enhanced by spiking neuron dynamics. The dual-evaluation method in GCL comprises global importance evaluation to identify nodes crucial for overall conversation context, and local importance evaluation to detect nodes significant for local semantics, generating augmented graph views that preserve both global and local information. This approach ensures balanced information processing throughout the pipeline, enhancing biological plausibility and achieving superior emotion recognition.

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IROS Conference 2025 Conference Paper

DRTT: A Diffusion-based Framework for 4DCT Generation, Robust Thoracic Registration and Tumor Deformation Tracking

Dongyuan Li
Yixin Shan
Yuxuan Mao
Haochen Shi
Shenghao Huang
Weiyan Sun
Chang Chen
Xiaojun Chen

In minimally invasive robotic thoracic surgery, the unavoidable respiratory motion of the patient causes lung lesions to move and deform, making precise tumor localiza-tion a significant challenge for surgeons. To address this, we introduce an RDDM (Recursive Deformable Diffusion Model)-based framework designed for real-time intraoperative tumor tracking, which can be used for registration and navigation in robot-assisted thoracic surgery. The RDDM reduces training complexity and enhances dataset utilization by employing a simplified DDM (Diffusion Deformable Model) iteratively, significantly lowering computational demands while maximizing the extraction of valuable information from limited 4D-CT (four-dimensional computed tomography) datasets. Considering the robustness required for intraoperative registration and navigation, we incorporate an ICP (Iterative Closest Point)-based point cloud registration method into the framework and validate our approach using publicly available datasets and volunteer trials. This innovation has the potential to reduce radiation exposure, trauma, and the risk of complications for patients undergoing minimally invasive thoracic surgery, and enables downstream tasks such as RAPNB (robot-assisted percutaneous needle biopsy) and radiation therapy.

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NeurIPS Conference 2025 Conference Paper

DyG-Mamba: Continuous State Space Modeling on Dynamic Graphs

Dongyuan Li
Shiyin Tan
Ying Zhang
Ming Jin
Shirui Pan
Manabu Okumura
Renhe Jiang

Dynamic graph modeling aims to uncover evolutionary patterns in real-world systems, enabling accurate social recommendation and early detection of cancer cells. Inspired by the success of recent state space models in efficiently capturing long-term dependencies, we propose DyG-Mamba by translating dynamic graph modeling into a long-term sequence modeling problem. Specifically, inspired by Ebbinghaus' forgetting curve, we treat the irregular timespans between events as control signals, allowing DyG-Mamba to dynamically adjust the forgetting of historical information. This mechanism ensures effective usage of irregular timespans, thereby improving both model effectiveness and inductive capability. In addition, inspired by Ebbinghaus' review cycle, we redefine core parameters to ensure that DyG-Mamba selectively reviews historical information and filters out noisy inputs, further enhancing the model’s robustness. Through exhaustive experiments on 12 datasets covering dynamic link prediction and node classification tasks, we show that DyG-Mamba achieves state-of-the-art performance on most datasets, while demonstrating significantly improved computational and memory efficiency. Our code is available at https: //github. com/Clearloveyuan/DyG-Mamba.

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ICLR Conference 2025 Conference Paper

MMQA: Evaluating LLMs with Multi-Table Multi-Hop Complex Questions

Jian Wu 0037
Linyi Yang
Dongyuan Li
Yuliang Ji
Manabu Okumura
Yue Zhang 0004

While large language models (LLMs) have made strides in understanding tabular data, current tabular evaluation benchmarks, such as WikiTableQuestions and WikiSQL, are focus on single-table scenarios, which cannot necessarily reflect the complexity of real-world applications. To bridge this gap, we present a \textbf{M}ulti-table and Multi-hop Question Answering (MMQA) dataset to assess LLMs' understanding and reasoning capabilities in handling multi-table tasks. The MMQA dataset demands that models perform multiple inferences by drawing evidence from various tables, which are designed to be connected with each other and require models to identify and utilize relationships such as foreign and primary keys. Then, we introduce a comprehensive evaluation framework that tailors to assess LLMs' capabilities in several aspects including Multi-Table Retrieval, Text-to-SQL Generation, Multi-Table QA, Primary Key Selection, and Foreign Key Selection. Finally, we propose a novel multi-table retrieval method that achieves state-of-the-art (SOTA) performance on the MMQA dataset compared to several strong baselines. Our experiment results reveal that, compared with human performance, both open-source and commercial LLMs leave significant performance room for improvements in multi-table understanding and reasoning tasks. We believe that the MMQA benchmark will enhance and facilitate LLMs' multi-table capabilities in real-world scenarios.

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AAAI Conference 2025 Conference Paper

Thermal-Aware Low-Light Image Enhancement: A Real-World Benchmark and a New Light-Weight Model

Zhen Wang
Yaozu Wu
Dongyuan Li
Shiyin Tan
Zhishuai Yin

Enhancing images captured under low-light conditions has been a topic of research for several years. Nonetheless, existing image restoration techniques mainly concentrate on reconstructing images from RGB data, often neglecting the possibility of utilizing additional modalities. With the progress in handheld technology, capturing thermal images with mobile devices has become straightforward. Investigating the integration of thermal data into image restoration presents a valuable research opportunity. Therefore, in this paper, we propose a multimodal low-light image enhancement task based on thermal information and establish a dataset named TLIE (Thermal-aware Low-light Image Enhancement), consisting of 1,113 samples. Each sample in our dataset includes a low-light image, a normal-light image, and the corresponding thermal map. Additionally, based on TLIE dataset, we develop a multimodal approach that simultaneously processes input images and thermal map data to produce the predicted normal-light images. We compare our method with previous unimodal and multimodal state-of-the-art LIE methods, and the experimental results and detailed ablation studies prove the effectiveness of our method.

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ICML Conference 2024 Conference Paper

Community-Invariant Graph Contrastive Learning

Shiyin Tan
Dongyuan Li
Renhe Jiang
Ying Zhang 0065
Manabu Okumura

Graph augmentation has received great attention in recent years for graph contrastive learning (GCL) to learn well-generalized node/graph representations. However, mainstream GCL methods often favor randomly disrupting graphs for augmentation, which shows limited generalization and inevitably leads to the corruption of high-level graph information, i. e. , the graph community. Moreover, current knowledge-based graph augmentation methods can only focus on either topology or node features, causing the model to lack robustness against various types of noise. To address these limitations, this research investigated the role of the graph community in graph augmentation and figured out its crucial advantage for learnable graph augmentation. Based on our observations, we propose a community-invariant GCL framework to maintain graph community structure during learnable graph augmentation. By maximizing the spectral changes, this framework unifies the constraints of both topology and feature augmentation, enhancing the model’s robustness. Empirical evidence on 21 benchmark datasets demonstrates the exclusive merits of our framework. Code is released on Github (https: //github. com/ShiyinTan/CI-GCL. git).

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