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Yudong Li

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6 papers

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AAAI Conference 2026 Conference Paper

OncoCoT: A Temporal-causal Chain-of-Thought Dataset for Oncologic Decision-Making

Peiru Yang
Yudong Li
Shiting Wang
Xinyi Liu
Haotian Gan
Xintian Li
Qingyu Gao
Yongfeng Huang

Long Chain-of-Thought (CoT) reasoning has shown great promise in complex reasoning tasks, but its application to medical decision-making presents unique challenges. Unlike structured tasks relying on static verification frameworks, medical decision-making requires dynamic validation through longitudinal clinical outcomes, exhibiting temporal-causal dependencies that complicate the verification of reasoning processes. Therefore, we introduce a novel data construction framework specifically designed for medical decision-making. First, the framework analyzes real-world clinical cases to construct a timeline of medical events and identify critical decision points, including examination, diagnosis, and treatment. Subsequently, it employs a clinical causality-aware strategy to generate decision-making questions at the identified points, along with reasoning traces and corresponding answers. Finally, information drawn from future nodes serves as clinical logic-constrained criteria to re-evaluate and refine the soundness of the generated reasoning and responses. Building on this, we present OncoCoT, an oncologic decision-making dataset derived from clinical records over the past four years across eight common cancer types. Furthermore, we distill a subset of OncoCoT into a dedicated benchmark, OncoEval, to facilitate systematic evaluation of clinical reasoning capabilities in LLMs. Evaluation results show that existing state-of-the-art reasoning models, such as Deepseek-r1 and GPT-o3, exhibit limited capability in addressing clinical problems in OncoEval, highlighting the need for further improvement.

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NeurIPS Conference 2025 Conference Paper

Cross City Traffic Flow Generation via Retrieval Augmented Diffusion Model

Yudong Li
Jingyuan Wang
Xie Yu
Peiyu Wang
Qian Huang

Traffic flow data are of great value in smart city applications. However, limited by data collection costs and privacy sensitivity, it is rather difficult to obtain large-scale traffic flow data. Therefore, various data generation methods have been proposed in the literature. Nevertheless, these methods often require data from a specific city for training and are difficult to directly apply to new cities lacking data. To address this problem, this paper proposes a retrieval-augmented diffusion generation model with representation alignment. We use data from multiple source cities for training, extract consistent representations across multiple cities, and leverage retrieval-augmented generation (RAG) technology to incorporate historical data from source cities under similar conditions into the condition, aiming to improve the accuracy of data generation in the target city. Experiments on four real-world datasets demonstrate that, compared with existing deep learning methods, our method achieves better cross-city transfer performance.

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EAAI Journal 2025 Journal Article

Detail-focused and polarization-guided multi-modality fusion for underwater image clarity enhancing

Mingze Yao
Huibing Wang
Yudong Li
Wenzhe Liu
Xianping Fu

Underwater optics imaging typically suffer from the impurities scattering and light absorption in dynamic and complex underwater environment, which significantly effect the clarity and visibility of images. Existing cutting-edge Underwater Image Enhancement (UIE) methods mostly focus on color correction and contrast enhancement neglect the textual and detail information of objects, leading to imbalance exposure and edge features missing. To overcome these problems, we propose a novel detail-focused and polarization guided multi-modality fusion network (DFPG-Net), for enhancing underwater images. Unlike the previous methods, we first construct a Detail-Focused Convolution (DFC) block for extracting features from underwater multimodal images, which integrates difference convolutions to capture prior and edge information. Meanwhile, polarization information is introduced with a Multi-scale Polarization Guided (MPG) fusion module, which intends to maintain and enhance the texture and details information from the degree of polarization information and angle of polarization information obtained from the polarized image. Additionally, a parallel progressive attention network is designed to explore and combine the valuable and discriminative information in feature learning stage. Extensive experiments on the constructed underwater dataset validate the effectiveness and superior performance of the proposed DFPG-Net, which against state-of-the-art methods in both machine evaluation metrics and visual perception.

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AAAI Conference 2025 Conference Paper

GTG: Generalizable Trajectory Generation Model for Urban Mobility

Jingyuan Wang
Yujing Lin
Yudong Li

Trajectory data mining is crucial for smart city management. However, collecting large-scale trajectory datasets is challenging due to factors such as commercial conflicts and privacy regulations. Therefore, we urgently need trajectory generation techniques to address this issue. Existing trajectory generation methods rely on the global road network structure of cities. When the road network structure changes, these methods are often not transferable to other cities. In fact, there exist invariant mobility patterns between different cities: 1) People prefer paths with the minimal travel cost; 2) The travel cost of roads has an invariant relationship with the topological features of the road network. Based on the above insight, this paper proposes a Generalizable Trajectory Generation model (GTG). The model consists of three parts: 1) Extracting city-invariant road representation based on Space Syntax method; 2) Cross-city travel cost prediction through disentangled adversarial training; 3) Travel preference learning by shortest path search and preference update. By learning invariant movement patterns, the model is capable of generating trajectories in new cities. Experiments on three datasets demonstrates that our model significantly outperforms existing models in terms of generalization ability.

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NeurIPS Conference 2023 Conference Paper

Learning Visual Prior via Generative Pre-Training

Jinheng Xie
Kai Ye
Yudong Li
Yuexiang Li
Kevin Qinghong Lin
Yefeng Zheng
Linlin Shen
Mike Zheng Shou

Various stuff and things in visual data possess specific traits, which can be learned by deep neural networks and are implicitly represented as the visual prior, e. g. , object location and shape, in the model. Such prior potentially impacts many vision tasks. For example, in conditional image synthesis, spatial conditions failing to adhere to the prior can result in visually inaccurate synthetic results. This work aims to explicitly learn the visual prior and enable the customization of sampling. Inspired by advances in language modeling, we propose to learn Visual prior via Generative Pre-Training, dubbed VisorGPT. By discretizing visual locations, e. g. , bounding boxes, human pose, and instance masks, into sequences, VisorGPT can model visual prior through likelihood maximization. Besides, prompt engineering is investigated to unify various visual locations and enable customized sampling of sequential outputs from the learned prior. Experimental results demonstrate the effectiveness of VisorGPT in modeling visual prior and extrapolating to novel scenes, potentially motivating that discrete visual locations can be integrated into the learning paradigm of current language models to further perceive visual world. Code is available at https: //sierkinhane. github. io/visor-gpt.

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NeurIPS Conference 2022 Conference Paper

Tenrec: A Large-scale Multipurpose Benchmark Dataset for Recommender Systems

Guanghu Yuan
Fajie Yuan
Yudong Li
Beibei Kong
Shujie Li
Lei Chen
Min Yang
Chenyun Yu

Existing benchmark datasets for recommender systems (RS) either are created at a small scale or involve very limited forms of user feedback. RS models evaluated on such datasets often lack practical values for large-scale real-world applications. In this paper, we describe Tenrec, a novel and publicly available data collection for RS that records various user feedback from four different recommendation scenarios. To be specific, Tenrec has the following five characteristics: (1) it is large-scale, containing around 5 million users and 140 million interactions; (2) it has not only positive user feedback, but also true negative feedback (vs. one-class recommendation); (3) it contains overlapped users and items across four different scenarios; (4) it contains various types of user positive feedback, in forms of clicking, liking, sharing, and following, etc; (5) it contains additional features beyond the user IDs and item IDs. We verify Tenrec on ten diverse recommendation tasks by running several classical baseline models per task. Tenrec has the potential to become a useful benchmark dataset for a majority of popular recommendation tasks. Our source codes and datasets will be included in supplementary materials.

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