Author name cluster

Ming Wang

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9 papers

2 author rows

AAAI Conference 2026 Conference Paper

DeepWriter: A Multi-Agent Collaboration Framework for Information-rich Ultra-long Book Writing

Ming Wang
Minghao Hu
Xiuli Kang
Li He
Yu Tian
Chunming Liu
Han Shi
Zhunchen Luo

Long-form books are among the most information-rich and structurally complex forms of written content, often exceeding 100,000 words. While recent methods have enabled basic long-text generation, they remain limited in two key aspects: the inability to generate ultra-long content at book scale, and the lack of mechanisms for integrating rich factual information. To address these limitations, we propose DeepWriter, a multi-agent collaborative framework that follows a structured planning-then-generation paradigm. It first constructs a detailed book outline with narrative arcs and chapter semantics, then incrementally generates content conditioned on retrieved knowledge and contextual signals. DeepWriter supports controllable generation of full-length books exceeding 100,000 words, enriched with citations, trivia and images. To support evaluation beyond surface-level fluency, we introduce DeepWriter-Bench, a bilingual benchmark of 18 annotated books designed to assess book-scale coherence, richness, and factual grounding. Additionally, we propose BookScore, a unified 100-point metric for quantifying book maturity. Experimental results show that DeepWriter achieves a state-of-the-art BookScore of 80.92, consistently outperforming strong baselines.

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JBHI Journal 2026 Journal Article

Parallel Multi-Attention and Gated Fusion for Visual Question Localized Answering in Surgical Scenes

Zeyu Wang
Ming Wang
Peixi Peng
Wanshu Fan
Zhongbin Han
Xin Yang
Dongsheng Zhou

Surgical Visual Question Localized Answering (Surgical-VQLA) is an emerging task that supports surgical education by generating accurate answers and localizing relevant anatomical regions based on visual content and textual queries. This task requires precise spatial reasoning and tight semantic alignment across modalities, which remain challenging for current models due to limited spatial sensitivity and insufficient semantic integration. Mitigating these limitations, we propose EndoVisLoc, a dedicated framework that enhances visual-textual interaction through structured attention and gated fusion. Specifically, we design a Parallel Multi Attention Module (PMAM) to capture different visual features, improving the perception of anatomical structures. We further develop a Dynamic Gated Fusion Module (DGFM) to adaptively inject semantic priors into visual features via gated control, facilitating robust cross-modal fusion. Finally, we introduce a Hierarchical Classifier Head (HCH) to refine the fused representations and jointly optimize answer prediction and spatial localization. Extensive experiments on the EndoVis-18-VQLA and EndoVis-17-VQLA datasets demonstrate the superior performance of EndoVisLoc, surpassing the state-of-the-art OTAS model by +5. 72% ACC, +5. 73% F-score, and +1. 82% mIoU on EndoVis-18-VQLA and by +1. 59% ACC, +2. 48% F-score, and +0. 33% mIoU on EndoVis-17-VQLA. These results confirm the consistent advantage of EndoVisLoc in both answer accuracy and precise anatomical localization.

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JAIR Journal 2026 Journal Article

T-COL: Generating Counterfactual Explanations for General User Preferences on Variable Machine Learning Systems

Ming Wang
Daling Wang
Wenfang Wu
Shi Feng
Yifei Zhang

To address the interpretability challenge in machine learning (ML) systems, counterfactual explanations (CEs) have emerged as a promising solution. CEs are unique as they provide workable suggestions to users, instead of explaining why a certain outcome was predicted. The application of CEs encounters two main challenges: general user preferences and variable ML systems. On one hand, user preferences for specific values can vary depending on the task and scenario. On the other hand, the ML systems for verification may change while the CEs are performed. Thus, user preferences tend to be general rather than specific, and CEs need to be adaptable to variable ML models while maintaining robustness even as these models change. Facing these challenges, we propose general user preferences based on insights from psychology and behavioral science, and add the challenge of non-static ML systems as one preference. Moreover, we introduce a novel method, Tree-based Conditions Optional Links (T-COL) for generating CEs adaptable to general user preferences. Moreover, we employ T-COL to enhance the robustness of CEs with specific conditions, making CEs robust even when the ML models are replaced. To assess subjectivity preferences, we define LLM-based autonomous agents to simulate users and align them with real users. Experiments show that T-COL outperforms all baselines in adapting to general user preferences.

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AAAI Conference 2026 Conference Paper

WikiMAG: A Multi-Agent Guided Framework for Generating Structured Wikipedia-like Articles

Xiuli Kang
Yinlong Xiao
Minghao Hu
Yuan Huang
Bin Mao
Ming Wang
Fang Wang
Zhunchen Luo

Wikipedia serves as the world's largest and most popular online reference encyclopedia, rich in structured knowledge and authoritative citations. Recently, numerous works have leveraged large language models to automatically generate Wikipedia-like articles. However, existing approaches primarily focus on producing singular narrative-type content, overlooking higher information-density structured elements such as timeline and table. To address these limitations, we propose WikiMAG, a multi-agent guided framework for generating structured Wikipedia-like articles. This framework employs a collaborative multi-agent mechanism to orchestrate the creation process, featuring three synergistic core components: Progressive planner first constructs the coarse-grained outline framework and then annotate fine-grained types for outline units, encompassing narrative, timeline, and table formats; Reflective inspector dynamically curates high-quality references via multi-round interactive feedback, thereby enhancing the authority and relevance of citations; Versatile writer integrates fine-grained outline details and high-quality reference information to generate information-rich articles, incorporating the three annotated formats. We evaluate WikiMAG on two public datasets, FreshWiki and WikiGenBen, across outline, writing, and verifiability dimensions. Compared with the best baseline method, our method achieves an average improvement of 6.73 points and 4.39 points in Heading Soft Recall and the METEOR metric (a machine translation and text generation evaluation metric) respectively, and an average increase of 16.84 percentage points in Citation Rate.

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JBHI Journal 2025 Journal Article

MEDL-DDI: Example-Driven Learning With Multi-Source Features for Predicting Drug-Drug Interaction

Haixue Zhao
Yunjiong Liu
Peiliang Zhang
Xiaoping Min
Ming Wang
Chao Che

Accurate drug–drug interaction (DDI) prediction is crucial for optimizing the efficacy of combination therapies and minimizing adverse effects. Most existing methods rely on single features and struggle to integrate structural and sequential drug information. Additionally, prediction bias caused by class imbalance remains a significant challenge. To address these issues, this study proposes a multi-source example-driven learning framework for DDI (MEDL-DDI) that jointly models structural and sequential drug representations to achieve robust multimodal fusion and mitigate class imbalance. MEDL-DDI enriches SMILES with chemical knowledge, extracts global semantic features via a Transformer, and identifies key substructures through a graph information bottleneck. Moreover, an example-driven mechanism guided by example centers enhances the model's ability to recognize minority classes. Experimental results on three benchmark datasets validate that MEDL-DDI outperforms state-of-the-art methods. The case study on cardiovascular drug interactions further highlights MEDL-DDI's practical value and applicability.

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AAAI Conference 2023 Conference Paper

Supervised Contrastive Few-Shot Learning for High-Frequency Time Series

Xi Chen
Cheng Ge
Ming Wang
Jin Wang

Significant progress has been made in representation learning, especially with recent success on self-supervised contrastive learning. However, for time series with less intuitive or semantic meaning, sampling bias may be inevitably encountered in unsupervised approaches. Although supervised contrastive learning has shown superior performance by leveraging label information, it may also suffer from class collapse. In this study, we consider a realistic scenario in industry with limited annotation information available. A supervised contrastive framework is developed for high-frequency time series representation and classification, wherein a novel variant of supervised contrastive loss is proposed to include multiple augmentations while induce spread within each class. Experiments on four mainstream public datasets as well as a series of sensitivity and ablation analyses demonstrate that the learned representations are effective and robust compared with the direct supervised learning and self-supervised learning, notably under the minimal few-shot situation.

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YNIMG Journal 2022 Journal Article

Evidence for predictions established by phantom sound

Yun-Yi Qi
Zi-Hao Guo
Xiao-Tao Guo
Rui-Rui Guan
Bin Luo
Jing-Wu Sun
Ming Wang
Hua-Wei Li

Predictions, the bridge between the internal and external worlds, are established by prior experience and updated by sensory stimuli. Responses to omitted but unexpected stimuli, known as omission responses, can break the one-to-one mapping of stimulus-response and can expose predictions established by the preceding stimulus built up. While research into exogenous predictions (driven by external stimuli) is often reported, that into endogenous predictions (driven by internal percepts) is rarely available in the literature. Here, we report evidence for endogenous predictions established by the Zwicker tone illusion, a phantom pure-tone-like auditory percept following notch noises. We found that MMN, P300, and theta oscillations could be recorded using an omission paradigm in subjects who can perceive Zwicker tone illusions, but could not in those who cannot. The MMN and P300 responses relied on attention, but theta oscillations did not. In-depth analysis shows that an increase in single-trial theta power, including total and induced theta, with the endogenous prediction, is lateralized to the left frontal brain areas. Our study depicts that the brain automatically analyzes internal perception, progressively establishes predictions and yields prediction errors in the left frontal region when a violation occurs.

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JMLR Journal 2020 Journal Article

Semi-parametric Learning of Structured Temporal Point Processes

Ganggang Xu
Ming Wang
Jiangze Bian
Hui Huang
Timothy R. Burch
Sandro C. Andrade
Jingfei Zhang
Yongtao Guan

We propose a general framework of using a multi-level log-Gaussian Cox process to model repeatedly observed point processes with complex structures; such type of data has become increasingly available in various areas including medical research, social sciences, economics, and finance due to technological advances. A novel nonparametric approach is developed to efficiently and consistently estimate the covariance functions of the latent Gaussian processes at all levels. To predict the functional principal component scores, we propose a consistent estimation procedure by maximizing the conditional likelihood of super-positions of point processes. We further extend our procedure to the bivariate point process case in which potential correlations between the processes can be assessed. Asymptotic properties of the proposed estimators are investigated, and the effectiveness of our procedures is illustrated through a simulation study and an application to a stock trading dataset. [abs] [ pdf ][ bib ] &copy JMLR 2020. ( edit, beta )

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ICRA Conference 2002 Conference Paper

300mm Full Automation Integration Test Methodology and Experience

Ming Wang
Eric Chang
Jason Liu

Taiwan Semiconductor Manufacturing Company launched the first 300 mm fully automation foundry in 1999. The newly introduced technology will automate the 300 mm fab for better manufacturing control and less human labor. Based on the accumulated TSMC data from year 1999 to year 2000, it is found that to maintain is more difficult than to build up the system. It is a great challenge to integrate so many hardware and software components closely. In fact, few 300 mm foundries are successful from the maintenance point of view. TSMC is still continuing to improve the full fab automation by strict integration test methodology and refer to daily problem analysis system. TSMC enlarges the acceptance test scope to cover the process equipment, transportation, material control and manufacturing system. In order to make sure the integration is smooth and on the right track, TSMC has developed a methodology to execute hardware and software integration. Here TSMC exposes some of the critical issues and shares the experience for unmanned factory. TSMC believes these can help the 300 mm fab to speed up the system construction task and find out their own problems by TSMC's methodology and experience.

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