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Hui Wang

9 papers indexed

arxivcs.AI2026-07-23

AREX: Towards a Recursively Self-Improving Agent for Deep Research

Shuqi Lu, Chaofan Li, Kun Luo, Zhang Zhang, Hui Wang, Hongwang Xiao, et al.

Deep research requires agents to find answers that jointly satisfy multiple constraints. Discovering such answers is costly, whereas verifying a candidate can often be decomposed into tractable constraint-wise checks. This discovery--verification asymmetry suggests that a researc…

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arxiveess.SPcs.AIcs.LG2026-07-17

Map as a Prompt: Learning Multi-Modal Spatial-Signal Foundation Models for Cross-scenario Wireless Localization

Yong Chu, Xun Zhou, Zenglin Xu, Hui Wang, Yue Yu

Accurate and robust wireless localization is a critical enabler for emerging 5G/6G applications, including autonomous driving, extended reality, and smart manufacturing. Despite its importance, achieving precise localization across diverse environments remains challenging due to…

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arxivcs.AI2026-07-13

Comparative Analysis of GAT and BERT for Human-Like Playtesting

Kleio Fragkedaki, Theodoros Panagiotakopoulos, Matteo Biasielli, Hui Wang

Accurately modeling and understanding player experience is crucial for designing engaging puzzle games. To achieve this, a common approach involves collecting diverse user data to train predictive playtesting models that mimic player behavior. However, existing data-driven method…

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arxivcs.CRcs.AI2026-07-12

PromptGraph: Graph-Guided Prompt Sanitization for Balancing Privacy and Utility in LLM Inference

Chen Gu, Hui Wan, Donghui Hu, Hui Wang, Zhuoer Gu

Large Language Model (LLM) services introduce a fundamental privacy challenge. Sensitive information may be inferred not only from explicit identifiers, such as names or phone numbers, but also from contextual associations among otherwise innocuous spans. Existing sanitizers typi…

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arxiveess.SY2026-07-10

An Improved Deep Reinforcement Learning Control Strategy for Traction Dual Rectifiers in EMUs

Zhigang Liu, Mingwei Tang, Xiangyu Meng, Hui Wang, Qiao Zhang, Haoyu Wang, et al.

Due to the use of PI-based d q current decoupling in the pulse rectifier of CRH5 high-speed trains, the PI parameters directly affect the traction system's control performance. Linearized control may have issues with reference trajectory changes or model mismatches, leading to a…

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crossref2026-07-02

Predicting Prognosis of Locoregionally Advanced Nasopharyngeal Carcinoma Using Machine Learning Models Based on Plasma Proteomics : A retrospectively registered Study

Yuyi Li, Chao Tan, Xiaoyu Chen, Weichang Zhu, Cuihong Jiang, Lili He, et al.

Abstract Background Patients with locoregionally advanced nasopharyngeal carcinoma (LA-NPC) exhibit heterogeneous short-term responses despite induction chemotherapy plus concurrent chemoradiotherapy, and effective plasma protein prognostic markers are lacking. This study aimed t…

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arxivcs.LGcs.AI2026-06-28

Autonomous mechanistic discovery of colorectal cancer vulnerabilities via multi-scale AI swarms

Christopher Baker, Tianyu Ren, Karen Rafferty, Hui Wang, Simon McDade

The acceleration of automated scientific discovery has been fundamentally bottlenecked by the epistemic gap between the semantic reasoning of large language models (LLMs) and the deterministic physics of mammalian biology. While recent multi-agent frameworks have achieved autonom…

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crossrefInternational Journal of Molecular Sciences2025-07-21Cited by 1

Machine Learning-Based Prognostic Signature in Breast Cancer: Regulatory T Cells, Stemness, and Deep Learning for Synergistic Drug Discovery

Samina Gul, Jianyu Pang, Yongzhi Chen, Qi Qi, Yuheng Tang, Yingjie Sun, et al.

Regulatory T cells (Tregs) have multiple roles in the tumor microenvironment (TME), which maintain a balance between autoimmunity and immunosuppression. This research aimed to investigate the interaction between cancer stemness and Regulatory T cells (Tregs) in the breast cancer…

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