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Xuebin Wang

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3 papers

2 author rows

AAAI Conference 2026 Conference Paper

ExoTimer: Leveraging Large Language Models for Time Series Forecasting with Exogenous Variables

Lan Wu
Xuebin Wang
Chenglong Ge
Ruijuan Chu
LinYu Wang

Real-world systems often exhibit complex behaviors and are influenced by various external factors, making the integration of exogenous variables essential for accurate and robust time series forecasting. However, modeling time series with exogenous variables remains challenging due to dynamic cross-variable dependencies and the semantic gap between numerical time series data and external contextual knowledge. Large language models (LLMs) have demonstrated powerful language understanding and knowledge representation capabilities in real-world systems, offering a promising solution to bridge this gap. Motivated by this, we propose ExoTimer, a framework that deeply integrates LLMs for time series modeling with exogenous variables. We begin by introducing an Exo-Aware Endogenous Encoder to dynamically incorporate important exogenous variable information and generate patch-level representations for endogenous variables. To leverage the rich knowledge in LLMs, a Multi-Attribute Prompt Embedding module is elaborately designed to convert heterogeneous temporal features, contextual information and task specifications into LLM-interpretable textual prompts. Additionally, we propose Bi-Hash Alignment, a lightweight cross-modal alignment mechanism that bridges textual and temporal modalities in a shared hash space. Finally, a Dual-Branch Predictor with a learnable coefficient is employed to obtain the final time series prediction by integrating temporal-text and text-temporal representations. Extensive experiments on twelve real-world datasets demonstrate that ExoTimer achieves state-of-the-art performance and exhibits generalizability and scalability in both few-shot and zero-shot scenarios.

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AAAI Conference 2026 Conference Paper

PriAgent: A Collaborative Multi-Agent Framework for Auditing Android Privacy Compliance

Ziwei Zhang
Zhao Li
Zhuojun Jiang
Jiangyi Yin
Xuebin Wang
Jiangchao Chen
Qingyun Liu

Stringent regulations like General Data Protection Regulation (GDPR) mandate that an application's code-level data handling must align with its natural-language privacy policy, creating a critical auditing challenge. However, existing methods, predominantly reliant on static analysis, suffer from a critical limitation: in their pursuit of soundness via over-approximation, they exhibit "semantic blindness"—detecting what data flows exist but not why. This leads to an overwhelming volume of false positives, rendering automated auditing impractical. To bridge this gap, we introduce PriAgent, a novel framework that approaches compliance auditing as a multi-stage, AI-driven reasoning task. Instead of a monolithic model, PriAgent deploys a team of specialized agents that execute a divide-and-conquer strategy. They systematically prune the analysis space by abstracting data flows, pinpoint semantic loci critical for inspection, and perform on-demand summarization of large code blocks to ensure scalability. PriAgent leverages Retrieval-Augmented Generation (RAG) with a curated knowledge base of Android APIs, equipping agents to discern potentially non-compliant behavior from benign functionality. By correlating code-level evidence with the app's stated privacy policy, PriAgent delivers a holistic and explainable verdict for each potential violation. Our evaluations demonstrate that PriAgent significantly reduces false positives, enabling a more scalable and precise compliance audit.

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UAI Conference 2025 Conference Paper

FlightPatchNet: Multi-Scale Patch Network with Differential Coding for Short-Term Flight Trajectory Prediction

Lan Wu
Xuebin Wang
Ruijuan Chu
Guangyi Liu
Jing Zhang
Linyu Wang

Accurate multi-step flight trajectory prediction plays an important role in Air Traffic Control, which can ensure the safety of air transportation. Two main issues limit the flight trajectory prediction performance of existing works. The first issue is the negative impact on prediction accuracy caused by the significant differences in data range. The second issue is that real-world flight trajectories involve underlying temporal dependencies, and most existing methods fail to reveal the hidden complex temporal variations and extract features from one single time scale. To address the above issues, we propose FlightPatchNet, a multi-scale patch network with differential coding for flight trajectory prediction. Specifically, FlightPatchNet first utilizes differential coding to encode the original values of longitude and latitude into first-order differences and generates embeddings for all variables at each time step. Then, global temporal attention is introduced to explore the dependencies between different time steps. To fully explore the diverse temporal patterns in flight trajectories, a multi-scale patch network is delicately designed to serve as the backbone. The multi-scale patch network exploits stacked patch mixer blocks to capture inter- and intra-patch dependencies under different time scales, and further integrates multi-scale temporal features across different scales and variables. Finally, FlightPatchNet ensembles multiple predictors to make direct multi-step prediction. Extensive experiments on ADS-B datasets demonstrate that our model outperforms the competitive baselines.

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