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Zhongyuan Wang

6 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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arxivcs.CV2026-07-18

InfoDense: Density-Aware Regional Decisive Replay for Memory-Efficient Incremental Face Forgery Detection

Jikang Cheng, Hao Shen, Xueyi Zhang, Guangcheng Wang, Zhongyuan Wang, Renye Yan, et al.

The rapid evolution of face forgery techniques has introduced an increasing variety of manipulations. Incremental Face Forgery Detection (IFFD), which incrementally adds new forgery data to fine-tune previously trained models, has emerged as a promising approach to handle evolvin…

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arxivcs.CV2026-07-12

Physics-inspired Pseudo Anomaly Generation and Prototype Feature Guidance for 3D Anomaly Detection

Jian Ning, Qin Zou, Linchun Wu, Yuanhao Yue, Kunmo Li, Shoubin Chen, et al.

3D point cloud anomaly detection plays a vital role in industrial manufacturing, yet it faces significant challenges due to the scarcity and high acquisition cost of real anomalous samples. The inherently anomaly-free training data further hinders detection methods from effective…

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arxivcs.CLcs.AIcs.MA2026-07-09

WebSwarm: Recursive Multi-Agent Orchestration for Deep-and-Wide Web Search

Xiaoshuai Song, Liancheng Zhang, Kangzhi Zhao, Yutao Zhu, Zhongyuan Wang, Guanting Dong, et al.

Large language model (LLM)-based web search agents are transforming information seeking from simple factoid question answering into complex, deep-and-wide search and research-oriented tasks. A single ReAct-style agent is constrained by one long trajectory and limited context, mak…

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arxivcs.AIcs.CLcs.IR2026-06-25

AgentX: Towards Agent-Driven Self-Iteration of Industrial Recommender Systems

Changxin Lao, Fei Pan, Guozhuang Ma, Han Li, Huihuang Lin, Jijun Shi, et al.

Recommendation algorithm iteration is moving from an artisanal, engineer-bound process toward an industrialized research loop, but this transition remains blocked by a structural execution bottleneck: the idea-to-launch cycle still depends on human engineers to generate hypothese…

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