Built with and by Teycir Ben Soltane•
How to Use•FAQ•GitHub•arXiv.org•
Share:
ArXivCSExplorer
☆☆Bookmarks🏆RSSHow to UseFAQ
Home/Authors/Xiang Xu

Xiang Xu

4 indexed papers

Recent (6 mo)
4
With code
0
Influential cites
0
Benchmarked
0

Publications per year

4
26

Top categories

AI×3Vision×1Robotics×1Multiagent×1

Frequent co-authors

Alan Liang1×
Youquan Liu1×
Xian Sun1×
Linfeng Li1×
Lingdong Kong1×
Ziwei Liu1×

Research Timeline

2026
Orthogonal Concept Erasure for Diffusion Models

The paper proposes Orthogonal Concept Erasure (OCE), a novel multiplicative parameter update method that achieves precise concept erasure in diffusion models while independently preserving overall generative capacity.

From "Weak" Signals to Strong Models: Preference Delta Aggregation with LoRA Merging

The paper proposes Preference Delta Aggregation (PDA), a framework that aggregates multiple weak preference signals derived from smaller model pairs using LoRA merging to significantly boost the performance of a strong large language model.

Healthcare Mechanisms from Policy-as-Code Search under Strategic Provider Response

The paper models healthcare mechanism design as program synthesis, demonstrating that an optimized, mixed-objective program can eliminate up-coding and reduce patient rejection while maintaining financial viability.

Not All Points Are Equal: Uncertainty-Aware 4D LiDAR Scene Synthesis

The paper introduces U4D, an uncertainty-aware framework that synthesizes 4D LiDAR scenes by prioritizing the reconstruction of geometrically difficult and uncertain regions first, leading to state-of-the-art fidelity and temporal consistency.

Highlighted terms show continued research focus across papers

Papers

cs.CVcs.RORecentJun 1, 2026

Not All Points Are Equal: Uncertainty-Aware 4D LiDAR Scene Synthesis

Xiang Xu, Alan Liang, Youquan Liu, Xian Sun +4 more

The paper introduces U4D, an uncertainty-aware framework that synthesizes 4D LiDAR scenes by prioritizing the reconstruction of geometrically difficult and uncertain regions first, leading to state-of…

View →
cs.AIRecentMay 29, 2026

From "Weak" Signals to Strong Models: Preference Delta Aggregation with LoRA Merging

Qi Sun, Siyue Zhang, Yulin Chen, Yuxiang Xue +2 more

The paper proposes Preference Delta Aggregation (PDA), a framework that aggregates multiple weak preference signals derived from smaller model pairs using LoRA merging to significantly boost the perfo…

View →
cs.AIcs.MARecentMay 29, 2026

Healthcare Mechanisms from Policy-as-Code Search under Strategic Provider Response

Zihan Wang, Xiang Xu, Hongyuan Zha, Wenhao Li

The paper models healthcare mechanism design as program synthesis, demonstrating that an optimized, mixed-objective program can eliminate up-coding and reduce patient rejection while maintaining finan…

View →
cs.AIRecentMay 27, 2026

Orthogonal Concept Erasure for Diffusion Models

Yuhao Sun, Lingyun Yu, Haoxiang Xu, Fengyuan Miao +2 more

The paper proposes Orthogonal Concept Erasure (OCE), a novel multiplicative parameter update method that achieves precise concept erasure in diffusion models while independently preserving overall gen…

View →