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Pandora: Leveraging Code-driven Knowledge Transfer for Unified Structured Knowledge Reasoning
Yongrui Chen, Junhao He, Linbo Fu, Shenyu Zhang, Rihui Jin, Xinbang Dai, Jiaqi Li, Dehai Min, Nan Hu, Yuxin Zhang, Guilin Qi, Yi Huang, Tongtong Wu
https://arxiv.org/abs/2508.17905

Pandora: Leveraging Code-driven Knowledge Transfer for Unified Structured Knowledge Reasoning
Unified Structured Knowledge Reasoning (USKR) aims to answer natural language questions by using structured sources such as tables, databases, and knowledge graphs in a unified way. Existing USKR methods rely on task-specific strategies or bespoke representations, which hinder their ability to dismantle barriers between different SKR tasks, thereby constraining their overall performance in cross-task scenarios. In this paper, we introduce \textsc{Pandora}, a novel USKR framework that addresses …

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High-efficiency infrared upconversion imaging with nonlinear silicon metasurfaces empowered by quasi-bound states in the continuum
Tingting Liu, Jumin Qiu, Meibao Qin, Xu Tu, Huifu Qiu, Feng Wu, Tianbao Yu, Qiegen Liu, Shuyuan Xiao
https://arxiv.org/abs/2508.21782

High-efficiency infrared upconversion imaging with nonlinear silicon metasurfaces empowered by quasi-bound states in the continuum
Infrared imaging is indispensable for its ability to penetrate obscurants and visualize thermal signatures, yet its practical use is hindered by the intrinsic limitations of conventional detectors. Nonlinear upconversion, which converts infrared light into the visible band, offers a promising pathway to address these challenges. Here, we demonstrate high-efficiency infrared upconversion imaging using nonlinear silicon metasurfaces. By strategically breaking in-plane symmetry, the metasurface su…