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Jun Jiang

5 papers indexed

arxiveess.SP2026-07-22

JEPA-CFM: A Joint Embedding Predictive Architecture-based Channel Foundation Model for Robust Fluid Antenna Systems

Yuan Gao, Yiming Liu, Jun Jiang, Jianbo Du, Shunqing Zhang, Xiaoli Chu, et al.

Fluid antenna systems (FAS) have emerged as a promising technology for sixth-generation (6G) wireless networks. By allowing antenna elements to move freely within a compact region, FAS can exploit rich spatial diversity without additional hardware. However, acquiring real-time ch…

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arxivcs.CRcs.AIcs.MM2026-07-19

Between Safe Boundaries: Exploiting Temporal Consistency for Jailbreaking Text-To-Video Generation Models

Xingkai Peng, Jun Jiang, Jiayang Liu, Kejiang Chen, Weiming Zhang

Recently, text-to-video (T2V) models have been widely deployed, sparking growing concerns over their robustness against jailbreak attacks. Existing jailbreak methods, mostly adapted from text-to-image attacks, suffer notable drawbacks when applied to T2V systems. They fail to ful…

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arxivcs.AI2026-07-16

CFM-Bench: A Unified Multi-Domain, Multi-Task Benchmark for Channel Foundation Models

Yuan Gao, Wenjun Yu, Jun Jiang, Yunfan Li, Xinyu Guo, Shugong Xu

Channel foundation models (CFMs) are developing rapidly, with recent studies reporting benefits from pretraining across downstream wireless tasks. Yet CFMs are commonly evaluated in model-specific pipelines with different data, radio configurations, partitions, adaptation procedu…

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arxivcs.AIcs.SC2026-07-05

Language models guide symbolic equation discovery by controlling search

Zikai Xie, Wenmei Li, Man Luo, Jun Jiang, Linjiang Chen

Scientific equation discovery must combine broad domain priors with strict numerical testing. Symbolic regression supplies numerical grounding but faces a combinatorial search space, whereas many language-model systems ask the model to propose or select formulas directly. We test…

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arxivcs.RO2026-06-30

Labimus: A Simulation and Benchmark for Humanoid Dexterous Manipulation in Chemical Laboratory

Yuhan Wu, Zhao Jin, Tao Li, Yuheng Zhang, Zhichao Wang, Shuo Wang, et al.

Laboratory automation has made remarkable progress through robotic platforms and AI-driven scientific reasoning. However, many laboratory operations (e.g., solid--solid transfer) remain inherently dynamic and require real-time adaptation to different materials and experimental co…

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