activity
20242026
most citedTowards Few-shot Self-explaining Graph Neural Networks

1 citations · 1 across the 3 of their papers we have counts for

collaborators

6 papers

cs.CV2026

How to Utilize Complementary Vision-Text Information for 2D Structure Understanding

Jiancheng Dong, Pengyue Jia, Derong Xu +9

LLMs typically linearize 2D tables into 1D sequences to fit their autoregressive architecture, which weakens row-column adjacency and other layout cues. In contrast, purely visual…

cs.AI2026

MOSAIC: Composable Safety Alignment with Modular Control Tokens

Jingyu Peng, Hongyu Chen, Jiancheng Dong +5

Safety alignment in large language models (LLMs) is commonly implemented as a single static policy embedded in model parameters. However, real-world deployments often require conte…

cs.CL2025

AdaSwitch: Balancing Exploration and Guidance in Knowledge Distillation via Adaptive Switching

Jingyu Peng, Maolin Wang, Hengyi Cai +5

Small language models (SLMs) are crucial for applications with strict latency and computational constraints, yet achieving high performance remains challenging. Knowledge distillat…

cs.CL2025

Logic Jailbreak: Efficiently Unlocking LLM Safety Restrictions Through Formal Logical Expression

Jingyu Peng, Maolin Wang, Nan Wang +7

Despite substantial advancements in aligning large language models (LLMs) with human values, current safety mechanisms remain susceptible to jailbreak attacks. We hypothesize that…

cs.AI2024

Stepwise Reasoning Error Disruption Attack of LLMs

Jingyu Peng, Maolin Wang, Xiangyu Zhao +6

Large language models (LLMs) have made remarkable strides in complex reasoning tasks, but their safety and robustness in reasoning processes remain underexplored. Existing attacks…

cs.LG20241 cited

Towards Few-shot Self-explaining Graph Neural Networks

Jingyu Peng, Qi Liu, Linan Yue +3

Recent advancements in Graph Neural Networks (GNNs) have spurred an upsurge of research dedicated to enhancing the explainability of GNNs, particularly in critical domains such as…