1 citations · 1 across the 9 of their papers we have counts for
9 papers
IstGPT: LLM-based Anomaly Detection for Spatial-Temporal Graph in Industrial Systems
Yuchen Zhang, Ning Xi, Pengbin Feng +5
Industrial Internet systems face increasing threats from sophisticated industrial control system (ICS) attacks, resulting in critical safety incidents. However, existing tools exhi…
Counterfactual Trace Auditing of LLM Agent Skills
Xiaolin Zhou, Jinbo Liu, Li Li +2
Large Language Model agents are increasingly augmented with agent skills. Current evaluation methods for skills remain limited. Most deployed benchmarks report only pass rate befor…
Mind Dreamer: Untethering Imagination via Active Causal Intervention on Latent Manifolds
Shaojun Xu, Xiaoling Zhou, Yihan Lin +4
Model-Based Reinforcement Learning yields sample efficiency via latent imagination, yet remains constrained by Historical Tethering: imagination is typically initialized from obser…
When Simulation Lies: A Sim-to-Real Benchmark and Domain-Randomized RL Recipe for Tool-Use Agents
Xiaolin Zhou, Aojie Yuan, Zheng Luo +12
Tool-use language agents are evaluated on benchmarks that assume clean inputs, unambiguous tool registries, and reliable APIs. Real deployments violate all these assumptions: user…
Decoupling Safety into Orthogonal Subspace: Cost-Efficient and Performance-Preserving Alignment for Large Language Models
Yutao Mou, Xiaoling Zhou, Yuxiao Luo +2
Safety alignment is essential for building trustworthy artificial intelligence, yet it remains challenging to enhance model safety without degrading general performance. Current ap…
Seed&Steer: Guiding Large Language Models with Compilable Prefix and Branch Signals for Unit Test Generation
Shuaiyu Zhou, Zhengran Zeng, Xiaoling Zhou +3
Unit tests play a vital role in the software development lifecycle. Recent advances in Large Language Model (LLM)-based approaches have significantly improved automated test genera…
Mitigating Spurious Correlations with Causal Logit Perturbation
Xiaoling Zhou, Wei Ye, Rui Xie +1
Deep learning has seen widespread success in various domains such as science, industry, and society. However, it is acknowledged that certain approaches suffer from non-robustness,…
Enhancing In-Context Learning via Implicit Demonstration Augmentation
Xiaoling Zhou, Wei Ye, Yidong Wang +4
The emergence of in-context learning (ICL) enables large pre-trained language models (PLMs) to make predictions for unseen inputs without updating parameters. Despite its potential…
Boosting Model Resilience via Implicit Adversarial Data Augmentation
Xiaoling Zhou, Wei Ye, Zhemg Lee +2
Data augmentation plays a pivotal role in enhancing and diversifying training data. Nonetheless, consistently improving model performance in varied learning scenarios, especially t…