1 citations · 1 across the 8 of their papers we have counts for
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Understanding and Mitigating Hallucination Escape in Tool-Using LLM Agents
Peigui Qi, Kunsheng Tang, Yide Song +2
Large language models (LLMs) increasingly serve as autonomous agents that invoke external tools. However, this capability introduces tool hallucination, selecting incorrect tools o…
GlitchPatch: Repairing Glitch Tokens in Frozen Language Models via Local Retokenization
Kunsheng Tang, Peigui Qi, Yide Song +3
Glitch tokens are anomalous vocabulary entries that can cause large language models (LLMs) to produce outputs inconsistent with their inputs. Existing repair methods require access…
VertMark: A Unified Training-Free Robust Watermarking Framework for Vertical Domain Pre-trained Language Models
Cong Kong, Xin Cheng, Zhaoxia Yin +3
With the application of vertical domain pre-trained language models (VPLMs) in specialized fields such as medical, finance, and law, model parameters and inference capabilities hav…
State-Dependent Safety Failures in Multi-Turn Language Model Interaction
Pengcheng Li, Jie Zhang, Tianwei Zhang +5
Safety alignment in large language models is typically evaluated under isolated queries, yet real-world use is inherently multi-turn. Although multi-turn jailbreaks are empirically…
Character as a Latent Variable in Large Language Models: A Mechanistic Account of Emergent Misalignment and Conditional Safety Failures
Yanghao Su, Wenbo Zhou, Tianwei Zhang +4
Emergent Misalignment refers to a failure mode in which fine-tuning large language models (LLMs) on narrowly scoped data induces broadly misaligned behavior. Prior explanations mai…
GUIGuard-Bench: Toward a General Evaluation for Privacy-Preserving GUI Agents
Yanxi Wang, Zhiling Zhang, Wenbo Zhou +6
As GUI agents increasingly rely on screenshots to perceive and operate digital environments, they may inadvertently expose sensitive information such as identities, accounts, locat…