12 citations · 13 across the 5 of their papers we have counts for
7 papers
LLM-Assisted Automatic Security Proofs for Cryptographic Protocols: How Far Are We?
Tianjian Liu, Shicheng Feng, Jin'ao Shang +5
Large language models (LLMs) have shown strong potential for assisting software and security analysis tasks, yet their effectiveness in cryptographic symbolic protocol verification…
The Marathon of Scientific Reasoning: Robustness of Scientific Agents to Perturbations in Multi-Turn Interactions
Xiaoting Lyu, Xinbo Ma, Yufei Han +4
Large language model (LLM)-based scientific agents are increasingly used for scientific problem solving, yet their robustness to imperfections arising during multi-turn interaction…
AgentLeak: Cloning Stronger LLM Agent Capabilities onto Weaker Agents Beyond Skill Stealing
Xiaoting Lyu, Yuhong Wu, Yufei Han +5
Large language model (LLM) agents increasingly achieve long-horizon tasks by combining foundation models with explicit skills and implicit procedural knowledge acquired through exe…
Lurking in the shadows: Unveiling Stealthy Backdoor Attacks against Personalized Federated Learning
Xiaoting Lyu, Yufei Han, Wei Wang +5
Federated Learning (FL) is a collaborative machine learning technique where multiple clients work together with a central server to train a global model without sharing their priva…
MolX: Enhancing Large Language Models for Molecular Understanding With A Multi-Modal Extension
Khiem Le, Zhichun Guo, Kaiwen Dong +8
Large Language Models (LLMs) with their strong task-handling capabilities have shown remarkable advancements across a spectrum of fields, moving beyond natural language understandi…
Cross-Context Backdoor Attacks against Graph Prompt Learning
Xiaoting Lyu, Yufei Han, Wei Wang +3
Graph Prompt Learning (GPL) bridges significant disparities between pretraining and downstream applications to alleviate the knowledge transfer bottleneck in real-world graph learn…
Graph-based Molecular Representation Learning
Zhichun Guo, Kehan Guo, Bozhao Nan +8
Molecular representation learning (MRL) is a key step to build the connection between machine learning and chemical science. In particular, it encodes molecules as numerical vector…