4 citations · 7 across the 7 of their papers we have counts for
10 papers
TRACESTEER: Trace Driven Activation Steering for Hallucination Mitigation in Diffusion Language Models
Xin Zhang, Yili Wang, Yue Tan +5
Diffusion large language models (dLLMs) have emerged as a promising alternative to autoregressive LLMs, yet remain susceptible to hallucination. Activation steering offers a promis…
SAIGuard: Communication-State Simulation for Proactive Defense of LLM Multi-Agent Systems
Ruxue Shi, Yili Wang, Mengnan Du +4
LLM-based multi-agent systems (MAS) solve complex tasks through inter-agent collaboration, but their communication-driven nature also allows security risks to spread across agents…
Detecting Unfaithful Chain-of-Thought via Circuit-Guided Internal-External Discrepancy
Xu Shen, Zhen Tan, Song Wang +4
Chain-of-thought (CoT) reasoning improves the problem-solving ability of large language models (LLMs), but generated reasoning traces may not faithfully reflect the model's actual…
BlindGuard: Safeguarding LLM-based Multi-Agent Systems under Unknown Attacks
Rui Miao, Yixin Liu, Yili Wang +5
The security of LLM-based multi-agent systems (MAS) is critically threatened by propagation vulnerability, where malicious agents can distort collective decision-making through int…
Understanding the Information Propagation Effects of Communication Topologies in LLM-based Multi-Agent Systems
Xu Shen, Yixin Liu, Yiwei Dai +5
The communication topology in large language model-based multi-agent systems fundamentally governs inter-agent collaboration patterns, critically shaping both the efficiency and ef…
Raising the Bar in Graph OOD Generalization: Invariant Learning Beyond Explicit Environment Modeling
Xu Shen, Yixin Liu, Yili Wang +5
Out-of-distribution (OOD) generalization has emerged as a critical challenge in graph learning, as real-world graph data often exhibit diverse and shifting environments that tradit…
Mamba-Based Graph Convolutional Networks: Tackling Over-smoothing with Selective State Space
Xin He, Yili Wang, Wenqi Fan +4
Graph Neural Networks (GNNs) have shown great success in various graph-based learning tasks. However, it often faces the issue of over-smoothing as the model depth increases, which…
Graph Defense Diffusion Model
Xin He, Wenqi Fan, Yili Wang +4
Graph Neural Networks (GNNs) are highly vulnerable to adversarial attacks, which can greatly degrade their performance. Existing graph purification methods attempt to address this…
Unifying Unsupervised Graph-Level Anomaly Detection and Out-of-Distribution Detection: A Benchmark
Yili Wang, Yixin Liu, Xu Shen +6
To build safe and reliable graph machine learning systems, unsupervised graph-level anomaly detection (GLAD) and unsupervised graph-level out-of-distribution (OOD) detection (GLOD)…
Rethinking Independent Cross-Entropy Loss For Graph-Structured Data
Rui Miao, Kaixiong Zhou, Yili Wang +3
Graph neural networks (GNNs) have exhibited prominent performance in learning graph-structured data. Considering node classification task, based on the i.i.d assumption among node…