15 citations · 36 across the 29 of their papers we have counts for
20 papers · 1 filter
Bayes-Sufficient Compression Is Not Enough: How Does Communication Help Multi-Agent Systems?
Yi Xie, Zhanke Zhou, Yi Fan +3
Multi-agent LLM systems pair a sender with broad context and an executor with a limited local view. We study when a short message improves the executor's next decision, when raw co…
DICE: Entropy-Regularized Equilibrium Selection for Stable Multi-Agent LLM Coordination
Yi Xie, Zhanke Zhou, Chentao Cao +2
Multi-agent large language model (LLM) systems often fail to reliably outperform a single strong model equipped with best-of-N sampling. We argue that a core source of this instabi…
Beyond Homophily: Towards Generalized Graph Reconstruction Attack and Defense
Zhanke Zhou, Bo Han, Xuan Li +3
Graph neural networks (GNNs) are widely deployed on relational data, yet they can leak sensitive or proprietary information about the training graph adjacency, e.g., social ties, t…
The Easy, the Hard, and the Learnable: Confidence and Difficulty-Adaptive Policy Optimization for LLM Reasoning
Zhanke Zhou, Xiangyu Lu, Chentao Cao +4
RL with verifiable rewards can substantially improve LLM reasoning, yet standard GRPO-style training often treats easy, hard, and learnable questions alike through uniform sampling…
Reference-guided Policy Optimization for Molecular Optimization via LLM Reasoning
Xuan Li, Zhanke Zhou, Zongze Li +4
Large language models (LLMs) benefit substantially from supervised fine-tuning (SFT) and reinforcement learning with verifiable rewards (RLVR) in reasoning tasks. However, these re…
Co-rewarding: Stable Self-supervised RL for Eliciting Reasoning in Large Language Models
Zizhuo Zhang, Jianing Zhu, Xinmu Ge +6
While reinforcement learning with verifiable rewards (RLVR) is effective to improve the reasoning ability of large language models (LLMs), its reliance on human-annotated labels le…
From Passive to Active Reasoning: Can Large Language Models Ask the Right Questions under Incomplete Information?
Zhanke Zhou, Xiao Feng, Zhaocheng Zhu +3
While existing benchmarks probe the reasoning abilities of large language models (LLMs) across diverse domains, they predominantly assess passive reasoning, providing models with a…
From Debate to Equilibrium: Belief-Driven Multi-Agent LLM Reasoning via Bayesian Nash Equilibrium
Xie Yi, Zhanke Zhou, Chentao Cao +3
Multi-agent frameworks can substantially boost the reasoning power of large language models (LLMs), but they typically incur heavy computational costs and lack convergence guarante…
Landscape of Thoughts: Visualizing the Reasoning Process of Large Language Models
Zhanke Zhou, Zhaocheng Zhu, Xuan Li +5
Numerous applications of large language models (LLMs) rely on their ability to perform step-by-step reasoning. However, the reasoning behavior of LLMs remains poorly understood, po…
Rethinking LLM Unlearning Objectives: A Gradient Perspective and Go Beyond
Qizhou Wang, Jin Peng Zhou, Zhanke Zhou +3
Large language models (LLMs) should undergo rigorous audits to identify potential risks, such as copyright and privacy infringements. Once these risks emerge, timely updates are cr…
Noisy Test-Time Adaptation in Vision-Language Models
Chentao Cao, Zhun Zhong, Zhanke Zhou +4
Test-time adaptation (TTA) aims to address distribution shifts between source and target data by relying solely on target data during testing. In open-world scenarios, models often…
Model Inversion Attacks: A Survey of Approaches and Countermeasures
Zhanke Zhou, Jianing Zhu, Fengfei Yu +4
Deep neural networks have enabled numerous studies and applications on both Euclidean data, such as images and text, and non-Euclidean data, such as graphs. Because these networks…
Envisioning Outlier Exposure by Large Language Models for Out-of-Distribution Detection
Chentao Cao, Zhun Zhong, Zhanke Zhou +3
Detecting out-of-distribution (OOD) samples is essential when deploying machine learning models in open-world scenarios. Zero-shot OOD detection, requiring no training on in-distri…
Combating Bilateral Edge Noise for Robust Link Prediction
Zhanke Zhou, Jiangchao Yao, Jiaxu Liu +6
Although link prediction on graphs has achieved great success with the development of graph neural networks (GNNs), the potential robustness under the edge noise is still less inve…
DeepInception: Hypnotize Large Language Model to Be Jailbreaker
Xuan Li, Zhanke Zhou, Jianing Zhu +3
Large language models (LLMs) have succeeded significantly in various applications but remain susceptible to adversarial jailbreaks that void their safety guardrails. Previous attem…
Neural Atoms: Propagating Long-range Interaction in Molecular Graphs through Efficient Communication Channel
Xuan Li, Zhanke Zhou, Jiangchao Yao +3
Graph Neural Networks (GNNs) have been widely adopted for drug discovery with molecular graphs. Nevertheless, current GNNs mainly excel in leveraging short-range interactions (SRI)…
Understanding Fairness Surrogate Functions in Algorithmic Fairness
Wei Yao, Zhanke Zhou, Zhicong Li +2
It has been observed that machine learning algorithms exhibit biased predictions against certain population groups. To mitigate such bias while achieving comparable accuracy, a pro…
On Strengthening and Defending Graph Reconstruction Attack with Markov Chain Approximation
Zhanke Zhou, Chenyu Zhou, Xuan Li +3
Although powerful graph neural networks (GNNs) have boosted numerous real-world applications, the potential privacy risk is still underexplored. To close this gap, we perform the f…
KGTuner: Efficient Hyper-parameter Search for Knowledge Graph Learning
Yongqi Zhang, Zhanke Zhou, Quanming Yao +1
While hyper-parameters (HPs) are important for knowledge graph (KG) learning, existing methods fail to search them efficiently. To solve this problem, we first analyze the properti…
AdaProp: Learning Adaptive Propagation for Graph Neural Network based Knowledge Graph Reasoning
Yongqi Zhang, Zhanke Zhou, Quanming Yao +2
Due to the popularity of Graph Neural Networks (GNNs), various GNN-based methods have been designed to reason on knowledge graphs (KGs). An important design component of GNN-based…