1 citations · 1 across the 5 of their papers we have counts for
11 papers
Memory as Belief: Bayesian Evolution of Memory for Self-Evolving LLM Agents
Yiming Xu, Qian Yang, Zihan Chen +4
Large language model (LLM) agents increasingly rely on external memory to accumulate past experiences for continual adaptation. However, effectively reusing memory requires accurat…
DecepEval: A Benchmark for Evaluating Deception in LLM Agents
Yiming Xu, Hongyue Yu, Beihua Yang +8
As large language model (LLM) agents become increasingly autonomous, they may pursue task performance through deception, raising concerns about their reliable deployment. Existing…
When Should Agents Check External State? Budgeting Observations for Stored Intentions
Zhengkun Di, Bin Shi, Kai Sun +2
Prospective memory allows an agent to retain an intention tied to a future condition, but the stored intention does not reveal whether that condition currently holds. Checking it m…
Cross-Relational Preference Learning for Better LLM Instruction Following
Runsheng Li, Kai Sun, Bin Shi +1
Large Language Models (LLMs) still exhibit limited capability in following complex instructions. While existing approaches often rely on preference learning to enhance this ability…
TED: Related Party Transaction guided Tax Evasion Detection on Heterogeneous Graph
Yiming Xu, Bin Shi, Bo Dong +3
Tax evasion causes severe losses of government revenues and disturbs the economic order of fair competition. To help alleviate this problem, the latest tax evasion detection soluti…
Mitigating Instance Entanglement in Instance-Dependent Partial Label Learning
Rui Zhao, Bin Shi, Kai Sun +1
Partial label learning is a prominent weakly supervised classification task, where each training instance is ambiguously labeled with a set of candidate labels. In real-world scena…
Feature Bank Enhancement for Distance-based Out-of-Distribution Detection
Yuhang Liu, Yuefei Wu, Bin Shi +1
Out-of-distribution (OOD) detection is critical to ensuring the reliability of deep learning applications and has attracted significant attention in recent years. A rich body of li…
Out-of-Distribution Generalization on Graphs via Progressive Inference
Yiming Xu, Bin Shi, Zhen Peng +3
The development and evaluation of graph neural networks (GNNs) generally follow the independent and identically distributed (i.i.d.) assumption. Yet this assumption is often untena…
Label Ensemble Perspective for Reduced-Complexity Graph Node Classification under Label Noise
Rui Zhao, Bin Shi, Zhiming Liang +4
Graph Neural Networks (GNNs) have been widely employed for semi-supervised node classification tasks on graphs. However, the performance of GNNs is significantly affected by \emph{…
Estimating Noisy Class Posterior with Part-level Labels for Noisy Label Learning
Rui Zhao, Bin Shi, Jianfei Ruan +2
In noisy label learning, estimating noisy class posteriors plays a fundamental role for developing consistent classifiers, as it forms the basis for estimating clean class posterio…
GraphPub: Generation of Differential Privacy Graph with High Availability
Wanghan Xu, Bin Shi, Ao Liu +2
In recent years, with the rapid development of graph neural networks (GNN), more and more graph datasets have been published for GNN tasks. However, when an upstream data owner pub…