1 citations · 1 across the 5 of their papers we have counts for
6 papers · 1 filter
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…
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…
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{…