4 citations · 6 across the 13 of their papers we have counts for
14 papers
Long-Horizon Textual World Modeling through Structured Reasoning
Fangxin Wang, Xiang Gao, Yuguang Yao +3
World models must predict how an environment evolves under sequences of actions, enabling agents to compare possible futures and reason about counterfactual actions before acting.…
Textual Belief States for World Models: Identifiable Representation Learning Under Strict Mediation
Xiang Gao, Kaiwen Dong, Yuguang Yao +2
World models in partially observed environments rely on latent representations that summarize interaction history, but in many modern LLM-based architectures predictive performance…
Goal-Conditioned Supervised Learning for LLM Fine-Tuning
Shijun Li, Kaiwen Dong, Xiang Gao +1
Large language models often require fine-tuning to better align their behavior with user intent at deployment. Existing approaches are commonly divided into online and offline para…
Learning to Rewrite Tool Descriptions for Reliable LLM-Agent Tool Use
Ruocheng Guo, Kaiwen Dong, Xiang Gao +1
While most efforts to improve LLM-based tool-using agents focus on the agent itself - through larger models, better prompting, or fine-tuning - agent performance increasingly plate…
RIMRULE: Improving Tool-Using Language Agents via MDL-Guided Rule Learning
Xiang Gao, Yuguang Yao, Qi Zhang +5
Large language models (LLMs) often struggle to use tools reliably in domain-specific settings, where APIs may be idiosyncratic, under-documented, or tailored to private workflows.…
Transaction Categorization with Relational Deep Learning in QuickBooks
Kaiwen Dong, Padmaja Jonnalagedda, Xiang Gao +5
Automatic transaction categorization is crucial for enhancing the customer experience in QuickBooks by providing accurate accounting and bookkeeping. The distinct challenges in thi…
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…
CORE: Data Augmentation for Link Prediction via Information Bottleneck
Kaiwen Dong, Zhichun Guo, Nitesh V. Chawla
Link prediction (LP) is a fundamental task in graph representation learning, with numerous applications in diverse domains. However, the generalizability of LP models is often comp…
You do not have to train Graph Neural Networks at all on text-attributed graphs
Kaiwen Dong, Zhichun Guo, Nitesh V. Chawla
Graph structured data, specifically text-attributed graphs (TAG), effectively represent relationships among varied entities. Such graphs are essential for semi-supervised node clas…
Universal Link Predictor By In-Context Learning on Graphs
Kaiwen Dong, Haitao Mao, Zhichun Guo +1
Link prediction is a crucial task in graph machine learning, where the goal is to infer missing or future links within a graph. Traditional approaches leverage heuristic methods ba…
Node Duplication Improves Cold-start Link Prediction
Zhichun Guo, Tong Zhao, Yozen Liu +5
Graph Neural Networks (GNNs) are prominent in graph machine learning and have shown state-of-the-art performance in Link Prediction (LP) tasks. Nonetheless, recent studies show tha…
Pure Message Passing Can Estimate Common Neighbor for Link Prediction
Kaiwen Dong, Zhichun Guo, Nitesh V. Chawla
Message Passing Neural Networks (MPNNs) have emerged as the {\em de facto} standard in graph representation learning. However, when it comes to link prediction, they often struggle…
FakeEdge: Alleviate Dataset Shift in Link Prediction
Kaiwen Dong, Yijun Tian, Zhichun Guo +2
Link prediction is a crucial problem in graph-structured data. Due to the recent success of graph neural networks (GNNs), a variety of GNN-based models were proposed to tackle the…
Heterogeneous Graph Masked Autoencoders
Yijun Tian, Kaiwen Dong, Chunhui Zhang +2
Generative self-supervised learning (SSL), especially masked autoencoders, has become one of the most exciting learning paradigms and has shown great potential in handling graph da…