903 citations · 1.1k across the 16 of their papers we have counts for
17 papers
From Knowing to Acting: Benchmarking Self-Awareness Capability of LLM Agents
Yifan Li, Shengbin Yue, Boyu Feng +6
The integration of external tools has transitioned LLM agents from passive responders to autonomous systems. However, current benchmarks prioritize execution success, neglecting se…
Contrast to Detect: Dynamic Graph Contrastive Regularization for Unsupervised Anomaly Detection in Multivariate Time Series
Yunhua Pei, Zixing Song, Jin Zheng +1
Anomaly detection in multivariate time series (MTS) is hindered by dynamic inter-variable dependencies and feature entanglement under spectral noise, and in practice, is further co…
A market-calibrated accelerated failure time model for in-play football forecasting
Lawrence Clegg, Zixing Song, John Cartlidge
In-play football forecasting models have struggled to match the accuracy of betting exchange prices, which aggregate information from many market participants. We close this gap by…
Distill-Belief: Closed-Loop Inverse Source Localization and Characterization in Physical Fields
Yiwei Shi, Zixing Song, Mengyue Yang +2
{Closed-loop inverse source localization and characterization (ISLC) requires a mobile agent to select measurements that localize sources and infer latent field parameters under st…
Semi-supervised Instruction Tuning for Large Language Models on Text-Attributed Graphs
Zixing Song, Irwin King
The emergent reasoning capabilities of Large Language Models (LLMs) offer a transformative paradigm for analyzing text-attributed graphs. While instruction tuning is the prevailing…
MMPG: MoE-based Adaptive Multi-Perspective Graph Fusion for Protein Representation Learning
Yusong Wang, Jialun Shen, Zhihao Wu +6
Graph Neural Networks (GNNs) have been widely adopted for Protein Representation Learning (PRL), as residue interaction networks can be naturally represented as graphs. Current GNN…
From Evidence to Trajectory: Abductive Reasoning Path Synthesis for Retrieval-Augmented Generation Agents Development
Muzhi Li, Jinhu Qi, Yihong Wu +9
Retrieval-augmented generation (RAG) agent development is hindered by the lack of executable ground-truth agent-environment interaction trajectories. Existing datasets provide ques…
Mitigating Over-Squashing in Graph Neural Networks by Spectrum-Preserving Sparsification
Langzhang Liang, Fanchen Bu, Zixing Song +3
The message-passing paradigm of Graph Neural Networks often struggles with exchanging information across distant nodes typically due to structural bottlenecks in certain graph regi…
Context-aware Inductive Knowledge Graph Completion with Latent Type Constraints and Subgraph Reasoning
Muzhi Li, Cehao Yang, Chengjin Xu +5
Inductive knowledge graph completion (KGC) aims to predict missing triples with unseen entities. Recent works focus on modeling reasoning paths between the head and tail entity as…
A Survey on Vision-Language-Action Models for Embodied AI
Yueen Ma, Zixing Song, Yuzheng Zhuang +2
Embodied AI is widely recognized as a cornerstone of artificial general intelligence (AGI) because it involves controlling embodied agents to perform tasks in the physical world. B…
WSFE: Wasserstein Sub-graph Feature Encoder for Effective User Segmentation in Collaborative Filtering
Yankai Chen, Yifei Zhang, Menglin Yang +3
Maximizing the user-item engagement based on vectorized embeddings is a standard procedure of recent recommender models. Despite the superior performance for item recommendations,…
Spectral Feature Augmentation for Graph Contrastive Learning and Beyond
Yifei Zhang, Hao Zhu, Zixing Song +2
Although augmentations (e.g., perturbation of graph edges, image crops) boost the efficiency of Contrastive Learning (CL), feature level augmentation is another plausible, compleme…
COSTA: Covariance-Preserving Feature Augmentation for Graph Contrastive Learning
Yifei Zhang, Hao Zhu, Zixing Song +2
Graph contrastive learning (GCL) improves graph representation learning, leading to SOTA on various downstream tasks. The graph augmentation step is a vital but scarcely studied st…
Graph Component Contrastive Learning for Concept Relatedness Estimation
Yueen Ma, Zixing Song, Xuming Hu +3
Concept relatedness estimation (CRE) aims to determine whether two given concepts are related. Existing methods only consider the pairwise relationship between concepts, while over…
ResNorm: Tackling Long-tailed Degree Distribution Issue in Graph Neural Networks via Normalization
Langzhang Liang, Zenglin Xu, Zixing Song +3
Graph Neural Networks (GNNs) have attracted much attention due to their ability in learning representations from graph-structured data. Despite the successful applications of GNNs…
A Survey on Deep Semi-supervised Learning
Xiangli Yang, Zixing Song, Irwin King +1
Deep semi-supervised learning is a fast-growing field with a range of practical applications. This paper provides a comprehensive survey on both fundamentals and recent advances in…
Graph-based Semi-supervised Learning: A Comprehensive Review
Zixing Song, Xiangli Yang, Zenglin Xu +1
Semi-supervised learning (SSL) has tremendous value in practice due to its ability to utilize both labeled data and unlabelled data. An important class of SSL methods is to natural…