activity
20242026
most citedConservation-informed Graph Learning for Spatiotemporal Dynamics Prediction

1 citations · 1 across the 5 of their papers we have counts for

collaborators

5 papers

cs.SE2026

SWE-World: Building Software Engineering Agents in Docker-Free Environments

Shuang Sun, Huatong Song, Lisheng Huang +11

Recent advances in large language models (LLMs) have enabled software engineering agents to tackle complex code modification tasks. Most existing approaches rely on execution feedb…

cs.AI2026

TheoremForge: Scaling up Formal Data Synthesis with Low-Budget Agentic Workflow

Yicheng Tao, Hongteng Xu

The high cost of agentic workflows in formal mathematics hinders large-scale data synthesis, exacerbating the scarcity of open-source corpora. To address this, we introduce \textbf…

cs.LG2025

Lighter-X: An Efficient and Plug-and-play Strategy for Graph-based Recommendation through Decoupled Propagation

Yanping Zheng, Zhewei Wei, Frank de Hoog +4

Graph Neural Networks (GNNs) have demonstrated remarkable effectiveness in recommendation systems. However, conventional graph-based recommenders, such as LightGCN, require maintai…

cs.LG20251 cited

Conservation-informed Graph Learning for Spatiotemporal Dynamics Prediction

Yuan Mi, Pu Ren, Hongteng Xu +6

Data-centric methods have shown great potential in understanding and predicting spatiotemporal dynamics, enabling better design and control of the object system. However, deep lear…

cs.LG2024

A Bayesian Mixture Model of Temporal Point Processes with Determinantal Point Process Prior

Yiwei Dong, Shaoxin Ye, Yuwen Cao +3

Asynchronous event sequence clustering aims to group similar event sequences in an unsupervised manner. Mixture models of temporal point processes have been proposed to solve this…