5 papers
ReCast: Recasting Learning Signals for Reinforcement Learning in Generative Recommendation
Peiyan Zhang, Hanmo Liu, Chengxuan Tong +3
Generic group-based RL assumes that sampled rollout groups are already usable learning signals. We show that this assumption breaks down in sparse-hit generative recommendation, wh…
Learning to Compose for Cross-domain Agentic Workflow Generation
Jialiang Wang, Shengxiang Xu, Hanmo Liu +5
Automatically generating agentic workflows -- executable operator graphs or codes that orchestrate reasoning, verification, and repair -- has become a practical way to solve comple…
When Speed meets Accuracy: an Efficient and Effective Graph Model for Temporal Link Prediction
Haoyang Li, Yuming Xu, Yiming Li +5
Temporal link prediction in dynamic graphs is a critical task with applications in diverse domains such as social networks, recommendation systems, and e-commerce platforms. While…
Real-time Two-tape Control System in Vine robots
Hanmo Liu, Kayleen Smith, Zimu Yang +1
This paper focuses on how to make a growing Vine robot steer in different directions with a novel approach to real-time steering control by autonomously applying adhesive tape to i…
A Selective Learning Method for Temporal Graph Continual Learning
Hanmo Liu, Shimin Di, Haoyang Li +3
Node classification is a key task in temporal graph learning (TGL). Real-life temporal graphs often introduce new node classes over time, but existing TGL methods assume a fixed se…