5 papers
Revision, Not Restart: Revisable Visual Plans for Closed-Loop World-Action Models
Pengyiang Liu, Junbo Niu, Wenhao Zheng +5
World-action models use predicted visual futures to condition robot actions, yet execution feedback can invalidate parts of a prediction while leaving its task structure useful. We…
Trinity: A Scenario-Aware Recommendation Framework for Large-Scale Cold-Start Users
Wenhao Zheng, Wang Lu, Fangshuang Tang +4
Early-stage users in a new scenario intensify cold-start challenges, yet prior works often address only parts of the problem through model architecture. Launching a new user experi…
Reinforcing User Interest Evolution in Multi-Scenario Learning for recommender systems
Zhijian Feng, Wenhao Zheng, Xuanji Xiao
In real-world recommendation systems, users would engage in variety scenarios, such as homepages, search pages, and related recommendation pages. Each of these scenarios would refl…
UAV-Flow Colosseo: A Real-World Benchmark for Flying-on-a-Word UAV Imitation Learning
Xiangyu Wang, Donglin Yang, Yue Liao +5
Unmanned Aerial Vehicles (UAVs) are evolving into language-interactive platforms, enabling more intuitive forms of human-drone interaction. While prior works have primarily focused…
Anyprefer: An Agentic Framework for Preference Data Synthesis
Yiyang Zhou, Zhaoyang Wang, Tianle Wang +13
High-quality preference data is essential for aligning foundation models with human values through preference learning. However, manual annotation of such data is often time-consum…