7 papers
When Top-ranked Recommendations Fail: Modeling Multi-Granular Negative Feedback for Explainable and Robust Video Recommendation
Siran Chen, Boyu Chen, Chenyun Yu +5
Existing video recommendation systems, relying mainly on ID-based embedding mapping and collaborative filtering, often fail to capture in-depth video content semantics. Moreover, m…
Text2Mem: A Unified Memory Operation Language for Memory Operating System
Yi Wang, Lihai Yang, Boyu Chen +6
Large language model agents increasingly depend on memory to sustain long horizon interaction, but existing frameworks remain limited. Most expose only a few basic primitives such…
G-UBS: Towards Robust Understanding of Implicit Feedback via Group-Aware User Behavior Simulation
Boyu Chen, Siran Chen, Zhengrong Yue +7
User feedback is critical for refining recommendation systems, yet explicit feedback (e.g., likes or dislikes) remains scarce in practice. As a more feasible alternative, inferring…
VRAgent-R1: Boosting Video Recommendation with MLLM-based Agents via Reinforcement Learning
Siran Chen, Boyu Chen, Chenyun Yu +6
Owing to powerful natural language processing and generative capabilities, large language model (LLM) agents have emerged as a promising solution for enhancing recommendation syste…
Super Encoding Network: Recursive Association of Multi-Modal Encoders for Video Understanding
Boyu Chen, Siran Chen, Kunchang Li +3
Video understanding has been considered as one critical step towards world modeling, which is an important long-term problem in AI research. Recently, multimodal foundation models…
Unveiling Hidden Vulnerabilities in Digital Human Generation via Adversarial Attacks
Zhiying Li, Yeying Jin, Fan Shen +9
Expressive human pose and shape estimation (EHPS) is crucial for digital human generation, especially in applications like live streaming. While existing research primarily focuses…