5 papers
Enabling Agents to Communicate Entirely in Latent Space
Zhuoyun Du, Runze Wang, Huiyu Bai +6
While natural language is the de facto communication medium for LLM-based agents, it presents a fundamental constraint. The process of downsampling rich, internal latent states int…
Learning to Evolve: A Self-Improving Framework for Multi-Agent Systems via Textual Parameter Graph Optimization
Shan He, Runze Wang, Zhuoyun Du +4
Designing and optimizing multi-agent systems (MAS) is a complex, labor-intensive process of "Agent Engineering." Existing automatic optimization methods, primarily focused on flat…
Online-PVLM: Advancing Personalized VLMs with Online Concept Learning
Huiyu Bai, Runze Wang, Zhuoyun Du +6
Personalized Visual Language Models (VLMs) are gaining increasing attention for their formidable ability in user-specific concepts aligned interactions (e.g., identifying a user's…
GEM: Generative Entropy-Guided Preference Modeling for Few-shot Alignment of LLMs
Yiyang Zhao, Huiyu Bai, Xuejiao Zhao
Alignment of large language models (LLMs) with human preferences typically relies on supervised reward models or external judges that demand abundant annotations. However, in field…
GFRIEND: Generative Few-shot Reward Inference through EfficieNt DPO
Yiyang Zhao, Huiyu Bai, Xuejiao Zhao
The ability to train high-performing reward models with few-shot data is critical for enhancing the efficiency and scalability of Reinforcement Learning from Human Feedback (RLHF).…