5 papers
The Hidden Power of Scaling Factor in LoRA Optimization
Zicheng Zhang, Haoran Li, Jiaxing Wang +10
In Low-Rank Adaptation (LoRA), the scaling factor is often treated as a mere complement to the learning rate, yet its role in optimization remains poorly understood. In this p…
Hierarchical Attacks for Multi-Modal Multi-Agent Reasoning
Hao Zhou, Tiru Wu, Yan Jiang +3
Multi-modal multi-agent systems (MM-MAS) have gained increasing attention for their capacity to enable complex reasoning and coordination across diverse modalities. As these system…
OxyGent: Making Multi-Agent Systems Modular, Observable, and Evolvable via Oxy Abstraction
Junxing Hu, Tianlong Li, Lei Yu +1
Deploying production-ready multi-agent systems (MAS) in complex industrial environments remains challenging due to limitations in scalability, observability, and autonomous evoluti…
HiMA-Ecom: Enabling Joint Training of Hierarchical Multi-Agent E-commerce Assistants
Junxing Hu, Ai Han, Haolan Zhan +7
Hierarchical multi-agent systems based on large language models (LLMs) have become a common paradigm for building AI assistants in vertical domains such as e-commerce, where a mast…
The Primacy of Magnitude in Low-Rank Adaptation
Zicheng Zhang, Haoran Li, Yifeng Zhang +5
Low-Rank Adaptation (LoRA) offers a parameter-efficient paradigm for tuning large models. While recent spectral initialization methods improve convergence and performance over the…