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cs.AI2026
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…
cs.AI2025
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…