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cs.AI2025
On the Importance of Task Complexity in Evaluating LLM-Based Multi-Agent Systems
Bohan Tang, Huidong Liang, Keyue Jiang +1
Large language model multi-agent systems (LLM-MAS) offer a promising paradigm for harnessing collective intelligence to achieve more advanced forms of AI behaviour. While recent st…
cs.AI2025
Synthesizing Post-Training Data for LLMs through Multi-Agent Simulation
Shuo Tang, Xianghe Pang, Zexi Liu +6
Post-training is essential for enabling large language models (LLMs) to follow human instructions. However, its effectiveness depends on high-quality instruction data, which is cha…