Showing cs.AIShow all
3 papers · 1 filter
cs.AI2026
ADIAS: Automated Design of Interactive Agentic Systems
Lekang Jiang, Bohan Tang, Stephan Goetz +1
Automated agent design improves agent harnesses through iterative revision, evaluation, and feedback summarization. Existing methods are largely candidate-centric: cross-round expe…
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…