2 citations · 3 across the 4 of their papers we have counts for
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cs.AI2026
Position: Agentic Evolution is the Path to Evolving LLMs
Minhua Lin, Hanqing Lu, Zhan Shi +11
As Large Language Models (LLMs) move from curated training sets into open-ended real-world environments, a fundamental limitation emerges: static training cannot keep pace with con…
cs.AI2026★ 2 cited
Phase Transition for Budgeted Multi-Agent Synergy
Bang Liu, Linglong Kong, Jian Pei
Multi-agent systems can improve reliability, yet under a fixed inference budget they often help, saturate, or even collapse. We develop a minimal and calibratable theory that predi…
cs.AI2025
Advances and Challenges in Foundation Agents: From Brain-Inspired Intelligence to Evolutionary, Collaborative, and Safe Systems
Bang Liu, Xinfeng Li, Jiayi Zhang +45
The advent of large language models (LLMs) has catalyzed a transformative shift in artificial intelligence, paving the way for advanced intelligent agents capable of sophisticated…