1 citations · 1 across the 6 of their papers we have counts for
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Principle-Evolvable Scientific Discovery via Uncertainty Minimization
Yingming Pu, Tao Lin, Hongyu Chen
Large Language Model (LLM)-based scientific agents have accelerated scientific discovery, yet they often suffer from significant inefficiencies due to adherence to fixed initial pr…
PiFlow: Principle-Aware Scientific Discovery with Multi-Agent Collaboration
Yingming Pu, Tao Lin, Hongyu Chen
Large Language Model (LLM)-based multi-agent systems (MAS) demonstrate remarkable potential for scientific discovery. Existing approaches, however, often automate scientific discov…
PriM: Principle-Inspired Material Discovery through Multi-Agent Collaboration
Zheyuan Lai, Yingming Pu
Complex chemical space and limited knowledge scope with biases holds immense challenge for human scientists, yet in automated materials discovery. Existing intelligent methods reli…
Leveraging large language models for nano synthesis mechanism explanation: solid foundations or mere conjectures?
Yingming Pu, Liping Huang, Tao Lin +1
With the rapid development of artificial intelligence (AI), large language models (LLMs) such as GPT-4 have garnered significant attention in the scientific community, demonstratin…