5 papers · 1 filter
Triage-PiEvo: Evidence Admission for Principle-Evolving Scientific Agents
Peize Li, Yingming Pu, Xingguo Xu +3
Large language model (LLM)-based AI Scientists advance scientific discovery, yet they suffer from inefficiencies due to noisy observations. Existing principle-evolving agents act o…
Collaborative Principle Evolution via Evidence Transfer for Scientific Discovery
Yingming Pu, Hongyu Chen, Tao Lin
Large Language Model (LLM)-based agents promise to automate scientific discovery, yet exploring the vast hypothesis space remains costly. Existing principle-evolution methods accel…
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…
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…