6 papers
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…
Mechanisms of Matter: Language Inferential Benchmark on Physicochemical Hypothesis in Materials Synthesis
Yingming Pu, Tao Lin, Hongyu Chen
The capacity of Large Language Models (LLMs) to generate valid scientific hypotheses for materials synthesis remains largely unquantified, hindered by the absence of benchmarks pro…
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…