activity
20242026
most citedPriM: Principle-Inspired Material Discovery through Multi-Agent Collaboration

1 citations · 1 across the 6 of their papers we have counts for

collaborators

8 papers

cs.LG2026

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…

cs.LG2026

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…

cs.LG2026

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…

cond-mat.mtrl-sci2025

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…

cs.AI2025

Airalogy: AI-empowered universal data digitization for research automation

Zijie Yang, Qiji Zhou, Fang Guo +19

Research data are the foundation of Artificial Intelligence (AI)-driven science, yet current AI applications remain limited to a few fields with readily available, well-structured,…

cs.LG2025

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…

cs.LG2025★ 1 cited

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…

cs.LG2024

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…