PiFlow: Principle-aware Scientific Discovery with Multi-Agent
  Collaboration
Large Language Model (LLM)-based multi-agent systems (MAS) demonstrate
remarkable potential for scientific discovery. Existing approaches, however,
often automate scientific discovery using predefined workflows that lack
rationality constraints. This often leads to aimless hypothesizing and a
failure to consistently link hypotheses with evidence, thereby hindering
systematic uncertainty reduction. Overcoming these limitations fundamentally
requires systematic uncertainty reduction. We introduce PiFlow, an
information-theoretical framework, treating automated scientific discovery as a
structured uncertainty reduction problem guided by principles (e.g., scientific
laws). In evaluations across three distinct scientific domains -- discovering
nanomaterial structures, bio-molecules, and superconductor candidates with
targeted properties -- our method significantly improves discovery efficiency,
reflected by a 73.55\% increase in the Area Under the Curve (AUC) of property
values versus exploration steps, and enhances solution quality by 94.06\%
compared to a vanilla agent system. Overall, PiFlow serves as a
Plug-and-Play method, establishing a novel paradigm shift in highly efficient
automated scientific discovery, paving the way for more robust and accelerated
AI-driven research. Code is publicly available at our
https://github.com/amair-lab/PiFlow{GitHub}.
682e8a7651706f69070c40d3
Yingming Pu
682e8a7651706f69070c40d4
Tao Lin
682e8a7651706f69070c40d5
Hongyu Chen
PiFlow: Principle-aware Scientific Discovery with Multi-Agent
  Collaboration
Large Language Model (LLM)-based multi-agent systems (MAS) demonstrate
remarkable potential for scientific discovery. Existing approaches, however,
often automate scientific discovery using predefined workflows that lack
rationality constraints. This often leads to aimless hypothesizing and a
failure to consistently link hypotheses with evidence, thereby hindering
systematic uncertainty reduction. Overcoming these limitations fundamentally
requires systematic uncertainty reduction. We introduce PiFlow, an
information-theoretical framework, treating automated scientific discovery as a
structured uncertainty reduction problem guided by principles (e.g., scientific
laws). In evaluations across three distinct scientific domains -- discovering
nanomaterial structures, bio-molecules, and superconductor candidates with
targeted properties -- our method significantly improves discovery efficiency,
reflected by a 73.55\% increase in the Area Under the Curve (AUC) of property
values versus exploration steps, and enhances solution quality by 94.06\%
compared to a vanilla agent system. Overall, PiFlow serves as a
Plug-and-Play method, establishing a novel paradigm shift in highly efficient
automated scientific discovery, paving the way for more robust and accelerated
AI-driven research. Code is publicly available at our
https://github.com/amair-lab/PiFlow{GitHub}.
PiFlow, an information-theoretical framework, improves automated scientific discovery by systematically reducing uncertainty and enhancing solution quality across various scientific domains.
Large Language Model multi-agent systems automated scientific discovery predefined workflows scientific discovery information-theoretical framework uncertainty reduction hypothesis evidence Area Under the Curve exploration steps solution quality Plug-and-Play method