title: PiFlow: Principle-aware Scientific Discovery with Multi-Agent
    Collaboration
summary: 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}.
paper_authors._id: 682e8a7651706f69070c40d3
paper_authors.name: Yingming Pu
paper_authors._id: 682e8a7651706f69070c40d4
paper_authors.name: Tao Lin
paper_authors._id: 682e8a7651706f69070c40d5
paper_authors.name: Hongyu Chen
paper_title: PiFlow: Principle-aware Scientific Discovery with Multi-Agent
    Collaboration
paper_summary: 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}.
paper_ai_summary: PiFlow, an information-theoretical framework, improves automated scientific discovery by systematically reducing uncertainty and enhancing solution quality across various scientific domains.
paper_ai_keywords: 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