PiFlow: Principle-aware Scientific Discovery with Multi-Agent Collaboration

• URL: http://arxiv.org/abs/2505.15047v1
• Date: Wed, 21 May 2025 03:09:39 GMT
• Title: PiFlow: Principle-aware Scientific Discovery with Multi-Agent Collaboration
• Authors: Yingming Pu, Tao Lin, Hongyu Chen,
• Abstract summary: We introduce textttPiFlow, an information-theoretical framework for automated scientific discovery.<n>Our method significantly improves discovery efficiency, reflected by a 73.55% increase in the Area Under the Curve.<n>Overall, textttPiFlow serves as a Plug-and-Play method, establishing a novel paradigm shift in highly efficient automated scientific discovery.
• Score: 9.640689981816852
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 \texttt{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, \texttt{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 \href{https://github.com/amair-lab/PiFlow}{GitHub}.

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