Build Your Personalized Research Group: A Multiagent Framework for Continual and Interactive Science Automation
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908599560699904 |
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| author | Li, Ed Ren, Junyu Pan, Xintian Yan, Cat Li, Chuanhao Bergemann, Dirk Yang, Zhuoran |
| author_facet | Li, Ed Ren, Junyu Pan, Xintian Yan, Cat Li, Chuanhao Bergemann, Dirk Yang, Zhuoran |
| contents | The automation of scientific discovery represents a critical milestone in Artificial Intelligence (AI) research. However, existing agentic systems for science suffer from two fundamental limitations: rigid, pre-programmed workflows that cannot adapt to intermediate findings, and inadequate context management that hinders long-horizon research. We present \texttt{freephdlabor}, an open-source multiagent framework featuring \textit{fully dynamic workflows} determined by real-time agent reasoning and a \coloremph{\textit{modular architecture}} enabling seamless customization -- users can modify, add, or remove agents to address domain-specific requirements. The framework provides comprehensive infrastructure including \textit{automatic context compaction}, \textit{workspace-based communication} to prevent information degradation, \textit{memory persistence} across sessions, and \textit{non-blocking human intervention} mechanisms. These features collectively transform automated research from isolated, single-run attempts into \textit{continual research programs} that build systematically on prior explorations and incorporate human feedback. By providing both the architectural principles and practical implementation for building customizable co-scientist systems, this work aims to facilitate broader adoption of automated research across scientific domains, enabling practitioners to deploy interactive multiagent systems that autonomously conduct end-to-end research -- from ideation through experimentation to publication-ready manuscripts. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_15624 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Build Your Personalized Research Group: A Multiagent Framework for Continual and Interactive Science Automation Li, Ed Ren, Junyu Pan, Xintian Yan, Cat Li, Chuanhao Bergemann, Dirk Yang, Zhuoran Artificial Intelligence Computation and Language Machine Learning Multiagent Systems The automation of scientific discovery represents a critical milestone in Artificial Intelligence (AI) research. However, existing agentic systems for science suffer from two fundamental limitations: rigid, pre-programmed workflows that cannot adapt to intermediate findings, and inadequate context management that hinders long-horizon research. We present \texttt{freephdlabor}, an open-source multiagent framework featuring \textit{fully dynamic workflows} determined by real-time agent reasoning and a \coloremph{\textit{modular architecture}} enabling seamless customization -- users can modify, add, or remove agents to address domain-specific requirements. The framework provides comprehensive infrastructure including \textit{automatic context compaction}, \textit{workspace-based communication} to prevent information degradation, \textit{memory persistence} across sessions, and \textit{non-blocking human intervention} mechanisms. These features collectively transform automated research from isolated, single-run attempts into \textit{continual research programs} that build systematically on prior explorations and incorporate human feedback. By providing both the architectural principles and practical implementation for building customizable co-scientist systems, this work aims to facilitate broader adoption of automated research across scientific domains, enabling practitioners to deploy interactive multiagent systems that autonomously conduct end-to-end research -- from ideation through experimentation to publication-ready manuscripts. |
| title | Build Your Personalized Research Group: A Multiagent Framework for Continual and Interactive Science Automation |
| topic | Artificial Intelligence Computation and Language Machine Learning Multiagent Systems |
| url | https://arxiv.org/abs/2510.15624 |