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arxiv_ai 90% Match Research Paper AI Researchers,Computational Scientists,Research Engineers,Developers of AI Agents 2 weeks ago

Build Your Personalized Research Group: A Multiagent Framework for Continual and Interactive Science Automation

reinforcement-learning › multi-agent
📄 Abstract

Abstract: 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.
Authors (7)
Ed Li
Junyu Ren
Xintian Pan
Cat Yan
Chuanhao Li
Dirk Bergemann
+1 more
Submitted
October 17, 2025
arXiv Category
cs.AI
arXiv PDF Code

Key Contributions

Presents freephdlabor, an open-source multiagent framework for continual and interactive science automation. It features fully dynamic workflows driven by real-time agent reasoning and a modular architecture for customization. Key innovations include automatic context compaction, workspace-based communication, memory persistence, and non-blocking human intervention, addressing limitations of rigid workflows and poor context management in existing agentic systems.

Business Value

Accelerates the pace of scientific discovery and innovation by providing a flexible and powerful platform for automating complex research tasks, fostering collaboration between humans and AI agents.

View Code on GitHub