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📄 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
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.