arXiv:2510.15624v1 Announce Type: cross
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.
