SCP: Accelerating Discovery with a Global Web of Autonomous Scientific Agents
Abstract
We introduce SCP: the Science Context Protocol, an open-source standard designed to accelerate discovery by enabling a global network of autonomous scientific agents. SCP is built on two foundational pillars: (1) Unified Resource Integration: At its core, SCP provides a universal specification for describing and invoking scientific resources, spanning software tools, models, datasets, and physical instruments. This protocol-level standardization enables AI agents and applications to discover, call, and compose capabilities seamlessly across disparate platforms and institutional boundaries. (2) Orchestrated Experiment Lifecycle Management: SCP complements the protocol with a secure service architecture, which comprises a centralized SCP Hub and federated SCP Servers. This architecture manages the complete experiment lifecycle (registration, planning, execution, monitoring, and archival), enforces fine-grained authentication and authorization, and orchestrates traceable, end-to-end workflows that bridge computational and physical laboratories. Based on SCP, we have constructed a scientific discovery platform that offers researchers and agents a large-scale ecosystem of more than 1,600 tool resources. Across diverse use cases, SCP facilitates secure, large-scale collaboration between heterogeneous AI systems and human researchers while significantly reducing integration overhead and enhancing reproducibility. By standardizing scientific context and tool orchestration at the protocol level, SCP establishes essential infrastructure for scalable, multi-institution, agent-driven science.
Keywords
Cite
@article{arxiv.2512.24189,
title = {SCP: Accelerating Discovery with a Global Web of Autonomous Scientific Agents},
author = {Yankai Jiang and Wenjie Lou and Lilong Wang and Zhenyu Tang and Shiyang Feng and Jiaxuan Lu and Haoran Sun and Yaning Pan and Shuang Gu and Haoyang Su and Feng Liu and Wangxu Wei and Pan Tan and Dongzhan Zhou and Fenghua Ling and Cheng Tan and Bo Zhang and Xiaosong Wang and Lei Bai and Bowen Zhou},
journal= {arXiv preprint arXiv:2512.24189},
year = {2026}
}