SCP: Accelerating Discovery with a Global Web of Autonomous Scientific Agents
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| Main Authors: | , , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866917177411502080 |
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| author | Jiang, Yankai Lou, Wenjie Wang, Lilong Tang, Zhenyu Feng, Shiyang Lu, Jiaxuan Sun, Haoran Pan, Yaning Gu, Shuang Su, Haoyang Liu, Feng Wei, Wangxu Tan, Pan Zhou, Dongzhan Ling, Fenghua Tan, Cheng Zhang, Bo Wang, Xiaosong Bai, Lei Zhou, Bowen |
| author_facet | Jiang, Yankai Lou, Wenjie Wang, Lilong Tang, Zhenyu Feng, Shiyang Lu, Jiaxuan Sun, Haoran Pan, Yaning Gu, Shuang Su, Haoyang Liu, Feng Wei, Wangxu Tan, Pan Zhou, Dongzhan Ling, Fenghua Tan, Cheng Zhang, Bo Wang, Xiaosong Bai, Lei Zhou, Bowen |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_24189 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | SCP: Accelerating Discovery with a Global Web of Autonomous Scientific Agents Jiang, Yankai Lou, Wenjie Wang, Lilong Tang, Zhenyu Feng, Shiyang Lu, Jiaxuan Sun, Haoran Pan, Yaning Gu, Shuang Su, Haoyang Liu, Feng Wei, Wangxu Tan, Pan Zhou, Dongzhan Ling, Fenghua Tan, Cheng Zhang, Bo Wang, Xiaosong Bai, Lei Zhou, Bowen Artificial Intelligence Multiagent Systems 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. |
| title | SCP: Accelerating Discovery with a Global Web of Autonomous Scientific Agents |
| topic | Artificial Intelligence Multiagent Systems |
| url | https://arxiv.org/abs/2512.24189 |