SCP: Accelerating Discovery with a Global Web of Autonomous Scientific Agents
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Abstract (arXiv)
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.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 30 Dec 2025.
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Citing papers per year: 2026: 6.
6 papers.
- 2026InternAgent-1.5: A Unified Agentic Framework for Long-Horizon Autonomous Scientific Discoverycited by 27
- 2026FinToolBench: Evaluating LLM Agents for Real-World Financial Tool Usecited by 10
- 2026MolClaw: An Autonomous Agent with Hierarchical Skills for Drug Molecule Evaluation, Screening, and Optimizationcited by 7
- 2026SciDataCopilot: An Agentic Data Preparation Framework for AGI-driven Scientific Discoverycited by 4
- 2026LAP: An Agent-to-Instrument Protocol for Autonomous Sciencecited by 2
- 2026Embodied Science: Closing the Discovery Loop with Agentic Embodied AIcited by 1
- 2026LAP: An Agent-to-Instrument Protocol for Autonomous Science2 Jun 2026
- 2026MolClaw: An Autonomous Agent with Hierarchical Skills for Drug Molecule Evaluation, Screening, and Optimization2 Apr 2026
- 2026Embodied Science: Closing the Discovery Loop with Agentic Embodied AI20 Mar 2026
- 2026FinToolBench: Evaluating LLM Agents for Real-World Financial Tool Use9 Mar 2026
- 2026SciDataCopilot: An Agentic Data Preparation Framework for AGI-driven Scientific Discovery9 Feb 2026
- 2026InternAgent-1.5: A Unified Agentic Framework for Long-Horizon Autonomous Scientific Discovery9 Feb 2026