SCP: Accelerating Discovery with a Global Web of Autonomous Scientific Agents

• URL: http://arxiv.org/abs/2512.24189v1
• Date: Tue, 30 Dec 2025 12:45:32 GMT
• Title: SCP: Accelerating Discovery with a Global Web of Autonomous Scientific Agents
• Abstract summary: We introduce SCP: the Science Context Protocol, an open-source standard designed to accelerate discovery by enabling a global network of autonomous scientific agents.<n>At its core, SCP provides a universal specification for describing and invoking scientific resources.<n> SCP complements the protocol with a secure service architecture, which comprises a centralized SCP Hub and federated SCP Servers.
• Score: 39.076395839537476
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• CSAR: Containerized System Architecture for Robotics [0.12045539806824919]
  We present CSAR (Containerized System Architecture for Robotics), a container-centric architectural framework designed specifically for robotics teams and the edge-cloud.<n>CSAR combines LXC/LXD-based system containerization, ROS 2/DDS-based communication, and a three-layer edge infrastructure to organize computation into hardware-affine, persistent execution environments.<n>We describe a real deployment of CSAR in an academic robotics laboratory and evaluate it through representative use cases involving edge-offloaded 3D SLAM and GPU-accelerated semantic mapping.
  arXiv  Detail & Related papers  (2026-06-29T13:36:42Z)
• A Technical Taxonomy of LLM Agent Communication Protocols [60.76747983053368]
  This study develops a technical taxonomy to classify and analyze large language models (LLMs) agent communication protocols.<n>The framework guides protocol selection and highlights open research gaps such as privacy and policy enforcement.
  arXiv  Detail & Related papers  (2026-06-17T14:45:20Z)
• Delivering Science as a Service: Sci-Orchestra's Cloud-Native Approach to HPC [0.01826019335709531]
  Sci-Orchestra is a layered orchestration framework designed to fully automate experimental operations.<n>By abstracting execution through an API-driven interface, the system assumes responsibility for secure authentication, resource management, and scalable deployment.<n>A key innovation of Sci-Orchestra is its autonomous marketplace, which serves as a catalyst for cross-institutional collaboration.
  arXiv  Detail & Related papers  (2026-05-08T19:03:40Z)
• From Agent-Only Social Networks to Autonomous Scientific Research: Lessons from OpenClaw and Moltbook, and the Architecture of ClawdLab and Beach.Science [0.0]
  OpenClaw and Moltbook produced a large-scale dataset of autonomous AI-to-AI interaction in January 2026.<n>This study conducts a multivocal literature review of that ecosystem and presents two complementary platforms for autonomous scientific research.
  arXiv  Detail & Related papers  (2026-02-23T13:10:01Z)
• InternAgent-1.5: A Unified Agentic Framework for Long-Horizon Autonomous Scientific Discovery [138.0404718571971]
  We introduce InternAgent-1.5, a unified system designed for end-to-end scientific discovery.<n>The system is built on a structured architecture composed of three coordinated subsystems for generation, verification, and evolution.<n>We evaluate InternAgent-1.5 on scientific reasoning benchmarks such as GAIA, HLE, GPQA, and FrontierScience.
  arXiv  Detail & Related papers  (2026-02-09T18:36:06Z)
• S1-NexusAgent: a Self-Evolving Agent Framework for Multidisciplinary Scientific Research [0.0]
  We propose S1-NexusAgent, a self-evolving agent framework for scientific research.<n>S1-NexusAgent adopts a hierarchical Plan-and-CodeAct execution paradigm, decoupling global scientific planning from subtask-level tool execution.<n>S1-NexusAgent achieves state-of-the-art generalization performance, validating its effectiveness and capability in complex scientific tasks.
  arXiv  Detail & Related papers  (2026-02-02T02:33:25Z)
• Bohrium + SciMaster: Building the Infrastructure and Ecosystem for Agentic Science at Scale [82.20980951765891]
  We argue that scaling agentic science requires an infrastructure-and-ecosystem approach, instantiated Bohrium+SciMaster.<n>Bohrium acts as a managed, traceable hub for AI4S assets that turns diverse scientific data, software, compute, and laboratory systems into agent-ready capabilities.<n>SciMaster orchestrates these capabilities into long-horizon scientific, on which scientific agents can be composed and executed.
  arXiv  Detail & Related papers  (2025-12-23T16:04:41Z)
• An Agentic Framework for Autonomous Materials Computation [70.24472585135929]
  Large Language Models (LLMs) have emerged as powerful tools for accelerating scientific discovery.<n>Recent advances integrate LLMs into agentic frameworks, enabling retrieval, reasoning, and tool use for complex scientific experiments.<n>Here, we present a domain-specialized agent designed for reliable automation of first-principles materials computations.
  arXiv  Detail & Related papers  (2025-12-22T15:03:57Z)
• Unleashing Scientific Reasoning for Bio-experimental Protocol Generation via Structured Component-based Reward Mechanism [26.631284893550056]
  We introduce SciRecipe, a large-scale dataset of over 12K structured protocols spanning 27 biological subfields.<n>To further improve protocol generation, we propose the "Sketch-and-Fill" paradigm, which separates analysis, structuring, and expression.<n>We develop Thoth, trained through a staged Knowledge-to-Action process that progresses from knowledge acquisition to operational reasoning.
  arXiv  Detail & Related papers  (2025-10-17T12:47:50Z)
• Technical Implementation of Tippy: Multi-Agent Architecture and System Design for Drug Discovery Laboratory Automation [0.0]
  This paper provides a comprehensive technical analysis of Tippy's multi-agent system implementation for drug discovery laboratory automation.<n>We present a distributed architecture featuring five specialized agents (Supervisor, Molecule, Lab, Analysis, and Report) that coordinate through OpenAI Agents SDK.<n>The system architecture encompasses agent-specific tool integration, asynchronous communication patterns, and comprehensive configuration tracking through Git-based tracking.
  arXiv  Detail & Related papers  (2025-07-18T17:57:40Z)
• A Large-Scale Evolvable Dataset for Model Context Protocol Ecosystem and Security Analysis [8.943261888363622]
  We introduce MCPCorpus, a large-scale dataset containing around 14K MCP servers and 300 MCP clients.<n>Each artifact is annotated with 20+ normalized attributes capturing its identity, interface configuration, GitHub activity, and metadata.<n> MCPCorpus provides a reproducible snapshot of the real-world MCP ecosystem, enabling studies of adoption trends, ecosystem health, and implementation diversity.
  arXiv  Detail & Related papers  (2025-06-30T02:37:27Z)
• Position: Intelligent Science Laboratory Requires the Integration of Cognitive and Embodied AI [98.19195693735487]
  We propose the paradigm of Intelligent Science Laboratories (ISLs)<n>ISLs are a multi-layered, closed-loop framework that deeply integrates cognitive and embodied intelligence.<n>We argue that such systems are essential for overcoming the current limitations of scientific discovery.
  arXiv  Detail & Related papers  (2025-06-24T13:31:44Z)
• ScienceBoard: Evaluating Multimodal Autonomous Agents in Realistic Scientific Workflows [82.07367406991678]
  Large Language Models (LLMs) have extended their impact beyond Natural Language Processing.<n>Among these, computer-using agents are capable of interacting with operating systems as humans do.<n>We introduce ScienceBoard, which encompasses a realistic, multi-domain environment featuring dynamic and visually rich scientific software.
  arXiv  Detail & Related papers  (2025-05-26T12:27:27Z)
• Towards a Dynamic Composability Approach for using Heterogeneous Systems in Remote Sensing [0.0]
  We present a novel approach for using composable systems in the intersection between scientific computing, artificial intelligence (AI), and remote sensing domain. We describe the architecture of a first working example of a composable infrastructure that federates Expanse, an NSF-funded supercomputer, with Nautilus, a geo-distributed cluster.
  arXiv  Detail & Related papers  (2022-11-13T14:48:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.