Clarus让多个AI或人类研究者像团队一样协作，能自动拆解科研任务并记录每个贡献，适合想做复杂科学项目的团队。
Clarus是一个用于协调自主研究代理的协作基础设施，定义了项目-代理-资源对象模型。它通过研究应用、数字协作、物理基板和物理世界四个层组织科学协作。在受控论文生成案例研究中，Clarus将研究目标组织成可追溯、可审查、可归因且累积的协作网络。Clarus已在clarus.holosai.io开放访问。
Clarus: Coordinating Autonomous Research Agents toward Web-Scale Scientific Collaboration
Existing autonomous research agents can support parts of the research process, but most systems still treat research as either an isolated assistant task or a closed workflow. Therefore, autonomous science needs a collaboration infrastructure that coordinates projects, agents, and digital and physical resources. We identify this as a shift from code-centered execution loops to research-oriented collaboration processes, where questions, evidence, participants, and resources must be coordinated under uncertainty. In this framing, an agent may be an AI system, a human researcher, a team, a laboratory, or an organization-backed participant. To this end, we present Clarus, a collaboration infrastructure for coordinating autonomous research agents toward web-scale scientific collaboration. Clarus reformulates research as an open, auditable, attributable, and resource-aware multi-phase collaboration process. It defines a minimal project-agent-resource object model and organizes scientific collaboration through four layers including Research Application, Digital Collaboration, Physical Substrate, and Physical World. Core modules are implemented as pluggable mechanisms, allowing Clarus to adapt to task risk, collaboration structure, and resource constraints. Through a controlled paper-generation case study, we show that Clarus can organize a research goal into a traceable, reviewable, attributable, and accumulative collaboration network across phases, tasks, and participants. Together, the object model, collaboration protocol, trust mechanisms, and prototype validation provide an initial foundation for open research networks. Clarus is now available at clarus.holosai.io.