让AI团队自主研究,可实时监控、回滚和协作。
Claw AI Lab: An Autonomous Multi-Agent Research Team

- 用一个提示生成多智能体研究团队,支持自定义角色与协作流程。
- 在5个案例中实验完成度更高,专家评分在创新性、完整性上更优。
- 内置代码集成工具,提升实验可追踪性与论文结果可靠性。
我们提出Claw AI Lab,一个原生的自主研究平台,将自动化研究从隐式的提示到论文流程升级为可交互的AI实验室。用户仅需一个提示即可创建具备定制角色、协作流程、实时监控、成果检查及回滚/续跑控制的完整研究团队。平台支持探索、多智能体讨论和复现三种研究模式,显著提升研究的可控性与实验室化程度。核心贡献在于Claw-Code Harness,该工具连接本地代码库、数据集与模型检查点,实现可运行实验并反馈执行结果,从而增强执行集成、实验完成度与结果一致性:实验更易审查、迭代和准确转为论文,减少部分运行和结果格式错误等常见问题。在内部对五个AI研究案例的评估中,相比AutoResearchClaw基线,专家评委一致认为Claw AI Lab在想法新颖性、实验完整性和论文呈现质量上更优。我们认为Claw AI Lab是迈向可使用、可交互、可靠感知的自主科研基础设施的重要一步。
原文摘要 · Abstract (English)
We present Claw AI Lab, a lab-native autonomous research platform that advances automated research from a hidden prompt-to-paper pipeline into an interactive AI laboratory. Rather than centering the system around a single agent or a fixed serial workflow, we allow users to instantiate a full research team from one prompt, with customizable roles, collaborative workflows, real-time monitoring, artifact inspection, and rollback/resume control through a unified dashboard. The platform also supports distinct research modes for exploration, multi-agent discussion, and reproduction, making autonomous research substantially more steerable and laboratory-like in practice. A key practical contribution of Claw AI Lab lies in its Claw-Code Harness, which connects local codebases, datasets, and checkpoints to runnable experiments and feeds execution artifacts back into the research loop. As a result, the harness improves not only execution integration, but also experimental completion and result integrity: experiments are easier to inspect, iterate on, and faithfully transfer into final papers, reducing common failure modes such as partial runs and malformed result reporting. In our internal evaluation on five AI research case studies, using AutoResearchClaw as the baseline, Claw AI Lab is consistently preferred by AI expert judges on idea novelty, experiment completeness, and paper presentation quality. We view Claw AI Lab as an early step toward a new paradigm: autonomous research as usable, interactive, and reliability-aware scientific infrastructure.
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