Intelligent systems have traditionally been designed as tools rather than collaborators, often lacking critical characteristics that collaboration partnerships require. Recent advances in large language model (LLM) agents open new opportunities for human-LLM-agent collaboration by enabling natural communication and various social and cognitive behaviors. Yet it remains unclear whether principles of computer-mediated collaboration established in HCI and CSCW persist, change, or fail when humans collaborate with LLM agents. To support systematic investigations of these questions, we introduce an open and configurable research platform for HCI researchers. The platform's modular design allows seamless adaptation of classic CSCW experiments and manipulation of theory-grounded interaction controls. We demonstrate the platform's research efficacy and usability through three case studies: (1) two Shape Factory experiments for resource negotiation with 16 participants, (2) one Hidden Profile experiment for information pooling with 16 participants, and (3) a participatory cognitive walkthrough with five HCI researchers to refine workflows of researcher interface for experiment setup and analysis.

翻译：传统智能系统通常被设计为工具而非协作者，往往缺乏协作伙伴关系所需的关键特性。大型语言模型（LLM）智能体的最新进展通过实现自然交流及多样化的社会与认知行为，为人类与LLM智能体协作开辟了新途径。然而，当人类与LLM智能体协作时，人机交互（HCI）与计算机支持协同工作（CSCW）领域确立的计算机中介协作原则究竟是持续有效、发生变化还是彻底失效，目前仍不明确。为支持对这些问题的系统性研究，我们为HCI研究者开发了一个开放可配置的研究平台。该平台的模块化设计能够无缝适配经典CSCW实验，并支持基于理论的交互控制变量操纵。我们通过三个案例研究验证了平台的研究效能与可用性：（1）涉及16名参与者的两项Shape Factory资源协商实验，（2）涉及16名参与者的Hidden Profile信息整合实验，以及（3）与五位HCI研究者共同开展的参与式认知走查，以优化实验设置与分析阶段的研究者界面工作流程。