AI 中文总结
本研讨会聚焦智能体介导协作中的贡献消解问题，将召集研究者与从业者，通过多元活动探讨应对方案，旨在形成共享研究议程与基础设施应对基础。
AI 中文摘要
协作性知识工作正发生超越披露或透明度的变化：大语言模型（LLM）智能体现已嵌入团队研究、设计、撰写与决策的过程中，在成员间起中介作用、综合输入、重构想法并起草共享产出。它们不仅促进协作，还在贡献形成的当下嵌入工作流，在此过程中可能破坏贡献可被见证、归因与问责的社会条件。本次研讨会召集研究者与从业者应对我们所称的“贡献消解”——智能体介导协作工作中归因、原创性与问责制的模糊化。我们认为，这种消解始于协作之前，源于个体工作者对哪些内容真正属于自己的不确定性，并通过协作关系传播，瓦解促成富有成效的知识交流所需的可靠性。参与者将通过立场声明、映射练习与实操活动，揭示将问责制视为文档问题（如AI使用声明、水印、溯源日志）的框架，忽视了问责制产生的条件。我们的目标是形成共享研究议程，为协作性知识工作中的贡献消解构建基础设施应对方案的基础。
英文摘要
Collaborative knowledge work is changing in ways that go beyond disclosure or transparency. LLM agents are now embedded in how teams research, design, write, and decide: mediating between members, synthesizing inputs, reformulating ideas, and drafting shared outputs. They do not only facilitate collaboration; they operate within the workflow at the moment contributions are being formed. In doing so, they risk undermining the social conditions under which contributions can be witnessed, attributed, and held accountable. This workshop brings together researchers and practitioners to confront what we call contribution dissolution: the blurring of attribution, originality, and accountability in agent-mediated collaborative work. We argue that this dissolution begins before collaboration itself, in the individual worker's own uncertainty about what is genuinely theirs, and propagates through collaborative relationships, collapsing the reliability that makes productive intellectual exchange possible. Through position statements, mapping exercises, and a hands-on activity, participants will surface how framing accountability as a documentation problem (e.g., AI use statements, watermarking, provenance logs) overlooks the conditions under which accountability is produced. Our goal is to produce a shared research agenda and the foundations of an infrastructural response to contribution dissolution in collaborative knowledge work.
