LLMs are increasingly presented as collaborators in programming, design, writing, and analysis. Yet the practical experience of working with them often falls short of this promise. In many settings, users must diagnose misunderstandings, reconstruct missing assumptions, and repeatedly repair misaligned responses. This poster introduces a conceptual framework for understanding why such collaboration remains fragile. Drawing on a constructivist grounded theory analysis of 16 interviews with designers, developers, and applied AI practitioners working on LLM-enabled systems, and informed by literature on human-AI collaboration, we argue that stable collaboration depends not only on model capability but on the interaction's grounding conditions. We distinguish three recurrent structures of human-AI work: one-shot assistance, weak collaboration with asymmetric repair, and grounded collaboration. We propose that collaboration breaks down when the appearance of partnership outpaces the grounding capacity of the interaction and contribute a framework for discussing grounding, repair, and interaction structure in LLM-enabled work.

翻译：大型语言模型日益被呈现为编程、设计、写作和分析领域的协作者。然而，在实际工作中，与它们协同的体验往往未能达到这一承诺。在许多场景中，用户必须诊断误解、重构缺失的假设，并反复修正不对齐的回应。本文提出一个概念框架，用以理解此类协作何以依然脆弱。基于对16位从事LLM驱动系统设计、开发与应用的人工智能从业者的建构主义扎根理论分析，并结合人机协作领域文献，我们认为：稳定的协作不仅依赖于模型能力，更取决于交互的“基础条件”（grounding conditions）。我们区分了三种循环出现的人机工作结构：一次性辅助、非对称修复的弱协作，以及有基础的协作。我们提出，当伙伴关系的外观超越了交互的基础承载能力时，协作就会崩溃，并贡献了一个用于讨论LLM驱动工作中的基础建立、修复与交互结构的框架。