Domain experts possess tacit knowledge that they cannot easily articulate through explicit specifications. When experts modify AI-generated artifacts by correcting terminology, restructuring arguments, and adjusting emphasis, these edits reveal domain understanding that remains latent in traditional prompt-based interactions. Current systems treat such modifications as endpoint corrections rather than as implicit specifications that could reshape subsequent reasoning. We propose context-mediated domain adaptation, a paradigm where user modifications to system-generated artifacts serve as implicit domain specification that reshapes LLM-powered multi-agent reasoning behavior. Through our system Seedentia, a web-based multi-agent framework for sense-making, we demonstrate bidirectional semantic links between generated artifacts and system reasoning. Our approach enables specification bootstrapping where vague initial prompts evolve into precise domain specifications through iterative human-AI collaboration, implicit knowledge transfer through reverse-engineered user edits, and in-context learning where agent behavior adapts based on observed correction patterns. We present results from an evaluation with domain experts who generated and modified research questions from academic papers. Our system extracted 46 domain knowledge entries from user modifications, demonstrating the feasibility of capturing implicit expertise through edit patterns, though the limited sample size constrains conclusions about systematic quality improvements.

翻译：领域专家拥有难以通过显式规范清晰表达的隐性知识。当专家通过纠正术语、重构论证和调整重点来修改AI生成的人工制品时，这些编辑操作揭示了在传统提示交互中仍处于潜伏状态的领域理解。现有系统将此类修改视为终点修正，而非可重塑后续推理的隐性规范。我们提出上下文中介领域自适应这一范式，其中用户对系统生成人工制品的修改作为隐性领域规范，能够重塑基于大语言模型的多智能体推理行为。通过我们系统Seedentia（一个基于网络的意义建构多智能体框架），我们展示了生成人工制品与系统推理之间的双向语义链接。该方法支持：通过迭代人机协作将模糊初始提示演化为精确领域规范的规范引导启动、通过逆向工程用户编辑实现的隐性知识迁移，以及基于观测修正模式自适应调整智能体行为的上下文学习。我们展示了与领域专家的评估结果，专家从学术论文中生成并修改研究问题。系统从用户修改中提取了46条领域知识条目，证明了通过编辑模式捕获隐性专业知识的可行性，尽管有限的样本量限制了关于系统性质量改进的结论。