发表机构
Korea Institute of Energy Technology (KENTECH)（韩国能源技术研究院（KENTECH））
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
Transfiver是一种人机协同推理架构，通过共享可编辑状态实现人机共同更新，分离共享参数与持久状态，目前扩展至丰富类型共享状态仍待研究。
AI 中文摘要
长期人机交互存在困难，因为引导推理的信息由模型隐式更新，用户无法直接检查或控制。我们提出了TRANSparent Framework for Interactive, Verifiable, Editable Representation（Transfiver，交互式可验证可编辑表示的透明框架），这是一种通过共享可编辑状态实现人机协同推理的架构。其核心思想是，交互特定信息维护在单一持久状态$(S_t)$中，模型和人类均可对其进行更新。Transfiver区分两种状态演化模式：在隐式流更新中，模型解释持续交互并决定新信息是修改现有状态项还是创建新项；在显式定向编辑中，人类检查并修改指定项。双方作用于同一底层状态，因此人类的修正会改变后续计算所读取的状态，而非添加其他指令或独立记录。该架构将普通使用前学习的共享参数$(\theta)$与部署期间演化且无需参数重新训练的持久状态$(S_t)$分离。将Transfiver扩展至丰富的自然语言、关系及大规模共享状态仍是未解决的问题。
英文摘要
Long-term human-AI interaction is difficult because the information that guides inference is updated implicitly by the model and is not directly inspectable or controllable by the user. We introduce the TRANSparent Framework for Interactive, Verifiable, Editable Representation (Transfiver), an architecture for human-AI co-inference through a shared editable state. Its central idea is that interaction-specific information is maintained in a single persistent state $(S_t)$ that both the model and the human update. Transfiver distinguishes two modes of state evolution. In an implicit stream update, the model interprets ongoing interaction and decides whether new information revises an existing state item or creates a new one. In an explicit directed edit, a human inspects and modifies an addressed item. Both act on the same underlying state, so a human correction changes the state that subsequent computation reads, rather than adding another instruction or separate record. The architecture separates shared parameters $(θ)$, learned before ordinary use, from the persistent state $(S_t)$, which evolves during deployment without parameter retraining. Extending Transfiver to rich natural-language, relational, and large-scale shared states remains open.
