Transfiver is a framework designed for long‑term human‑AI interaction, allowing users to directly inspect and edit the information that guides inference. The key idea is to keep all interaction‑specific data in a single persistent state $S_t$ that both the model and the human update.\ \ State evolution follows two modes:\
- Implicit stream update: the model parses ongoing dialogue and decides whether to revise an existing state item or to create a new one.\
- Explicit directed edit: a human inspects and directly modifies a addressed item. Because both parties act on the same underlying state, a human correction immediately changes what subsequent computation reads, rather than being added as a separate instruction.\ \ The architecture separates pre‑trained shared parameters $\theta$ from the persistent state $S_t$, which evolves during deployment without requiring parameter retraining.\ \ Current work focuses on relatively simple state representations; extending Transfiver to rich natural‑language, relational, or large‑scale shared states remains an open challenge.\ \ Review