Transfiver: Human-AI Co-Inference through a Shared Editable State

For people and AI systems to reason together, people need to inspect and correct the information the system uses in subsequent computation. 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 that both the model and the human update. Transfiver distinguishes two modes of state evolution. An implicit stream update requires the model to interpret ongoing interaction and determine whether new information revises an existing state item or creates a new one. In an explicit directed edit, a human inspects a record and changes it. 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. We evaluate two implementations that test complementary aspects of this design. Record-store experiments show two results. A retracted record stays out of the prompt when the system rebuilds its input for the next question. Its text stays readable for inspection and restoration. A learned model reads editable records while it generates each answer, so retracting the record that holds an answer removes that answer. These results support shared editable state as a basis for human-AI collaboration.