Transmitter: Signal-Transduction-Inspired Reusable Response Pathways Through Decoupled Discrete State Selection

Biological signaling provides a role-level analogy for separating response selection from response expression. Inspired by this principle, we introduce Transmitter, a vision backbone whose token-mixing operator makes three roles explicit: query fragments select a route in a learned receptor book, the route retrieves a paired vector from a signal book, and an input-dependent gain modulates the retrieved response. The assembled response is added to each head input, exposing route and response traces for fixed-checkpoint analysis. During training, the model learns both which response to select and how to express it for classification. The classification loss trains the selected response path, while an auxiliary loss learns the query-to-receptor matching used for discrete selection. At one frozen intermediate checkpoint, replacing the release gain of all eight blocks by its within-image spatial mean lowers ImageNet-1K top-1 accuracy by 21.58 percentage points (pp), while moving whole per-token gain vectors to other tokens lowers it by 15.78–16.18 pp; forcing the original routes leaves drops of 20.46 and 15.32–15.60 pp, so gain placement matters even without route reselection. In exploratory COCO traces, the same-minus-different-class code-affinity gap is 0.0078 larger in the last two blocks than in the first two. These results position route-explicit token mixing as an inspectable decomposition of visual computation.