Intelligence, composedlike movable type,for any task.
MTP, short for Movable Type Printing, splits every question into pieces, hands each to a model trained for that domain, and sets the answers back into one. No more stacking parameters.
MTP, short for Movable Type Printing, splits every question into pieces, hands each to a model trained for that domain, and sets the answers back into one. No more stacking parameters.
MTP · Movable Type Printing
A user asks something that needs law, tax and Japanese at once. MTP treats it like a page waiting to be typeset, not a prompt for one giant model to swallow whole.
The framework splits the question into independent sub-tasks. Each sub-task becomes a single piece of type that can be handled on its own.
Every domain has its own model, trained deeply for that field alone. Each piece is routed to the specialist that knows it best.
Only the models the question needs wake up. Small, focused models answer precisely, without carrying the weight of everything else.
MTP sets the partial answers back into one coherent response, the way a printer sets type into a finished page.
Why MTP
Bigger models buy capability with ever more parameters, and every question pays for all of them. MTP buys capability with structure.
Capability grows only by making the whole model larger, so cost and latency grow with it.
Each question lights up just the pieces of type it needs. The rest of the case stays idle.
Each specialist is trained deeply for one field, instead of one model trained shallowly for everything.
A question wakes only the specialists it needs, so cost scales with the task, not with the model.
New capability means training one new specialist and adding it to the case. Nothing else is retrained.