Internalizing Tools as Morphisms in Graded Transformers

We introduce a graded formulation of internal symbolic computation for transformers. The hidden space is endowed with a grading V=bigoplusg∈ GVg, and symbolic operations are realized as typed block maps (morphisms) φhleftarrow g:Vg→ Vh that are activated selectively by a differentiable routing policy. A self-supervised graded utility functional, defined as the loss reduction induced by a candidate morphism, governs activation and yields sparse, interpretable behavior. We develop the algebraic and geometric foundations: an internal model category whose objects are homogeneous components and whose morphisms are admissible grade transitions; adjoint pairs encoding typed round trips; and information-geometric interpretations in terms of KL gain, mirror descent with Bregman divergences, and Fisher natural gradients. Methodologically, we specify a utility--aware routing mechanism and objective that remain fully end-to-end differentiable. Analytic case studies and lightweight sanity checks illustrate selective morphic activation on hybrid symbolic-linguistic tasks. The framework unifies symbolic computation, geometry, and self--supervised learning within the graded transformer formalism citesh-89,sh-95, while subsuming prior external-tool paradigms (e.g., Toolformer citetoolformer2023) as a special case via functorial internalization.