Attach an evidential cost head to a neural graph model, representing each edge cost by a weighted set of interval boxes, and compress this representation before the downstream shortest-path or routing solver. Instead of minimizing Jaccard or Jousselme distance between the original and compressed mass functions, choose merges that minimize the induced cost error on the currently selected route, while enforcing a conservative monotonicity condition so that the resulting path regret is bounded.
Represent a 3D neural field using high-order polynomial coefficients attached to an adaptively refined tetrahedral mesh, with a small MLP predicting residual corrections from local coordinates. Refine only tetrahedra whose prediction, rendering, or PDE residual is large, and use globally ordered vertices so neighboring tetrahedra share identical face and edge coefficients without hanging-node constraint solves.
Replace dense token-to-token attention with attention over connected token groups, called polymers, while forbidding nearby polymers from being simultaneously selected. Each candidate group receives an exponentially decaying size and boundary penalty, and the layer sums or samples only compatible collections of groups. The construction should create structured sparsity and prevent redundant overlapping attention regions.