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.
Replace a single plug-in top-k router decision with a confidence correspondence containing every router parameter candidate and sparse expert assignment that remains compatible with calibration and the current input. Project this set onto a hierarchy of expert groups and return the finest group-level decision supported by all surviving explanations; otherwise coarsen the route or abstain. Active endpoint bracketing evaluates only candidates that could still change the projected routing report.
Represent a nonnegative attention or routing score matrix by two nonnegative vectors, X = uv^T, and learn only entries on a sparse bipartite graph of important query-key or token-expert interactions. Complete the remaining entries multiplicatively and monitor cycle residuals as a certificate of whether the sparse representation is compatible with rank one. Use local ratio violations to trigger additional edges or relax the rank-one approximation only where needed.