Translate a recursive neural architecture grammar into a combinatorial specification and use its generating function to sample architectures uniformly conditional on size. This removes the severe shape and branching biases of naive random tree generation, producing a better-covered architecture-search population at the same evaluation budget.
Equip an autoregressive model with a hard constraint that generated token ranks cannot contain a nondecreasing subsequence of length k. Maintain a patience-sorting-style state representing the smallest ending token rank achievable by subsequences of lengths 1 through k-1, mask tokens that would create a length-k subsequence, and optionally sample uniformly among valid continuations using exact suffix counts. This provides a mathematically controlled data-augmentation or decoding regime for…
Use a multivariate generating function to construct training examples or network variants with an exactly controlled amount of a structural attribute, such as graph branching, routing depth, skip connections, or the number of experts activated. Train by gradually changing the target coefficient slice instead of relying on an uncontrolled random curriculum.