This work introduces a method for NP-class combinatorial problems using a vanilla Transformer. By combining Sudoku rules and guesses, the approach achieves SOTA results (99.8%). Solution length is analyzed via the Min-Sum Set Cover problem.

We provide computationally efficient payoff-based learning algorithms which significantly improve upon prior work in terms of runtime for learning CCE

We introduce an adaptive kernel design method that leverages LLMs as genetic operators to dynamically evolve Gaussian process (GP) kernels during Bayesian optimization (BO)