G-RRM: Guiding Symbolic Solvers with Recurrent Reasoning Models

Classical symbolic solvers excel at guaranteeing correct solutions to constraint satisfaction problems but can be slow on large search spaces, while neural models are fast but not always reliable. G-RRM combines both by using symbol-equivariant recurrent reasoning models to generate solution proposals that guide symbolic solvers like backtracking algorithms and SAT solvers. The paper finds that neural guidance helps most when problems have large search spaces and when the solver can adaptively override imperfect neural hints. On Sudoku benchmarks, this yields substantial speedups for adaptable solvers, offering a promising hybrid strategy for combinatorial optimization and logic-based AI systems. Authors: Timo Bertram, Sidhant Bhavnani, Richard Freinschlag, Erich Kobler, Andreas Mayr, Günter Klambauer Paper: https://arxiv.org/abs/2607.02491v1