Solver-Aware Decompositions for Programming-by-Example: When Dividing Requires Knowing how to Conquer

תקציר מקורי באנגליתarXiv:2608.03461v2 Announce Type: replace Abstract: Decomposition-based Programming-by-example (PBE) scales performance by splitting tasks into subtasks that a learned synthesizer solves: a decomposer predicts intermediate subgoals, and a synthesizer generates programs conditioned on them. Execution-decomposition approaches such as ExeDec train the decomposer to imitate ground-truth (GT) subgoals, implicitly treating decomposition quality as intrinsic to the task. We challenge this assumption: for bounded solvers with fixed inductive biases, GT decompositions reflect the annotator's factorization choices - not the solver's search dynamics. A decomposer trained to match GT decompositions may therefore propose subgoals that are logically valid yet intractable for the solver. We propose Solve