RECAST trains a lightweight router to unify retrieval and computation as evidence construction, beating retrieval and program-generation baselines across six benchmarks
RECAST casts evidence construction as a sequential decision process over retrieval and computation operations: a lightweight RouterLM iteratively selects lexical, semantic, or relational primitives, or requests customized operations that a frozen CompilerLM translates into executable code, then passes accepted evidence to a frozen AnswerLM; RouterLM is trained with SFT followed by GRPO, achieving the strongest average success rate across six heterogeneous benchmarks and zero-shot generalization on three held-out benchmarks.