Instead of just retrieving relevant documents, RECAST learns to combine retrieval with computation—filtering, aggregating, and deriving answers across multiple sources—enabling LLMs to handle complex, multi-step reasoning tasks more effectively.
RECAST is a framework that helps language models solve complex tasks by learning to actively construct evidence through computation rather than just retrieving it. A lightweight router model decides which operations to perform on multiple information sources, a compiler translates those decisions into executable code, and an answer model produces the final result.