StepRel: Efficient Relational Feature Search beyond Full Interaction History

LLM agents can construct relational features by exploring databases, generating SQL programs, and revising them through validation feedback. As search progresses, they accumulate useful knowledge alongside a growing history of candidate programs and intermediate results. Retaining this history repeatedly processes information that could instead be reused selectively. We introduce StepRel, which maintains database knowledge, the current SQL program, and search evidence outside the conversation. It updates programs through local edits and periodically rebuilds the context from the retained knowledge, program, and selected evidence. This separation preserves an exact program for further revision while carrying forward useful outcomes from earlier attempts. Across 18 RelBench tasks and four LLM backbones, StepRel maintains competitive predictive performance under a shared evaluation protocol while reducing the context needed for continued search. These results demonstrate that effective relational feature search can continue without carrying its complete interaction history.