Curiosity-Driven Questioning for Engine-Agnostic LLM Research Ideation

Abstract

Scientific ideation is driven by curiosity: researchers ask questions that expose knowledge gaps, reveal competing hypotheses, and clarify missing evidence relevant to decision making. Yet, most LLM-based ideation systems optimize the idea text while leaving curiosity under-modeled, resulting in brittle, engine-specific gains. To address this, we propose Curiosity-Driven Questioning (CDQ), a training-free, engine-agnostic method that constructs a compact set of curiosity-driven questions from a topic corpus under black-box LLM access. CDQ scores questions with three corpus-relative indices (GapExposure, DisagrDiversity, ClarityGain) and uses a two-stage scoring with an entropic mirror-ascent reweighting over question meta-policies to concentrate on high-utility question policies. Our results show that prepending CDQ questions improves downstream ideation across single-pass, retrieval-augmented generation, and self-reflection engines with multiple base models under preference-based evaluation. Ablations and a human study further support the complementarity of the indices and the reliability of the evaluation. Our code and dataset are available at: https://github.com/Sikun-Skyler-Guo/CDQ