Asking the Right Question: Epistemic Inquiry as a Learnable Reasoning Skill for Scientific Discovery

Effective scientific reasoning depends not only on extending chains of thought, but on identifying what is missing, surfacing implicit assumptions and posing the right questions to unlock a solution. This epistemic dimension is central not just to discovery, but to scientific partnership: a genuine AI co-scientist must be able to articulate its knowledge gaps in a form that human experts can engage with, including gaps that can only be filled by tacit domain knowledge no model possesses. We introduce Chain-of-Questions (CoQ), a prompting and training framework in which a model explicitly generates targeted epistemic questions before attempting to solve a problem, producing a structured, inspectable representation of what it does not know. Across science, chemistry, mathematics, and coding benchmarks, CoQ consistently outperforms chain-of-thought prompting at matched token budgets, and question decompositions trained on one domain transfer to held-out chemistry targets without any chemistry training, suggesting that the skill of decomposing uncertainty into questions is a portable reasoning skill, and not a benchmark artifact. Together, these results point toward a concrete capability that separates a scientific tool from a scientific collaborator: not the ability to answer, but the ability to ask.