IntentSci-Graph: A Scientific Reasoning Layer for Autonomous Research Agents

Autonomous research agents are increasingly capable of executing scientific workflows, but reliable scientific discovery cannot be reduced to execution capability alone. Reliable scientific discovery also depends on whether an agent can reason over an underspecified research intent, organize a scientifically coherent plan, and determine what evidence is needed to support a conclusion. Existing research agents can organize this reasoning through planning and execution workflows, but the resulting organization is often incomplete or insufficiently verified. A method may not be clearly connected to the evidence required for the intended claim, or the final outputs may not be aligned with the criteria used to validate the result. To address this gap, we introduce IntentSci-Graph, a scientific reasoning layer for autonomous research. IntentSci-Graph casts autonomous research as a pre-execution scientific program synthesis problem, transforming research intent into an explicit and verifiable scientific program. Rather than asking a downstream executor to infer scientific structure while acting, IntentSci-Graph produces a structured interface that links research objectives, methods, required evidence, and validation criteria. The program is refined through structured synthesis and verification, making the intended study inspectable before execution and reusable across different downstream agents. On ASI-Bench, IntentSci-Graph effectively improves autonomous research performance, particularly under underspecified research prompts, demonstrating that explicit and verifiable scientific programs can provide a practical foundation for reliable autonomous research.