Can Broad Biomedical Knowledge Be Contextualized into Scenario-Grounded Knowledge?

Biomedical discovery often requires reconciling broad biomedical knowledge with specific experimental or clinical data. While background knowledge suggests useful biological mechanisms, it is typically too general to map directly onto concrete dataset variables. Conversely, data-driven patterns are often dataset-specific and lack mechanistic insight, creating a gap between abstract principles and concrete evidence. We formulate the bridging of this missing link as knowledge contextualization: transforming broad biomedical knowledge into evidence-supported, scenario-grounded propositions, with the goal of allowing domain experts to efficiently inspect, replay, and validate candidate hypotheses. To achieve this, we propose SCENE, a bi-level multi-agent framework that implements knowledge contextualization as an iterative search process. The upper level translates broad knowledge into search directions and maps them onto the dataset schema. The lower level then executes these grounded directions using multi-objective optimization to find concrete propositions that balance evidential strength with sufficient data support. A feedback loop between the two levels progressively refines these search directions. We evaluate SCENE in two distinct settings: discovering patient subgroups with heterogeneous treatment benefits in clinical trial scenarios, and identifying context-specific biological responses in LINCS L1000 studies. In clinical trial scenarios, SCENE outperforms existing baselines by discovering highly specific, strongly supported subgroups. Furthermore, SCENE uniquely enables the discovery of perturbational contexts with strong target-response matching and high positive rates in LINCS L1000 studies. These results demonstrate that SCENE effectively bridges the gap between broad knowledge and scenario-specific evidence, providing traceable and inspectable hypotheses for follow-up validation.