# Can Broad Biomedical Knowledge be Contextualized into Scenario-Grounded Propositions?

arXiv: https://arxiv.org/abs/2605.27082 · HTML: https://mlsanity.com/paper/2605.27082/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 35 references that are in the map.
- First posted on arXiv 26 May 2026.

## Builds on (most cited first, 11 of 11)

- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://mlsanity.com/paper/2308.08155/) (2023)
- [MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework](https://mlsanity.com/paper/2308.00352/) (2023)
- [AFlow: Automating Agentic Workflow Generation](https://mlsanity.com/paper/2410.10762/) (2024)
- [Generalized Random Forests](https://mlsanity.com/paper/1610.01271/) (2016)
- [Recursive Partitioning for Heterogeneous Causal Effects](https://mlsanity.com/paper/1504.01132/) (2015)
- [HypKG: Hypergraph-based Knowledge Graph Contextualization for Precision Healthcare](https://mlsanity.com/paper/2507.19726/) (2025)
- [FEDMEKI: A Benchmark for Scaling Medical Foundation Models via Federated Knowledge Injection](https://mlsanity.com/paper/2408.09227/) (2024)
- [Modern approaches for evaluating treatment effect heterogeneity from clinical trials and observational data](https://mlsanity.com/paper/2311.14889/) (2023)
- [CURLS: Causal Rule Learning for Subgroups with Significant Treatment Effect](https://mlsanity.com/paper/2407.01004/) (2024)
- [Learning Subgroups with Maximum Treatment Effects without Causal Heuristics](https://mlsanity.com/paper/2511.20189/) (2025)

## Built on by (most cited first, 1 of 1)

- [Robust Biomolecular Complex Design Across Protein Conformational Landscapes](https://mlsanity.com/paper/2609.33726/) (2026)

## Abstract (arXiv)

Biomedical discovery often requires connecting broad biomedical knowledge with specific experimental or clinical data. Background knowledge suggests relevant mechanisms but is usually too general to map directly onto dataset variables, while data-driven patterns can be dataset-specific and hard to interpret mechanistically. We study this missing link as knowledge contextualization: transforming broad biomedical knowledge into evidence-supported, scenario-grounded propositions that domain experts can inspect, replay, and validate. We propose SCENE, a bi-level multi-agent framework that treats knowledge contextualization as iterative search. The upper level converts broad knowledge into search directions and grounds them in the dataset schema. The lower level executes these directions through multi-objective optimization to identify concrete propositions that balance evidential strength and data support. Feedback between the two levels progressively refines the search. We evaluate SCENE in two settings: discovering patient subgroups with heterogeneous treatment benefits in clinical trial scenarios, and identifying context-specific biological responses in LINCS L1000 studies. In clinical trials, SCENE discovers specific, well-supported subgroups and outperforms existing baselines. In L1000 studies, SCENE identifies perturbational contexts with strong target-response matching and high positive rates. These results show that SCENE bridges broad knowledge and scenario-specific evidence, producing traceable, inspectable hypotheses for follow-up validation.