Risk Aware Compositional Evidence Learning for Heterogeneous Biomedical Observations

High dimensional continuous observations provide rich local information for representation learning in biomedical and scientific data. Recent advances in patch based modeling, structured encoding, and feature selection have improved the ability to capture local dependencies. However, existing methods typically aggregate local observations into a single deterministic representation or retain one preferred subset, making it difficult to preserve multiple useful evidence combinations when local information is redundant, heterogeneous, and unequally reliable. Our key idea is to learn an input conditioned, risk aware distribution over multiple evidence compositions rather than searching for a single optimal representation. Based on this idea, we propose Quantized Structured Flow Representation (QSFR). Specifically, QSFR maps continuously varying local observations into reusable discrete evidence units, constructs alternative evidence compositions through flow based sequential sampling, and models composition level utility as a distribution. The estimated utility and utility dispersion jointly reshape probability mass toward informative and reliable evidence states. We evaluate QSFR across multiple heterogeneous cohorts spanning different biological sources and acquisition conditions. The results show consistently favorable representation quality across multiple evaluation criteria. Mechanism analyses reveal improved local stability, expanded compositional support, and lower risk evidence organization.