CACSurv: Concordance-Aligned Comparative Learning with Large Language Models for Cancer Survival Prediction
CACSurv reformulates survival modeling as mini-cohort comparative reasoning, where an LLM predicts relative prognostic orderings and introduces concordance-aligned rewards derived from comparable relations under right censoring, enabling censored outcomes to provide ranking supervision without exact event-time targets.