ES4SURV: AN EVIDENCE-SEEKING MULTIMODAL LARGE LANGUAGE MODEL FOR WHOLE-SLIDE SURVIVAL PREDICTION

In digital pathology, accurate clinical predictions require integrating both global tissue context and local morphological details. While multimodal large language models (MLLMs) show strong potential, existing frameworks compress thousands of slide patches into a fixed set of global tokens, often discarding fine-grained, clinically decisive evidence. To overcome this limitation, we present ES4Surv, an autoregressive whole-slide image (WSI) MLLM that dynamically bridges macro- and micro-level features for survival analysis. ES4Surv first constructs a global slide representation, then uses that context to guide an adaptive, query-driven finding of high-resolution patch features aligned with predefined histologic concepts. These targeted patches are reinjected into the model, forming an interpretable reasoning pathway from global slide understanding to localized evidence and final prognosis. Across five TCGA benchmarks comprising 2,938 patients, ES4Surv consistently outperforms existing state-of-the-art slide-level models in term of macro disease-specific survival.