PaST-Q: Learning Future-Queryable Patient States for Tool-Augmented Clinical Agents

While clinical guidelines provide population-level recommendations, personalized cancer treatment requires considering both longitudinal patient history and future clinical evolution under candidate therapies. Existing approaches remain limited in either the use of longitudinal disease and treatment context or the richness of future predictions. To address these challenges, we propose PaST-Q, a patient modeling and multi-agent framework that connects historical context with future simulation for guideline-grounded treatment planning. PaST-Q organizes heterogeneous records into temporally ordered Observation-Action blocks and combines hierarchical set encoding with causal temporal modeling to construct a shared patient state. A unified predictor combines Future Query Tokens, a shared backbone, and role-specific experts to directly forecast future observation and action representations at specified horizons. By incorporating candidate treatments into the patient history, PaST-Q predicts patient-specific clinical evolution under each treatment. Through a shared tool interface, a multi-agent system translates these predictions into clinical evidence, compares candidate treatments, and generates treatment recommendations. We evaluate PaST-Q on the public MMRF CoMMpass dataset and a private cervical cancer cohort. Comparisons of patient history integration and future simulation methods, together with clinician evaluation, demonstrate the value of longitudinal context and patient-specific forecasting for treatment recommendation and clinical decision support.