Not All Old Knowledge Is Yours Alone: Direction-Wise Budgeted Release for Continual Multi-Agent Coordination in Comorbid Treatment

Clinical policies are usually learned one disease at a time, but real patients present with combinations of conditions whose treatments interact. We study offline reinforcement learning over a growing team of disease-specific agents, introduced in stages of increasing comorbidity. This couples two problems: agents must learn to condition on one another, yet the updates that install these dependencies overwrite representations earlier policies rely on. We let a single spectral memory serve both roles — a private singular-value subspace that identifies which historical directions an update would disturb, and a shared basis that defines the coordinates through which co-present agents exchange patient information. Rather than protecting all historical directions uniformly, we compare spectral responses across agents on time-aligned observations and allocate a bounded adaptation budget as a continuous knapsack problem. We evaluate on sepsis, kidney injury, and respiratory failure cohorts, measuring every checkpoint on all single-, two-, and three-disease test sets.