ClinAdapt: Self-Adaptive Multi-Agent Learning for Longitudinal Clinical Event Modeling in Lung Cancer Detection

Longitudinal clinical event modeling from electronic health records (EHRs) requires reasoning over sparse, noisy, and long-context multimodal sequences. Existing LLM-based multi-agent systems alleviate context-length constraints but process each patient in isolation, failing to emulate how clinicians exploit accumulated experience from similar prior cases. We introduce ClinAdapt, a self-adaptive multi-agent system with two complementary adaptation mechanisms. First, an Adaptive Experience Memory (AEM) serves as non-parametric memory, indexing rejection-sampled reasoning traces to retrieve similar patients as few-shot contexts. Second, Multi-Agent Policy Adaptation (MAPA) with reward-ranked fine-tuning parametrically optimizes inter-agent and agent-memory collaboration. A leave-one-out cross-retrieval strategy unifies these mechanisms, aligning training- and inference-time behavior under retrieval augmentation. On lung cancer prediction using up to five years of multimodal EHRs, ClinAdapt outperforms nine strong baselines on both the overall population and a challenging never-smoker population. Analyses of the adaptation dynamics reveal three key findings. First, as the AEM expands, the optimal retrieval strategy shifts from diverse to specific samples. Second, under MAPA, the manager agent’s prediction loss converges rapidly, while worker agents’ temporal reasoning continues to benefit from more verified patients. Third, the two mechanisms are complementary in predicted risk, with AEM improving specificity and MAPA improving sensitivity.