SAIL: Stable Anchoring with Individual-Conditioned Learning for Cross-Subject Time Series Classification

Cross-subject classification of biomedical time series is challenging because the same task semantics can be expressed differently across individuals, causing models trained on seen subjects to generalize poorly to unseen subjects. Existing methods typically suppress subject-specific information to learn invariant representations, which may compromise task-relevant semantics. In this paper, we rethink the relationship between task semantics and individual variation, viewing semantic representations as shared task semantics shaped by individual-specific variation. We further find that cross-subject variation can be decomposed into reusable shift modes, which can be recombined to adapt semantic representations to unseen individuals. Based on these observations, we propose SAIL, which extracts a physiology-grounded semantic anchor and employs a mixture-of-experts (MoE) architecture to model reusable variation patterns. Individual context conditionally selects and combines these patterns, while feature-wise gating controls their contribution to the final representation. Across five benchmark medical datasets, SAIL improves the average macro-F1 over the lightweight TeCh baseline by 5.12% , while achieving approximately 9× faster inference.